IJSRET Volume 12 Issue 1, Jan-Feb-2026

A Performance Assessment Of Machine Learning-Based Techniques For Image Restoration

Authors: Shradha Kumavat, Kapil Shah

Abstract: Image restoration is a fundamental task in image processing with wide-ranging applications in modern life, including medical imaging, remote sensing, radar imaging, and digital preservation of historical and museum artifacts. The objective of image restoration is to recover a high-quality image from degraded observation by reducing the effects of noise and blur. Effective restoration depends on understanding the degradation process; therefore, identifying the type of noise and the blur model is essential. In practical scenarios, images are often degraded by atmospheric and environmental conditions and restoring them requires appropriate restoration techniques tailored to the distortion characteristics. This paper reviews and assesses contemporary machine learning-based image restoration methods. The proposed evaluation reports quantitative performance across four standard benchmark datasets Kodak24, CBSD68, Urban100, and LIVE—using PSNR (dB), MSE, and SSIM as primary quality metrics. The achieved PSNR scores are 27.24 dB, 29.38 dB, 30.04 dB, and 30.91 dB on Kodak24, CBSD68, Urban100, and LIVE, respectively. The corresponding MSE values are 367.56, 224.88, 193.10, and 158.02, while SSIM values are 0.8690, 0.9337, 0.9432, and 0.8008. These results demonstrate the effectiveness of the evaluated approach in improving image quality across diverse image restoration benchmarks.

A Hybrid Approach for Leaf Disease Diagnosis Using Otsu–K-Means Segmentation and Convolutional Neural Networks

Authors: Umer Khan, Ranjan Thakur

Abstract: Image restoration the task of recovering degraded or damaged images has become essential across many technical domains, including space imaging, medical imaging, and several post-processing applications. Most restoration techniques begin by modeling the degradation process that corrupts an image, typically involving blur and noise, and then attempt to reconstruct an approximation of the original image. However, in real-world scenarios, degradation is often unknown, requiring the simultaneous estimation of both the true image and the blurring function directly from the observed degraded image, without relying on prior knowledge of the blur mechanism. This thesis proposes a novel digital image restoration approach based on punctual kriging, supported by multiple machine learning algorithms. The work focuses on restoring images corrupted by Gaussian noise by achieving an effective trade-off between two competing goals: producing smooth, visually pleasing results while preserving edge details and structural integrity.

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Adaptive Modulation And Coding Enhancement In 6G Wireless Networks Through Intelligent Algorithms

Authors: Nitu Shah, Saima Khan, Dr. Vikas Gupta, Sandip Nemade

Abstract: This study explores the application of smart algorithms on improved adaptive modulation and coding schemes in 6G systems. The rapid development of wireless communication requires system requirements advanced sufficient to dynamically maximize the spectral efficiency, simultaneously the ultra-reliable low-latency communication. By using machine learning-based intelligent modulation and coding scheme (MCS) selection, such as deep reinforcement learning with a Q-learning method and CNNs, this study attempts to fine-tune MCS selection in 6G networks. The conclusion from the proposed hypothesis is that intelligent algorithm driven adaptive MLC can deliver substantially higher performance in throughput/spectral efficiency/BER as compared to traditional lookup table and outer loop link adaptation methods. Results show that modular adaptive modulation and coding based on reinforcement learning achieves 10%-20% gain in terms of throughput over traditional outer loop link adaptation, and spectral efficiency gains lie between 12.64% ∼ 21.52% for different velocity conditions. The computational complexity of deep learning methodologies decrease up to 80%, with comparable block error rate performance. Results indicate that intelligent algorithms can achieve real-time channel quality adjustment, and improve key 6G performance metrics. This work paves the way for self- organizing wireless networks supporting a variety of quality-of-service demands in future generation communication systems.

DOI: http://doi.org/10.5281/zenodo.18231654

Plant-Leaf Disease Detection Using Deep Learning Techniques

Authors: Deepika Soni, Neelesh Shrivtastav, Pradeep Tripathi

Abstract: Using imaging technology, we suggest that plant disease detection systems automatically identify the symptoms that occur on the leaves and stems of a plant, allowing for the cultivation of healthy plants on a farm to be improved. It is these systems that monitor the plant's characteristics, such as its leaves or stem, and any variations that are seen from those characteristics will be automatically recognised and sent to the user. The purpose of this paper is to conduct an evaluation of the available disease detection methods in plants. The most recent breakthrough in deep learning-based convolutional neural networks (CNNs) has resulted in a significant improvement in picture categorization accuracy. This Thesis, which is motivated by CNN's success in picture classification, uses a pre-trained deep learning-based technique for identifying plant illnesses to detect plant diseases. The contribution of this work may be divided into two categories: Predictions for a dataset may be made using the most powerful large-scale architectures available today, such as AlexNet GoogleNet, which are utilised for illness detection and the usage of baseline and transfer learning techniques for predictions. CNN's suggested model was trained and tested using data sets gathered from the website, according to the network. The results of training, testing, and experiments demonstrate that the suggested architecture is capable of realising and increasing GoogleNet model getting to 99.10 percent. when compared to other models, the accuracy.

A Review Of Deep Learning Techniques For Automated Plant Leaf Disease Detection In Agriculture

Authors: Umer Khan

Abstract: Agriculture is a primary source of livelihood for a large population in India and remains essential for human survival and national economic development. However, variations in climate and local environmental conditions increase the risk of crop diseases, which can significantly reduce yield and quality. In many cases, the earliest symptoms of plant infections appear on leaves and, if not detected in time, the disease can spread throughout the plant and across the field, leading to major production losses. Early and accurate identification of plant diseases is therefore critical for reducing agricultural losses and improving the quality of farm produce. Since manual inspection is difficult and time-consuming due to the large number of plants in cultivation areas, automated disease detection methods are increasingly required. This work proposes an AI-based software approach for detecting leaf infections to enable fast and reliable diagnosis, followed by evaluation and actionable insights to prevent large-scale crop damage. The proposed framework involves key steps including image dataset collection, image preprocessing, feature extraction/selection from leaf images, model evaluation, and disease classification. The overall goal is to support timely disease management and improve crop productivity and profitability.

Deep Learning-Based Gender Recognition From Facial Images Using Benchmark Datasets

Authors: Gayatri Solanki, Abhay Mundra

Abstract: Gender identification from facial images is an important problem in computer vision and has attracted growing interest due to its applications in surveillance, security, and human-centered systems. Although humans can infer gender naturally, developing automated systems that perform reliably across real-world conditions remains challenging. This work presents a gender classification framework that leverages face recognition feature vectors for prediction. First, face images are detected, aligned, and preprocessed to obtain a standardized facial representation. Next, a face recognition network extracts compact feature embeddings that encode discriminative facial characteristics. Finally, machine learning and deep learning classifiers are applied to these embeddings to determine gender. The proposed system integrates advanced components including VGG-Face, Deep Belief Networks, and shifted filter responses to improve robustness. Multiple deep learning architectures were investigated—CNN, VGG16, ResNet50, InceptionV3, and EfficientNet—with ResNet152 showing the strongest overall performance. Experimental findings indicate that ResNet152 achieves approximately 9% improvement over leading alternatives and demonstrates enhanced resilience to anomalies and variations compared with earlier approaches.

A Comparative Study Of CNN, ResNet50, U-Net, YOLOv7, And InceptionResNetV2 For Brain Tumor Classification In MRI

Authors: Devendra Gupta, Abhay Mundra

Abstract: Brain tumor detection from magnetic resonance imaging (MRI) is essential for early diagnosis, treatment planning, and improved patient outcomes. This study conducts a comparative evaluation of five deep learning approaches—CNN, ResNet50, U-Net, YOLOv7, and InceptionResNetV2—for automated brain tumor classification. Prior to model training, the dataset was prepared through systematic preprocessing, including data cleaning, normalization, and augmentation to improve robustness and reduce overfitting. Model performance was assessed using standard classification metrics: accuracy, precision, recall, and F1-score. Experimental results indicate that all evaluated architectures achieved strong predictive performance; however, InceptionResNetV2 consistently outperformed the other models, achieving near-perfect scores across all evaluation measures. This strong performance suggests improved reliability in reducing both false-positive and false-negative predictions, making InceptionResNetV2 a promising candidate for clinical decision-support applications. Overall, the findings highlight the importance of advanced deep learning architectures in delivering accurate and dependable MRI-based brain tumor detection.

Comparative Review Of Brain Tumor Segmentation Techniques: Classical Methods, CNN/U-Net Models, And Hybrid Frameworks

Authors: Devendra Gupta, Abhay Mundra

Abstract: Brain tumor segmentation is a critical task in medical imaging, supporting accurate diagnosis, treatment planning, and continuous monitoring of tumor progression. Over the years, a variety of segmentation strategies have been proposed, each offering distinct advantages and limitations. Early traditional approaches—including thresholding, edge-based detection, and region-growing methods—are computationally efficient and simple to implement, but they often perform poorly in the presence of noise, intensity inhomogeneity, and complex or ambiguous tumor boundaries. Statistical and model-driven techniques, such as clustering methods and deformable models, improve adaptability to anatomical variability but typically require careful parameter selection and may involve higher computational cost. In recent years, machine learning and deep learning methods have transformed brain tumor segmentation, particularly through the use of Convolutional Neural Networks (CNNs) and U-Net-based architectures, which have demonstrated strong accuracy and robustness across large and diverse MRI datasets. More recently, hybrid methods that combine classical image processing with deep learning have gained attention for improving efficiency, interpretability, and generalization. This review summarizes the evolution of brain tumor segmentation methods, compares major categories of approaches, and discusses current challenges and promising future research directions.

Review Of Gender Identification Using Machine And Deep Learning

Authors: Gayatri Solanki, Abhay Mundra

Abstract: Gender identification has gained significant attention in recent years because it supports many real-world applications related to demographic analysis and human-centered systems. Gender classification refers to the automated process of predicting a person’s gender (typically male or female) based on visual appearance, most commonly from facial images. However, extracting reliable and discriminative facial features remains challenging due to variations in lighting, pose, facial expressions, occlusion, and image quality. With advances in machine learning and deep learning, automatic gender classification systems have become increasingly accurate and widely adopted across multiple domains. These systems can be useful in security and access-control environments, as well as in demographic analytics and personalized services. In certain contexts, gender identification may also be applied to manage access in gender-specific spaces and services, such as women-only transportation sections or gender-segregated facilities. This review summarizes key traditional machine learning approaches (e.g., SVM-based methods) and modern deep learning techniques (e.g., convolutional neural networks), and discusses commonly used benchmark datasets and evaluation practices for gender classification.

Role Of Secondary Alcohol In Affecting The Kinetics And Thermodynamic Extensive Properties Of Catalysed Solvolysis Of The Substituted Aliphatic Formate

Authors: Dr. Kumari Priyanka

Abstract: Role of a secondary alcohol (Propan-2-ol) in affecting the kinetics and the thermodynamic extensive properties of the acid catalysed solvolysis of iso-butyl formate has been highlighted by studying the kinetics of the reaction in different aquo-propan-2-ol reaction media having 20 to 80% (v/v) of propan-2-ol and at five different temperatures rising from 20 to 40°C at interval of 5°C It was found that with increase in Jemperature of the reaction from 20 to 40°C from 0.172 to 1.344 molecules of water are associated with the activated complex and from this, it is inferred that mechanistic path followed by the reaction in presence of propan-2-ol is changed from bimolecular to unimolecular. The depletion and enhancement observed respectively in iso-composition and iso-dielectric activation energies reveal that the transition state is solvated and initial state is desolvated with addition of propan-2-ol in reaction media. Almost unity value of the slope of the plots of log k values against log [H+] values shows that the reaction follows AAC2 mechanism. From the values of iso-kinetic temperature, which comes to be 287, it may be concluded that in water-propan-2-ol reaction media, the reaction follows Barclay-Butler rule and there is weak but acceptable interaction between solvent and solute in aquo-propan-2-ol reaction media

DOI: https://doi.org/10.5281/zenodo.18373220

Smart Tourist Safety

Authors: Dev Dharrshan S, Divya Dharshini S, Anusree D, HemaPriya Vs, Avaneesh R, Deshika Sri KM, Dhavanithi M

Abstract: Smart Tourist Safety refers to the integration of digital technologies such as the Internet of Things (IoT), mobile applications, artificial intelligence, and real-time data analytics to enhance the safety and security of tourists. With the rapid growth of global tourism, ensuring tourist safety has become a critical concern for destinations and governments. Smart safety systems enable real-time monitoring, emergency response, location tracking, and risk prediction, helping tourists navigate unfamiliar environments securely. This study explores the concept of Smart Tourist Safety, examines key technologies involved, and discusses their role in improving emergency management, crime prevention, and overall tourist confidence. The findings highlight that smart safety solutions not only reduce risks but also enhance destination attractiveness and sustainability.

A Comprehensive Review Of Long-Term Variability And Recent Advances In Equatorial And Low-Latitude Ionospheric Research Over The Indian Region (2000–2025)

Authors: Lekshmi O Nair

Abstract: The equatorial and low-latitude ionosphere over the Indian region has undergone significant changes during the past 25 years (2000-2025), spanning three complete solar cycles. This comprehensive review synthesizes observational evidence from ground-based ionosondes, GPS Total Electron Content (TEC) measurements, satellite missions, and space weather monitoring networks across the Indian longitude sector (60°E-100°E). Key findings reveal systematic variations in the Equatorial Ionization Anomaly (EIA) morphology, with the northern crest showing enhanced TEC values during solar maximum periods and distinctive seasonal asymmetries. Equatorial Spread F (ESF) and plasma bubble occurrence demonstrate strong correlations with solar flux variations, geomagnetic activity, and atmospheric tidal forcing. The review highlights technological advances including the Indian Network for Space Weather Impact Monitoring (InSWIM), improved ionospheric models, and enhanced prediction capabilities. Significant space weather events, including the 2003 Halloween storms, 2015 St. Patrick's Day event, and recent solar cycle 25 disturbances, have provided insights into ionosphere-magnetosphere coupling processes. Climate change effects on the upper atmosphere are emerging as new research frontiers, with evidence for long-term trends in ionospheric parameters. Future challenges include understanding mesosphere-lower thermosphere-ionosphere coupling, developing regional ionospheric models for navigation applications, and preparing for increasing space weather threats to technological infrastructure.

DOI: http://doi.org/10.5281/zenodo.18242035

Performance Comparison Of Video-Based, Graph-Based, And Cloud-Hosted Vector Storage Systems: An Empirical Study Of MemVid, Qdrant, And Amazon S3 Vector

Authors: Vishrut Nath Jha, Joanne Anto, Athira KK

Abstract: Recent advancements in vector databases and embedding-based retrieval have transformed how large unstruc- tured datasets are indexed and searched. However, the perfor- mance of emerging storage and retrieval systems varies widely depending on their architectural design and optimization goals. This study presents a comparative evaluation of three distinct approaches MemVid, Qdrant, and Amazon S3 Vectors using a dataset of 10,417 medical-text chunks derived from research documents. Each system was assessed in terms of indexing efficiency, retrieval accuracy, latency, and resource utilization. Experimental results demonstrate that MemVid, which stores em- beddings in a video-encoded FAISS-based format, achieved lower query latency and higher retrieval precision for this corpus, while Qdrant exhibited superior scalability and flexibility in handling dynamic updates and metadata filtering. Amazon S3 Vectors, though currently in preview, offered cloud-native durability and seamless AWS integration with moderate performance overhead. The analysis reveals that no single vector system universally outperforms others; rather, each excels under specific workload conditions. These findings provide practical guidance for selecting appropriate vector storage backend based on corpus size, update frequency, and deployment environment.

DOI: http://doi.org/10.5281/zenodo.18243168

A Systematic Review on Detection of Fake Video Through Deep Learning

Authors: Suman Lata, Dr. Upendra Kumar Srivastava

Abstract: Generative models such as GANs and diffusion systems enable the creation of highly realistic fake videos, eroding confidence in online content across social, political, and legal domains. Synthesizing insights from more than 85 scholarly articles published between 2018 and 2025, this work categorizes detection methods into spatial CNNs that identify frame-level flaws, temporal RNNs/LSTMs for motion inconsistencies, RPG-based physiological cues, and fused audio-video approaches. Evaluations on datasets like Celebs and Wild Deepfake yield accuracies above 95% in controlled settings, but cross-dataset generalization and defenses against advanced forgeries falter. Hybrid architectures with transformers emerge as leaders, revealing critical gaps in real-time efficiency and edge-device applicability to steer forthcoming innovations.

DOI: https://doi.org/10.5281/zenodo.18253039

Glaucoma Detection Using Image Processing

Authors: Pavan M, Pavan V S, Pranav H Nayak, Sanath G, Dr. R Manjunatha

Abstract: Glaucoma is a serious eye disease that leads to irreversible vision loss if not detected at an early stage. It is primarily caused by increased intraocular pressure, which damages the optic nerve. Early diagnosis plays a crucial role in preventing permanent blindness; however, traditional diagnostic methods are time-consuming and require expert ophthalmologists. This project presents an automated glaucoma detection system using digital image processing techniques applied to retinal fundus images. The proposed system focuses on extracting key features such as the optic disc, optic cup, and calculating the cup-to-disc ratio (CDR), which is a significant indicator of glaucoma. Image preprocessing techniques including noise removal, contrast enhancement, and segmentation are employed to improve accuracy. The extracted features are then analyzed to classify the eye as normal or glaucomatous. The system aims to provide a cost-effective, efficient, and reliable method for early glaucoma screening, thereby assisting ophthalmologists in diagnosis and reducing the risk of vision loss.

The Opportunities And Risks Of Artificial Intelligence-driven Taxation From An International Perspective

Authors: James Anderson

Abstract: The use of artificial intelligence technologies in tax administration is becoming increasingly widespread worldwide to increase efficiency and detect fraud. Tools such as chatbots, risk ratings and predictive analytics optimise workflows, but their wider use in administrative decision-making raises legal and structural challenges. There is a critical difference between decision-supporting and autonomous artificial intelligence. Over-reliance on automated systems risks eroding legal expertise and obscuring decision-making, making it difficult for taxpayers to seek redress. Taxpayer profiling carries the risk of discriminatory treatment, so rigorous testing and minimisation of bias are necessary. In terms of methodological foundations, the study used dogmatic and transdisciplinary analysis to examine the opportunities and risks from an international perspective. The advantages of artificial intelligence include the real-time analysis of large amounts of data, which helps to filter out tax avoidance schemes and reduce the administrative burden on taxpayers (e.g. pre-filled tax returns). At the same time, the "black box" phenomenon violates the principle of transparency. The US and the OECD aim to improve efficiency and develop taxpayer services using artificial intelligence tools. The EU takes a risk-based approach, imposing strict requirements on high-risk artificial intelligence systems and emphasising the need for human oversight and legal remedies. Australian examples (Robodebt, Pintarich cases) highlight the legal and human rights risks of faulty algorithms, underlining the need for accountability. Success lies in striking a balance: while exploiting technological efficiency, it is necessary to guarantee human oversight, the accountability of algorithms and the protection of taxpayers' fundamental rights. Artificial intelligence should support fair law enforcement, not replace it.

DOI: http://doi.org/10.5281/zenodo.18265078

Understanding Cybercrime and Its Impact on Women: Legal and Societal Challenges

Authors: Arona Mumtaz

Abstract: Cybercrime has emerged as one of the most significant threats in the digital era, particularly impacting vulnerable groups such as women and children. With the growing use of the internet, cybercriminals exploit anonymity to engage in illegal activities that range from harassment to defamation and pornography. This paper examines various forms of cybercrime, with a particular focus on crimes targeting women, such as cyber harassment, cyber stalking, and cyber pornography. It discusses notable cases and the legislative framework in India aimed at combating these crimes. Despite existing laws, the paper highlights gaps in enforcement and the challenges posed by anonymity on the internet. Additionally, empirical evidence is presented to highlight the prevalence of cybercrime, its impact on victims, and the challenges in enforcement. The article concludes by offering suggestions for improving legal enforcement, public awareness, and privacy protection to combat the rising tide of cybercrime.

DOI: https://doi.org/10.5281/zenodo.18266294

(2, R2(r − 1))-Support Regular Graphs

Authors: Dr. N.R.Santhi Maheswari, L.Subhalakshmi

Abstract: A graph G is (2, r2(r − 1))-support regular, if the 2-support of every vertex is r2(r − 1), where r is the number of vertices at distance one and r(r − 1) is the number of vertices at distance 2 for every vertex. This paper deals with the concept that, for r > 1, a r-regular graph of girth ≥ 5 is (2, r2(r − 1)) support regular. The (2, r(r − 1)2) support regular graph’s existence is also proved and illustrated. Construction of such graphs is also given with some of its properties

Impact Study of Retrofitting a Smart City Project: The Case Study on Surat Castle (Old Fort)

Authors: Rashi N. Sadadiwala, Ashwani Raj, Dr. Krupesh A Chauhan

Abstract: Surat, a historically significant urban center in western India, is undergoing rapid urbanization, placing considerable pressure on its cultural heritage assets such as Surat Castle (Old Fort), the Dutch Cemetery, and several other historic precincts. Renowned for its textile and diamond industries, the city contributes substantially to the national economy and today spans approximately 461.60 sq. km, accommodating a population of nearly 8 million. Constructed in 1540—41 as a defensive bastion against Portuguese incursions, Surat Fort has transitioned through multiple regimes—serving as a Mughal military stronghold, a British administrative establishment, and later State Government offices. Each occupation period has left architectural and spatial imprints, collectively narrating the city's evolving political and cultural trajectory. Recognizing the fort's heritage value, the Surat Municipal Corporation (SMC) initiated a comprehensive retrofitting and conservation program under the Smart City Mission launched in 2015. The Surat Municipal Corporation (SMC) carried out the development in three distinct phases. Phase 1 focusing on foundation and primary restoration, phase 2 focusing on adaptive reuse and cultural hub whereas phase 3 focusing on heritage square and urban integration. Quantitative analysis of visitor data from January 2019 to November 2025 demonstrates a significant shift in tourism dynamics. This study critically evaluates the impacts of these interventions, particularly how heritage conservation can be harmonized with contemporary urban renewal strategies. The retrofitting works have catalyzed the revitalization of the Chowk precinct, enhanced tourism potential, and strengthened civic identity. The transformation of the one-kilometer radius urban fabric surrounding the fort, thereby reinforcing Surat's image as a dynamic yet culturally rooted urban environment. The Chowk area now emerges as a city center and heritage square, seamlessly integrating with key urban nodes—Andrews Library, J.J. Training College, the Old Civil Hospital, the Anglican Church, Gandhi Baug, local bazaars, and the SMC Muglisara institutional cluster—alongside the newly developed metro station. This research also provides a structured repository of recorded observations and spatial analyses, serving as a reference framework for future scholars and practitioners. Overall, Surat Fort stands as a model for adaptive reuse. The study further aids policymakers, administrators, consultants, and researchers in replicating similar heritage-led retrofitting initiatives in other cities.

Thermo-Mechanical Modeling And Residual Stress Analysis In Additively Manufactured AlSi10Mg: A Review

Authors: Pankaj Kumar Rai, Dr. P. N. Ahirwar

Abstract: Additive manufacturing (AM) processes qualifies in producing high-performance, complex design component with an efficient use of material. However, processing of fusion based additive manufacturing processes such as Laser Powder Bed Fusion Processes (LPBF) generates thermal stresses due to rapid heating and cooling cycles. The accumulation of these residual stresses in the printed component is undesirable and may result in dimensional distortion, anisotropy, and premature failure of components during service. Aluminium alloys such as AlSi10Mg are processed through LPBF route of AM due its excellent printability and its application in aerospace applications due to its superior fly to weight ratio. However, the printed AlSi10Mg faces challenges due to its high thermal conductivity and residual stresses. These stresses hinder dimensional tolerances and worsen mechanical performance. This review provides the overview of additive manufacturing processes with the physics of residual stress development and residual stresses in AlSi10Mg. A detailed discussion on residual stress generation, measurement and management are presented. The residual measurement strategies involving destructive, semi-destructive, and non-destructive and state-of-the-art numerical modeling approaches, including finite element–based and data-driven methods. This review aims to provide a comprehensive insight of the residual stress in additively manufactured AlSi10Mg to help in designing of component for practical application.

DOI: http://doi.org/10.5281/zenodo.18279481

Taxonomic Diversity And GC Content Variation In Bacteria Community Of Plantain (Musa Paradisiaca L.) Rhizosphere_874

Authors: Wofu, N. B, Nwauzoma, A. B, Chuku, E. C, Nmom F. W.

Abstract: Plantain (Musa paradisiaca L.), Nigeria’s third most important starchy staple, depends on rhizosphere bacteria for nutrient acquisition and stress tolerance, yet its microbial profile remains underexplored. This study applied 16S rRNA amplicon sequencing to characterize bacterial diversity and GC content in plantain rhizosphere from Rivers State, Nigeria. Diseased plantain roots were collected from the Rivers State Institute of Agriculture Research and Teaching (RIART) Farm, Port Harcourt, Nigeria. Genomic DNA was extracted from plantain roots and amplicons sequenced following Laragen’s validated proprietary. The metagenomic data were analyzed using Laragen’s proprietary in-house pipeline based on BLAST searches for taxonomic classification. The results revealed that Proteobacteria dominated (54.81%), followed by Verrucomicrobia (16.48%), Bacteroidetes (12.28%), Actinobacteria (8.10%), and Planctomycetes (3.16%). Alphaproteobacteria (29.1%), Gammaproteobacteria (21.4%), and Rhizobiales (23.1%) were prevalent at class and order levels. Dominant genera included Luteolibacter sp. (14.5%), Pseudoxanthomonas sp. (14.5%), and Devosia sp. (13.8%), with unclassified taxa reaching 38.4% at genus/species levels. GC content varied widely (<30% to ~70%), highest in Gordonia sp. and Paracoccus sp., lowest in Paludibacter sp. and Pseudoxanthomonas sp. The study revealed marked genomic diversity in the rhizosphere of plantain. Future studies should use shotgun metagenomics, isolate key taxa, and develop targeted bioinoculants to improve plantain productivity and sustainability in Nigerian agroecosystems.

DOI: http://doi.org/10.5281/zenodo.18279647

Cloud Computing in Education: A review of Architecture, Applications, and Integration Challenges

Authors: Swetha Pradeep, Shreedharini Y

Abstract: Cloud computing has emerged in recent times as a disruptive technology that has favourably influenced the functioning of many businesses, organizations, and institutions. The utilisation and prevalence of cloud computing arise from an on- demand model that provides computing services via the internet. Several academic institutions have incorporated cloud computing into the educational process to enhance pedagogical outcomes. The review aims to examine cloud computing in education and the need for educational institutions to comprehend its primary advantages. In this review, we discussed the architectural integrations of cloud computing services in education, encompassing Software as a Service (SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS) models. The outcome of this study includes a visual representation of the educational trends in cloud computing, the impact of cloud educational technologies, and the major challenges facing its adoption. This review will augment literature on cloud computing, its application in educational institutions, and anticipated challenges.

Role of AI and Big Data in promoting Sustainable Investment Strategies

Authors: Prof. Abhijit Chakraborty, Vinayak Ramakrishna Bhat, Samartha Vinayak Hegde, Prasanna Vighneshwara Hegde

Abstract: The rapid expansion of sustainable investment has created an urgent demand for reliable, comprehensive, and verifiable ESG information. Yet, despite its growing importance, the field continues to struggle with fragmented reporting standards, selective disclosures, and the increasing prevalence of greenwashing, all of which undermine the integrity of sustainability-focused financial decisions. This study provides an in-depth qualitative exploration of how Artificial Intelligence (AI) and Big Data are beginning to address these persistent issues and reshape the foundations of sustainable investment strategies. Drawing on a wide range of scholarly publications, institutional reports, and contemporary discussions from 2020 to 2025, the research examines how emerging digital tools are being used to interpret and validate sustainability performance. The literature shows that AI techniques such as natural language processing, intelligent screening algorithms, and pattern recognition models are improving the credibility of ESG evaluations by identifying inconsistencies, revealing undisclosed risks. Big Data strengthens this process by incorporating diverse and externally sourced evidence, including satellite-based environmental monitoring, supply-chain traceability systems, climate-risk datasets, and real-time operational. These combined capabilities not only enhance transparency but also reduce information asymmetry, allowing investors to form a more accurate understanding of a firm’s sustainability behavior and long-term risk exposure. The study finds that the use of AI and Big Data is gradually shifting sustainable investing from a disclosure-driven model dependent on voluntary corporate reporting to a more evidence-based and analytically rigorous approach. This transition supports stronger investor confidence, encourages more accountable corporate behavior, and aligns investment decisions more closely with global sustainability objectives. While challenges remain, including data standardization and ethical concerns in AI use, the overall trajectory suggests that AI and Big Data are poised to become essential pillars of future sustainable finance. This study examines how advanced analytical technologies contribute to improving the reliability, transparency, and investment relevance of ESG assessments. Using secondary data from a sample of forty publicly listed companies across multiple industry sectors, the study employs descriptive statistics, sector-wise analysis, regression modelling, and one-way ANOVA to examine patterns in ESG performance and sustainability risk. The analysis reveals significant sectoral differences in ESG scores, with technology and healthcare firms demonstrating relatively higher sustainability performance compared to energy and manufacturing sectors. Regression results indicate a strong negative relationship between ESG scores and sustainability risk, suggesting that higher sustainability performance is associated with improved risk management outcomes. The findings also show that firms adopting AI-driven analytics and structured sustainability reporting practices tend to achieve higher ESG scores and lower risk exposure.

Iot manhole monitor for manhole cover gas and temperature sensor

Authors: Gauri Bhambere, Roshani Gaikwad, laxmi Ingole, Sanjivini Gore, prof. Prachi Walunj

Abstract: Urban drainage and sewer manholes often pose serious safety risks due to the accumulation of toxic gases and abnormal temperature rise, which can lead to explosions, health hazards, and infrastructure damage. This project presents an IoT-based Manhole Monitoring System designed to continuously monitor hazardous gas levels and temperature inside manholes in real time. The system employs gas sensors to detect harmful gases such as methane, hydrogen sulfide, and carbon monoxide, along with temperature sensors to identify overheating or fire risks. Sensor data is collected by a microcontroller and transmitted wirelessly to a cloud platform using IoT communication technologies. When abnormal conditions are detected, instant alerts are sent to authorities via a web or mobile application, enabling quick preventive action. The proposed system improves worker safety, enhances public safety, reduces manual inspection efforts, and supports smart city infrastructure by providing reliable, real-time monitoring of underground manhole conditions.

DOI: http://doi.org/

Tunnel Electrification For Road Using Esp32 Based Smart Lighting And Safety System

Authors: Sandhyarani Balasaheb Kunjir, Suraj Rajiv Jaybhaye, Pradeep Sanjay Kapse, Abhishek Sunil Adagale, Mrs. Adagle P. M

Abstract: Tunnel electrification is essential for road safety and visibility inside tunnels. Conventional tunnel lighting consumes high power because lights remain ON continuously irrespective of traffic density. This research paper presents an ESP32 based smart lighting and safety system for road tunnels. The system uses sensors such as IR/Ultrasonic sensor for vehicle detection and LDR sensor for ambient light detection. Based on real-time sensor input, the ESP32 controller activates tunnel lighting section-wise, which reduces unnecessary electricity consumption. The system is further expandable with IoT monitoring through Wi-Fi for remote supervision and fault indication. The proposed smart tunnel electrification system improves energy efficiency, enhances safety and provides an economical solution for smart city infrastructure.

Hybrid Deep and Ensemble Learning for Adaptive Financial Time-Series Forecasting

Authors: Vansh Shisodia, Saibee Alam, Anish Kushwaha, Aarchi Goyal

Abstract: This research constructs a hybrid system for one- step-ahead (H=1) stock forecasting, addressing the non-linear and non-stationary nature of financial time-series. The objective is twofold: a regression task for the Adjusted Close Price and a binary classification task for directional movement. The pro- posed ensemble design combines three model families: classical econometrics (ARIMA), deep learning (LSTM) for long-term dependencies, and ensemble tree methods (XGBoost, RF) for non- linear feature interactions. The methodology emphasizes rigorous feature engineering, including technical indicators and GARCH- derived volatility features, and robust validation using Time Series Cross-Validation (TSCV) and Nested Cross-Validation (nCV). The system culminates in a stacked ensemble (blending layer) and utilizes advanced loss functions like Huber Loss to manage heavy-tailed return distributions Evaluation is based on both statistical fit and financial utility metrics, such as directional accuracy and the Sharpe Ratio.

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Beyond Statistical Fairness: A Systematic Review Of Novel Metrics For Identifying Algorithmic Bias In AI-Driven Governance

Authors: Abubakar Sadiq Yusha’u, Aminu Aliyu Abdullahi

Abstract: Artificial Intelligence (AI) systems are increasingly embedded in public governance for decision-making in areas such as welfare distribution, predictive policing, taxation, immigration, and electoral administration. While these systems promise efficiency and scalability, they also introduce significant risks of algorithmic bias with direct implications for equity, accountability, and democratic legitimacy. This study presents a systematic literature review (SLR) on metrics for identifying algorithmic bias in AI-driven governance models, with a particular emphasis on novel and governance-aware measurement approaches. The review follows PRISMA guidelines and analyzes peer-reviewed journal articles, conference proceedings, and high-impact policy reports published between 2014 and 2025. Literature was sourced from Scopus, Web of Science, IEEE Xplore, ACM Digital Library, and SpringerLink using structured search strings related to algorithmic bias, fairness metrics, and AI governance. After a multi-stage screening and eligibility process, the selected studies were subjected to qualitative thematic synthesis and comparative analysis. The results reveal that traditional statistical fairness metrics such as demographic parity, equalized odds, and predictive parity are widely used but insufficient for governance contexts due to their lack of contextual, temporal, and institutional sensitivity. The review identifies and classifies emerging bias metrics into five major categories: causal metrics, intersectional metrics, temporal and dynamic metrics, structural–institutional metrics, and explainability-driven indicators. These novel metrics demonstrate stronger alignment with governance principles, particularly in addressing power asymmetries, historical discrimination, and policy constraints. The study contributes a consolidated taxonomy of bias metrics and proposes an integrated, multi-dimensional framework for evaluating algorithmic bias in AI-driven governance systems. The findings offer practical guidance for policymakers, regulators, and system designers, while highlighting critical research gaps related to standardization, empirical validation, and Global South governance contexts.

DOI: http://doi.org/10.5281/zenodo.18312609

The Future of Learning with AI Systems

Authors: R.S. Singaravel, R.S. Jothirajan

Abstract: This article focuses on how AI enabled systems can adapt to context in instructional approaches and school learning. And also it emphasizes AI enabled systems fools, models, etc., Recommendations in this paper seek to engage teachers educational leaders, Policy makers, researchers and educational technology innovators and providers as they work together on pressing policy issues that arise as Artificial Intelligence(AI) is used in education Examples of AI supporting learning principles in this section include the following: AI based tutoring for students as they solve math problems, adapting to Learners with special need. Formative assessment is traditionally a key use of edtech because feedback loops are vital to improving teaching and learning. As seen in voice assistants mapping tools and other familiar applications AI may enhance educational services. In summary, it is imperative to address the role of AI in education now in order to realize key opportunities, prevent and mitigate emerging risks, and manage unintended consequences.

Experimental Investigation About Textile Reinforced Concrete for Repairing and Strengthening Reinforced Concrete Beams

Authors: Salla Arun Tejadhar Reddy, Prashant S. Lanjewar

Abstract: Textile reinforced concrete (TRC) is a relatively new and innovative composite material developed to provide improved performance characteristics when used in the repair and retrofitting of reinforced concrete (RC) beams. TRC offers superior tensile and flexural strength compared to traditional RC materials and has been widely used in civil engineering projects for repair and strengthening applications. The paper provides an experimental investigation into the performance of TRC when used in the repair and strengthening of RC beams.

Design and Implementation of a Three Phase Failure Detector Using Arduino Nano

Authors: Aarti Bhowal, Sanskruti Deshmukh, Raj Darade, Dhruv Dhage, Prof.Ananta Walekar

Abstract: Three-phase power systems are widely used in industrial and commercial applications due to their efficiency and reliability. However, failures such as phase loss, voltage imbalance, and phase interruption can cause serious damage to electrical equipment. This paper presents the design and implementation of a Three Phase Failure Detector using an Arduino Nano. The system continuously monitors the voltage of each phase using voltage sensor modules. When a fault condition is detected, the system provides visual and audible alerts using an OLED display, LED indicators, and an active buzzer. The proposed system is compact, low- cost, and suitable for real-time monitoring applications.

Footstep power genrator using piezo electric senser

Authors: Omkar Dhas, Sarthak Kulthe, Akhilesh Barate, Pruthviraj Zinge, Prof. Sonali Navale. S

Abstract: In this paper, the design of a footstep-based power generation system using piezoelectric sensors is presented. The increasing demand for energy due to rapid population growth has led to the depletion of conventional power resources. Therefore, the utilization of renewable and alternative energy sources has become essential. This proposed system focuses on harvesting mechanical energy generated from human footsteps and converting it into electrical energy. The concept is highly suitable for densely populated countries like India and China, where public places such as railway stations, bus stands, and streets experience continuous human movement. By implementing this system, the mechanical energy produced during walking can be efficiently converted into usable electrical energy.

Personal Health Advisor App For People

Authors: Walid Bebal, Joydeb Nandi, Taha Ghole, Prof.S.E.Gawali

Abstract: This paper presents HealthAI, a mobile-based per- sonal health advisor application designed to improve preventive healthcare awareness. The application integrates multiple public APIs to deliver real-time weather-based health insights, nutrition analysis, drug safety alerts, and health-related news through a unified mobile interface. A modular client–server architecture is adopted to ensure scalability, reliability, and low response latency. Experimental evaluation demonstrates that the system performs efficiently under varying workloads, making it suitable for real- world mobile health applications.

DOI: http://doi.org/10.5281/zenodo.18428124

SNIPP- A Remote Interview Platform with Integrated Code Editor

Authors: Keshav Jangir, Manish Saini, Rupali Tanwar, Hridyansh Pradhan

Abstract: SNIPP is a smart remote interview platform that allows secure technical assessments through real-time coding and AI evaluation. It includes MediaPipe-based proctoring, role-based question generation, fullscreen enforcement, and automated handling of violations. These features help ensure fair and reliable interviews. The platform uses a full-stack setup with Next.js for both the frontend and backend. It uses Convex for real-time data synchronization, Clerk for secure authentication, and Monaco Editor for an interactive coding environment. It supports conflict-free interview scheduling, automatic email notifications, and real-time updates through Convex mutations. The system allows browser-based code execution across multiple programming languages with a responsive and device-optimized interface. Key features include MediaPipe AI proctoring, AI-generated questions, a 6-strike violation policy, automatic interview termination, and enforced fullscreen mode. It provides detailed violation reports after interviews, while role-based access control helps manage sessions securely and maintains data integrity. Thorough testing confirmed the platform's effectiveness and reliability. It achieved a 100% functional test pass rate and can handle up to 50 concurrent users. The average API response time is 1.5 seconds. The platform is fully secure, implementing JWT-based authentication and input validation, and maintained 99.9% uptime during load testing. SNIPP delivers a scalable, robust solution that reduces scheduling conflicts in remote interviews, removes the need for infrastructure setup for coding assessments, and helps recruiters work efficiently.

DOI: http://doi.org/10.5281/zenodo.18337196

Revolutionizing Agriculture: Emerging and Unconventional Applications of Artificial Intelligence

Authors: Gopal Ghosh

Abstract: The integration of Artificial Intelligence (AI) in agriculture has transcended traditional applications like precision farming and crop monitoring. This paper explores cutting-edge and unconventional uses of AI, such as predictive climate-resilient agriculture, AI-driven bioengineering, autonomous swarms, and ethical AI for sustainable farming practices. It delves into the role of AI in reshaping the agricultural landscape by optimizing not only productivity but also sustainability and resilience against climate change. The paper concludes with a discussion on future possibilities, challenges, and the need for inclusive AI solutions in agriculture.

Comparative Analysis Of Private, Public, And Hybrid Cloud Models For Academic Library Data Storage Security

Authors: Mr. Abhay Pathak

Abstract: The rapid expansion of digital resources and user expectations in academic environments has driven universities and research institutions to adopt cloud-based data storage solutions for their libraries. With the growing volume of sensitive academic content, user records, metadata, and digital archives, the security of academic library data has emerged as one of the most critical concerns for library administrators, IT personnel, and stakeholders. This paper presents a comprehensive comparative analysis of private, public, and hybrid cloud models with a specific focus on data storage security in academic library environments. The study examines the fundamental architecture, security mechanisms, governance controls, performance trade-offs, legal and compliance implications, and cost considerations associated with each cloud model. Private cloud solutions, hosted either on-premises or in secure managed environments, offer strong data control and customizable security policies, but may require substantial operational investment and in-house expertise. Public cloud services, provided by global vendors such as AWS, Microsoft Azure, and Google Cloud Platform, deliver scalable storage, advanced built-in security features, and cost flexibility, but they introduce concerns related to multi-tenant exposure, third-party dependency, and complex regulatory compliance across jurisdictions. Hybrid cloud architecture emerges as a middle ground, combining the on-site control of private clouds with the scalability of public clouds, but also introduces additional complexity in secure integration, data partitioning, and unified policy enforcement. The abstract highlights that despite the rapid adoption of cloud technologies, academic libraries face nuanced security challenges that extend beyond basic encryption or access control. Issues such as secure data migration, key management, identity and access governance, incident response, and threat monitoring differ significantly depending on the chosen cloud model. This study utilizes comparative security metrics such as data confidentiality, integrity assurance, availability guarantees, authentication strength, and compliance readiness to evaluate each cloud paradigm. The research employs both qualitative expert assessment and quantitative performance measurements derived from simulated workloads on representative cloud environments. Results indicate that while public clouds often lead in raw scalability and advanced automated threat detection capabilities, private clouds consistently provide higher levels of administrative control and predictable performance under peak load. Hybrid solutions show promise for balancing security needs, cost, and flexibility, especially in libraries with mixed data classification levels — segregating highly sensitive materials in private segments while maintaining open access resources in public segments. Importantly, this paper also explores the human and governance factors associated with cloud security, including staff training, shared responsibility models, contract nuances with cloud vendors, and audit transparency.

DOI: http://doi.org/10.5281/zenodo.18348933

Artificial Intelligence–Based Mock Interviews for Performance Improvement

Authors: Mr.Santosh Handignoor, Mr.Himanshu Singh, Prof. Vaishali Suryawanshi, Prof. Dipak Kadve

Abstract: Interview readiness is a decisive factor in determining employability and professional advancement; however, a large number of students struggle to perform effectively due to limited practice opportunities, anxiety, and the absence of structured, objective feedback. Recent developments in Artificial Intelligence (AI) have enabled the creation of intelligent systems capable of simulating interview scenarios and evaluating candidates in a consistent and data-driven manner. This research examines an AI-based mock interview framework that utilizes Natural Language Processing for response evaluation, speech analytics for assessing confidence and fluency, and facial expression analysis for understanding non-verbal behavior. By combining these AI techniques, the system delivers personalized feedback that highlights communication gaps, confidence issues, and knowledge deficiencies. Unlike traditional mock interviews, the proposed approach allows repeated practice without dependency on human evaluators, ensuring scalability and fairness. The study is supported by quantitative analysis conducted on a student dataset, revealing notable improvements in interview performance, self-confidence, and communication effectiveness after exposure to AI-driven mock interviews. The results demonstrate that AI-based interview preparation tools can significantly enhance interview readiness and serve as an effective alternative to conventional training methods. This work reinforces the growing role of AI in employability skill development and its potential to transform interview preparation practices in academic and recruitment environments.

DOI: https://doi.org/10.5281/zenodo.18387039

Modeling COVID-19 Spread in Cameroon Using Gompertz Distribution Techniques

Authors: Leo. Tanyam. Encho, Abraham Okolo

Abstract: The Gompertz distribution is widely applied in describing human mortality, establishing actuarial tables, and various other fields. Historically, it was originally introduced by Benjamin Gompertz (1825) in connection with human mortality. This study aims to derive and analyze the mathematical and statistical properties of the Gompertz distribution, providing explicit expressions for parameter estimation from both frequentist and Bayesian perspectives. We then apply these estimation methodologies to analyze COVID-19 data in Cameroon. We investigate and compare numerous frequentist approaches for parameter estimation, including maximum likelihood, method of moments, pseudo-moments, modified moments, L-moments, percentile-based, least squares (including weighted), maximum product of spacings, minimum spacing absolute distance, minimum spacing absolute-log distance, Cramér-von-Mises, and Anderson-Darling (including right-tail) estimators. Their performance is evaluated using extensive numerical simulations, and their coverage probabilities are also assessed. Our results indicate that among the frequentist estimators, modified moments and moments estimators generally perform better than their counterparts. For Bayesian estimators, those based on the Mean Squared Error Loss Function (MSELF) and Kullback-Leibler Loss Function (KLF) demonstrate superior performance. The maximum product of spacings estimators also exhibit competitive performance.

DOI: https://doi.org/10.5281/zenodo.18359495

A Comparative Study Of Rule-Based AI Vs. Generative AI Models In Decision-Making Systems

Authors: Mohammad Sameer Hussain, Jaspreet Kaur, Er. Gundeep Kaur

Abstract: Decision making systems are using a combination of style rules and new style artificial intelligence to help people make good choices. The old style rules are good because they are clear and easy to understand and they make sure people follow the rules. The old style rules have some problems though. They are hard to scale up. They cost a lot to maintain. Decision making systems that use style rules do not adapt well to new situations. On the hand the new style artificial intelligence like the kind that understands human language can find patterns and help with tough decisions. The style artificial intelligence is really good, at helping people make good choices because it can understand what people are saying and find patterns that the old style rules cannot. The style artificial intelligence is a big help to decision making systems because it can do things that the old style rules cannot. Decision making systems that use the style artificial intelligence can make better choices because they have more information and can understand what people are saying. This kind of intelligence has some problems. Artificial intelligence can make things up. It can be hard to understand intelligence. Also when something goes wrong with intelligence systems like these artificial intelligence systems it is not clear who is responsible, for the artificial intelligence. This paper reviews expert perspectives on both approaches and compares them in terms of interpretability, robustness, data dependence, deployment constraints, and evaluation. Evidence across multiple domains suggests that hybrid architectures integrating explicit rules, structured knowledge, and generative components provide a practical path toward trustworthy and adaptive decision- making.

DOI: http://doi.org/10.5281/zenodo.18359722

Applications And Challenges Of AI-Driven Systems In The Modern Food Industry

Authors: Jigarkumar Ambalal Patel, Mayur Girish Taunk

Abstract: The food industry is one of the largest global employers, yet it faces ongoing challenges in demand–supply chain management and food safety due to heavy reliance on manual processes and human error. Artificial Intelligence (AI) and Machine Learning (ML) are increasingly being adopted to transform the industry across the entire "farm to fork" pipeline by improving efficiency, accuracy, and safety. This paper reviews key AI- and ML-driven applications, including smart farming for crop monitoring and yield optimization, automated product sorting and grading, electronic noses for spoilage detection, and vision-based dietary assessment. Despite these advances, significant challenges remain, such as inaccurate image segmentation, high intra-class variation in food appearance, and the lack of large, standardized datasets. Overcoming these limitations is crucial for enabling reliable and scalable real-world deployment of AI.

DOI: http://doi.org/10.5281/zenodo.18359803

Applications And Challenges Of AI-Driven Systems In The Modern Food Industry

Authors: Jigarkumar Ambalal Patel, Mayur Girish Taunk

Abstract: The food industry is one of the largest global employers, yet it faces ongoing challenges in demand–supply chain management and food safety due to heavy reliance on manual processes and human error. Artificial Intelligence (AI) and Machine Learning (ML) are increasingly being adopted to transform the industry across the entire "farm to fork" pipeline by improving efficiency, accuracy, and safety. This paper reviews key AI- and ML-driven applications, including smart farming for crop monitoring and yield optimization, automated product sorting and grading, electronic noses for spoilage detection, and vision-based dietary assessment. Despite these advances, significant challenges remain, such as inaccurate image segmentation, high intra-class variation in food appearance, and the lack of large, standardized datasets. Overcoming these limitations is crucial for enabling reliable and scalable real-world deployment of AI.

DOI: http://doi.org/10.5281/zenodo.18359803

Determination of Ascorbic Acid Content in Different Fruit Juices Under Various Storage Conditions Using Iodometric Titration

Authors: Ibrahim Abdurrashid, Ms. Ritu Sharma, Dr. Harish Saraswat, Dr. Giriraj, Jeevan Singh, Abubakar Musa Shuaibu

Abstract: This study investigated the impact of storage conditions room temperature, heat and cold on the levels of ascorbic acid (vitamin C) of chosen fruit juices like lemon, orange, apple, tomato and mango. Vitamin C was quantified by iodometric titration and the concentration of each fruit was recorded for the three conditions. the results revealed significant discrepancies both among the different fruits and the storage methods. Lemon juice always maintained the maximum ascorbic acid content of 2.1 at room temperature, 2.0 heated and 2.05 refrigerated, followed by orange at 1.8, 1.72 and 1.76 respectively. Mango has 1.1, 1.0 and 1.07, and apple at 0.92, 0.83 and 0.88 were moderately present, while tomato contained the lowest levels 0.72, 0.64 and 0.71. a common trend suggested that warming reduced ascorbic acid content in all fruit juices, validating vitamin C is heat labile nature. alternatively refrigeration preserved ascorbic acid content significantly better than room temperature and warming with values closer to initial concentrations.

DOI: https://doi.org/10.5281/zenodo.18372692

Self-Assistive Tool for Deaf and Dumb Beginners to Learn Volleyball with Hand Gestures

Authors: Dr. Kalyana Rajasekhar Babu

Abstract: Deaf and dumb individuals often face significant barriers in learning and engaging with team sports such as volleyball, primarily due to challenges in communication and instruction. Recent advancements in computer vision and machine learning have enabled the development of hand gesture recognition systems that can bridge this gap. This paper proposes a self-assistive tool that leverages hand gesture recognition for facilitating the learning of volleyball among deaf and dumb beginners. By integrating gesture interpretation, real-time feedback, and interactive instruction, this approach aims to foster inclusivity within sports education. Drawing upon recent studies in gesture recognition, human-computer interaction, and assistive technologies, this research outlines the system’s architecture, underlying algorithms, and potential impact on accessibility in sports training. The findings indicate that such tools, grounded in deep learning and computer vision frameworks, can empower deaf and dumb learners, enhance communication, and foster greater participation in athletic activities.

Transforming Clinical Practice: A Comprehensive Review of Artificial Intelligence in Medical Diagnosis and Treatment Planning

Authors: David Mark Abayomi, Obafaiye Pauline Olayemi

Abstract: The integration of Artificial Intelligence (AI) into healthcare is revolutionizing the paradigms of diagnosis and treatment (Topol, 2019). This paper provides a comprehensive review of contemporary AI applications, focusing on machine learning (ML) and deep learning (DL) models in image analysis, predictive analytics, and precision medicine. We conducted a systematic literature review of peer-reviewed articles and major clinical trials published between 2018 and 2023. Our analysis demonstrates that AI algorithms, particularly con- volutional neural networks (CNNs), now match or exceed human expert performance in diagnosing specific conditions from radiological (e.g., mammography, chest X-rays) and pathological images (Liu et al., 2021). In treatment, AI-driven tools are enhancing radiotherapy planning, predicting patient-specific drug responses, and powering clinical decision support systems (He et al., 2019). The discussion highlights transformative case studies, including AI for early sepsis detection and diabetic retinopathy screening, while critically addressing significant challenges: algorithmic bias (Obermeyer et al., 2019), the ”black box” problem, data privacy concerns, and the necessity for robust clinical vali- dation and regulatory frameworks (FDA, 2021). We conclude that AI holds immense potential to augment clinical decision-making, improve diagnostic accuracy, personalize treatment, and alleviate administrative burdens. However, its successful translation into routine care necessitates a collaborative focus on ethical AI development, interdisciplinary education, and human-centered design to ensure these tools are equitable, transparent, and effectively integrated into the clinical workflow.

DOI: https://doi.org/10.5281/zenodo.18372826

Smart Multi-Modal Analysis System

Authors: M. Gowsalya,, N. Devapriya, K. Abinaya

Abstract: In the modern digital era, the increasing demand for intelligent monitoring systems has become a critical concern across domains such as healthcare, surveillance, and smart environments. Conventional monitoring approaches primarily rely on single- modality data sources, which often limit their accuracy, reliability, and adaptability in real-world conditions. To address these limitations, this paper proposes a Smart Multimodal Analysis System (SMAS) that integrates multiple data modalities, including visual, audio, sensor, and textual information, into a unified intelligent framework. The proposed system leverages advanced machine learning and deep learning techniques to perform real-time data acquisition, preprocessing, feature extraction, and multimodal fusion. By combining information at both feature and decision levels, SMAS enhances detection accuracy and robustness, even in the presence of noisy or incomplete data. The system supports intelligent classification, anomaly detection, and predictive analysis, enabling timely alerts and informed decision-making. Experimental evaluation demonstrates that the multimodal approach outperforms traditional single-modality systems in terms of accuracy and reliability. The results highlight the potential of SMAS as an effective and scalable solution for next-generation smart monitoring applications.

DOI: https://doi.org/10.5281/zenodo.18410791

Architectural Disturbances In Generative Analytics Systems: A Demographic And Organizational Simulation Perspective (GASF Framework)

Authors: Neh Sharma

Abstract: GenAI has altered how businesses think about data and how they use it to make decisions. After 2020, better large language models (LLMs), retrieval-augmented generation (RAG), and agentic pipelines have made it possible for analytics systems to go from only reporting on data to coming up with fresh insights. But this change makes people very worried about fairness, openness, and data privacy, especially since models affect how businesses make decisions and how people from different backgrounds work together. This paper looks at new developments in architecture and talks about the ongoing ethical and evaluative problems that come up in generative analytics. A single Generative Analytics System Framework (GASF) is proposed, integrating architectural, evaluative, and ethical dimensions to achieve a balance between analytical efficacy and accountability. A simulation demonstrates that various departments and demographic user groups utilise LLM-based analytics in distinct manners. The findings indicate that user skill and contextual diversity influence factual accuracy, delay, and trust in distinct ways. This means we need to make systems that are fair and keep people's information safe. The report concludes with a proposal for research aimed at developing generative analytics ecosystems that are ethical, comprehensible, and adaptable to diverse populations.

Customer Churn Prediction In The Banking Sector: A Machine Learning And Deep Learning-based Hybrid Approach

Authors: Sangeeta Rani, Vikram Singh, Tanisha Mittal

Abstract: Customer churn poses a significant challenge to businesses, necessitating robust predictive solutions. We propose a novel hybrid stacking framework that integrates four diverse base classifiers—logistic regression (LR), random forest (RF), artificial neural network (ANN), and XGBoost—with a meta-learner to enhance churn prediction performance. In the first stage (Level 0), the base models independently learn from preprocessed customer behaviour and demographic features, capturing both linear and non-linear patterns. Their predicted class probabilities subsequently serve as input features to a deep feedforward neural network at Level 1, which functions as the meta-learner. This architecture is trained using categorical cross-entropy loss with the Adam optimiser, incorporating dropout to mitigate overfitting. The stacking ensemble leverages the complementary strengths of the base models (e.g., interpretability from LR, decision-boundary flexibility from RF, complex pattern recognition from ANN, and from XGBoost to achieve superior predictive accuracy and generalisation compared to any individual classifier. Experimental results on a real-world churn dataset demonstrate that the hybrid model consistently outperforms traditional baselines, achieving statistically significant improvements in AUC and F1-score. The findings suggest that stacking heterogeneous learners with a deep meta-model provides a powerful methodology for addressing the complexities of churn prediction.

DOI: http://doi.org/10.5281/zenodo.18385968

Enhanced AES Cryptography Algorithm For Secured Health Information Exchange

Authors: Mary M. Asia, Dr. John Lenon E. Agatep

Abstract: The healthcare industry is a critical sector globally, directly influencing human life. Ensuring the confidentiality, integrity, and authenticity of health data is paramount for protecting individual privacy. While Advanced Encryption Standard (AES) is widely recognized encryption technique, it has inherent vulnerabilities, particularly in secure key sharing. Compromises in these channels can undermine the overall strength of AES encryption. In response to the rising threat of data breaches, numerous cryptographic algorithms have been developed to protect digital health records and communication. These include symmetric algorithms like the Advanced Encryption Standard (AES) and Data Encryption Standard (DES), and asymmetric algorithms like RSA and Elliptic Curve Cryptography (ECC). This study presents an enhanced AES algorithm integrated with Elliptic Curve Diffie-Hellman (ECDH), which strengthens key management by offering secure key generation and additional cryptographic layers. The research employed an experimental design, utilizing PyCryptodome for implementation, alongside tools such as NumPy, psutil, and Matplotlib for performance testing and analysis. Comparative evaluations between the enhanced AES-ECDH and standard AES algorithm were conducted in terms of execution time, CPU usage, memory consumption, and security analysis. To uphold ethical standards, dummy datasets were used, ensuring no sensitive information was compromised during testing. The findings revealed that while the enhanced AES-ECDH algorithm significantly improves security—offering features like forward secrecy and heightened resistance to various attacks—it comes at the expense of increased resource consumption. Despite this trade-off, enhanced algorithm is highly suitable for scenarios that prioritize data protection over system performance, especially in healthcare environments.

Assessing The Ethical Challenges Of AI-Driven Decision-Making In Criminal Justice

Authors: Mr. Shantanu

Abstract: Artificial Intelligence (AI) is increasingly integrated into criminal justice systems worldwide, influencing decisions related to policing, bail, sentencing, and parole. While AI-driven tools promise efficiency, consistency, and predictive accuracy, their deployment raises serious ethical concerns. Issues such as algorithmic bias, lack of transparency, accountability gaps, and threats to fundamental rights challenge the legitimacy of AI-based decision-making. This paper critically examines the ethical challenges associated with AI in criminal justice, evaluates their implications for fairness and due process, and emphasizes the need for ethical governance frameworks. The study adopts an analytical and doctrinal approach, drawing on existing literature, case studies, and ethical theories to assess how AI can be aligned with principles of justice, equality, and human dignity.

DOI: http://doi.org/10.5281/zenodo.18401805

Virtualization Techniques in Cloud Computing

Authors: Sanjive R, Arjun A G

Abstract: Cloud computing has emerged as a revolutionary technology that enables on-demand access to shared computing resources such as storage, applications, and processing power through the internet. In recent years, educational institutions have increasingly adopted cloud computing to modernize teaching, learning, and administrative processes. This shift is driven by the growing demand for flexible learning environments, digital collaboration, remote accessibility, and cost-effective infrastructure management. Traditional educational systems rely heavily on physical hardware and locally installed software, which often leads to high maintenance costs, limited scalability, and restricted access to learning resources. Cloud computing overcomes these limitations by offering scalable, reliable, and affordable solutions tailored to academic needs. This paper explores the adoption of cloud computing in educational institutions, focusing on its architecture, service models, and practical applications. Cloud-based platforms such as Learning Management Systems (LMS), virtual classrooms, digital libraries, and online assessment tools have transformed the educational ecosystem by enabling anytime-anywhere learning. The study highlights key benefits of cloud adoption, including reduced operational costs, improved collaboration among students and faculty, enhanced data storage and backup capabilities, and increased institutional efficiency. Additionally, cloud computing supports innovation in education by integrating emerging technologies such as artificial intelligence, big data analytics, and smart learning environments. Despite its advantages, the adoption of cloud computing in education also presents challenges such as data security, privacy concerns, internet dependency, and vendor lock-in. This paper discusses these challenges and emphasizes the importance of implementing strong security policies, data protection mechanisms, and regulatory compliance to ensure safe and effective cloud usage. The study concludes that cloud computing plays a vital role in the digital transformation of educational institutions and has the potential to significantly improve the quality, accessibility, and sustainability of education. With proper planning and governance, cloud computing can serve as a powerful enabler for the future of education. Cloud computing has revolutionized the way computing resources are provisioned, managed, and consumed by enabling on-demand access to scalable infrastructure and services over the internet. At the core of this paradigm lies virtualization, a foundational technology that enables efficient utilization of physical resources by abstracting hardware and allowing multiple isolated computing environments to coexist on a single physical system. Virtualization techniques play a critical role in achieving the essential characteristics of cloud computing, including scalability, elasticity, fault tolerance, resource pooling, and cost efficiency. This paper presents a comprehensive study of virtualization techniques in cloud computing, focusing on their architecture, types, operational mechanisms, and performance implications. The study explores key virtualization approaches such as hardware virtualization, operating system–level virtualization, para-virtualization, full virtualization, and container-based virtualization, highlighting their advantages, limitations, and suitability for different cloud service models (IaaS, PaaS, and SaaS). Hypervisors such as VMware ESXi, Xen, KVM, and Hyper-V are discussed as critical enablers that manage virtual machines and ensure isolation, security, and efficient resource allocation. In addition, modern lightweight virtualization through containers (e.g., Docker and Kubernetes) is examined due to its growing adoption in cloud-native environments. The abstract also emphasizes the role of virtualization in supporting dynamic workload management, live migration, high availability, disaster recovery, and multi-tenancy, which are essential for large-scale cloud data centers. Performance overhead, security vulnerabilities, and resource contention are identified as major challenges associated with virtualization, along with emerging solutions such as hardware-assisted virtualization, AI-driven resource optimization, and secure enclave technologies. Furthermore, the paper highlights the importance of virtualization in enabling emerging trends such as edge computing, serverless architectures, and hybrid cloud environments. Overall, this study demonstrates that virtualization remains a cornerstone of cloud computing, continuously evolving to meet the demands of modern applications. By providing insights into current techniques and future directions, the paper aims to assist researchers, cloud architects, and practitioners in selecting appropriate virtualization strategies to build efficient, secure, and scalable cloud infrastructures.

Smart Food Donation and Waste Reduction System

Authors: Thangadurai M, Hareshwar M S, Manonmani R, Manimaran R

Abstract: Food waste has become a major global concern, with tons of edible food discarded daily while millions of people remain hungry. Traditional food donation systems rely on manual coordination and delayed communication, leading to inefficiency and limited outreach. To address this issue, the Smart Food Donation and Waste Reduction System is proposed a web-based application designed to connect restaurants, supermarkets, and NGOs for the efficient redistribution of surplus food. The platform features dedicated donor and receiver interfaces, supported by centralized cloud database for seamless data management. Using real-time location matching powered by the Google Maps API, the system identifies the nearest NGOs for available donations. Instant notifications and automated alerts ensure quick food collection before spoilage occurs. All data related to food type, quantity, and pickup time are processed through RESTful APIs, while integrated analytics dashboards visualize donation trends and track food waste reduction. Ultimately, it provides a reliable, realtime, and sustainable solution that contributes to the Zero Hunger goal.

DOI: https://doi.org/10.5281/zenodo.18410917

A Bibliometric Analysis of Sentiment Analysis Research in Customer Reviews: Trends, Hotspots, and Future Directions

Authors: Sudipkumar Ghanvat, Aditi Shintre, Sohail Hawaldar

Abstract: The study gives a comprehensive bibliometric study of the sentiment analysis research in customer reviews using Scopus as major data source. The analysis, spanning the 2010 to 2024 period, covers trends in publication, key contributors, collaborative networks, thematic hotspots and emerging research directions. The results indicate that the publication of sentiment analysis increased significantly in artificial intelligence, business, e commerce, fields over the last decade. Top institutions including Universiti Sains Malaysia and Vellore Institute of Technology have done most of the work along with leading authors like Hashimoto, K. and Okada, M. In terms of geography, India, China, the United States, Malaysia and Indonesia are the countries that dominate global research contributions. Thematic analysis shows that machine learning, deep learning, natural language processing, and explainable AI are popular subjects, along with practical applications in customer satisfaction and recommendation systems. The work also suggests promising future directions such as real time sentiment dashboards, multilingual and multimodal approaches and integration of ethical and privacy aware practices. This bibliometric review highlights influential authors, institutions, and research themes to enable both researchers and industry practitioners to understand the history and future of sentiment analysis.

DOI: https://doi.org/10.5281/zenodo.18412327

A Multi-Layer Approach For Email Threat Detection

Authors: Mustakim Khan, Ashok Yadav

Abstract: We present a multi-layer email threat detection system that integrates header authentication analysis, URL/attachment reputation checks via threat intelligence, and machine learning classification. The system parses incoming emails, verifies SPF/DKIM/DMARC results, extracts URLs and attachment hashes, and queries VirusTotal for each indicator. It then applies a trained ML model (TF-IDF + Logistic Regression) to classify the email as phishing or benign. Finally, a scoring engine correlates all signals into a composite risk score. In testing, the system successfully identified simulated phishing emails: for example, a malicious email with known bad links and spoofed headers was flagged as Phishing with high confidence, while benign messages were rated low-risk. The GUI (Figures 1–2) displays the analysis report, including header results, VirusTotal findings, ML verdict, and final threat score. Our multi-layer method leverages complementary techniques to improve detection accuracy and reduce false negatives compared to single- method approaches.

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Test Paper Title By Saquib 122429012026

Authors: Mohd saquib siddiqui, ashar ahmed

Abstract: Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged. It was popularised in the 1960s with the release of Letraset sheets containing Lorem Ipsum passages, and more recently with desktop publishing software like Aldus PageMaker including versions of Lorem Ipsum.

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Social Media Engagement and Value Orientation among College Students in Tamil Nadu

Authors: Jasmine A, Dr. G. Arul Selvi

Abstract: Social media has become an inseparable part of young people’s everyday life, particularly among college students. This article examines how social media engagement influences value orientation among college students in Tamil Nadu, with specific reference to empathy, morality and civic engagement. Drawing on empirical observations among undergraduate students from rural and urban backgrounds, the study shows that excessive and unregulated social media use is associated with weakened empathy, reduced family bonding and diminished moral responsibility. At the same time, responsible and reflective engagement with social media platforms enhances prosocial values, civic awareness and social sensitivity. The article emphasises the need for value-based digital literacy in higher education to ensure ethical and socially responsible digital citizenship.

Approaching Integration Of Artificial Intelligence With Robotic Surgical Systems

Authors: Mr. Danish Ishfaq, Ms. Aasifa Jan

Abstract: Artificial Intelligence (AI) and robotic surgical systems represent transformative technologies in modern healthcare, with profound potential for enhancing surgical precision, reducing operative risk, and improving patient outcomes. In the Indian context, research and clinical practice are increasingly exploring this convergence, encompassing both academic inquiry and real-world deployments. This paper synthesizes recent literature on AI integration with robotic surgery, highlights Indian research efforts, examines clinical case developments, identifies technical and ethical challenges, and discusses future directions for advancing AI-enabled surgical robotics within India’s healthcare ecosystem.

DOI: https://doi.org/10.5281/zenodo.18426331

Mathematics: The Core Engine Behind AI Systems

Authors: Mr. Rushikesh Kalhale, Mr. Venkatesh Bansode, Mr. Utkarsh Maske, Prof.Deepa Shivshimpi

Abstract: Mathematics is at the base of all Artificial Intelligence (AI) systems. Throughout the AI lifecycle, mathematics is the pillar for representing data at the start, learning, reasoning on behalf of the human user and adapting in the mid-section, and finally optimizing any algorithm or data driven model at the end. This paper will discuss how the main mathematics will start to emerge as critical constructs for AI – linear algebra, calculus, probability and statistics, and optimization. We will demonstrate the pertinence of mathematical models as a pathway for the development of neural networks, machine learning algorithms, and data driven decision systems. In demonstrating examples of how mathematics has evolved as part of the responsive development of Artificial Intelligence, we can clearly delineate the ongoing, sometimes inescapable, role mathematics will have in defining intelligent systems in the future.

DOI: https://doi.org/10.5281/zenodo.18427209

Alcohol Detection with Engine Locking System for Vehicle Safety

Authors: Aher Pratiksha Mahendra, Auti Samiksha Ankush, Adak Dnyaneshwari Santosh, Adinath shankar satpute

Abstract: This paper presents the design and implementation of an Alcohol Detection with Engine Locking system for vehicles using the MQ-3 alcohol sensor, HC-SR04 ultrasonic sensor, and Arduino UNO as the Master Control Unit (MCU). The system continuously monitors alcohol concentration in the vehicle cabin and automatically locks the engine if the alcohol concentration exceeds the predefined threshold level. The proposed system also incorporates a SIM900A GSM module to send alert messages regarding the vehicle's whereabouts to designated authorities. Additionally, the ultrasonic sensor measures the distance between vehicles and activates warning indicators when the safe following distance is compromised. Experimental results demonstrate that the system provides an efficient and reliable solution to control accidents caused by drunk driving.

DOI: https://doi.org/10.5281/zenodo.18429030

Vehicle Entry Monitoring System Using YoLo V8

Authors: Nishant Kadam, Swarup Chaudhari, Rushikesh Patil, Hrishikesh Kakade

Abstract: Automated vehicle monitoring is a cornerstone of modern security infrastructure, essential for maintaining safety and operational efficiency in high-traffic environments such as industrial complexes, gated communities, and public facilities. Traditional manual surveillance methods are frequently plagued by human error, significant labor costs, and operational bottlenecks that compromise the integrity of security protocols. This paper presents a robust framework for an automated Vehicle Entry Monitoring System (VEMS) utilizing the state-of-the-art You Only Look Once (YOLO) object detection architecture. The proposed system integrates real-time video stream processing with advanced deep learning models to achieve high-speed detection and classification of various vehicle types, including cars, trucks, and motorcycles. A critical component of the methodology involves the integration of Optical Character Recognition (OCR) and tracking algorithms, such as DeepSORT, to automatically extract alphanumeric license plate data and maintain unique vehicle identities across consecutive frames. This integration enables the creation of a comprehensive, searchable database that cross-references detected plates with authorized whitelists for proactive access control. Experimental results demonstrate that the system ensures near 100% operational uptime by automating the data trail for security auditing and regulatory compliance. The framework provides a scalable solution for intelligent transportation management, significantly reducing manpower dependency while enhancing the reliability of entry logs. By combining real-time detection overlays with a centralized monitoring dashboard, this research offers a sophisticated, data-driven approach to facility security, fostering safer and more efficient urban mobility environments.

Beyond Static Secrecy: A Self-Adaptive, Noise-Aware Privacy Amplification Framework for Heterogeneous 6G Quantum-Secured Networks.

Authors: Okai Tettey-Antie Samuel

Abstract: Modern Quantum Key Distribution (QKD) often fails in highly dynamic mobile environments due to rigid post-processing architectures. This paper introduces a pioneering self-adaptive privacy amplification (SAPA) framework that replaces traditional static compression with a closed-loop controller. By integrating twelve distinct quantum noise models—including Non-Markovian and Gaussian Bosonic channels—we demonstrate that real-time entropy estimation can reclaim up to 25% of secure key material previously lost in mobile-induced fluctuations. Our results establish a new paradigm for "living" security in future 6G ecosystems.

DOI: https://doi.org/10.5281/zenodo.18429991

Solar Powered Smart Street Light with Motion Detection

Authors: Ms. Chavhan S.S, Ms. Humbe P.D, Ms. Bodake S.S, Ms. Devkar M.R, Ms. Jadhav N.

Abstract: Street lighting plays a vital role in public safety but consumes a significant amount of electrical energy. Conventional street lights operate continuously throughout the night, leading to unnecessary power wastage. This paper presents the design and implementation of a solar powered smart street lighting system with motion detection to improve energy efficiency. The proposed system uses solar energy as the primary power source and a motion sensor to control light intensity based on human or vehicle movement. During periods of no motion, the light remains in dim mode, and it switches to full brightness when motion is detected. The system is controlled using a microcontroller and operates automatically without manual intervention. Experimental results show that the proposed system significantly reduces energy consumption and maintenance costs, making it suitable for smart city applications.

Sacred Ecology: Understanding UKS Through Community Narratives On Culturally Important Plants

Authors: Dr. Ruchita Sujai Chowdhary

Abstract: Sacred plants have long played an integral role in shaping ecological consciousness, ritual performances, and cultural identity within Indian society. Among these, Tulsi (Ocimum sanctum) and Peepal (Ficus religiosa) hold a distinctive presence as sacred, medicinal, and symbolic botanical entities embedded deeply in everyday religious and cultural practices. This qualitative study examines the Use, Knowledge, and Significance (UKS) surrounding these plants through community narratives in both rural and urban settings in North India. Utilizing narrative inquiry and ethnographic approaches, the research documents oral histories, lived experiences, ritual participation, and ecological perceptions expressed by diverse community members. Findings reveal that Tulsi and Peepal function not only as religious icons but also as powerful conveyors of environmental values, emotional wellbeing, and intergenerational continuity. Despite rapid modernization and urban transformations disrupting traditional practices, the enduring relevance of these plants demonstrates their potential as culturally grounded tools for ecological communication. The study argues that sacred plant traditions embody a form of “sacred ecology,” offering insights into sustainable cultural-environmental relationships and highlighting the need for preserving traditional knowledge systems.

DOI: https://doi.org/10.5281/zenodo.18430477

RfID Door Lock Using Arduino

Authors: Sahil Shinde, Pushkar Rahane, Sudarshan Suryavanshi, Krishna Tayde, Prof. Bhagawat S. Mohite

Abstract: This research Security is a major concern in homes, offices, and restricted areas. Traditional lock systems using mechanical keys have limitations such as key loss, duplication, and lack of access control. To overcome these issu es, this project presents the design and implementation of an RFID Door Lock using Arduino. The proposed system uses Radio Frequency Identification (RFID) technology to allow only authorized users to access the door. An RFID reader reads the unique ID of the RFID card or tag and sends it to the Arduino microco ntroller. The Arduino compares the scanned ID with the pre-stored authorized IDs. If the ID matches, the system.

Early Detection Of Unrecoverable Loans Using Machine Learning On Nepal Rastra Bank N002 Regulatory Data

Authors: Krishna Prisad Bajgai, Dr. Bhoj Raj Ghimire

Abstract: Early identification of unrecoverable loans is a critical requirement for financial institutions to maintain portfolio quality, comply with regulatory provisioning standards, and minimize credit losses. In Nepal, microfinance institutions and banks are mandated to report loan performance using the Nepal Rastra Bank (NRB) N002 monitoring framework, which contains borrower demographics, loan characteristics, delinquency behavior, and provisioning information. Despite the availability of structured regulatory data, most institutions continue to rely on rule-based aging mechanisms that fail to capture complex nonlinear risk patterns. This study proposes a machine learning-based framework for predicting unrecoverable loans using NRB N002-compliant datasets. A supervised classification problem is formulated, where loans are labeled as unrecoverable based on regulatory delinquency thresholds (Days Past Due >180 or Provision ≥50%). Three models—Logistic Regression, Random Forest, and Extreme Gradient Boosting (XGBoost)—are implemented and evaluated using recall, precision, F1-score, and ROC-AUC metrics, with special emphasis on recall to minimize false negatives in high-risk loan identification. Experimental results demonstrate that XGBoost achieves superior performance with near-perfect recall for unrecoverable loans and an ROC-AUC exceeding 0.97, significantly outperforming traditional statistical approaches. Explainability is ensured using SHAP-based feature attribution. highlighting delinquency duration, overdue principal, outstanding exposure, and provisioning ratios as dominant predictors. The findings confirm that machine learning models can substantially enhance early warning credit risk systems within Nepalese financial institutions while maintaining regulatory transparency and operational interpretability.

DOI: https://doi.org/10.5281/zenodo.18440690

The “How Much” Vs. “How Bad”: Impact Of Quantitative,Hyper-Personalized Moderation Advice On User Comprehension And Dietary Intent

Authors: Vishal Singh, Hemant Singh, Ajay Rawat, Shivam Kumar Jha

Abstract: Nutrition-analysis applications traditionally provide qualitative, binary guidance such as “healthy,” “unhealthy,” or “avoid.” However, recent advances in generative artificial intelligence (AI) enable hyper-personalized, quantitative moderation advice that recommends specific serving sizes, risk thresholds, and actionable alternatives. This paper investigates whether quantitative, personalized recommendations enhance user comprehension, confidence, and dietary intent compared to generic, qualitative warnings. We conduct a randomized controlled A/B user study with 100 participants and compare a qualitative control interface against a quantitative, generative-AI- powered interface offering explicit serving guidance and alternatives. Results show that quantitative moderation advice significantly improves comprehension accuracy, user confidence, trust, and positive dietary intent. These findings provide strong HCI evidence supporting the integration of precise, personalized guidance in digital nutrition applications.

An Empirical And Analytical Study Of Risk–Return Relationship In Equity Investments.

Authors: P. Vijetha, Sk Maqbool basha

Abstract: The risk–return relationship is a fundamental concept in finance, guiding investment decisions and portfolio management. This study empirically examines the relationship between risk and return among 10 actively traded equity stocks over a five-year period (2019–2024). Both systematic risk (beta) and total risk measures (standard deviation and variance) are analyzed to determine their influence on equity returns. Secondary data from NSE, BSE, and financial databases were used, and statistical techniques including descriptive statistics, correlation analysis, regression analysis, and t-tests were employed. The findings reveal a positive and statistically significant relationship between risk and return, with beta emerging as the strongest predictor. Regression results indicate that risk measures collectively explain over 50% of the variance in returns. The study validates the traditional risk–return tradeoff and highlights the importance of incorporating multiple risk metrics for informed investment decisions. Implications for investors, portfolio managers, and policymakers are discussed, emphasizing strategies for optimizing returns while managing risk in dynamic equity markets.

AI–Powered Interview System Based On Resume Analysiss

Authors: Ms. Nirzhara Suryawanshi, Ms. Manasi Patange, Ms. Pallavi Thakare, Ms. Harshada Sonar, Mrs Samiksha Gawali

Abstract: The AI-Powered Interview System Based on Resume Analysis is an intelligent and interactive platform designed to enhance students’ interview preparation through automation and personalized evaluation. The system enables users to upload their resumes, which are then processed using Natural Language Processing (NLP) techniques to extract key skills, academic achievements, and relevant experience. Based on this analysis, the system dynamically generates field-specific interview questions tailored to the candidate’s profile. To simulate a realistic interview environment, the platform incorporates voice-based interaction using speech-to-text and text-to-speech technologies. Users respond to questions through audio, and the system evaluates their answers in real time using NLP- based answer analysis. The system further provides performance ratings, personalized feedback, and improvement suggestions, helping candidates identify their strengths and areas of development.

DOI: https://doi.org/10.5281/zenodo.18448371

Artificial Intelligence In Cybersecurity: A Comprehensive Survey

Authors: Hansikaa M

Abstract: The rapid growth of digital technologies and interconnected systems has greatly increased the complexity and scale of cyber threats. Traditional cybersecurity methods, which depend on predefined rules and signature-based detection, often have difficulty identifying advanced, dynamic, and new attacks. In this situation, artificial intelligence has emerged as a powerful tool for improving cybersecurity by enabling smart, flexible, and automated defence systems. This research offers a thorough look at how artificial intelligence is used in cybersecurity, focusing on machine learning, deep learning, and anomaly detection techniques for identifying and responding to threats. The study reviews current security methods, examines their weaknesses, and discusses how AI-driven approaches enhance detection accuracy, lower false positives, and support proactive security management. An AI-based cybersecurity framework is also presented to show how smart models can work with security monitoring, data processing, and automated response features. The effectiveness of AI-based cybersecurity solutions is assessed through performance analysis and discussions of experimental results, highlighting improvements in real-time threat detection and system efficiency. Additionally, the research explores important application areas, challenges, and future directions, including explainable artificial intelligence, privacy-preserving learning, and autonomous security operations. Overall, this study emphasizes the important role of artificial intelligence in strengthening modern cybersecurity systems and underscores its potential to tackle evolving cyber threats through ongoing learning and smart decision-making.

Proportional Odds Modelling Of Hiv Infection Among Pregnant Women (A Case Study Of Federal Medical Centre, Owerri).

Authors: Nwagwu, Glory C, Obasi, Chinedu K, Nduka, Modestus U

Abstract: The HIV virus is a cankerworm that is bedeviling human-kind, with sequel advancement to Acquired Immuno-deficiency Syndrome (AIDS), if not properly managed can have effect on the socio-demographic factors. The study aimed at determining the impact of socio-demographic factors that affects HIV status of pregnant women in Imo state using a Proportional Odds Model. It was discovered that single women within the Age (15-19) years and resident in the rural area were the factors that contributed to the reason why these pregnant women are prone to contacting HIV/AIDS infection in Imo State.

DOI: https://doi.org/10.5281/zenodo.18454133

SunVolt: A Sustainable Solar-Powered Battery Charger In Rural Off-Grid Communities

Authors: Rodolfo L. Rabia, Ashley Nicole L. Tizon, Arvey Faith B. Paquibot, Ritchen G. Ibañez, Samuel P. Tabuena, Regine R. Ruallo

Abstract: The objectives of this project were to design and develop SunVolt, a solar-powered battery charging system using an Arduino Uno R3, to address energy shortages and high electricity costs in rural off-grid areas. SunVolt enables efficient solar-powered battery charging to support household and agricultural activities in locations with limited or unstable electricity access. The system integrated a solar panel, lead-acid battery, MPPT charge controller, and sensors that monitor light, temperature, and voltage, managed by the microcontroller. SunVolt independently finds an optimal energy conversion rate, prevents overcharging, and displays visual notifications with performance in real time to the user. Performance testing evaluated daily energy output, charging efficiency, and long-term reliability under real-world conditions. The results demonstrate that SunVolt effectively stores solar energy, meets residential and agricultural energy needs, and remains durable under varying environmental conditions. Overall, SunVolt offers a practical solution for improving energy self-sufficiency, reducing reliance on fossil fuels, and promoting sustainable development in undeserved communities.

DOI: https://doi.org/10.5281/zenodo.18454945

Cloud Computing In Artificial Intelligence and Machine Learning

Authors: Vignesh P, Sribharath K

Abstract: The rapid growth of Artificial Intelligence (AI) and Machine Learning (ML) has significantly increased the demand for high computational power, massive data storage, and efficient model deployment. Traditional on-premise infrastructures often fail to meet these requirements due to high cost, limited scalability, and maintenance complexity. Cloud computing provides a flexible, scalable, and cost-effective platform that supports the complete lifecycle of AI and ML systems. By offering powerful computing resources such as GPUs, TPUs, distributed storage, and pre-built AI services, cloud computing enables faster innovation and real-time intelligent applications. This paper presents an in-depth study of cloud computing and its role in AI and ML, covering architecture, service models, platforms, applications, benefits, challenges, security concerns, and future scope.

Amihans Breath: Development Of An IoT-Integrated Arduino System For Real-Time Indoor Air Quality Monitoring, Alert Notification, And Filtration

Authors: Jaydonn Arvin H. Santillana, Jay Laurence L. Quimque, John M. Pagaran, Benjo R. Saraka, Joseph Ramos, Jaine D. Luz

Abstract: This study describes Amihan’s Breath, an Internet-of-Things–based air quality management system using Arduino R4 Wi-Fi that monitors and regulates indoor air quality (IAQ) in Davao City. The system measures 12 air quality and thermal parameters, including PM1.0, PM2.5, PM10, NOx, CO, O₃, NH₃, VOCs, temperature, and humidity, and provides real-time monitoring, alerts, and air-filtering functions. Sensor accuracy ranged from 97–100% before and after filtration. Initial IAQ analysis indicated moderate pollution levels, with PM10 averaging 85.64 µg/m³ and NOx averaging 96.89 µg/m³. After filtration, pollutant levels significantly decreased, including a 29% reduction in NOx and substantial reductions in particulate matter. Overall, Amihan’s Breath is an effective and cost-efficient IAQ management system recommended for high-risk environments such as schools and healthcare facilities.

DOI: https://doi.org/10.5281/zenodo.18457385

SignBridge: An Offline Bidirectional Indian Sign Language Translation System

Authors: Samruddhi Pramod Bangar, Prasad Sakharam Khose,, Nikita Sandeep Ubhe, Sonali Dongare

Abstract: Communication barriers persist for hearing and speech-impaired individuals due to the lack of real-time, offline, bidirectional Indian Sign Language (ISL) translation tools. This paper presents SignBridge, an offline bidirectional system supporting Sign-to-Text/Speech and Text-to-Sign translation. MediaPipe is used for extracting hand and pose landmarks, while a TensorFlow-based hybrid CNN– Transformer model performs dynamic gesture recognition. A full-stack implementation using React.js and Flask ensures real-time interaction, and an avatar-based rendering module generates visual sign outputs. The system is designed for low- resource environments with improved privacy and reduced dependency on internet connectivity.

Serverless Computing in Cloud Environments: Architecture, Performance, and Challenges

Authors: Vishmitha. E, Madhumitha. M

Abstract: Serverless computing is an emerging paradigm in cloud computing that abstracts infrastructure management from developers and enables fully event-driven execution of applications. Unlike traditional cloud models that rely on continuously running virtual machines, serverless platforms dynamically allocate resources and execute functions only in response to events, thereby improving scalability and resource utilization. This paper presents a comprehensive analysis of serverless computing, focusing on its architectural design, performance characteristics, advantages, and inherent challenges. The core components of serverless architecture, namely Function-as-a-Service (FaaS) and Backend- as-a-Service (BaaS), are examined in detail to illustrate how they support stateless execution, automatic scaling, and rapid application development. A comparative study between serverless computing and traditional virtual machine-based cloud models is conducted with respect to scalability, latency, cost efficiency, and operational complexity. Performance factors such as cold start latency, execution overhead, and throughput under varying workloads are analyzed to highlight the trade-offs involved in adopting serverless systems. Furthermore, this paper discusses critical challenges including security concerns arising from multi-tenancy, vendor lock-in due to provider- specific services, limitations in observability and debugging, and complexities in state management. Finally, the paper outlines future research directions aimed at reducing latency, improving portability, enhancing security mechanisms, and integrating serverless computing with edge and hybrid cloud environments to support next-generation distributed applications.

A review on synthesis and features of different types of Carbon nanostructures deposited by RF-PECVD

Authors: Dr.B Purna chandra rao, Dr. K. Subbarao, Dr. S. Kondala Rao, B. V. Rama Rao

Abstract: This review is about the synthesis of different types of carbon nanostructures by Radio Frequency Plasma Enhanced Chemical vapor deposition (RF-PECVD) and its feasibility to grow variety of carbon nanostructures and their features. A variety of carbon nanostructures like carbon nanosheets, carbon nanoparticles, carbon nanotubes, nanoellipse like structures, nanorods and other islands like carbon nanostructures were grown at possibly low synthesis temperatures was reported at various international and national level journals is a part of my own research work. With the mission of make benefit for the easy understanding of the graduate students, scholars, academicians and researchers, it is presenting as a review report. In this report, first section contains a review on different types of synthesis techniques and their failure in the growth of pure, individual and aligned carbon nanostructures at low synthesis temperatures and the feasibility of RF-PECD in the growth of carbon nanostructures for full filing the above-mentioned requirements is discussed. Second section deals about the RF-PECVD technic and it’s inbuilt facilities for the growth of carbon nanostructures compared to the other techniques. Third section presents about the different types of grown carbon nanostructures during the period of my own research work using RF-PECVD. Fourth section presents about the applications of these carbon nanostructures in various fields.

DOI: https://doi.org/10.5281/zenodo.18466013

Pythons Computational Ecosystem: Foundations, Innovations, And Future Trajectories

Authors: Vineet Hemendra Mehta

Abstract: Python has emerged as a foundational technology in modern software development despite the emergence of numerous specialized programming languages. This paper examines Python’s sustained adoption across critical domains such as artificial intelligence, data science, web development, automation, and edge computing. The study analyzes Python’s design philosophy, ecosystem maturity, and recent toolchain innovations. A comparative analysis with other popular programming languages is presented to highlight Python’s strengths in productivity and ecosystem support. The paper concludes that Python’s adaptability and community-driven evolution ensure its continued relevance in both academic research and industrial applications.

DOI: http://doi.org/10.5281/zenodo.18466137

Six Approaches To Measuring Algorithmic Bias: An Empirical Evaluation Of Fairness Metrics In Machine Learning

Authors: Abubakar Sadiq Yusha’u, Aminu Aliyu Abdullahi

Abstract: Fairness metrics have become central instruments for identifying, quantifying, and mitigating bias in machine learning (ML) systems deployed in high-stakes decision-making contexts such as credit scoring, employment screening, welfare allocation, and criminal risk assessment. However, the rapid proliferation of fairness definitions has introduced substantial ambiguity regarding how algorithmic bias should be measured, interpreted, and governed in practice. This paper presents a comprehensive conceptual and empirical analysis of six widely adopted fairness metrics: Statistical Parity, Disparate Impact, Equalized Odds, Predictive Parity, Calibration, and Individual Fairness. Using a supervised classification task on a benchmark dataset, we empirically evaluate how fairness assessments vary across metrics under identical modeling conditions and decision thresholds. Our findings reveal substantial divergence among fairness metrics, with models satisfying one fairness criterion frequently violating others. These results demonstrate that algorithmic fairness is inherently multidimensional and context-dependent. We conclude that responsible AI governance requires multi-metric auditing, transparent metric selection, and domain-specific interpretation rather than reliance on any single fairness definition.

DOI: https://doi.org/10.5281/zenodo.18466710

An Automated Framework For Early Identification Of Pre-Eclampsia

Authors: Suhirdham K G, Abinaya S , Induja M K, Kanimozhi S

Abstract: Pre-eclampsia is one of the most severe pregnancy-related disorders and continues to be a major contributor to maternal and infant morbidity globally. The early detection of this disorder is difficult owing to the intricate relationship between clinical, demographic, and pregnancy- related variables. Traditional screening methods are highly dependent on manual analysis and are often ineffective in identifying high-risk cases at an early stage. This paper proposes an automated, non-IoT, machine learning-based clinical decision support system for the early detection of pre-eclampsia using routine antenatal data. Patients are classified into low, moderate, and high-risk categories to help clinicians take early action. To improve interpretability and reliability, artificial intelligence methods are integrated to identify prominent risk factors contributing to each prediction. Experimental results show that the proposed system enhances the accuracy of early risk detection while maintaining clinical interpretability, there by bridging the gap between artificial intelligence research and maternal healthcare practice.

DOI: http://doi.org/10.5281/zenodo.18467300

Cybersecurity Threats In The Age Of Cloud Computing

Authors: Sathya Seelan J, Dharshini S

Abstract: Cloud computing has become a foundational technology for modern organizations, enabling scalable, flexible, and cost-efficient access to computing resources through the internet. Enterprises across sectors increasingly rely on cloud services for data storage, application deployment, business operations, and critical decision-making processes. The flexibility offered by cloud computing allows organizations to dynamically scale resources, reduce operational costs, and rapidly deploy innovative applications. Despite these significant advantages, the widespread adoption of cloud computing has introduced complex cybersecurity challenges that threaten data confidentiality, integrity, and availability, creating an urgent need for robust security frameworks. The shared and distributed nature of cloud environments, coupled with multi-tenancy, virtualization, and third-party service management, expands the attack surface and exposes systems to a variety of sophisticated cyber threats. These threats are further amplified by rapid technological advancements, including the integration of Internet of Things (IoT) devices, edge computing, and artificial intelligence (AI) applications in cloud platforms, which increase connectivity but also add layers of vulnerability. Malicious actors exploit misconfigurations, weak authentication mechanisms, and software vulnerabilities to gain unauthorized access, steal sensitive information, or disrupt services, highlighting the importance of proactive security measures. This research paper provides a comprehensive analysis of major cybersecurity threats associated with cloud computing and evaluates existing and emerging security mechanisms employed to mitigate these risks. Key threats discussed include data breaches, account hijacking, insecure application programming interfaces (APIs), insider threats, denial-of-service (DoS) attacks, ransomware, and compliance-related vulnerabilities. Data breaches remain one of the most critical concerns, as attackers can access sensitive information stored in cloud systems through technical exploits, human errors, or inadequate security policies. Account hijacking, often achieved through phishing attacks, malware injection, or credential theft, allows attackers to manipulate cloud resources, disrupt services, or launch further attacks within an organization’s network. Insecure APIs, which serve as communication gateways between applications and cloud services, pose substantial risks if improperly designed or inadequately secured, enabling unauthorized access, data manipulation, or denial-of-service attacks. Insider threats, whether intentional or accidental, continue to be a persistent challenge due to the trusted access employees or contractors have to cloud resources. The paper also explores the shared responsibility model in cloud computing security, which delineates the division of security obligations between cloud service providers and cloud users. While providers are tasked with securing the underlying infrastructure, including physical hardware, virtualization layers, and platform services, users are responsible for securing data, applications, access credentials, and configurations. Misunderstanding or neglecting these responsibilities can result in security gaps, misconfigurations, and increased exposure to cyberattacks. To address these challenges, the study analyzes a range of mitigation strategies, including advanced encryption techniques for data at rest and in transit, identity and access management (IAM) solutions, multi-factor authentication, continuous monitoring, intrusion detection and prevention systems, and compliance with international security standards such as ISO/IEC 27001, NIST frameworks, and GDPR.

An Intelligent System For Carbon Footprint Prediction Using Ensemble Regression

Authors: Ms. V. Dhanalakshmi, Sanjuga S K, Sindulaxme J, Soundarya M

Abstract: Carbon dioxide (CO₂) emissions from industrial and organizational operations such as energy consumption, transportation, and operational processes significantly impact environmental sustainability. Accurate carbon footprint prediction is essential for reliable emission analysis and informed reduction planning. However, traditional systems rely on static calculation methods, which fail to capture dynamic operational patterns and complex emission relationships. The proposed system employs a machine learning–based framework to predict carbon footprint in industrial and organizational environments. Activity-based operational data such as electricity consumption, fuel usage, and transportation parameters are first subjected to data preprocessing and feature engineering. The processed data are then utilized in ensemble regression modeling to generate reliable emission predictions. The system predicts total carbon emissions and provides category-wise emission analysis to identify major emission-contributing activities. The proposed solution enables data-driven decision-making for sustainable operational planning and emission reduction, fostering environmentally responsible practices through analytical assessment of carbon emissions.

DOI: https://doi.org/10.5281/zenodo.18468154

Artificial Intelligence In Aviation And Aerospace

Authors: Sanjith Rajesh, Prof Ankit Shrimankar

Abstract: Artificial Intelligence (AI) is quickly changing aerospace, fields traditionally shaped by human creativity and engineering skill. AI helps optimize rocket trajectories and allows for autonomous spacecraft navigation. It has become a crucial part of modern exploration. Its capacity to handle large amounts of data in real time enables engineers to foresee mechanical failures before they happen. It also helps design more efficient propulsion systems and simulate complex missions to distant planets. It can also pre-calculate whether our expectations from an aircraft are met as per design conjectures. As humanity aims to colonize Mars and expand the limits of space travel, AI serves as both a driving force and a protector. It is transforming how we build, launch, and maintain the machines that help us to circumnavigate and go beyond the Earth. The objective of this hybrid review is to find and abstractly define AI’s use in aviation. analyze faults that can occur due to its use from real published fault reports and extrapolate its use in Aeronautics and in some cases Astronautics. All inferences are concluded based on exhaustive review of research by reports published by credible government recognized sources on events occurring from the date of induction of AI in the field of aerospace. Multiple angles were viewed mostly from the consumer, the manufacturer and regular civilians.

A Low-Code CRM Architecture for Fuel Booking and Inventory Control

Authors: Akhilash Pennam

Abstract: This paper proposes a cloud-based CRM solution developed on the Salesforce platform to modernize gas station operations by automating fuel booking, inventory management, supplier coordination, and customer interactions. The system uses custom objects, role-based security, low-code automation through Flows, and Apex triggers to enforce business rules and reduce manual effort. Real-time dashboards and analytical reports provide insights into fuel consumption, inventory status, and revenue trends. Testing and validation results indicate improved operational efficiency, data accuracy, and service responsiveness, confirming that the proposed CRM solution is scalable, secure, and suitable for multi-branch deployment and future enhancements.

DOI: https://doi.org/10.5281/zenodo.18477450

A Comprehensive Analysis Of The OSI Model In Modern Networking

Authors: Sachin Kumar

Abstract: Smart Tourist Safety refers to the integration of digital technologies such as the Internet of Things (IoT), mobile applications, artificial intelligence, and real-time data analytics to enhance the safety and security of tourists. With the rapid growth of global tourism, ensuring tourist safety has become a critical concern for destinations and governments. Smart safety systems enable real-time monitoring, emergency response, location tracking, and risk prediction, helping tourists navigate unfamiliar environments securely. This study explores the concept of Smart Tourist Safety, examines key technologies involved, and discusses their role in improving emergency management, crime prevention, and overall tourist confidence. The findings highlight that smart safety solutions not only reduce risks but also enhance destination attractiveness and sustainability

DOI: https://doi.org/10.5281/zenodo.18492762

Innovations In Sustainability: Green Airports Integrating Renewable Energy And Smart Waste Systems

Authors: Prachi Kishan Varu

Abstract: The aviation industry is among the most energy-intensive, producing about 2.5% of total CO₂ emissions worldwide. As demand for air travel continues to grow and airports as major energy consumers and infrastructure hubs continue to develop, the role of green airports is increasingly evident. Green airports are transforming aviation infrastructure through renewable energy systems, sustainable technology, and intelligent and rational waste management systems. This paper investigates the innovations in sustainability that have the potential to contemporize airport operations, including the application of renewable energy deployment systems, smart waste systems, and digital technologies that reduce environmental footprint. This paper employs global and India examples to highlight best practices, policy enablers, and challenges as traditional airports transition to sustainable airports that are ready for the future.

DOI: https://doi.org/10.5281/zenodo.18492944

Impact Analysis of a 100MW Solar PV System Integration into Port-Harcourt Mains 132kV Transmission Network

Authors: Olamilekan Emmanuel Solomon, Hachimenum Nyebuchi Amadi, Barididum P. Biragbara, Richeal Chinaeche Ijeoma

Abstract: This study aims to analyze the impact of integrating a 100MW solar photovoltaic (PV) system into the Port Harcourt 132kV transmission network, specifically to assess its effects on grid performance and stability. The increasing incorporation of renewable energy, especially solar PV, presents operational challenges such as voltage fluctuations, reactive power imbalances, harmonic distortion, and frequency instability. If left unmanaged, these issues can lead to transformer overloading, grid congestion, and increased system losses. To address these challenges, we conducted load flow, voltage stability, and harmonic analyses using the Electrical Transient Analysis Program (ETAP) to model the existing network and evaluate the integration of the 100 MW solar PV systems alongside a battery energy storage system (BESS). The simulation results indicated that prior to integration, critical buses (T1A = 89%, T2A = 89.1%, T3A = 90.8%) and transformers (T1A = 112.8%, T2A = 111.8%, T3A = 91.7%) were operating beyond acceptable limits. After integration, the bus voltages improved to T1A = 96.06%, T2A = 96.11%, and T3A = 97.36%. Additionally, transformer loading decreased to T1A = 71%, T2A = 70%, and T3A = 46.8%, while total network losses significantly reduced from 6086.8 kW + j32740.7 kvar to 1093.172 kW + j9392.581 kvar. These findings demonstrate that the coordinated integration of solar PV and BESS can enhance voltage stability, reduce system losses, and minimize transformer stress. The study recommends supportive policy frameworks to encourage large-scale solar PV integration with energy storage, representing a sustainable approach to improving grid reliability and advancing Nigeria’s transition to renewable energy.

DOI: https://doi.org/10.5281/zenodo.18493122

The Influence Of System Configuration On The Performance Of Power Transformer Differential Protection Scheme In Corner Stone, Port Harcourt

Authors: Hachimenum Nyebuchi Amadi, Biobele A. Wokoma, Barineka Richard Zarakpege, Richeal Chinaeche Ijeoma

Abstract: Transformer differential protection can experience false trips and mis-coordination, especially during external feeder faults and magnetizing inrush conditions. These malfunctions can compromise supply continuity, reduce system reliability, and put unnecessary stress on critical substation equipment. This research examines the reliability of the existing differential protection scheme at Corner Stone Substation and develops an enhanced adaptive configuration aimed at mitigating false operations while ensuring secure and selective fault clearance. To establish a performance baseline, historical relay event records from 2024 to 2025 were analyzed. A detailed MATLAB/Simulink model of the 15MVA, 33/11kV transformer protection system was created. The baseline protection scheme was tested under internal faults, external feeder faults, and transformer energization conditions. Subsequently, an improved protection configuration that integrates adaptive directional logic was implemented and validated through comparative simulations. The study found that the existing differential protection at Corner Stone Substation was reliable during internal faults, operating within 100 to 120 milliseconds. However, it was prone to false tripping during transformer energization, which produced an inrush current of approximately 6000 A with significant second harmonic distortion. Additionally, mis-coordination occurred during external feeder faults exceeding 7kA, with trip times ranging from 60 to 100 milliseconds. By integrating adaptive directional logic, the new scheme achieved secure restraint during external faults while maintaining rapid isolation of internal faults in less than 120 milliseconds. MATLAB simulations confirmed that the improved configuration enhanced selectivity, minimized false operations, and ensured reliable coordination between transformer and feeder protections. The findings indicate that adaptive directional differential protection improves selectivity, reduces false operations, and ensures robust coordination between transformer and feeder protections. This advancement contributes to enhancing protection strategies for modern substations and has potential applications for mitigating relay misoperations in other high-voltage grid systems.

DOI: https://doi.org/10.5281/zenodo.18493328

Federated Deep Learning for Privacy-Preserving Healthcare (FedMed)

Authors: A. Priyadharsini

Abstract: The rapid adoption of artificial intelligence in the healthcare sector has led to an increased demand for high-quality medical datasets. However, the sensitive nature of patient information and the strict regulatory requirements surrounding healthcare data often restrict institutions from sharing data with external entities. Federated Medical Learning (FedMed) presents a promising solution by enabling multiple healthcare institutions to collaboratively train deep learning models without exposing raw patient data. This paper proposes a robust FedMed framework that integrates federated averaging, secure aggregation, and advanced privacy-preserving techniques to ensure confidentiality while maintaining high model performance. Experiments conducted using medical imaging datasets demonstrate that the FedMed model achieves accuracy levels comparable to centralized deep learning approaches, while significantly reducing privacy risks. The findings highlight the potential of FedMed to enable scalable, secure, and efficient AI-driven healthcare applications across diverse medical environments.

MosquiTect: A Multi-Sensor Automated System For Mosquito Detection And Environmental Surveillance

Authors: Sampang, Althea Mae A., Halik, Iederf Sean B, Labadan, David Andrew, Canada, Kevin Raymart C

Abstract: The researchers aimed to develop a multi-sensor automated system for mosquito detection and environmental surveillance, Mosquitect, having a purpose of offering support in the early assessment of dengue risks in areas prone to mosquito presence. MosquiTect is designed with the use of an Arduino UNO R4 Microcontroller that aids in tracking wingbeat frequencies, temperature, humidity, and visual-based detection of mosquitoes through a camera module. The data analysis is performed by utilizing percentages and means. During the 14-day testing, the findings show that MosquiTect had a 97.64% of success rate in terms of detecting wingbeat frequencies and gender identification signals; temperature and humidity provided a 100% success rate in monitoring environmental parameters; and a 77.07% success rate in terms of visual-detection of mosquitoes. The result shows that MosquiTect holds high relevance in the facilitation of preventive steps against dengue, especially in tropical areas. MosquiTect also possesses strong practicality for aiding governmental departments in forming preventive measures for dengue. This cultivates improvements in the capability of optical detection and image recognition, energy efficiency, environmental surveillance, and predictive modelling for the population of mosquitoes and their potential dengue outbreaks by the public health agencies.

DOI: https://doi.org/10.5281/zenodo.18494907

Awareness Of Artificial Intelligence: Benefits, Risks, And Ethical Implications

Authors: Sushovan Chandra, Barsha Maity, Swagatam Biswas, Angshuman Ghosh, Angshuman Ghosh

Abstract: Artificial Intelligence (AI) has emerged as one of the most transformative technologies of the 21st century, influencing nearly every sector of society. From healthcare and education to finance and governance, AI-driven systems are reshaping how decisions are made and services are delivered. Despite its growing adoption, public awareness and understanding of AI remain limited, particularly regarding its risks and ethical challenges. This research paper examines the positive and negative impacts of AI, highlights key ethical and social concerns, and emphasizes the importance of awareness, regulation, and responsible implementation. The study aims to provide a balanced perspective on AI, encouraging informed usage that maximizes benefits while minimizing harm.

OTP Door Lock System

Authors: Ms.Walunj P.D, Ms.Sangme S.R, Ms.Suryawanshi P.P, Ms.Hanumante K.B, Ms. Upase M.S

Abstract: This paper presents the design and implementation of an OTP (One-Time Password) based door lock system using Arduino. The system enhances security by allowing access only after successful OTP verification. The OTP is generated and transmitted to the authorized user via a GSM module. The proposed system is low-cost, reliable, and suitable for homes, offices, and restricted areas. Experimental results show that the system provides improved security compared to traditional lock systems. Security of residential and commercial premises is a major concern in today’s world. Conventional locking systems such as mechanical keys and password-based locks are vulnerable to theft, duplication, and unauthorized access. To overcome these limitations, this project presents an OTP (One-Time Password) based door lock system that provides enhanced security and flexibility. The proposed system generates a unique, time-limited OTP for every access request, which is sent to the authorized user’s registered mobile number through a GSM module. The user must enter the received OTP using a keypad or mobile interface to unlock the door. A microcontroller controls the verification process and activates a relay to operate the electronic door lock. Since the OTP is valid for only one use and for a short duration, the chances of unauthorized entry are significantly reduced. The system is simple, cost-effective, and suitable for homes, offices, and restricted areas, offering a reliable solution for modern security needs.

Drone-Based Traffic Surveillance

Authors: M.Selvam, Dr A.Shiny Pradeepa

Abstract: Drone deployment has become crucial in a variety of applications, including solutions to traffic issues in metropolitan areas and highways. On the other hand, data collected via drones suffers from several problems, including a wide range of object scales, angle variations, truncation, and occlusion. Rapid urbanization and the continuous growth of vehicle population have placed immense pressure on existing traffic management systems. Conventional traffic surveillance methods, such as fixed cameras, loop detectors, and manual monitoring, often suffer from limited coverage, high infrastructure costs, and lack of real-time adaptability. Therefore, this project proposes a drone-based traffic surveillance system operates through the coordinated functioning of power, sensing, control, communication, and actuation modules. The system is powered by a 3.7V Li-ion/Li-Po battery, which supplies energy to all onboard components through a battery protection and charging circuit to ensure safe and stable operation. The flight controller acts as the central processing unit, receiving real-time data from sensors such as the gyroscope and accelerometer to maintain flight stability, orientation, and balance. Front and bottom cameras capture aerial and ground-level traffic footage, which can be switched using the camera switching module depending on surveillance requirements. The optical flow sensor assists in position holding and low-altitude navigation. User commands are transmitted via a 2.4 GHz transmitter and receiver, enabling remote control and mission updates. Based on sensor inputs and control commands, the flight controller generates appropriate signals to the motor driver, which regulates the speed of the coreless DC motors for precise manoeuvring. Additionally, the LED lighting module enhances visibility during low-light or night-time operations. Through this integrated workflow, the drone efficiently captures real-time traffic data while maintaining stable and controlled flight.

Survey On Climate Change Awareness In Indian Students

Authors: Sanjana Sunilkumar Dubey, Dr Vipin Kumar

Abstract: Education of school and college students on climate change is highly important in influencing mitigation and adaptation behaviours in the long term especially on the climate prone countries like India. This research is a survey-based evaluation of climate change awareness, risk perception, self efficacy, and pro environmental behavioural intention among Indian students, with a special interest in the variations of these variables according to the urban and rural geographical location, the type of school, and the exposure to climate education programmes. The questionnaire comprised a structured questionnaire that was delivered through a stratified sampling design to the participants that were secondary school students (Classes 912) and first-year undergraduate programmes. The measure consisted of climate knowledge, perceived risk, self-efficacy, behavioural intention, and information sources on climate. The analysis of data was done using descriptive statistics, group comparison, and multiple regression modelling to determine the predictors of behavioural intention toward climate action. Using an exemplary sample size (N = 600), the findings show that, although students will exhibit average knowledge of climate as a whole, there exist significant disparities in knowledge of health-related climatic effects and locally applicable strategies of adaptation. Students in urban areas always claim more knowledge and perception of risk of the climate than rural students due to the information availability and access to education. The results also indicate that perceived risk and self-efficacy have a stronger effect on behavioural intention than knowledge does. Being members of eco-clubs and having undergone climate-focused school-based climate modules are both substantially linked with intentions to participate in climate-positive behaviours.

Heart Disease Prediction (XGBoost, Random Forest, And KNN)

Authors: Riya Jaiswal, Simran Sahu, Prince Pandey, Vandana Thripathi

Abstract: Heart disease continues to be a major global health concern, accounting for a significant number of premature deaths each year. Early detection can improve survival rates, yet traditional diagnostic methods are time-consuming and often dependent on expert interpretation. This study applies machine learning techniques to clinical data to develop a predictive model capable of estimating heart disease risk. Various algorithms—including Logistic Regression, Random Forest, Support Vector Machine (SVM), and XGBoost—were evaluated. The results show that ensemble models deliver the highest accuracy, demonstrating strong potential for supporting clinical decision-making.

TerraGrow: A Soil Analysis Device For Optimal Crop Selection

Authors: Delos Santos, Greg, Galgo, Lady Nathalie A, Gubat, Karyll D., Zamora, Daisy Anne

Abstract: The purpose of this study is to design and develop a soil testing device known as TerraGrow using IoT technology that could help farmers test soil properties and recommend the appropriate crops for growing. The soil testing device could measure EC values, soil moisture levels, and soil temperatures to acquire valuable soil information, which could then be interpreted using a mobile or web application. The results obtained were analyzed using mean and percentage to test the accuracy of the soil testing device. The results revealed that the soil testing device TerraGrow could measure and interpret soil properties with greater accuracy and efficiency. The application of IoT technology made it easy for the soil testing device to store data and provide recommendations for growing appropriate crops based on soil quality. The study results showed that the soil testing device TerraGrow could work with greater efficiency and ease compared to traditional methods. The study concluded that the application of TerraGrow has made a significant contribution to modern agricultural practices. The soil testing device could be made even better with suggestions like automatic calibration and solar power.

DOI: https://doi.org/10.5281/zenodo.18539454

Eduable: A Multimodal AI-Learning For Disabilities

Authors: Mrs. M. Lavanya, Ms. R. Kavinila, Ms. M. Harini, Ms. K. Keerthana

Abstract: Education for students with disabilities continues to face challenges due to inadequate accessibility tools, lack of adaptive content, and poor connectivity in rural areas. Existing technologies such as screen readers, speech-to-text converters, and sign language translators function independently, resulting in fragmented learning experiences. EduAble, is a multi modal AI-powered learning platform designed to support students with visual, hearing, mobility, and neurodiverse challenges. It integrates Text-to-Speech (TTS), Speech-to-Text (STT), sign language and gesture recognition, and adaptive content simplification to create a unified, inclusive learning environment. EduAble is developed using Django with Django REST Framework for backend processing and React Native for cross-platform mobile accessibility, supported by PostgreSQL for data storage.The platform employs advanced AI models such as gTTS for speech synthesis, CNN with MediaPipe and OpenCV for gesture and sign language detection, and BERT for text simplification using TensorFlow which collectively enhance learning accessibility and provide a more effective and integrated assistive education system.

DOI: https://doi.org/10.5281/zenodo.18585544

Design And Structural Analysis Of Helical Gear With Varying Helix Angle

Authors: Neha Sahu, Prof. Ruchika Saini

Abstract: This study focuses on the design and structural analysis of helical gears with varying helix angles to investigate their influence on mechanical performance. By designing helical gears with different helix angles and analyzing them under identical loading and boundary conditions, the study aims to evaluate changes in bending stress, contact stress, deformation, and axial force. The results of this investigation will help identify optimal helix angle ranges that enhance gear strength and longevity while minimizing undesirable effects such as excessive axial loads and material failure. The findings of this study are expected to contribute to improved gear design practices by providing insights into the relationship between helix angle variation and structural performance. Such insights are valuable for engineers and designers seeking to develop efficient, durable, and high-performance gear systems for modern mechanical applications.

Smart Mental Health Assistant -An Ai Based Support System For Emotional Well-Being

Authors: Aliza Sayyad, Dr. Pravin Khatkale

Abstract: The prevalence of mental health conditions in- cluding stress, anxiety, and depression is on the rise worldwide, but stigma, ignorance, and a lack of mental health experts con- tinue to hinder early detection and ongoing emotional support. The Smart Mental Health Assistant, an AI-powered support sys- tem intended to assess user symptoms, forecast potential mental health issues, and offer tailored self-care advice, is the idea behind this project. The system incorporates a chatbot interface for user interaction and advice, Random Forest Classifier for mental health prediction, and Natural Language Processing (NLP) for symptom extraction. In order to effectively diagnose mental health disorders, the system transforms retrieved symptoms into binary vectors using datasets that include symptoms, severity lev- els, and preventative measures. This paper examines the body of research on AI in mental health, pinpoints important variables affecting technology uptake, and emphasizes the significance of scalable and easily accessible mental health resources. The re- sults show that early diagnosis, emotional monitoring, and pre- ventive treatments could all be enhanced by AI-based screening systems. By facilitating ongoing assistance, lowering stigma, and enhancing psychological well-being, the suggested assistant bene- fits the mental health ecosystem.

DOI: https://doi.org/10.5281/zenodo.18595836

Challenges In Indian Agriculture And Government Interventions: A Review

Authors: Ashwini Shinde, Dr. Kiran Wakchaure

Abstract: India’s agriculture sector remains the backbone of rural livelihoods and national food security, contributing substantially to economic growth and employment. However, farmers continue to encounter a wide range of structural and socio-economic barriers, including small and fragmented landholdings, heavy reliance on monsoon rains, inadequate technological adoption, post-harvest inefficiencies, financial vulnerabilities, and unstable market prices. Additional constraints such as rising labour expenses, low levels of mechanization, limited irrigation coverage, and insufficient knowledge of sustainable practices further limit agricultural productivity. This review paper explores these complex challenges in detail while assessing the effectiveness of major government programmes designed to address them. Key schemes—such as the Pradhan Mantri Fasal Bima Yojana (PMFBY), PM-Kisan income support, Soil Health Card initiative, e-NAM digital marketplace, Pradhan Mantri Krishi Sinchai Yojana (PMKSY), Minimum Support Price (MSP) mechanisms, and emerging digital agriculture efforts—are evaluated for their role in improving productivity, farmer income, and risk management. The study identifies notable policy successes as well as areas requiring improvement, emphasizing the need for integrated, technology-oriented, and farmer-focused strategies.

DOI: https://doi.org/10.5281/zenodo.18595876

Voice-Activated Al Safety Pendant For Women With Real-Time Location Sharing And Emergency Alert Transmission To Contacts Via Mobile App

Authors: Jayashree Chava, Prasad Chavan, Dr. Pritish Vibhute

Abstract: Women’s safety continues to be a pressing concern globally, and timely access to help often determines the outcome of critical situations. With rapid advances in electronics and communication technology, there is growing potential to build practical tools that can offer support when it is needed most. This work presents a compact, AI-enabled wearable safety device developed specifically to assist women during emergencies. The device operates hands-free and relies on on-device voice recognition, implemented on an ESP32-S3 microcontroller trained using Edge Impulse. It uses Bluetooth Low Energy (BLE) to connect with a companion Android application. When the system recognizes the spoken keyword “Help! Help!” it functions entirely offline to activate the mobile app. The app then automatically fetches the user’s GPS location and sends an SOS alert to selected emergency contacts. It also uses the Google Places API to identify nearby police stations for quicker support. To strengthen post-incident reporting, the wearable includes an AI-based motion and image-capture module that records relevant visual evidence through its built-in camera. The prototype is designed to be power-efficient, affordable, and mindful of user privacy, making it suitable for both rural and urban environments. Overall, the proposed system shows how edge AI and IoT connectivity can be combined to create a practical and reliable personal-safety solution.

DOI: https://doi.org/10.5281/zenodo.18595926

Smart Agriculture System Using IoT And Machine Learning For Automated Irrigation Management

Authors: Kewal Manish Patel, Gaurav Tushar Kokate, Durvesh Amit Amale, Shubham Musmade, Atharva Gare

Abstract: Agriculture in India faces challenges such as unpredictable rainfall, improper irrigation planning, and inefficient use of water resources. To address these issues, this paper proposes a Smart Agriculture System that integrates Internet of Things (IoT) sensors with a lightweight Machine Learning model to optimize irrigation. The system collects real-time soil moisture, temperature, humidity, and light intensity data using low-cost sensors such as the soil moisture sensor and DHT11. The data is sent to a cloud platform through an ESP8266/NodeMCU microcontroller for monitoring. A simple ML model, such as Linear Regression or Decision Tree, predicts the required watering level based on sensor patterns. When moisture falls below the predicted threshold, the system automatically activates a water pump and sends an alert to the farmer’s mobile dashboard. The proposed solution reduces water wastage, increases crop health, and facilitates precision agriculture. This work demonstrates how IoT and ML together can support sustainable agricultural practices, contributing to UN Sustainable Development Goals (SDG-2 and SDG-12). The prototype is easy to implement, low-cost, and scalable for real-world applications.

DOI: https://doi.org/10.5281/zenodo.18595980

Design And Analysis Of Neural Networks, Fuzzy Logic, And Expert Systems For Intelligent Decision-Making

Authors: Mr. Viraj Kishor Chitte, Mr. Om Anant Aher, Mr. Darshan Santosh Bhandari, Mr. Sai Yogesh More, Mrs. Smita Manohar Dighe

Abstract: Neural networks, fuzzy logic, and expert systems are fundamental to the development of intelligent systems capable of addressing complex decision-making challenges across various domains. Neural networks, inspired by the structure of the human brain, demonstrate proficiency in pattern recognition, data classification, and high-accuracy prediction. Fuzzy logic facilitates reasoning under uncertainty, enabling systems to process imprecise inputs and generate responses that resemble human reasoning. Expert systems employ rule-based reasoning to emulate expert decision-making, delivering reliable solutions across healthcare, diagnostics, and industrial automation. This paper examines the underlying principles, strengths, limitations, and applications of these three artificial intelligence techniques. Through comparative analysis, it highlights their performance distinctions and unique contributions to intelligent problem-solving. Additionally, the study investigates the advantages of integrating these methods to create hybrid intelligent systems with improved adaptability, accuracy, and reliability. Such integrated approaches have the potential to advance AI-driven solutions in smart systems, real-time monitoring, and automated decision support.

DOI: https://doi.org/10.5281/zenodo.18596011

Lightweight Real-Time Footfall Counting System Using YOLOv8 And Centroid Tracking For Resource-Constrained Environmen

Authors: Piyush Kotkar, Pratik Halnor, Sakshi Kapse, Harshal Adhav, Atharva Dhawale

Abstract: Real-time foot traffic monitoring is now a key part of retail analytics, campus management, and smart surveillance. However, limitations in computing power make it hard to use heavy deep-learning models in low-power settings. This paper introduces a lightweight footfall counting system that uses YOLOv8n and YOLOv8s along with a centroid-based tracking method for effective ID persistence and directional counting. Experimental results indicate that YOLOv8n reaches 4.1 FPS on CPU-only systems with 98–99% ID stability, surpassing YOLOv8s in real-time performance. The system works well for embedded platforms, public monitoring, and budget-sensitive deployments.

DOI: https://doi.org/10.5281/zenodo.18596112

BreathSafe: AI For Respiratory Health Care

Authors: Yash Solunke, Om Nikam, Shubham Chavan, Rutuja Raut, Pallavi Gulia

Abstract: BreathSafe is an innovative AIdriven system designed to monitor and diagnose respiratory conditions through breath analysis and real-time data processing. By leveraging machine learning algorithms on sensor data from wearable devices, BreathSafe enables early detection of diseases like COPD, asthma, and lung infections with over 90% accuracy in clinical trials. This paper presents the system's architecture, implementation, and evaluation for sustainable healthcare innovation.

DOI: http://doi.org/10.5281/zenodo.18619328

Design And Development Of An Ai-Powered Sustanable Irrigation Advisor

Authors: Yash Solunke, Ketan Bharambe, Nidhi Gandhi, Himani Suryawanshi, Khushi Raktate

Abstract: Sustainable irrigation is a critical component of modern agriculture due to increasing water scarcity, climate variability, and the need for precision resource management. Traditional irrigation systems, often based on fixed schedules or coarse environmental data, frequently lead to over-irrigation, under-irrigation, and inefficient water use. To address these limitations, this work introduces an AI-powered irrigation advisory framework that combines microclimate simulation, machine learning models, and real-time field-level sensing to generate accurate and adaptive water-use recommendations. The proposed system models localized microclimate parameters, including soil moisture, evapotranspiration, humidity flux, and temperature gradients, to provide more accurate short-term water demand estimates than traditional farm-level predictions. Machine learning algorithms continuously optimize the system, forecast crop-specific water needs, and dynamically identify patterns. To ensure robustness across diverse farming scenarios, the framework incorporates adaptive calibration mechanisms that adjust recommendations based on changing crop phenology and environmental conditions. We describe the implementation of this software-driven decision-support tool and its validation using both simulated and real-world agricultural datasets. Results demonstrate improved prediction reliability, a reduction in irrigation waste, and enhanced water-use efficiency compared to conventional scheduling methods. The proposed AI-powered sustainable irrigation advisor illustrates how microclimate-aware systems can advance next-generation smart agriculture, supporting productivity, environmental sustainability, and water conservation.

DOI: https://doi.org/10.5281/zenodo.18596198

Predictive Mobility Management In 6G Networks Using Long Short-Term Memory (LSTM) Networks

Authors:Sachin Kumar

Abstract: The rapid evolution of wireless communication technologies has led to the emergence of sixth-generation (6G) networks, which aim to support ultra-low latency, massive connectivity, and intelligent network automation. One of the critical challenges in 6G is efficient mobility management due to highly dynamic user behavior, ultra-dense networks, and heterogeneous access technologies. Traditional mobility management schemes rely on reactive handover mechanisms that often result in increased latency, packet loss, and signaling overhead. To address these limitations, predictive mobility management has gained significant attention. This paper proposes the use of Long Short-Term Memory (LSTM) networks, a type of deep learning model well-suited for sequential data, to predict user mobility patterns in 6G networks. By leveraging historical mobility data, the LSTM-based approach enables proactive handover decisions, improved resource allocation, and enhanced Quality of Service (QoS). The paper discusses the architecture, working principle, advantages, and applicability of LSTM-based predictive mobility management in 6G environments, highlighting its potential to enable intelligent and autonomous network operations

DOI: https://doi.org/10.5281/zenodo.18596515

Fake News Detection Using Machine Learning

Authors: Vishlesha Anil Habib, Vidya Gorakh Jagtap, Shrawani Ravindra Gaikwad, Nagraj Yashwant Kherud, Vaijayanti Pradip Kolhe

Abstract: The exponential growth of online platforms has enabled rapid dissemination of information, but it has also facilitated the widespread propagation of fake news. Fake news has negatively impacted political stability, public health, social harmony, and digital trust. This paper presents a comprehensive study and implementation of machine learning (ML) and Natural Language Processing (NLP)-based techniques for detecting fake news. The proposed system uses advanced text preprocessing, TF-IDF feature extraction, and multiple ML algorithms such as Logistic Regression, Support Vector Machine (SVM), Random Forest, and Naïve Bayes. Experimental results show that SVM achieves the highest accuracy of 94.8%, outperforming other models. This work demonstrates that combining linguistic features and machine learning provides a scalable and reliable approach to combat misinformation. Future enhancements include using transformer-based deep learning models and multilingual datasets

DOI: https://doi.org/10.5281/zenodo.18596939

Leveraging Business Analytics For Smart And Sustainable Business Decisions

Authors: Sarvesh Bhandari, Rohan Wakchaure, Aarya Mahajan, Gauri Gadakh, Vaibhav Bhokare

Abstract: In the competitive and rapidly changing business world of today, organizations make greater use of business analytics to inform smart, sustainable choices. This paper discusses how analytics tools and techniques can help an organization enhance operational efficiency, time its forecasts better, and embrace strategies that could ensure long-term sustainability. Integrating descriptive analytics with diagnostic, predictive, and prescriptive analytics helps turn raw data into actionable insights for businesses to drive efficient resource utilization, better customer understanding, and strategic planning. The role of modern technologies, such as machine learning, business intelligence systems, and real-time dashboards, has also been discussed in enhancing the data-driven decisioning process. It also investigates how the application of business analytics can result in environmental, social, and economic sustainability by minimizing waste, optimizing operations, and encouraging responsible business operations. The study points out that based on the literature review and practical applications, there is a strong need for analytical competencies and a data-driven culture within organizations. The conclusions highlight that leveraging business analytics is an important pathway not only to attaining competitive advantage but also to sustainable and resilient business growth. This paper also emphasizes the importance of integrating sustainability goals into the analytical models that support balanced and responsible decision making. With the pressure by stakeholders, regulators, and consumers increasing in sustainability matters, being able to link performance metrics together with environmental and social indicators increasingly becomes a priority competency for business. Business analytics lets organizations assess the effects of their long-term decisions, measure sustainability performance, and helps organizations make decisions that not only benefit them but also align with global standards like ESG frameworks. By highlighting practical examples of emerging trends, the paper shows how analytics-driven insights empower organizations to innovate, reduce risks, and build sustainable value for all.

DOI: https://doi.org/10.5281/zenodo.18597175

Smart Wardrobe Management System Using Ai&ml

Authors: Preethi Wilson G, Gokulakrishnan R, Dhivya dhanasree S S, Sumanth BKM

Abstract: A virtual try-on system is an advanced AI-powered platform that allows users to visualize how clothing items would appear on their bodies without physically wearing them. These systems are transforming the way people shop online by offering a digital fitting room experience using computer vision, deep learning, and generative models. in recent years, the demand for online fashion experiences has increased, encouraging the development of systems like Style VTON, which not only allows users to try on clothes virtually but also supports multiple body poses and preserves personal identity and clothing details. By using a combination of input images (user photo, clothing image, and target pose), such systems generate a highly realistic image of the user wearing the desired outfit in a new posture

Pulsatile Non- Newtonian Blood Flow Under The Influence Of A Transverse Magnetic Field: A Magnetohydrodynamic Study

Authors: Annu Singh, Basant Kumar Mishra

Abstract: Blood flow in human arteries is inherently pulsatile and exhibits non-Newtonian behavior, driven by the rhythmic cardiac cycle and influenced by shear-dependent viscosity arising from plasma and cellular interactions. This study investigates the magnetohydrodynamic (MHD) effects of a transverse magnetic field on pulsatile non-Newtonian blood flow, with particular emphasis on velocity distribution, wall shear stress (WSS), flow resistance, and hemodynamic responses in stenosed arteries. Blood is modeled as a Casson fluid, capturing shear-thinning and yield stress characteristics, while the transverse magnetic field generates a Lorentz force opposing flow. Governing momentum equations are formulated in cylindrical coordinates and solved using analytical techniques (Finite Hankel transforms) complemented by numerical simulations for pathological and pulsatile conditions. The analysis reveals that increasing the Hartmann number (Ha) significantly reduces centerline velocity, flattens velocity profiles, and decreases WSS, whereas higher Casson parameters (β) produce blunter, plug-like profiles with higher central velocity and lower boundary shear. Pulsatility, represented by the Womersley number (α), introduces phase-lagged oscillations, and stenosis severity amplifies local velocities and WSS, increasing flow resistance. Additionally, Joule heating due to induced currents modestly raises blood temperature, relevant for hyperthermia therapy. These findings have significant implications for MRI safety, magnetic drug targeting, and vascular disease management, providing quantitative insight into the interplay of magnetic fields, non-Newtonian rheology, and pulsatile hemodynamics in arteries.

Self-Reinforced Composites: Materials, Processing, Properties, And Emerging Applications – A Review

Authors: A.Swarna

Abstract: Self-reinforced composites (SRCs), also termed single-polymer composites, are engineered so that the reinforcing phase and matrix belong to the same polymer family. By eliminating chemical mismatch at the interface, SRCs typically show improved interfacial integrity, low density, and high impact tolerance while remaining compatible with single-stream recycling. Recent work (2020–2025) has emphasized processing control, bio-based SRC platforms, and microstructure-driven property tailoring.”This review provides a comprehensive discussion of SRC fundamentals, fabrication strategies, structure–property relationships, environmental advantages, application sectors, and future research directions.

Design Of A Deep Learning Based Model For Leukemia Detection

Authors: Ms. Jyoti Ahlawat, Research Scholar, Dr. Banita, Associate Professor

Abstract: Leukemia is a life-threatening hematological malignancy that requires early and accurate diagnosis to improve patient outcomes. Manual examination of microscopic blood smear images is time-consuming, subjective, and highly dependent on expert pathologists. With recent advances in artificial intelligence, deep learning has emerged as a powerful tool for automated medical image analysis. The goal of this research paper is to develop a deep learning-based model that can accurately detect leukaemia from medical images, with a focus on optimizing the model’s performance using advanced techniques such as transfer learning, hyper parameter tuning, and regularization methods. Evaluation metrics such as accuracy, precision, recall, F1 score, and the ROC-AUC curve will be used to assess the model’s diagnostic ability. By building a robust and scalable deep learning model for leukaemia detection, this study aims to contribute to the growing body of research on AI-driven medical diagnostics and provide a practical tool to assist healthcare professionals in early leukaemia diagnosis.

Indian Highway Rehabilitation Strategies For Urban Bituminous Surface Road

Authors: Kartik Dadore, Jitendra Chouhan

Abstract: In India, the road traffic volume has increased manifolds during the post-independence period. The traffic axle loading may also in many cases be much heavier than the specified limit. As a result of which, the existing road network has been subjected to severe deterioration leading to premature failure of the pavements. In such a scenario, development of the effective pavement management strategies would furnish useful information to ensure the compatible and cost- effective decisions so as to keep the existing road network intact. The pavement deterioration models can prove to be an effective tool which can assist highway agencies to forecast economic and technical outcome of possible investment decisions regarding maintenance management of pavements. The optimum maintenance and rehabilitation strategies developed in this study would be useful in planning pavement maintenance strategies in a scientific manner and ensuring rational utilization of limited maintenance funds. Once this strategy for urban road network is implemented and made operational; this would serve as window to the other urban road network of different regions.

Review On Novel Approach To Enhancement MRI Image Brain Tumor Detection Using SVM And Artificial Neural Network Algorithm.

Authors: Chinmay Chouhan, Srashti Thakur

Abstract: Brain tumor segmentation is an important task in medical image processing. Early diagnosis of brain tumors plays an important role in improving treatment possibilities and increases the survival rate of the patients. Manual segmentation of the brain tumors for cancer diagnosis, from large amount of MRI images generated in clinical routine, is a difficult and time consuming task. There is a need for automatic brain tumor image segmentation. The purpose of this paper is to provide a review of MRI-based brain tumor segmentation methods. Recently, automatic segmentation using deep learning methods proved popular since these methods achieve the state-of-the-art results and can address this problem better than other methods. Deep learning methods can also enable efficient processing and objective evaluation of the large amounts of MRI-based image data. There are number of existing review papers, focusing on traditional methods for MRI-based brain tumor image segmentation. Different than others, in this paper, we focus on the recent trend of deep learning methods in this field. First, an introduction to brain tumors and methods for brain tumor segmentation is given. Then, the state-of-the-art algorithms with a focus on recent trend of deep learning methods are discussed. Finally, an assessment of the current state is presented and future developments to standardize MRI-based brain tumor segmentation methods into daily clinical routine are addressed.

An Empirical Study Of Stock Market Trends And Investor Behavior

Authors: K. Perarasu

Abstract: The stock market is a crucial component of the financial system, playing a significant role in economic development and wealth creation. Traditional financial theories assume that investors behave rationally and make decisions based on complete information. However, practical market observations reveal that investor behavior is often influenced by psychological, emotional, and social factors. This study aims to examine stock market trends and analyze how investor behavior impacts market movements. The research adopts an empirical approach using both primary and secondary data. Primary data is collected through structured questionnaires administered to individual investors, while secondary data is obtained from stock market indices, financial reports, and published research studies. Statistical tools such as percentage analysis, correlation, and graphical interpretation are employed to analyze the data. The findings reveal that investors frequently exhibit behavioral biases such as herd behavior, overconfidence, loss aversion, and risk aversion. Market trends show significant volatility during periods of economic uncertainty, indicating emotionally driven investment decisions. The study concludes that investor behavior plays a vital role in shaping stock market trends and that incorporating behavioral finance concepts can enhance investment decision-making and market stability.

Blended Learning: A Transformative Instructional Paradigm For Revitalizing Teaching Practices

Authors: Showkat Hussain Bhat

Abstract: Nowadays, the teaching and learning landscape is embracing a number of new pedagogical innovations and some of these involve the use of e-learning through Blended Learning (BL). This study attempts to assess the need of blended learning as an instructional paradigm to rejuvenate teaching. In this connection, it is substantial that innovative pedagogical approach must be embraced in the classrooms. Teaching classes could be completely combined together by using numerous synchronous and asynchronous gadgets. The way of fully integrating technologies could be helpful to increase styles of communication, mentor-learner engagement, learner satisfaction, academic motivation and performance of students. This study suggests that instructors could use blended learning pedagogy because students shifted to e-learning as an alternate to in-person classroom because of rising usage of smart phones because of anytime and anywhere class.

Role Of Clinical Pharmacist In Management Of Diabetes Mellitus_915

Authors: Anand Kumar Gupta, Arshita Kumari, Swarangi Karangale, Shalni Kumar, Paramanand Kumar Bharti

Abstract: Objective: To systematically review and synthesize recent evidence (2020–2025) on the role of clinical pharmacists in type 2 diabetes management, focusing on clinical outcomes, patient education, adherence, and cost-effectiveness. Methods: Literature from PubMed, Scopus, and other databases (2020–2025) was reviewed, including randomized controlled trials, cohort studies, and systematic reviews examining pharmacist interventions in diabetes care. Results: Pharmacist-led interventions achieved significant reductions in HbA₁c (0.52 to 3.59%), improved patient adherence, and enhanced cost-effectiveness. Structured clinics such as DMTAC demonstrated consistent improvements in glycemic control and cardiovascular risk parameters. Conclusion: Clinical pharmacists enhance diabetes management through collaborative care, education, and therapy optimization, resulting in improved patient outcomes and reduced complications.

Architecture and Performance Evaluation of IoT- Enabled Wireless Sensor Networks in Precision Crop Monitoring

Authors: Khushboo Mishra

Abstract: The combination of Internet of Things (IoT) and Wireless Sensor Networks (WSNs) has transformed the practice of the modern agricultural sector by providing the possibility to monitor the crops precisely, make decisions immediately, and take care of the resources. Conventional agricultural practices tend to assume homogenous application of inputs and manual monitoring, which ignore spatial and temporal changes in the soil, climatic and crop conditions which result in wasteful utilization of water, fertilizers and energy. IoT based WSNs overcome this shortfall by supporting distributed sensor nodes that continuously gather environmental and crop related data such as soil moisture, temperature, humidity, nutrient level, and health of the plant. They have low-power microcontrollers (e.g., ESP32, Arduino, NodeMCU) and can be connected through wireless networks, including LoRaWAN, Zigbee, WiFi, and NB-IoT, sending data to wireless access points (gateways), and cloud or edge computing platforms to be processed and analyzed. Predictive insights, early alerts to crop stress, pest infestations, and nutrient deficiencies can be made through advanced machine learning models and edge AI with 92-95.9 percent success in environmental and crop condition prediction. According to performance reviews, there are vast energy efficiency improvements (up to 67 percent), resource use (water and fertilizer savings up to 40 percent), network reliability (PDR >95 percent), and crop yield (up to 30 percent). The selection of the protocol, hierarchical clustering (LEACH), and the low-power architecture make network lifetime and coverage to be optimized. The main issues are environmental interference, power constraint, security of data as well as interoperability between heterogeneous sensors and communication protocols.

DOI: https://doi.org/10.5281/zenodo.18617318

Securing Social Media Interactions Through Bloom Filter-based Spam Control and User Access Management

Authors: Karthikeyan R, Akshith G, Charukesh S, Monica Lakshmi R M.E

Abstract: This project develops a Spam Comment Detection and User Blocking System for a social media web application, designed to enhance user experience and maintain a secure online environment. Users can register, log in, send friend requests, chat, and post text or images, which may receive likes, dislikes, and comments. The system employs an advanced classifier algorithm to detect and filter negative or spam comments in both the chat and post sections. If a user exceeds 10 spam attempts, their IP address is blocked, preventing further access to the platform. Users can also create and share local events, which are visible to other users. The admin has oversight capabilities, including viewing user activity, managing events, and monitoring time spent on the platform through graphical analysis. The admin can also intervene by sending warnings to users displaying addictive behavior. The system integrates HAM algorithms and Bloom Filter data structures to improve spam detection efficiency and ensure optimal performance. This solution helps foster a safe, interactive environment by reducing harmful content and promoting responsible usage.

AI-Powered Traffic Flow Prediction Using Drones

Authors: Dr. M. L Kiran, J. Divya, G. Vineetha, M. Mahitha, P. Likhita

Abstract: The exponential growth of urban vehicular traffic has rendered traditional timer-based signal control systems inefficient, leading to increased congestion, fuel wastage, and carbon emissions. This paper proposes a novel Drone-Based Traffic Density Control System that leverages Unmanned Aerial Vehicles (UAVs) equipped with ESP32-CAM modules for real-time, aerial surveillance of road intersections. Unlike fixed infrastructure, the proposed system utilizes a rotating camera mechanism to provide 360-degree coverage, eliminating blind spots. The system employs Edge AI for vehicle detection and density estimation, transmitting telemetry data via ESP-NOW Protocol to a ground-based traffic controller. This paper presents the mathematical modeling of the traffic flow using Webster’s optimization logic and the PID stability analysis of the drone flight controller. Experimental results demonstrate that the system successfully adapts signal timing based on real-time density, significantly reducing average waiting time at intersections. In addition,the system incorporates emergency vehicle detection from the camera feed and immediately grants priority green to the corresponding approach, pre-empting the normal phase sequence to reduce emergency response time.

DOI: https://doi.org/10.5281/zenodo.18619404

Longitudinal Structural MRI-Based Deep Learning And Radiomics Features For Predicting Alzheimer’s Disease Progression

Authors: Diksha Pawar, Prof. Jayshree Boaddh, Prof. Rahul Patidar

Abstract: Alzheimer's disease (AD), the leading cause of dementia worldwide, affects more than 55 million individuals and gen-erates annual healthcare costs exceeding two trillion USD [14]. A substantial proportion (30–40% per year) of pa-tients with mild cognitive impairment (MCI) progress to AD [2], making early and accurate prognostication essential for timely intervention, trial enrichment, and resource allocation. This paper presents a comprehensive review of a re-cent longitudinal MRI-based study by Aghajanian et al. [1], which integrates three-dimensional (3D) convolutional neural networks (CNNs), time-aware long short-term memory (T-LSTM) networks with attention mechanisms, and radiomics features to predict MCI-to-AD conversion using structural MRI. The cohort comprises 228 ADNI MCI participants with at least three T1-weighted MRI scans over an 18-month window (684 scans in total) [1]. A 3D Res-Net-18 backbone [9] extracts volumetric features, fed into a T-LSTM incorporating inter-scan intervals and attention mechanisms [10]. The best longitudinal model achieves a concordance index (c-index) of 0.90, with time-specific AUCs of 0.96, 0.94, and 0.89 for 2-, 3-, and 5-year conversion prediction, respectively, and an approximate 11-fold hazard ratio between high- and low-risk groups [1]. This review analyzes the methodology, highlights its strengths and weaknesses, and discusses key implications for clinical translation.

Iot-Based Intelligent Battery Management and Monitoring System for Electric Vehicle Applications

Authors: Balaganesh.S, Mrs.S. Indhumathi,M.E, Dr.A.Shiny Pradeepa, M.E

Abstract: Electric vehicles rely heavily on battery performance, safety, and lifespan, making efficient battery management essential. Existing battery systems face drawbacks such as inaccurate state estimation, poor thermal management, cell imbalance, and limited real-time fault detection, leading to reduced efficiency and safety risks. A Battery Management and Monitoring System addresses these issues by continuously supervising battery parameters to ensure safe, reliable, and optimal EV operation. Therefore, this project proposes a smart, connected, and predictive solution for effective battery management in electric vehicles. The system utilizes both an ESP32 and a Raspberry Pi Pico as central controllers to enhance data processing and control capabilities. Sensors such as voltage, current, and temperature (DHT11) are used to continuously monitor the battery’s key parameters. The ESP32 handles IoT connectivity, transmitting real-time data to a cloud platform (like Blynk), and allowing users to remotely monitor battery status and control motor operations via the internet. Meanwhile, the Raspberry Pi Pico is employed to manage local data acquisition, signal processing, and protective control logic. This division ensures faster and more reliable responses to critical conditions. A relay driver and electronic relay are used to regulate the DC gear motor, ensuring optimal power management based on the sensed data. In case of abnormalities such as overvoltage, overcurrent, or overheating, the system can automatically trigger protective actions to prevent battery damage. This intelligent and connected solution not only improves operational efficiency and reliability but also promotes the advancement of sustainable electric vehicle technology through smart, dual-controller energy management. The combined use of ESP32 and Raspberry Pi Pico provides both robust cloud integration and precise local control, making the system highly responsive and reliable.

Creating Robot Control Car Using Wi-fi

Authors: Komal Bhatkar, Gauri Gadhave, pragati Ingale, Ankita Gunjite, prof. Prachi Walunj

Abstract: The “Creating Wi-Fi Using Arduino Robot Car System” project focuses on the design and implementation of a smart robotic car that can be controlled wirelessly through a Wi-Fi network. The main objective of this project is to develop a low-cost, flexible, and user-friendly robotic system capable of remote operation using a smartphone or computer. The system utilizes an Arduino microcontroller integrated with an ESP8266 Wi-Fi module to establish wireless communication between the car and the user’s device. Through this setup, the user can send commands via a web-based interface or mobile application, which are then processed by the Arduino to control the car’s motion, such as forward, backward, left, and right movements.

AQuaRIUSV: Aquatic Quality Real-Time Information Using Surface Vehicle For Coastal Waters

Authors: Jay-An T. Biscocho, Keanne R. Noval, Lebron F. Calunsag, Nathalie G. Tangonan

Abstract: The objective of this study is to develop an automated surface vehicle prototype called AQuaRIUSV (Aquatic Quality Real-Time Information Using Surface Vehicle) designed to monitor water quality in marine ecosystems. The sensor being utilized by the prototype consists of pH, turbidity, and TDS sensors measuring key water quality parameters in real time. The system consists of a pH, turbidity, and TDS sensor; an Arduino Uno R4 Wi-Fi microcontroller for data processing and control; a Neo-6M GPS module for location tracking; an L298N motor driver operating dual DC motors for movement; SG90 micro servo motors for steering; and an ultrasonic sensor for obstacle detection. Monitoring was enabled by Blynk through an IoT dashboard. Performance was evaluated for accuracy, consistency, and reliability. Results show effective real-time monitoring via the IoT platform. The study concludes that AQuaRIUSV is a reliable, efficient, and sustainable system for continuous marine water quality monitoring.

DOI: http://doi.org/10.5281/zenodo.18627861

Indian Highway Study On Causes Of Failure Of Bituminous Pavement

Authors: Sourabh Upadhyay, Professor Jitendra Chouhan

Abstract: One of the main purposes of Highway bituminous pavement failure and its maintenance is to provide a better road surface for the road users and carry traffic smoothly and safely with minimum cost. Paved roads in tropical and sub-tropical climates often deteriorate in different ways to those in temperate regions, because of the harsh climatic conditions, lack of proper design and quality control, high loads and inadequate assessment for identifying causes of distresses before carrying out maintenance and rehabilitation. A pavement distress that occurs at the surface can have a number of different causes which must be properly identified before corrective action is taken. Proper maintenance is very essential for longer life of the road surface.

Food Waste and Cloth Donation for Orphanage

Authors: Deepa Kumar M, Sutha K

Abstract: The systemic mismanagement of surplus food and clothing creates significant economic and social waste, necessitating a transition from manual, fragmented charity methods to automated, data-driven platforms. This paper analyzes a web-based Digital Redistribution System developed using PHP and MySQL to facilitate real-time resource allocation between donors (restaurants, individuals) and orphanages. By shifting from a "reactive" model—where surplus often spoils before discovery—to a "proactive" digital ecosystem, the system ensures timely collection and transparent tracking. The study highlights the effectiveness of Centralized Data Management and Validation Testing in reducing manual overhead and ensuring data integrity, ultimately proposing a scalable framework for minimizing waste in urban environments. By shifting from manual, often inefficient donation methods to an automated online system, this project aims to reduce hunger and minimize environmental waste.

Smart Crop Disease Using CNN Model

Authors: Anitha Rajathi, Pellakuru Mahathi, Bhavya G, B Harshitha Reddy

Abstract: Agriculture continues to face significant challenges due to crop diseases that result in reduced yield, economic losses, and delayed intervention, particularly in developing regions where access to expert diagnosis is limited. Traditional disease identification methods rely on manual inspection, which is time-consuming, subjective, and not scalable. This paper presents a Smart Crop Disease Detection System using Convolutional Neural Networks (CNNs) for automated and accurate identification of plant diseases from leaf images. The proposed system leverages deep learning techniques trained on real-world agricultural image data obtained from the PlantDoc dataset, which contains healthy and diseased crop leaves captured under diverse field conditions. A lightweight and efficient CNN architecture, MobileNetV2, is adopted to enable real-time disease detection with reduced computational overhead, making the system suitable for mobile and low-power devices. The model performs image classification to identify disease categories and assess plant health conditions. Experimental evaluation demonstrates that the proposed model achieves an accuracy of 85%, outperforming other baseline architectures. To enhance deployability, the trained model is converted into TensorFlow Lite, enabling seamless integration into mobile and web-based applications. The proposed framework facilitates early disease detection, supports timely preventive measures, and contributes to improved agricultural productivity through intelligent decision support.

Effect Of Modern Lifestyle On The Subconscious Mind

Authors: Dr. Suman Dhawan, Prof. Sonali Ingole, Mr. Rohit Rajpurohit, Mr. Kartikesh Pachkawade , Prof. Deepa Shivshimpi

Abstract: The rapid growth of technology and lifestyle modernization has significantly influenced the human mind, behavior, and emotional balance. This study investigates the impact of the modern lifestyle on the subconscious mind — the part of the human psyche that governs thoughts, emotions, and decisions beyond conscious awareness. A structured questionnaire was administered to 305 respondents, including students and professionals, to examine how daily habits such as screen time, sleep patterns, stress, and mindfulness practices affect subconscious stability. Findings show that excessive device use, irregular sleep, and frequent stress strongly affect subconscious calmness and self-awareness. Participants who maintained mindfulness routines reported greater emotional balance. The study concludes that while modernization improves efficiency, it disrupts subconscious harmony, emphasizing the need for balanced routines and conscious mental care.

DOI: http://doi.org/10.5281/zenodo.18629253

Winter Season Bird Migration Patterns At Nawabganj Bird Sanctuary Unnao

Authors: Dr Amit Kumar Awasthi

Abstract: Nawabganj Bird Sanctuary, a Ramsar-designated wetland in the Unnao district of Uttar Pradesh, India, serves as a critical wintering habitat and stopover site for a multitude of migratory bird species traversing the Central Asian Flyway (CAF). This comprehensive review paper synthesizes four decades of ornithological data, ecological studies, and management reports to analyze the patterns, drivers, and conservation status of avian migration at this vital sanctuary. The analysis confirms Nawabganj’s role as a key refuge for over 250 bird species, with a significant influx of Palaearctic migrants between November and March. Dominant families include Anatidae (ducks, geese), Ardeidae (herons, egrets), Rallidae (coots, moorhens), and a diverse array of waders (Charadriiformes). Migration timing and species composition are primarily driven by photoperiodic cues in breeding grounds and the availability of wetland habitat, forage resources, and thermal cover in the sanctuary. However, the review identifies a multifaceted crisis threatening this ecological function. Severe anthropogenic pressuresincluding water scarcity due to upstream diversion and erratic rainfall, invasive plant species (Eichhornia crassipes, Prosopis juliflora) encroachment, agricultural runoff leading to eutrophication, unsustainable tourism, and increasing human-wildlife conflict in the surrounding landscapeare degrading habitat quality. Emerging evidence suggests shifts in arrival/departure timings and a potential decline in populations of certain diving ducks and sensitive waders, possibly linked to climate change and local habitat degradation. This paper concludes that while Nawabganj remains a biodiversity haven, its long-term viability as a migratory bird sanctuary is precarious. The review advocates for an urgent, science-based, and integrated management approach. Key recommendations include securing ecological water flows, implementing systematic habitat restoration (invasive species removal, creation of deeper zones), strengthening community-based conservation, establishing long-term ecological monitoring programs, and promoting regulated, eco-sensitive tourism. The findings underscore that the sanctuary's future is contingent on translating its protected status into effective, on-ground ecological security.

DOI: http://doi.org/10.5281/zenodo.18629460

Fingerprint Authentication Based Voting Machine

Authors: Mr Deshmukh Y.V, Shubham Tagad, Rahul Pisal, Abhishek Suryavanshi, Naveen Kumar

Abstract: India is the world's largest democracy, and the core of any democracy is that people elect their own representatives. However, in today's times, the integrity of the election process faces numerous challenges such as booth capturing, rigging, fake voting, and tampering with Electronic Voting Machines (EVMs). As responsible engineers, it is our duty to take action to address these issues. Commonly used EVMs conduct voting electronically, eliminating the need for ballot papers, which are time-consuming and prone to intentional or unintentional errors. Currently, verifying voter authenticity is a major concern, and it must be ensured that no individual can vote more than once. This problem can be solved by implementing a biometric voting system that verifies voter identity through fingerprints, ensuring the principle of one person, one genuine vote. In this project, a prototype biometric voting machine based on fingerprint recognition has been developed. It is proposed to integrate a feature linking the Aadhaar database of the Unique Identification Authority of India (UIDAI), Government of India, New Delhi. This integration would allow voters to register automatically on the portal, categorized by regions and constituencies based on their unique fingerprint identification. This would enable the device developed in this research to be applied nationwide during elections, significantly improving the Indian electoral system.

Relevance Of Sanskrit In Modern Indian Education: Policy, Pedagogy, And Contemporary Significance

Authors: Madhura S. Khandekar, Seema Singh

Abstract: Sanskrit in the contemporary Indian education has been a topic of national and scholarly significance once again, especially due to the implementation of the National Education Policy (NEP) 2020. Sanskrit, the ancient language believed to be one of the classics and sacred languages, has played a major role in the intellectual tradition of India in the fields of philosophy, science, linguistics, mathematics, medicine, and aesthetics. Nevertheless, its role in modern education systems has been disputed on many occasions because of the challenges of accessibility, relevancy, and employability. This paper is an empirical study of the relevance of Sanskrit in contemporary Indian education based on policy frameworks, curriculum reforms, pedagogical practices, and empirical research. The study is based on a qualitative document analysis methodology involving national policy documents, curriculum frameworks, parliamentary reports and peer-reviewed scholarly literature. Results indicate that Sanskrit has a multidimensional impact; maintenance of cultural heritage, enhancement of cognitive and linguistics ability, facilitating interdisciplinary learning and provision of an Indian Knowledge Systems (IKS) framework. It is proposed in the study that the role of Sanskrit in contemporary learning has never been the revival of the language as a mandatory classical language but as a strategic intervention in the pedagogy of inclusivity, technology-intensive learning, and interdisciplinary interventions. The paper has been ended by some policy and pedagogical suggestions on how Sanskrit education can be made to meet the liability of equity, up to date skills, and international knowledge systems.

Number Plate Extraction

Authors: Bhushan Darekar, Omkar Borade, Atharv Kasture, Suyash Bhole, Samiksha Gawali

Abstract: This project presents an Automatic Number Plate Recognition (ANPR) system using the YOLO object detection model and Optical Character Recognition (OCR). The system detects vehicle number plates from images or video using YOLO, then extracts and preprocesses the plate region for better clarity. OCR is applied to recognize and convert the alphanumeric characters into machine-readable text. The proposed system provides a fast, accurate, and real-time solution for vehicle identification, useful in traffic monitoring, toll collection, parking management, and security applications. It reduces manual effort and improves efficiency through deep learning and image processing techniques.

Exploring Emotion Recognition Through Handwriting Analysis: A Comprehensive Review

Authors: Anjali Kumari Soni, Dipti Kumari

Abstract: Handwriting analysis is an important way to understand someone’s emotion focusing on his or her handwriting styles. By exploring the features of handwriting, we can create an outlay of emotions of a writer such as happiness, anger, sadness etc. As regular emotion of a human being configures a personality. The basic objective of this review paper is to review the different approaches used by the researcher to find the actual state of emotion in a human at that time. After inducing different types of emotions and then collect the handwriting samples to analyze handwriting features like Baseline, Pen Pressure, Slant, margin used, Zone of writing etc. will help in development of emotion recognition system which is going to be a very good tool for mental and emotional development of an individual of any age group, gender and professionals/learners to cope up with any situation in their daily life.

DOI: http://doi.org/10.5281/zenodo.18654108

NSE Stock Forecasting & Prediction System Using Machine Learning and Deep Learning

Authors: Mr. Sam Paul. T, Venkata Ramana Lingamgunta, Moovendhan S, Ramanakumar R

Abstract: Stock markets are complex, dynamic, and highly volatile systems influenced by macroeconomic indicators, corporate performance, geopolitical events, and investor psychology. Conventional stock forecasting approaches rely heavily on single predictive models, static technical indicators, or human intuition, which are inadequate in capturing non-linear dependencies, regime shifts, and predictive uncertainty inherent in financial time-series data. These limitations increase investment risk and reduce the reliability of automated trading systems, particularly for retail investors in emerging markets such as the National Stock Exchange (NSE) of India. This paper proposes an AI-driven NSE Stock Forecasting and Risk-Aware Trading Decision Support System that integrates classical machine learning, deep learning, market regime detection, and probabilistic uncertainty estimation within a unified multi-model framework. The system employs Linear Regression, Long Short-Term Memory (LSTM), Bidirectional Gated Recurrent Units (GRU), and Temporal Fusion Transformer (TFT) models for multi-horizon forecasting over one to fourteen days. A market regime detection module classifies market conditions into Bull, Bear, or Sideways states and dynamically adjusts model weights in a regime-aware ensemble mechanism, while Monte Carlo Dropout is utilized to generate ninety-five percent confidence intervals to support risk-aware decision-making. A prototype implementation is developed using Python, TensorFlow/Keras, Scikit-learn, Pandas, and Streamlit, operating on historical NSE OHLCV data enriched with thirty-two technical indicators. Experimental results demonstrate that the proposed ensemble framework outperforms single-model baselines in terms of prediction accuracy, variance reduction, and trading signal reliability. The system delivers interpretable forecasts, confidence bands, and automated BUY, SELL, or HOLD recommendations through an interactive dashboard, making it suitable for investors, traders, analysts, and researchers.

Licences Plate Recognition Using Esp32 –Cam

Authors: Ms. Kamble P. S, Ms. Midge S. M, Ms. Kardile S. R, Ms. More V. A, Ms. Aadhav kalyani

Abstract: The rapid growth of intelligent transportation systems has increased the need for automated vehicle identification solutions that are both cost-effective and easy to deploy. Traditional license plate recognition systems rely on high-resolution CCTV cameras and powerful processing units, which makes them expensive and unsuitable for small-scale or portable applications. To overcome these limitations, this project proposes a compact and economical License Plate Recognition (LPR) system using the ESP32-CAM module. The ESP32-CAM is an IoT-based microcontroller equipped with an OV2640 camera, onboard Wi-Fi, and sufficient computing capability to capture real-time images. In the proposed system, ESP32-CAM continuously monitors the vehicle’s presence, captures an image at the correct moment, and transmits it wirelessly to a server or computer for further processing. The backend system applies image preprocessing techniques—such as grayscale conversion, noise reduction, edge detection, and contour analysis—to isolate the license plate region. Optical Character Recognition (OCR) is then used to extract alphanumeric characters from the detected plate. This approach significantly reduces hardware cost, wiring complexity, and power consumption compared to conventional surveillance-based LPR systems. The designed setup is highly scalable and can be deployed in applications such as automated parking systems, gated community authentication, security checkpoints, toll management, and vehicle tracking solutions. The project demonstrates the potential of integrating embedded camera modules with machine learning-based OCR algorithms to create an accurate, portable, and low-power license plate recognition system. The results confirm that the ESP32-CAM can serve as a reliable foundation for intelligent vehicular monitoring in both academic research and practical field implementations.

Adaptive Control And Dynamic Optimization Of Hybrid RF–PON Access Networks Under Time-Variant Deployment And Traffic Constraints

Authors: Kasheera Gamith

Abstract: Hybrid fiber–wireless access networks have gained prominence as pragmatic solutions to deployment inefficiencies concentrated in the access segment of broadband infrastructure. Existing research has largely treated hybridization decisions as static design choices made at planning time, based on fixed assumptions regarding cost, performance, and feasibility. However, real-world access networks operate under time-variant conditions, including fluctuating traffic demand, dynamic interference environments, evolving regulatory constraints, and phased infrastructure availability. This paper proposes an adaptive control and dynamic optimization framework for hybrid RF–PON access networks that extends static segment-level substitution models into a time-dependent decision space. By integrating control theory principles, multi-objective optimization, and access network architecture models, the paper demonstrates how hybrid networks can continuously adjust the degree and location of wireless substitution to optimize deployment efficiency and service performance. The proposed framework redefines hybrid access networks as adaptive systems rather than fixed architectures, enabling resilience and efficiency under real-world variability. The paper contributes a novel analytical foundation for intelligent access network control and provides direction for future implementation and empirical validation.

DOI: http://doi.org/10.5281/zenodo.18655088

Computer Vision: Realtime Object Detection Using AI And Machine Learning Realtime Eye Strain Detection System

Authors: Snehal Pravin Pangavhane, Mayur Navnath Dhumal, Gayatri virendra patil, Harish Ravindra Badgujar, Mrs. Samiksha Gawali

Abstract: The project entitled “Computer Vision: Realtime object detection using AI and Machine Learning Realtime Eye Strain Detection System”, focuses on enhancing digital well-being by addressing the growing problem of Computer Vision Syndrome (CVS), commonly known as Digital Eye Strain. With the increasing dependency on digital devices for work, study, and entertainment, users often experience symptoms such as eye dryness, irritation, blurred vision, headaches, and reduced concentration. The proposed system utilizes Artificial Intelligence (AI) and Computer Vision (CV) technologies to monitor and analyze real-time indicators of visual fatigue. Using tools such as MediaPipe and OpenCV, it detects parameters like blink rate, eye aspect ratio (EAR), sitting distance, and ambient lighting. A user-friendly PyQt6 graphical interface enables seamless interaction, providing users with real-time alerts, adaptive feedback, and personalized wellness recommendations. By integrating AI APIs like Gemini or Grok, the system generates intelligent insights, preventive suggestions, and health trend reports. This promotes healthy screen habits and reduces the risk of long-term eye strain. The Vision Shield system contributes to digital wellness, productivity improvement, and AI-based health monitoring, offering a scalable solution for students, professionals, and organizations alike.

DOI: https://doi.org/10.5281/zenodo.18656354

Artificial Intelligence in FinTech: Enhancing Financial Inclusion and Risk Management in Nepal’s Microfinance Sector

Authors: Krishna Prisad Bajgai, Dr. Bhojraj Ghimire, Niraj Kumar Shah

Abstract: Artificial Intelligence (AI)-driven Financial Technology (FinTech) systems have emerged as transformative tools for enhancing financial inclusion and strengthening risk management in financial institutions. In developing economies such as Nepal, Microfinance Institutions (MFIs) play a critical role in poverty alleviation and access to finance but continue to face challenges related to credit risk, fraud, operational inefficiencies, and limited outreach to underserved populations. This systematic review synthesizes existing empirical and theoretical literature on AI-enabled credit scoring, fraud detection, explainable AI, and regulatory governance frameworks in financial services, with a specific focus on applicability to microfinance contexts. Following PRISMA-based screening and thematic synthesis, 42 peer-reviewed and institutional studies were analyzed. Findings indicate that machine learning models significantly outperform traditional statistical approaches in credit risk prediction and fraud detection, while explainable AI techniques such as SHAP and LIME enhance transparency and regulatory trust. However, substantial gaps remain regarding ethical governance, bias mitigation, and deployment in low-resource microfinance environments. The paper proposes a Nepal-specific conceptual framework aligned with Nepal Rastra Bank (NRB) policies and highlights research directions for responsible AI-driven FinTech adoption in microfinance sectors.

DOI: https://doi.org/10.5281/zenodo.18656780

FEA-Based Design and Evaluation of a Steering Knuckle Joint

Authors: Mr. Khartode V. M., Prof. S. P. Godase, Dr. S. D. Shinde

Abstract: The steering knuckle is a unique component that links the suspension, steering, braking systems, and wheel hub to the vehicle chassis. It bears vertical loads and is crucial for directional control. Given the diverse loads encountered in various situations, it is imperative to ensure high quality, durability, and precision without affecting the steering performance or the vehicle's overall behavior. In the automotive sector, reducing fuel consumption and achieving lightweight designs are critical requirements. A lighter steering knuckle improves performance and reduces production costs. This study aimed to optimize the material used for the steering knuckle joint. Currently, it is constructed from spheroidal cast iron, which provides good strength but is heavy and less resistant to corrosion than other materials. Thus, selecting a material with improved corrosion resistance and lower weight is necessary. The proposed approach investigates the use of Al matrix composites. Initially, the knuckle was designed analytically using mathematical equations. Subsequently, FEA was conducted for all alternative materials, and material optimization was performed. An experimental investigation was conducted to validate the results obtained from the FEA. Keywords: Steering Knuckle, Optimization, FEA, Matrix Composites, Lightweight Design.

DOI: https://doi.org/10.5281/zenodo.18657599

IoT Simulated Agriculture Platform for Data Driven Farming

Authors: Mrs. Yogita P. Gawde, Ms. Richa Mishra, Ms. Akansha Yadav, Ms. Isha Raghwani, Ms. Pritam Anil Yadav

Abstract: Agriculture is the backbone of many developing economies and plays a crucial role in ensuring food security. Traditional farming methods are largely dependent on manual observation and farmer experience, which may lead to inefficient use of water, fertilizers, and other resources. With the emergence of Internet of Things (IoT) and Artificial Intelligence (AI), agriculture is transforming into a data-driven domain. This paper proposes an IoT Simulated Agriculture Platform for Data Driven Farming that integrates simulated sensor data with real-time weather information and AI- based analytics. The platform provides intelligent recommendations related to crop selection, irrigation scheduling, fertilizer management, and soil health improvement. By using simulated IoT data, the system eliminates the need for expensive physical sensors, making it suitable for small-scale farmers, students, and researchers. Experimental evaluation shows that the system effectively supports decision making and promotes sustainable agricultural practices.

AI Based Vehicle Crash Detection & Emergency Notification System

Authors: Ganesh K. Bharaskar, Jayesh S. Chavan, Yash K. Pawar, Sagar R. Girase, Prof. Mohan T. Patel

Abstract: The imperative for minimizing response time in vehicular accident scenarios necessitates the development of robust, automated detection and notification systems. Conventional methods often rely on manual intervention, introducing critical delays that severely impact victim outcomes. This paper presents the architecture and performance evaluation of an AI-Based Vehicle Crash Detection and Emergency Notification System (AVC-DENS), designed to provide instantaneous, location-aware alerts upon the occurrence of a significant vehicular impact event. The AVC-DENS employs a tightly integrated Internet of Things (IoT) framework centered around the ESP32 microcontroller unit. Crash detection is predicated upon the real-time analysis of data streams derived from integrated vibration and inertial sensors, complemented by GPS modules for precise spatial localization. Upon algorithmic confirmation of a crash event, the system executes a multi-faceted notification protocol: captured video evidence and temporal-spatial coordinates are immediately transmitted to a secure cloud platform, specifically utilizing Firebase for reliable data persistence and retrieval.

DOI: http://doi.org/10.5281/zenodo.18668675

Leadership Practices Of Data Engineering For AI And Machine Learning

Authors: Khaleel Khan Mohammed

Abstract: Data engineering is now an essential subject for handling, processing, and analysing big data as the amount of data collected is increasing exponentially. This paper gives a future-focused overview of data engineering. The creation, building, upkeep, and optimization of data architecture, infrastructure, and pipelines are all essential components of data engineering, a field within data science. This paper presents a systematic study of data engineering pipelines with a focus on leakage-safe data splitting, preprocessing order, evaluation protocols, and reproducibility practices. We outline a canonical preprocessing workflow that enforces strict separation between training and evaluation data while ensuring that all data-dependent transformations are learned exclusively from training partitions. The paper further discusses suitable validation strategies for both static and time-dependent data, emphasizes the role of nested and repeated cross-validation, and highlights the importance of ablation and stability analysis in assessing model robustness. Finally, we examine provenance-aware logging and experiment tracking as essential components for reproducible and auditable machine learning systems. The proposed guidelines aim to support the development of trustworthy, scalable, and reproducible ML pipelines across data-intensive domains.

DOI: https://doi.org/10.5281/zenodo.18677279

Water Quality Analysis of Local Area and Its Environmental Impact

Authors: Shabbir I. Tamboli

Abstract: Water quality plays a important role in maintaining ecological balance and human health. Rapid urbanization, industrial discharge, and agricultural runoff have significantly affected surface and groundwater resources in many local regions of India. The present study evaluates the physicochemical parameters of water samples collected from selected locations in the local area. Parameters such as pH, turbidity, total dissolved solids (TDS), hardness, chloride content, and dissolved oxygen (DO) were analyzed using standard laboratory methods. The results were compared with BIS (Bureau of Indian Standards) drinking water standards. The study reveals that certain parameters such as TDS and hardness exceeded permissible limits in some locations, indicating potential environmental and health risks. The findings highlight the need for continuous monitoring and effective water management strategies to protect environmental sustainability.

Arc Fault Detection Using Wavelet Analysis–based Signal Processing Methods

Authors: Anurag Kumar, Dr Ashish Kumar Rai

Abstract: Arc faults present a significant risk to electrical power systems, potentially causing equipment damage, fires, and service interruptions if not detected quickly. Traditional protection methods often struggle to detect arc faults because of their nonlinear, low-current, and nonstationary behaviour. Wavelet-based analysis has proven effective due to its strong time–frequency resolution. By decomposing voltage and current signals into multiple frequency bands, wavelet transforms extract transient features linked to arc initiation and extinction. Indicators such as wavelet coefficients, high-frequency energy, and entropy help distinguish arc faults from normal conditions and disturbances. Discrete, continuous, and wavelet packet transforms, combined with intelligent classifiers, enhance detection accuracy, speed, and robustness in modern distribution systems.

Performance Evaluation Of Systematic Investment Plans In Gold Mutual Funds: An Empirical Study Of Axis Gold Fund Regular Growth (2015–2025)

Authors: Dr. S. Roslin

Abstract: In recent years, Systematic Investment Plans (SIPs) have emerged as a preferred investment strategy among Indian investors due to their disciplined structure, affordability, and ability to mitigate market volatility. Simultaneously, gold continues to retain its significance as a safe-haven asset, offering protection against inflation and economic uncertainty. This study aims to evaluate the performance of SIP investments in gold by empirically examining the Axis Gold Fund Regular–Growth scheme over a ten-year period from 2015 to 2025.The research adopts a quantitative research methodology and relies on secondary data to analyze SIP performance across multiple investment horizons, namely 10 years, 5 years, 3 years, 2 years, and 1 year. Key performance indicators such as total investment, latest value, absolute return, annualized return, Return on Investment (ROI), Investment Growth Factor, and Wealth Gain are employed to assess the effectiveness of gold SIPs as both short-term and long-term investment options. Overall, the study concludes that SIPs in gold mutual funds represent a reliable and structured investment avenue for wealth creation and portfolio diversification. The results offer valuable insights for investors, financial planners, and academicians by highlighting the relevance of gold SIPs in long-term financial planning and risk management strategies.

DOI: https://doi.org/10.5281/zenodo.18678948

AGRI-Connect: An Ai-Driven Unet–Vision Transformer Framework For Disease-Aware Crop Quality Grading And Direct Agricultural Marketplace Integration

Authors: Monica Lakshmi R, Parvathareddy Kavya Reddy, Prasiddhi S, Keerthana Devi S

Abstract: Small and marginal farmers in developing economies face challenges such as delayed crop disease detection, subjective quality assessment, and non-transparent pricing due to intermediary-dominated markets. To address these issues, this paper presents Agri-Connect, an AI-driven digital platform that integrates automated crop disease detection, quality grading, and a direct farmer–consumer marketplace. The proposed system employs a hybrid deep learning architecture, combining UNet-based semantic segmentation for precise diseased region extraction and a Vision Transformer (ViT) for robust disease classification and severity analysis. Experimental evaluation was conducted on a combined dataset consisting of ICAR images, drone-captured imagery, and real- world field images under varying environmental conditions. The proposed framework achieved a disease classification accuracy of approximately 94% and reliable quality grading performance across multiple produce categories, outperforming conventional CNN-based approaches. A multilingual, voice-enabled interface and AI- powered chatbot enhance usability for low-literacy users, while an integrated real-time marketplace enables transparent, quality-based pricing and direct trade. Agri-Connect demonstrates the practical potential of linking AI-verified crop analysis with digital market access to improve farmer income, transparency, and sustainable agricultural practices.

DOI: https://doi.org/10.5281/zenodo.18679741

Shadow Mode Robotic Arm

Authors: U.Meri Kishore, C.Aswini, S.Mahammad Thousif, V.Manasa, P.Bhargavi, Y.Vishnu Govardhan

Abstract: This paper presents the design and implementation of a Shadow Mode Robotic Arm, a high- precision teleoperation system designed to replicate human arm movements in real-time. The system leverages an ESP32-CAM module for vision-based pose estimation, utilizing the MediaPipe framework to track key upper-body landmarks. The extracted joint coordinates are processed and mapped to a multi-degree-of-freedom (DOF) robotic arm actuated by MG995 metal-gear servos. An ESP32microcontroller serves as the secondary control unit, generating Pulse Width Modulation (PWM) signals for synchronized actuation. The system features a dual-mode operation: a direct mimicry "Shadow Mode" and a remote web- dashboard control mode. Experimental results demonstrate a low-latency response (<100ms) and high kinematic fidelity, offering a cost-effective solution for hazardous environment handling, telemedicine, and industrial automation.

DOI: https://doi.org/10.5281/zenodo.18695456

Work-life Harmony and Employee

Authors: Vijay V E, Varshitha Srinivasa, Prerana K P, Priyanka Jain , Prithi M

Abstract: With the current competitive and technology aggressive workplace, workers are finding it more difficult to balance between professional duties and personal and family life. The life-long working hours, job demands and never ending connection have rendered employees unable to have healthy work and life integration. The concept of work-life harmony has thus become a critical issue which concentrates on good integration of work and personal roles as opposed to having a rigid balance. This research is aimed at investigation of the correlation between work-life harmony and job performance among employees. The work is conducted in accordance with descriptive research design and is supported with primary data obtained after the use of a structured questionnaire among the employees of various organizational backgrounds. The variables of workload, job stress, flexibility and organizational support were taken into consideration to determine their level of influence on work-life harmony and job performance. Analysis of the data was done using simple statistical tools like a percentage analysis and correlation analysis. Results of the research indicate that there is a positive and significant correlation between work-life harmony and job performance. Workers whose work and personal lives are in a positive balance are more productive, have quality work and greater job effectiveness. The research reveals the significance of organizational support and employee friendly policies that foster the work-life harmony and the overall performance of the employees.

Speech Recognition Based Medicine Vending Machine With Message Notification To The Hospital About The Patient

Authors: Mr. Sandeep Ramesh Sonaskar, Ms. Mohini Fulzele, Mrs. Neema Ukani

Abstract: This paper presents an advanced healthcare automation system that integrates speech recognition technology with an automated medicine vending mechanism and a GSM-based hospital notification system. The proposed system enables patients to obtain prescribed medicines using voice commands, thereby eliminating manual intervention and reducing human errors. The system verifies patient identity, matches voice input with stored prescription data, dispenses medicine accurately, and sends real-time SMS notifications to hospital authorities. The solution is especially beneficial in rural and remote healthcare environments where pharmacist availability is limited. The implementation demonstrates high speech recognition accuracy, reliable medicine dispensing, and effective communication with healthcare providers.

The Paradigm Shift: A Comprehensive Analysis Of Zero Trust Architecture In Cybersecurity

Authors: Rizwan Majeed

Abstract: The digital infrastructure of the 21st century has undergone a metamorphosis so profound that the security paradigms of the previous decades are not merely inefficient—they are dangerously obsolete. The traditional "castle-and-moat" model, which concentrated defensive resources on a hardened network perimeter while assuming implicit trust for all internal traffic, has collapsed under the weight of cloud computing, mobile workforces, and the proliferation of the Internet of Things (IoT). In its place, Zero Trust Architecture (ZTA) has emerged as the definitive framework for securing the modern enterprise. Unlike legacy models that relied on physical or network location as a proxy for trust, Zero Trust operates on the rigorous axiom of "Never Trust, Always Verify." This report offers an exhaustive examination of the Zero Trust landscape, tracing its intellectual genealogy from academic theory to the boardrooms of global corporations. It explores the operational complexities of replacing legacy Virtual Private Networks (VPNs) with Zero Trust Network Access (ZTNA), and analyses the critical role of Artificial Intelligence (AI) in automating dynamic policy enforcement. Furthermore, it addresses the existential threat posed by quantum computing, outlining the necessary migration to Post-Quantum Cryptography (PQC). By synthesizing historical context, technical specifications, and future market trends, this document serves as a foundational text for understanding Zero Trust not as a product, but as a strategic imperative for institutional resilience.

DOI: https://doi.org/10.5281/zenodo.18711740

IoT based Electricity Generation Tiles using Piezo-electric Material

Authors: Madhur M.Kamate, Parth S.Parik, Darshan B.Patil, Samarth N.Taralekar, Ms.S.C.Raynade

Abstract: In recent years, the rapid growth of global energy demand, along with the ongoing depletion of traditional fossil fuel resources, has made the need for sustainable, renewable, and decentralized energy solutions more urgent. Traditional energy generation methods are increasingly unable to meet rising consumption while ensuring environmental sustainability. Among the various alternative approaches, capturing human kinetic energy produced during everyday activities has emerged as a promising and eco-friendly solution. Human movement, especially in crowded public areas, is an underused and always available energy source. This paper presents the design and implementation of a footstep-based electricity generation system that uses piezoelectric sensors to convert human movements into usable electrical power. The system works on the principle of the piezoelectric effect, where mechanical pressure from footsteps is turned into electrical energy. The electrical output produced is usually alternating and highly variable. It is processed to ensure stable operation through rectification, filtering, and conditioning with appropriate power electronic circuits. A buck-boost converter regulates the voltage levels effectively and allows for the reliable storage of the collected energy in rechargeable batteries. An Arduino-based microcontroller is included for smart monitoring, which provides real- time readings of voltage levels, footstep counts, and system performance displayed on a 16×2 LCD module. The stored energy is used to power low- power DC devices like LEDs and mobile charging ports, as well as AC loads through an inverter circuit. Experimental tests show that the generated power increases with the load and footstep frequency. This demonstrates the potential for deploying the system in high-footfall public spaces such as railway stations, shopping malls, and smart city infrastructures.

G+3 Residential Building Design, Analysis and Estimation by Using Staad Pro and Revit Software

Authors: Dr. C.Chinna Suresh Babu, P.Subha Akhil, A.Dharani, M.M.Haneef

Abstract: The project titled “G+3 Residential Building Design, Analysis, and Estimation Using STAAD Pro and Revit Software” focuses on thestructural planning, analysis, and cost estimation of a Ground plus Three (G+3) storied residential building using advanced computer- aided design tools. The main objective of this project is to achieve a safe, economical, and functionally efficient structural design that complies with relevant building codes and standards. In this project, STAAD Pro is used for structural analysis and design to determine the strength, stability, and load-carrying capacity of structural components such as beams, columns, and slabs under various load conditions including dead load, live load, wind load, and seismic load. The Revit Architecture software is utilized for creating detailed 3D models, visualizations, and working drawings of the building, enabling better coordination among architectural and structural components. Overall, this study demonstrates the importance of using Building Information Modeling (BIM) and structural analysis software in modern civil engineering for producing sustainable, economical, and structurally sound residential buildings.

DOI: https://doi.org/10.5281/zenodo.18720515

MethaneMorphosis: A Smart Bioengineered Reactor For Efficient Methane Generation Through Adaptive Co-digestion Of Mixed Organic Substrates

Authors: Gabriel D. Gonzales, Nhyllarhy Myrrh Junsay, Gian Gaizel Pausta, Jannon V. Tagalog, Angel S. Salario, George I. Salvador

Abstract: This study focused on developing the MethaneMorphosis: The Smart Bioengineered Reactor as a more secure, automated, and affordable alternative to household biogas generation, addressing the challenges created by rapidly surging Liquefied Petroleum Gas (LPG) prices and initial cost and safety issues of manual biodigesters. The system included an Arduino Uno R4 Wifi microcontroller, and optimized automated co-digestion of household food waste and pig manure using Response Surface Methodology (RSM) and various sensors to monitor temperature, pressure, and methane concentration in real-time. The study found 100% of the reactor to be automated with the optimal mesophilic range of 36-39 degrees Celsius successfully maintained; the system achieved a remarkably high energy conversion ratio (ECR of 4968.03%), with recovered energy equivalent to nearly 5.94 kg of LPG utilized, all at a unit cost of methane at ₱12.26/m^3. Overall, the findings illustrate the importance of this system as a very efficient and robust option for cost-effective renewable energy generation and sustainable waste management options. The primary recommendation is for the local stakeholders to explore and adapt more innovative natural gas projects like MethaneMorphosis, which is a low-cost and automated reactor as an option for households that promote sustainable waste management and reduce household energy costs through generating renewable biogas.

DOI: https://doi.org/10.5281/zenodo.18721478

A Comparative Study Of Regression Test Case Prioritization Methods In Software Quality Assurance

Authors: Ms. Meenakshi, Dr. Shweta Mishra

Abstract: Regression testing plays a critical role in ensuring the continued functionality and quality of software as changes, updates, or bug fixes are implemented. Given the complexity and the growing size of modern software systems, managing large test suites efficiently is becoming increasingly difficult. This paper focuses on the importance of test case prioritization, a technique aimed at ordering test cases in a way that maximizes fault detection early while minimizing resource utilization. The study compares various theoretical models and methods of test case prioritization, including random, requirement-based, code-based, and model-based prioritization. It highlights the benefits of prioritization in improving test efficiency and the challenges associated with its implementation. Additionally, the paper reviews both traditional and advanced prioritization techniques, including risk-based prioritization and AI-driven methods, with a comparison of their effectiveness in different software development contexts. Finally, the paper discusses the challenges and limitations of current prioritization models and suggests future directions for improving prioritization techniques in regression testing.

Automatic Pet Food Dispenser

Authors: Shantanu Shahaji Gaikwad, Aryan Dattatray Vibhute, Sharvari Arun Shedage, Yogita Sunil Kamble

Abstract: In today’з modern and faзt-moving world, the demand for зmart and automated зolutionз haз increaзed зignificantly, eзpecially in the field of pet care. Thiз reзearch focuзeз on the development of an automatic pet food diзpenзer deзigned to provide automated, preciзe, and intelligent feeding зolutionз to зupport pet ownerз with buзy lifeзtyleз. Many pet ownerз face challengeз in maintaining conзiзtent feeding зcheduleз due to work commitmentз, travel, and other daily reзponзibilitieз. The propoзed зyзtem aimз to зolve thiз problem by enзuring that petз receive timely and accurate mealз without conзtant human зuperviзion. Theзe зyзtemз integrate microcontrollerз, зenзorз, real-time clock moduleз, and Internet of Thingз (IoT) technologieз to enable зcheduled feeding, accurate portion control, and remote monitoring through mobile applicationз. The microcontroller actз aз the central control unit, coordinating the timing mechaniзm and diзpenзing proceзз. Senзorз are uзed to detect food levelз, monitor bowl зtatuз, and enзure proper food diзtribution. Through IoT connectivity, uзerз can acceзз the зyзtem remotely, adjuзt feeding зcheduleз, control portion зizeз, and receive real- time notificationз regarding feeding activitieз and зyзtem performance. Advanced modelз incorporate data tracking and intelligent featureз that record feeding hiзtory, analyze conзumption patternз, and provide inзightз to improve feeding accuracy and maintain optimal pet health. Some зyзtemз may alзo include voice recording featureз, camera monitoring, and emergency alertз to further enhance functionality and зecurity. Theзe зmart featureз help prevent overfeeding and underfeeding, reduce food waзtage, and зupport balanced nutrition. Overall, automatic pet food diзpenзerз enhance convenience, conзiзtency, and reliability in daily feeding routineз. They contribute зignificantly to improved animal welfare by enзuring petз receive proper nutrition at the right time. At the зame time, they reduce human effort, minimize зtreзз for pet ownerз, and repreзent an important зtep toward the integration of зmart technology in modern pet care management.Top of Form Bottom of Form

DOI: https://doi.org/10.5281/zenodo.18721934

Security Challenges And Solutions In Online Examination Systems

Authors: Urgen Maharjan, Gakegni Ngaste Aperhy, Sahasra Nagaraj, Sahil Gupta, Prithi M

Abstract: The recent fast digitalization of the educational system has seen the introduction of online examination systems in universities, examining bodies and professional organizations in large numbers. Even though these platforms enhance accessibility, scalability, and operational efficiency, they present vital security and integrity issues that pose a threat t o the credibility of online tests. The significant security issues are impersonation by candidates, contract cheating, leaking of question papers, tampering with answer scripts, unauthorised collusion, malware-based attacks, network breach and massive distributed denial-of-service (DDoS) attacks. Also, the introduction of remote proctoring systems based on AI provokes serious concerns about privacy, ethical and data protection issues. The currently in place security systems like password-authenticated access and crude browser limitation are inadequate in stopping advanced methods of cheating as well as online threats. The modern studies suggest sophisticated measures such as multi-factor authentication, facial recognition-based and key-stroke dynamic-based biometric verification, end- to-end encryption algorithms to ensure safe data delivery, blockchain-based transparent record keeping, behavioral analytics, abnormality detection approaches that use AIs, and lockdown browsers. Nevertheless, such solutions tend to cover single facets of security and can pose issues of computational cost, scalability, implementation cost and user confidentiality. The paper provides the in-depth examination of security issues with online examination systems and reviews the modern research-based solutions in a systematic way. The comparative assessment framework is created to determine the efficiency, practicability and constraints of different security mechanisms. According to the results, a multi-layered security architecture will be introduced incorporating the elements of authentication, communication security, intelligent monitoring, and tamper-resistant data management to guarantee the examination integrity, confidentiality, availability, and accountability. The research concludes that a combination of defense in depth strategy is crucial towards the attainment of secure, scalable and reliable online examination ecosystems.

MediMinder: Smart Health Scheduler

Authors: S.Rutuja, P. Sanskruti, P. Shreya, B.Nandini, M.Priya

Abstract: The core aim of the given project is to create an Web application named MediMinder, that assists people in keeping track of their daily health routines without much effort. The application gives smart reminders to take medicine, attend the checkups and make appointments with the doctor.The machine learning (ML) will learn the habits of the user and get their schedules in order by creating personalized and voice-activated reminders that include particular date and time, and medicine information.With the application, all users can enjoy better health management and safety by receiving personalized, voice-based, and specifically set reminders with a specific date, time, and specific medicine information.

Addressing Cold-Start Problem In Movie Recommendation System Using Sequence Modeling

Authors: Rupal Bula, Dr. Vandan Tewari, Mrs Sonu Airen

Abstract: The recommendation system is a classification of machine learning [1] that uses features to help anticipate, compact and find what people are looking for an exponentially growing number of options. It is an artificial intelligence algorithm in machine learning, which uses big data to recommend more related items to consumers. These can be based on different criteria, which includes past purchase, search history, demographic information and other factors. These systems are designed to predict what a user might like based on various factors. They are extensively used in various domains including e-commerce, streaming services, social networks and content platforms. In this paper we have proposed a novel movie recommendation system that effectively addresses the cold start user problem by leveraging sequence modeling techniques [2]. Traditional recommendation systems struggle with new users due to the lack of historical interaction data. Our approach utilizes sequence modeling, specifically Long Short Term Memory (LSTM) networks, which predict user preferences based on initial interactions. By analyzing the sequence of the movie watched, our model can generate accurate recommendations even with minimal user data.

DOI: https://doi.org/10.5281/zenodo.18723361

AIRMATH: FULLY AUTOMATIC SOLAR CRASS CUTTER

Authors: Apurva Tanaji Bhosale, Gaurav Prabhakar Pandhare, Suman Ravi Rathod, Nikhil Megharaj Tikande

Abstract: The Fully Automatic Solar Grass Cutter is an innovative, eco-friendly solution designed to automate lawn maintenance while utilizing renewable energy. This system operates entirely on solar power, eliminating the need for conventional fuel or external electrical supply. The solar panel mounted on the device captures sunlight and converts it into electrical energy, which is stored in a rechargeable battery to power the DC motors and control unit.The grass cutter is equipped with automated navigation and obstacle detection mechanisms using sensors, enabling it to move independently across the lawn while avoiding collisions. A microcontroller is used to control the movement of the wheels and the cutting blade motor, ensuring efficient and uniform grass trimming. The automation reduces human effort, operational cost, and environmental pollution compared to traditional petrol-powered grass cutters.This project emphasizes sustainability, energy efficiency, and smart automation. It is suitable for residential lawns, gardens, parks, and institutional grounds. By integrating renewable energy with robotic technology, the Fully Automatic Solar Grass Cutter provides a cost-effective, low-maintenance, and environmentally friendly alternative for modern lawn care applications.energy, which is stored in a rechargeable battery. This stored energy powers the DC motors responsible for blade rotation and vehicle movement. A microcontroller-based control unit manages the overall operation of the system.The machine is equipped with sensors for obstacle detection and autonomous navigation, enabling it to operate without human intervention. The automatic mechanism ensures uniform grass cutting while reducing manual labor, fuel consumption, and environmental pollution. Compared to conventional petrol-driven grass cutters, this system offers low operational cost, minimal maintenance, and zero carbon emissions.

DOI:

AIRMATH: FULLY AUTOMATIC SOLAR CRASS CUTTER

Authors: Apurva Tanaji Bhosale, Gaurav Prabhakar Pandhare, Suman Ravi Rathod, Nikhil Megharaj Tikande

Abstract: The Fully Automatic Solar Grass Cutter is an innovative, eco-friendly solution designed to automate lawn maintenance while utilizing renewable energy. This system operates entirely on solar power, eliminating the need for conventional fuel or external electrical supply. The solar panel mounted on the device captures sunlight and converts it into electrical energy, which is stored in a rechargeable battery to power the DC motors and control unit.The grass cutter is equipped with automated navigation and obstacle detection mechanisms using sensors, enabling it to move independently across the lawn while avoiding collisions. A microcontroller is used to control the movement of the wheels and the cutting blade motor, ensuring efficient and uniform grass trimming. The automation reduces human effort, operational cost, and environmental pollution compared to traditional petrol-powered grass cutters.This project emphasizes sustainability, energy efficiency, and smart automation. It is suitable for residential lawns, gardens, parks, and institutional grounds. By integrating renewable energy with robotic technology, the Fully Automatic Solar Grass Cutter provides a cost-effective, low-maintenance, and environmentally friendly alternative for modern lawn care applications.energy, which is stored in a rechargeable battery. This stored energy powers the DC motors responsible for blade rotation and vehicle movement. A microcontroller-based control unit manages the overall operation of the system.The machine is equipped with sensors for obstacle detection and autonomous navigation, enabling it to operate without human intervention. The automatic mechanism ensures uniform grass cutting while reducing manual labor, fuel consumption, and environmental pollution. Compared to conventional petrol-driven grass cutters, this system offers low operational cost, minimal maintenance, and zero carbon emissions.

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Mealsphere Management System

Authors: Durgesh Nishad, Purvesh Patil, Vedant Chaudhary, Ghansham Bordekar, Dr.Umesh Pawar

Abstract: The MealSphere Management System is a modular, automated platform designed to streamline food ordering, inventory tracking, billing, and administrative operations. Traditional restaurant workflows reliant on handwritten logs and disconnected tools often result in delays, errors, and poor visibility. MealSphere resolves these inefficiencies through a centralized system that integrates authentication, menu management, inventory deduction, digital billing, and analytics. It supports both offline and online modes, ensuring operational continuity and real-time synchronization. This paper presents the system’ s architecture, implementation, and performance evaluation, demonstrating its scalability across restaurants, hostels, cloud kitchens, and canteens.

Voice And Text Based Chatbot

Authors: Zaibindah Rafeeq Pandit, Sabit Aslam, Rehana Jan, Irfan Rasool

Abstract: Conversational agents, or more popularly called virtual assistants or chatbots, are now a unifying interface for modern digital ecosystems, enabling seamless human-computer interaction. Fueled by unprecedented accelerations in Artificial Intelligence (AI), Machine Learning (ML), and Natural Language Processing (NLP), these agents evolved from script-based rules to sentient agents with the capability to understand context, sentiment, and intent. Transformer-based models such as GPT and BERT have significantly improved fluency, coherence, and chatbot response flexibility so that conversations could be more human-like. The present paper follows the historical progression of conversational agents from the initial symbolic systems such as ELIZA to modern-day deep learning models. It covers significant architectural components like intent recognition, conversation management, and response generation with emphasis placed on the intersection of speech-to-text (STT) and text-to-speech (TTS) for voice interaction. The book also looks into popular frameworks and toolkits used to develop and deploy chatbots into real-world applications across healthcare, education, customer support, and mental health. Moreover, the paper highlights major challenges hindering the robustness of current systems, including data bias, hallucination, context limitations, and lack of emotional intelligence. Moral implications—particularly of fairness, privacy, and explainability—are argued against in terms of novel guidelines and mitigation strategies. A modular, LLM-assisted architecture is suggested to demonstrate practical implementation with inherent evaluation metrics. Finally, the paper outlines guidance for subsequent research and development, calling for emotionally smart, multi-lingual, and culturally sensitive conversational agents that are ethics-compliant and highly accessible and performing.

DOI: https://doi.org/10.5281/zenodo.18739755

FOOTSTEP POWER GENERATION

Authors: Aditya Kale, Srushti Dani, Sonali Gawali, Om Menkudle

Abstract: In last few years low power electronic devices have been increased rapidly. The devices are used in a large number to comfort our daily lives. With the increase in energy consumption of these portable electronic devices, the concept of harvesting alternative renewable energy in human surroundings arises a new interest among us. In this project we try to develop a piezoelectric generator. That can produce energy from vibration and pressure available on some other term(Like people walking ). This project describes the use of piezoelectric materials in order to harvest energy from people walking vibration for genera ting and accumulating the energy. This concept is also applicable to some large vibration sources which can find from nature. Thisproject also represents a footstep of piezoelectric energy harvesting model which is cost effective and easy to implement.

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Cybersecurity Challenges In IoT-Enabled Supply Chains

Authors: Dr Anuranjita Dixit

Abstract: IoT has disrupted supply chains worldwide by incorporating smart sensors, RFID tags, cloud-based visibility systems, autonomous tracking devices, robotics, and data-driven logistics. IoT-SC provides considerable operational benefits, including real-time tracking, predictive maintenance, inventory automation, transportation optimization, and responsive decision-making. However, it simultaneously brings in serious cybersecurity challenges due to the distributed nature of IoT ecosystems, resource-constrained devices, heterogeneous communication protocols, and exposure to public networks, vulnerabilities in every supply chain layer-from procurement and manufacturing to warehousing, distribution, and last-mile delivery.This research paper will comprehensively analyze the threats, vulnerabilities, and risks in IoT-enabled supply chains in regard to cybersecurity. It reviews the existing literature, maps attack surfaces, and evaluates major cyberattacks affecting supply chain IoT infrastructure, such as malware propagation, DDoS attacks, side-channel attacks, data tampering, firmware manipulation, RFID spoofing, GPS jamming, and supply chain infiltration via compromised vendor devices. The paper will also propose a multi-layer security framework for IoT-based supply chains that includes device authentication, lightweight encryption, blockchain-based integrity, intrusion detection systems, AI-driven anomaly detection, ZTA, and post-quantum cryptography.The goal is to emphasize the importance of robust cybersecurity strategies that would effectively protect IoT-enabled supply chains against emerging threats without compromising efficiency, scalability, and interoperability. The paper concludes with some future research directions, emphasizing dynamic security adaptation powered by AI, threat simulation using digital twin concepts, and advanced cryptographic techniques appropriate for next-generation IoT ecosystems.

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Poly-Sorb: Synthesis of Waste-Derived Polysulfide Sorbents for Oil Spill Recovery and Environmental Remediation

Authors: Osorio Lolo iii, Daniel Adrian Labadan, Ginrie P. Villaruel, Kathrina Clariss P. Duliente, Jesson H. Cinto

Abstract: This study utilized sulfur, waste cooking canola oil (WCCO), and sodium chloride, a waste-derived polysulfide sorbent synthesized for potential use in oil spill recovery and remediation. Through thermal copolymerization at 170°C , washed, and dried. Three concentrations were produced (15–15–70, 20–20-60, and 25–25–50 wt%). Oil absorption capacity, reusability retention across three cycles, and oil removal efficiency were tested for the three concentrations of polysulfide sorbent. Based on the findings, all concentrations showed successful results in absorbing oil, with 15-15-70 wt% achieving the highest mean absorption (1.42 g/g) and reusability retention (38.73%). 25-25-50 wt% performed the highest in terms of oil removal efficiency (96.0%), followed by 15-15-70 wt% (95.0%). Among three different concentrations, one-way ANOVA showed no statistically significant difference at α = 0.05 in terms of absorption capacity. The polysulfide sorbent showed effective absorption, moderate reusability, and high removal efficiency generally, indicating for its potential as a low-cost and eco-friendly sorbent. To help improve durability and performance, enhancement and conducting extended testing under simulated environmental conditions are recommended for future studies.

DOI: https://doi.org/10.5281/zenodo.18742276

Womens Safety and Evaluation System in OSN

Authors: Akshada Ashok Bhor, Vaishnavi Arun Jadhav, Pranali Suresh Vadaje, Urvashi Raosaheb Mahajan, Prof Dr. Monika Deshmukh

Abstract: Women’s safety is a major socio-technological concern globally, with increasing cases of harassment, assault, and threats both in public and private spaces. There is a growing need for innovative solutions that ensure protection, provide quick assistance, and evaluate environmental risks. This project introduces a comprehensive Women Safety and Evaluation System that utilizes modern technologies such as GPS tracking, emergency communication, real-time alert generation, and data-based safety evaluation. The system allows users to trigger emergency alerts, share live location with trusted contacts, and notify authorities instantly. Additionally, it incorporates evaluation mechanisms to analyze unsafe zones based on past incidents, user feedback, and contextual factors. By integrating these features, the project provides a proactive and reactive safety framework aimed at minimizing response time, preventing harm, and enhancing awareness. The objective of this system is to create a secure environment where women feel protected, confident, and supported. The solution serves as a technological bridge between victims and responders, promoting safety, empowerment, and a more secure society.

DOI: https://doi.org/10.5281/zenodo.18753530

AI-Powered Platform for Personal Finance

Authors: Mrs.S.Subha, Jeeva Pandiyan S, Kaviyarasan E, Najeeb Ahmed S

Abstract: The AI-Powered Platform for Personal Finance aims to simplify and enhance individual financial management through intelligent, data-driven solutions. The platform utilizes artificial intelligence and machine learning algorithms to examine user financial records such as income, expenditures, savings, and investment behavior. Based on these analyses, the system generates personalized budgeting strategies, spending insights, and predictive forecasts to support better financial decision-making. Natural language processing is incorporated to enable intuitive, conversational interaction, allowing users to access financial guidance in real time. In addition, advanced analytics help identify potential financial risks and opportunities, assisting users in optimizing savings and investment plans. Strong security measures and privacy-aware data handling techniques are embedded to protect sensitive financial information and ensure compliance with regulatory standards. Performance evaluation indicates that the platform significantly improves financial awareness, encourages disciplined spending habits, and enhances long-term financial planning. Overall, the proposed AI-based personal finance platform provides a scalable, secure, and intelligent approach to managing personal finances, empowering users with actionable insights for achieving financial stability and sustainable economic growth.

DOI: https://doi.org/10.5281/zenodo.18753748

SMART INTEGRATED VEHICLE SAFETY SYSTEM FOR COLLISION PREVENTION AND EMERGENCY RESPONSE

Authors: P. Pradeep Kumar, A. Archana G, Usha Sree A, Jayachandra C. , Mohan Krishna

Abstract: Public transport buses in India face critical safety challenges, with over 130 fatalities recorded in bus fire accidents since 2013 and hundreds of fog-related collisions occurring annually during winter months. Current safety systems are inadequate, with non-functional fire extinguishers, blocked emergency exits, and poor visibility conditions contributing to preventable deaths. This paper proposes an integrated multi-sensor safety architecture that addresses three primary hazards: fog-induced collisions, onboard fire emergencies, and delayed evacuation during accidents. The proposed system employs LiDAR (Light Detection and Ranging) technology for real-time obstacle detection and collision avoidance in low-visibility conditions caused by dense fog or heavy rainfall. Unlike conventional camera-based systems that fail in adverse weather, LiDAR sensors penetrate fog particles and provide accurate distance measurements up to 300 meters, triggering graduated visual and audible alerts to prevent collisions. For fire safety, the system integrates multi-zone automatic fire detection and suppression using temperature sensors and smoke detectors connected to solenoid-controlled water mist nozzles distributed throughout the passenger compartment. Upon detecting fire conditions, the system automatically activates suppression mechanisms within 3-10 seconds while simultaneously triggering emergency evacuation protocols. The automated emergency evacuation system features motorized rear-frame emergency doors designed to open upward using linear actuators, eliminating manual operation delays during panic situations. Additionally, the system incorporates an automated hydrophobic coating spray mechanism for the driver's windshield that dispenses nano-coating solution to create water-beading effects,significantly improving driver visibility. The complete system is controlled by an ESP32 microcontroller with modular firmware architecture, enabling real-time sensor fusion and decision-making algorithms. This integrated approach provides comprehensive safety enhancement at an estimated implementation cost significantly lower than deploying separate commercial systems.

DOI: https://doi.org/10.5281/zenodo.18753773

IOT BASED SMART WATER RESOURCE MONITORING SYSTEM

Authors: Mrs K. Lakshmi Devi, S. Nandini, S. Sonia, C. Prashanthi, A. Venkata Suman, K. Sunil

Abstract: Water problems are getting worse in homes and small businesses. Urban sprawl, more people, and poor water management all play a role. The old way of sending someone to check the water and waiting for lab results takes too long. It’s slow, takes a lot of effort, and only gives you a quick peek at what’s actually going on. Meanwhile, people end up using dirty water or wasting it without even knowing. Here comes this new project. Act as an IoT-powered Smart Water Monitoring System, built around an ESP32 microcontroller. It teams up with sensors that track TDS, turbidity, temperature, and flow. The system sends live data straight to the cloud, so you can see what’s happening in real time on your phone. You get both instant updates and a record of what’s happened over time. Safety’s built in. If the water gets too murky, the system shuts off the pump automatically. It also keeps an eye on water flow to catch leaks or weird usage patterns before things get out of hand. The whole setup is modular, affordable, and easy to scale up, perfect for homes, apartments, or small businesses. With real-time monitoring, smart controls, and cloud analytics all working together, you get safer water, less waste, and a smarter way to manage one of our most important resources.

Community – Centric Health Intelligent System For Disease Monitoring And Awareness

Authors: Mrs. S. Revathi, Jeyamadheswari B, Kelda A

Abstract: Community healthcare systems often struggle with delayed disease identification, limited interaction between healthcare professionals and citizens, and the absence of real-time local health insights; these challenges result in late medical intervention and increased disease spread, especially at the ward and street level. This paper presents a community-centric health intelligence system for disease monitoring and awareness, a web-based, location- aware platform that leverages Natural Language Processing (NLP) to analyse user-reported symptoms and detect early disease patterns within small geographical communities. The proposed system groups users based on residential wards, processes unstructured text-based symptom data, and applies threshold-based analytics to classify health conditions as normal, awareness needed, or medical camp required. A doctor awareness module enables healthcare professionals to share educational content, schedule medical camps, and monitor community health through an interactive dashboard. The system operates without additional hardware, relies solely on user participation and cloud infrastructure, and aims to strengthen preventive healthcare by enabling early intervention and community-wide awareness. This approach contributes to Sustainable Development Goals (SDG 3: Good Health and Well-being), SDG 9 (Industry, Innovation, and Infrastructure), and SDG 11 (Sustainable Cities and Communities).

DOI: https://doi.org/10.5281/zenodo.18754745

Harnessing Technological Progress for Human Development: Contributions from the SSH (Social Sciences and Humanities)

Authors: Rakesh Manilal H. Patel, Dr. Anand K. Acharya, Dr. Pankaj Kumar B. Solanki, Dr. Dhinesha Ruwanthi Perera, Dr. Nayanesh A. Gadhavi, Dr. Haresh D. Chaudhari

Abstract: The recent global economic slowdown has led to significant cuts in funding for the humanities and social sciences (HSS). At the same time, students increasingly opt for courses that provide a direct path way to professional careers. Yet, HSS plays a vital role in shaping the economic, social, and cultural well-being of society. Unlike the sciences, whose contributions to industry are more immediately visible, HSS sustains a complex knowledge ecosystem, linking producers and beneficiaries of knowledge in diverse ways. Beyond providing a skilled workforce in business and finance and fostering cultural understanding, HSS is indispensable for (1) Informing public policy and (2) Enhancing social well-being. This role has become even more urgent in the context of rapid advances in information technology (IT). The exponential growth of the Web and mobile communication is reshaping social life across industrialized and developing nations. Emerging technologies such as Google Glass signal a shift toward pervasive and largely invisible augmentation, raising challenges of regulation, monitoring, and ethics. Oxford philosopher Luciano Floridi has described this as a “fourth revolution” in knowledge, where human interaction with reality generates unprecedented ethical concerns. Despite pressures from techno-society and liberal economic values, HSS has advanced a vision of human well-being not reducible to GDP. The contributions of Amartya Sen and Martha Nussbaum on human development highlight broader dimensions of freedom and capability essential for improving the human condition. This paper explores the challenges HSS faces in an IT-driven society and argues that it remains critical for shaping public policy, enhancing social well-being, and guiding the types of technological development that truly advance human flourishing. Key Flow: Crisis & Funding Cuts → Pressure on HSS. HSS responds by providing public policy insights, social well-being frameworks, and critiques of GDP-focused progress. Theoretical foundations from Sen & Nussbaum reinforce human development beyond economics. IT revolution & new technologies raise ethical and regulatory issues. Conclusion: HSS is indispensable for guiding technological and social progress toward human flourishing.

Harnessing Technological Progress for Human Development: Contributions from the SSH (Social Sciences and Humanities)

Authors: Rakesh Manilal H. Patel, Dr. Anand K. Acharya, Dr. Pankaj Kumar B. Solanki, Dr. Dhinesha Ruwanthi Perera, Dr. Nayanesh A. Gadhavi, Dr. Haresh D. Chaudhari

Abstract: The recent global economic slowdown has led to significant cuts in funding for the humanities and social sciences (HSS). At the same time, students increasingly opt for courses that provide a direct path way to professional careers. Yet, HSS plays a vital role in shaping the economic, social, and cultural well-being of society. Unlike the sciences, whose contributions to industry are more immediately visible, HSS sustains a complex knowledge ecosystem, linking producers and beneficiaries of knowledge in diverse ways. Beyond providing a skilled workforce in business and finance and fostering cultural understanding, HSS is indispensable for (1) Informing public policy and (2) Enhancing social well-being. This role has become even more urgent in the context of rapid advances in information technology (IT). The exponential growth of the Web and mobile communication is reshaping social life across industrialized and developing nations. Emerging technologies such as Google Glass signal a shift toward pervasive and largely invisible augmentation, raising challenges of regulation, monitoring, and ethics. Oxford philosopher Luciano Floridi has described this as a “fourth revolution” in knowledge, where human interaction with reality generates unprecedented ethical concerns. Despite pressures from techno-society and liberal economic values, HSS has advanced a vision of human well-being not reducible to GDP. The contributions of Amartya Sen and Martha Nussbaum on human development highlight broader dimensions of freedom and capability essential for improving the human condition. This paper explores the challenges HSS faces in an IT-driven society and argues that it remains critical for shaping public policy, enhancing social well-being, and guiding the types of technological development that truly advance human flourishing. Key Flow: Crisis & Funding Cuts → Pressure on HSS. HSS responds by providing public policy insights, social well-being frameworks, and critiques of GDP-focused progress. Theoretical foundations from Sen & Nussbaum reinforce human development beyond economics. IT revolution & new technologies raise ethical and regulatory issues. Conclusion: HSS is indispensable for guiding technological and social progress toward human flourishing.

Smart Decision Support System for Organic Farming and AI Analytics

Authors: Mr.C.Radhakrishnan, Suryaprakash S, Thayumanavan A

Abstract: Farmers face the complex challenge of managing multiple, dynamic variables from soil health and weather volatility to pest control and fluctuating market demands all while adhering to strict organic principles that prohibit synthetic chemicals. This project introduces a software platform designed to function as a comprehensive smart assistant specifically for the modern organic farmer. The primary objective is to bridge the gap between complex data and actionable, on-the-ground decisions, empowering farmers to adopt a proactive, data-driven management style. This system is engineered to automatically pull together, or "integrate," multiple crucial data streams. These include detailed soil characteristics from real-time weather conditions and forecasts, and data related to crop health. The platform moves beyond the mere presentation of raw data. It utilizes Artificial Intelligence (AI) to analyze this consolidated information and generate "intelligence"-smart, actionable recommendations. Its intelligence-driven recommendations cover strategic decisions such as what crops to grow (based on soil suitability), in-season farm management (such as organic pest and disease control), and post-harvest strategy (such as identifying the optimal time to sell for maximum profit).The platform is explicitly tailored to the principles of organic farming, ensuring all advice promotes sustainability, enhances soil health naturally, and strictly avoids synthetic inputs. By equipping the modern farmer with accessible technology for data driven decisions, Project aims to significantly improve the efficiency, sustainability, and profitability of organic farming operations.

DOI: https://doi.org/10.5281/zenodo.18755082

Explainable Artificial Intelligence (Xai)–based system for Bone Tumor Detection Using Deep Learning.

Authors: Mrs.T.Dheepa, Pradhakshna S, Vigiyalakshmi Muthu, Srividhya K

Abstract: Timely identification of bone tumors plays a vital role in improving diagnosis accuracy and treatment effectiveness. Although deep learning techniques have achieved remarkable success in medical image analysis, their lack of interpretability often restricts their acceptance in clinical environments. This study proposes an Explainable Artificial Intelligence (XAI)–driven framework for bone tumor detection using deep learning models. A convolutional neural network (CNN) is utilized to automatically extract relevant features from medical images such as X-rays or MRI scans to classify tumors into benign and malignant categories. To address the transparency issue, explainability techniques such as Gradient-weighted Class Activation Mapping (Grad-CAM) are incorporated to highlight critical image regions that influence the model’s predictions. These visual explanations assist clinicians in understanding and validating the system’s decisions. The experimental evaluation shows that the proposed model delivers reliable classification performance along with interpretable outputs, thereby supporting clinical decision-making and enhancing trust in AI-assisted diagnostic systems.

DOI: https://doi.org/10.5281/zenodo.18755185

The Role Of Cyber Security In Crime Scene Investigation: A Cyber–Physical Systems Engineering Framework For Enhancing Physical Evidence And Its Admissibility

Authors: Dr. Bhanu Prakash S V, Dr. Sowmya R

Abstract: Modern crime scenes increasingly operate as cyber–physical systems in which physical evidence is tightly coupled with digital infrastructure, embedded devices, and networked environments. Conventional crime scene investigation methods are insufficient to address threats arising from remote access, data manipulation, and system-level vulnerabilities. This review presents a step-by-step, technology-driven framework that integrates cyber security engineering principles into crime scene investigation to enhance the integrity, reliability, and admissibility of physical evidence. The proposed approach models the crime scene as a cyber–physical system and applies threat modeling, secure system isolation, forensic data acquisition pipelines, cryptographic integrity verification, and cyber–physical correlation mechanisms. Emphasis is placed on reproducibility, system verification, and automated chain-of-custody controls from an engineering perspective.

DOI: https://doi.org/10.5281/zenodo.18755493

Multi-Omics Approaches In Wastewater Bioprocess Systems: Advancing Sustainability And Resource Recovery

Authors: Pranabesh Ghosh, Abhishek Konar, Tahsina Tabia

Abstract: The microbial ecosystem within a wastewater treatment facility serves multiple functions-removal of contaminants, recycling nutrients, and recovering resources. However, conventional process monitoring focuses on bulk physicochemical data, limiting information on the microbial processes driving system function. Recent advancements in multi-omics technologies—metagenomics, metatranscriptomics, metaproteomics, and other omics domains—have significantly advanced the study of the microbial communities in wastewater treatment by allowing researchers to comprehensively describe a microbial community's composition, functional capacity, and metabolic activity. Omics technologies have improved knowledge of the biological processes governing the nitrogen, phosphorus, and carbon cycles in wastewater, and the technologies can identify new contaminants and antibiotic resistance genes. When paired with predictive bioprocess modelling, multi-omic data enhances operational control and system stability and promotes energy-efficient process design. Omics data have revealed opportunities for the modernized wastewater treatment plant (WWTP) to transition to a water resource recovery facility (WRRF), wherein the plant can produce methane, recover nutrients, generate biopolymers, and contribute to the carbon economy. Despite advancements utilizing multi-omics technologies, there remain obstacles. Ongoing and emerging challenges include the high cost of sequencing, data integration difficulties, limited applicability to real-time processes, and a lack of infrastructure in developing regions. Closing the gap on process-scale implementation of multi-omics technologies will require standardized testing, multi-disciplinary collaboration, and the use of artificial intelligence control systems. Multi-omics techniques signify a changing of the guard in precision ecological engineering and the development of sustainable, climate-resilient, and resource-efficient bioprocess systems for wastewater treatment. Sustained technological advancement and integration at the systems level will be essential for the future of energy-positive and carbon-neutral wastewater treatment.

DOI: https://doi.org/10.5281/zenodo.18755909

APPLICATION OF PIEZOELECTRIC POWERED FLOOR IN INDIA TO EFFICIENTLY INCREASE ELECTRICITY GENERATION

Authors: Dr. Amudha G, Porselvi B, Radha A, Usha Mahi Pon

Abstract: Rising electricity demand and the need for sustainable energy solutions have encouraged exploration of alternative micro-generation technologies. This study investigates the use of piezoelectric powered flooring systems to harvest mechanical energy from human footsteps in high-footfall areas such as railway stations, commercial buildings, and educational institutions in India. By converting mechanical stress into electrical energy, piezoelectric materials offer a method of generating supplementary power without requiring additional land or fuel resources. The proposed work presents the system design framework, working principle, and feasibility of large-scale implementation within public infrastructure. Performance estimations indicate that although piezoelectric flooring cannot replace conventional energy sources, it can contribute to localized power needs and support smart, energy-efficient urban environments. The study highlights the potential of integrating energy harvesting technologies into everyday infrastructure to promote sustainable development.

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Construction Site Safety Violation Detection

Authors: Gowri A, Mohamed Sathik Z, Moses Saveriyar A, Sanjay P

Abstract: Construction sites are among the most hazardous work environments due to unsafe practices and the improper use of Personal Protective Equipment (PPE). To address these safety challenges, this project proposes an AI-based Construction Site Safety Violation Detection System that automatically identifies unsafe behaviors and PPE violations in real-time video streams. The system utilizes computer vision and deep learning techniques to detect workers, safety gear such as helmets and vests, and hazardous actions including entry into restricted zones and working at heights without protection. A tracking-by-detection approach is employed to monitor individuals across video frames, while pose estimation and action recognition models analyze human posture and movements to classify unsafe activities. When a safety violation persists beyond a predefined duration, the system generates instant alerts to enable timely intervention. This automated approach enhances workplace safety, reduces human supervision effort, and helps construction organizations proactively prevent accidents and injuries.

DOI: https://doi.org/10.5281/zenodo.18766472

Positivity Problems And Conjectures In Algebraic Combinatory

Authors: Dr. Priyanka B. Shingade

Abstract: Positivity questions occupy a central place in algebraic combinatory: given a naturally occurring symmetric or quasisymmetric, or polynomial function, when does it expand with nonnegative coefficients in a preferred basis monomial, elementary, Schurz, etc. This survey/research-style paper organizes classical and recent positivity problems, summarizes principal techniques, records key breakthroughs, and lists open conjectures and directions. We emphasize (i) classical positivity phenomena Littlewoods–Richardson, Schurz- and e- positivity, (ii) structural conjectures such as the Stanley–Stem bridge and Macdonald positivity problems and their recent status, (iii) positivity for representation-theoretic multiplicities Kroenke, platysma, and (iv) modern tools that have proved or advanced these questions. We close with a curated bibliography of key references and suggested research directions.

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Load Analysis Of Off-Shore Steel Fixed Jacket Platform Structures Having Bracing

Authors: Khodke Pawan Rajendra, Prof. Dr.U.S.Ansari

Abstract: Offshore steel fixed jacket platforms are essential structural systems that support exploration and production operations in harsh marine environments. These platforms are subjected to complex and fluctuating environmental loads, including wind, waves, currents, and seismic forces, which significantly influence their stability and long-term performance. Bracing systems play a crucial role in enhancing lateral stiffness, improving load distribution, and reducing structural deformations in offshore jackets. This study investigates the structural behaviour of offshore steel fixed jacket platforms equipped with different bracing configurations using SAP 2000. A detailed finite element model was developed, incorporating realistic material properties, geometric parameters, and environmental load combinations based on standard design codes. Static and dynamic analyses were performed to evaluate displacement, base shear, stress distribution, natural frequency, and overall stiffness under combined environmental loading. The comparative assessment revealed notable differences in performance among various bracing layouts, with some configurations demonstrating superior resistance to deformation and improved stability. The study emphasizes the importance of selecting an optimal bracing system to enhance the safety, reliability, and efficiency of jacket platforms. The findings provide valuable insights for design optimization and contribute to the advancement of offshore structural engineering practices.

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Multiple Interface Configurable Smart IoT Device

Authors: Ketaki Nanaware, Vedant Dhopate, Atharv Hapse

Abstract: This research paper details the creation of a versatile smart IoT device using the ESP32 microcontroller, MIT App Inventor, and Google Firebase. The device integrates multiple interfaces for real-time data collection, processing, and control, making it ideal for applications in smart homes, industrial automation, and environmental monitoring. The ESP32 handles sensor data (e.g., temperature and humidity) and communicates with Google Firebase for real-time data storage. Users interact through a mobile app developed with MIT App Inventor, which allows monitoring, configuration, and control of connected devices, such as motors. The system also employs MDNS and HTTP servers for efficient local network communication. Key features include dynamic data collection, real-time visualization on the mobile app, and remote device control. The integration with Firebase ensures scalability and secure data handling. This IoT solution demonstrates flexibility and potential for diverse applications, with future enhancements possible through additional sensors and advanced analytics.

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AIRMATH: PARALYSIS PATIENT HEALTH CARE MONITORING SYSTEM

Authors: Tanvi Ramkumar Sunewad, Shahin Tanvir Shaikh, Divya Hemant Dhende, Diksha Datta Jethe

Abstract: Paralysis is a debilitating condition that significantly impairs a patient’s ability to move and communicate, often resulting from neurological disorders, spinal cord injuries, or diseases such as stroke. According to the World Health Organization, stroke and related neurological conditions are among the leading causes of long-term disability worldwide, emphasizing the need for continuous and reliable patient monitoring systems. The Paralysis Patient Health Care Monitoring System is an advanced, technology-driven solution designed to continuously monitor the vital parameters and safety of paralyzed patients in hospitals or home environments. The system integrates biomedical sensors to track essential health indicators such as heart rate, body temperature, blood pressure, oxygen saturation (SpO₂), and movement detection. These parameters are processed through a microcontroller-based unit and transmitted via wireless communication modules to caregivers or medical professionals in real time. The proposed system also incorporates emergency alert mechanisms, enabling patients to communicate distress signals through minimal physical input, such as eye blink detection or slight finger movement sensors. In critical situations, automated notifications are sent to caregivers or healthcare providers to ensure immediate medical intervention. Data collected by the system can be stored in a cloud-based database for continuous analysis, enabling long-term health trend monitoring and improved clinical decision-making. The system enhances patient safety, reduces the burden on caregivers, and supports timely medical response, ultimately improving the quality of life for paralysis patients. In conclusion, the Paralysis Patient Health Care Monitoring System provides an efficient, reliable, and cost-effective approach to continuous health monitoring, promoting better patient outcomes through real-time data tracking, remote accessibility, and intelligent alert mechanisms.

DOI: https://doi.org/10.5281/zenodo.18769831

Investigation Of Steel Building For Analysis Of Seismic Performance

Authors: Divya Ravindra kshirsagar, Dr. Ansari U.S

Abstract: Eanhquakes pose a significant threat to the safety and stability of built structures. making seismic performance evaluation an essential aspect of modern structural engineering. Steel buildings, known for their high ductility, strength-to-weight ratio, and superior energy dissipation capacity, have emerged as a preferred solution in seismic regions. This study investigates the seismic behavior of a multi-storey steel building using advanced analytical tools and codal provisions, with the objective of understanding its dynamic response under earthquake loading. A derailed 3D structural model was developed using ETABS/STAAD Pro, and seismic forces were evaluated in accordance with IS 1893:2016, while member design considerations followed IS 800:2007. Response Spectrum Analysis (RSA) and Time—History Analysis (THA) were performed to assess critical parameters such as base shear, storey drift, lateral displacement, and structural stability. The results highlight the significance of structural configuration, bracing system selection, and stiffness distribution in determining overall seismic performance. Observed trends indicate that proper detailing and optimized member design substantially improve ductility and reduce seismic demand. The study provides valuable insights into the dynamic characteristics of steel buildings and offers recommendations for enhancing seismic resilience through performance-based design. The findings contribute to safer, more efficient, and code-compliant steel construction in earthquake-prone areas.

Mechanical Properties Of CalaSSAG: A Bioplastic Wrapper With Potential Antifungal Properties As An Alternative To Commercially Plastic Wrappers

Authors: Dave A. Camuta, Bryan B. Ortouste, Jen Rose A. Limentang, Krisha Nicole G. Bacolod, Jesson H. Cinto

Abstract: This study focused on developing a biodegradable food wrapper CalaSSAG, made from bio-based materials such as calamansi peel powder, starch, sodium alginate, and glycerol. The research aimed on making an alternative to plastic packaging that causes pollution. Calamansi is responsible for antimicrobial and antifungal properties. An experimental design was used to test how durable and thick the CalaSSAG bioplastic wrapper was, using three different concentrations of calamansi peel powder, 50%, 75%, and 100%. Testing its thickness and tensile strength. The results showed that the 100% calamansi mixture made the wrapper (1.26 mm), while the 50% mixture had the highest tensile strength (0.165 MPa). However, the control group was stronger than the alternative experimental group in terms of tensile strength with a tensile strength of 3.33 MPa. Statistical analysis using ANOVA confirmed that there is a significant difference in mean tensive strength among CalaSSAG bioplastic wrapper containing varying proportions of calamansi powder and commercially produced plastic wrapper. CalaSSAG was not as strong as the traditional control group, it showed to be flexible, biodegradable, and made from natural materials. It offers a safer, eco-friendly choice for food packaging. Future research is advised to test the antifungal properties of calamansi in the wrapper and its real-time application. CalaSSAG shows potential in reducing plastic pollution and waste.

DOI: https://doi.org/10.5281/zenodo.18781357

SkinGuard AI: Deep Learning-Based Dermatology Assistant with Email Notification

Authors: G. Lavanya, B. Balaki, Dr. Bhuvana. R

Abstract: Skin diseases are among the most common health problems worldwide, affecting individuals regardless of age, gender, or geographical location. Early detection and appropriate treatment are essential to prevent complications and psychological distress. However, limited access to dermatologists, especially in rural and underserved areas, delays timely diagnosis. This paper presents SkinGuard AI, a deep learning-based dermatology assistant that utilizes a Convolutional Neural Network (CNN) for image-based skin disease classification. The system enables users to upload images of affected skin areas through a web interface, where the images are preprocessed and analyzed using a trained CNN model. The system predicts the disease category along with a confidence score and provides personalized treatment recommendations. Additionally, it integrates an intelligent chatbot for interactive assistance and an automated email notification module to send diagnostic reports to registered guardians. The proposed solution enhances accessibility, reduces dependency on immediate hospital visits, and provides cost-effective preliminary dermatology support. Experimental results demonstrate high classification accuracy and reliable real-time performance.

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Smart Door Mate Security System

Authors: Ashish Siddharth Kokare, Ayushraj Ashok Ankush, Aadarsh Rajesh Kamble, Punit Krishna Fasage

Abstract: Women Visitors arrive unannounced. Sometimes welcome, sometimes not. We built a doormat that watches. Raspberry Pi 4B+ waits quietly. Pressure sensor under mat feels weight, sends signal, system wakes. Telegram fires to owner: "Someone at door." Servo rotates camera, captures face, transmits image. Remote eyes where none existed. No subscription, no cloud lock-in, no monthly fees. Telegram free, Pi owned, code open. Cost under ₹5,000, works on any door, alerts any phone. Continuous monitoring eliminated—mat sleeps, Pi dozes, power sips. Only presence triggers, only relevance notifies. Smart because selective, not because complex.

DOI: https://doi.org/10.5281/zenodo.18786973

Eco-concrete: Utilizing Cotton Textile Waste Strips and Broken Bottles as Partial Replacement of Fine Aggregates and Coarse Aggregates in Concrete Production

Authors: Melody Krystel Limboy, Jesson H. Cinto

Abstract: This study utilized cotton textile waste strips and broken glass as partial replacement for fine aggregates and coarse aggregates for development of eco-concretes. Through mixing, casting, and curing, three mixtures of eco-concretes and commercially produced concretes were produced. Areal density, bulk density, density, and compressive strength were tested for the three mixtures and the control group. Based on the findings, all mixtures showed successful results in compressive strength test with Mixture 3 achieving the highest average compressive strength (2.1 MPa). Among the mixtures, F- test of Independent Means showed no significant difference in the mean compressive strength among concrete mixes containing varying proportions of cotton waste textile strips and broken glass, and commercially produced concrete. This implied that the concrete with cotton textile waste strips and broken bottles can be a good substitute for commercial concrete and can also enhance more strength to the concrete made. To help improve compressive strength, use different drying days of curing days for concrete production

DOI: https://doi.org/10.5281/zenodo.18801235

FitAi Research Paper

Authors: Mrs. Thorat J.B, Saste Ghanshyam, Atharv Saste, Varun Salunkhe, Dnyaneshwar Shinde

Abstract: The current digital health landscape is saturated with generic fitness applications that fail to address the unique physiological and lifestyle constraints of individual users. This project, "FitAI Professional," addresses this challenge by developing a full-stack web application that leverages Generative AI for hyper-personalized coaching. Built using a React (Vite + TypeScript) frontend and a Node.js (Express) backend, the system integrates the Google Gemini model to act as an intelligent, context-aware planning engine. Unlike traditional rule-based systems, FitAI Professional interprets complex user profiles—including biometrics, equipment availability, and injuries—to generate structured, scientifically sound regimens. Key innovations include strict JSON schema enforcement for AI outputs and multimodal food analysis for seamless nutrition logging.

The Benefits Of Mindfulness Practice In Mathematics Education

Authors: Narinder Sharma

Abstract: Mathematics education frequently confronts significant cognitive, emotional, and motivational barriers that impede student learning. Recent research suggests that mindfulness practice—the cultivation of intentional, nonjudgmental awareness of present-moment experience—can enhance learners’ academic engagement and performance. This article explores the theoretical foundations and empirical evidence connecting mindfulness to mathematics learning, examining how mindfulness influences attention, anxiety regulation, metacognition, motivation, and classroom climate. Drawing from cognitive science, educational psychology, and pedagogical practice, the article outlines practical strategies for integrating mindfulness into mathematics instruction, highlights measurable benefits, and discusses future research directions. The findings suggest that mindfulness may contribute to more resilient, reflective, and motivated mathematical learners.

Oil Sentry: Enhancing Oil Spill Response Through Autonomous Suction and Navigation

Authors: Matthew Aaron Salvador, Deejay Mark Cardinas, Utada Aaliyah Maclay, Jemaica Bayale, Engr. Carlo G. Quitos

Abstract: The study focused on the design and development of Oil Sentry, an environmentally friendly, fully automated device that uses Arduino Uno technology to improve oil spill detection and collection. It was intended as a preventive measure against the harmful effects of oil spills on water- based ecosystems, specifically the coastal areas of the Davao Gulf. Oil Sentry integrates a motorized suction system for oil removal, an infrared sensor for oil detection, and a GPS-based navigation system for surface movement. An obstacle detection mechanism was also incorporated to ensure safe and efficient operation during cleanup. The device underwent laboratory testing to evaluate suction capacity, performance efficiency, detection accuracy, and navigation stability. Results showed that Oil Sentry accurately detected and collected oil while maintaining stable movement on the water surface. The system demonstrated effective navigation with minimal water disturbance, highlighting the potential of robotic solutions for marine environmental sustainability applications.

DOI: https://doi.org/10.5281/zenodo.18811894

Monitoring of Selective Pest Fall Armyworm (Spodoptera Frugiperda),Corn Earworm (Helicoverpa Armigera), Corn Leafhopper (Dalbulus Maidis) Occurence in The Maize Crop (Zea Mays).

Authors: S.Sathiyavathi, B.Keerthika, T.Saranya, V.Pavithra Vedhavalli

Abstract: Maize (Zea mays L.) is one of the most important cereal crops cultivated worldwide for food, feed, and industrial purposes. However, its production is significantly affected by various insect pests at different growth stages. Major pests of maize include the Fall armyworm, Corn earworm, corn leafhopper, stem borers such as leafhoppers. These pests cause damage by feeding on leaves, stems, tassels, and ears, leading to reduced yield and poor grain quality.

DOI: https://doi.org/10.5281/zenodo.18813089

Resumentor: AI-Powered Resume Analyser And Adaptive Mock Interview System

Authors: Punit Chauhan, Aakash Chouhan, Sunny Maurya, Siddhesh Mundhe, Prof. Shilpa Doke

Abstract: In today's highly competitive job market, candidates often struggle to optimize their resumes for Applicant Tracking Systems (ATS) and lack access to realistic interview preparation environments. This paper presents ResuMentor, a full-stack, AI-driven web platform designed to bridge this gap by providing intelligent resume analysis and real-time mock interview simulation. The system accepts user-uploaded resumes in PDF or DOCX format alongside a specified job role or description, and leverages the OpenAI GPT-4o API via Spring AI to generate ATS compatibility scores, keyword gap analysis, and actionable improvement suggestions tailored to the target job profile. For interview preparation, ResuMentor deploys an AI voice agent that conducts a structured, 30-minute mock interview session, dynamically generating questions ranging from beginner to advanced level based on the parsed resume content. The platform employs the Web Speech API for real-time speech-to-text transcription, providing a live transcript visible to the user during the session. Post-session, a detailed feedback report evaluates the clarity, conciseness, and relevance of the candidate's responses with specific examples drawn from the transcript. The backend is developed using Java Spring Boot 3.3 with Spring Security and OAuth2 for Google-authenticated login, MySQL as the relational database, and Apache Tika for resume parsing. The frontend is built with plain HTML, CSS, and JavaScript, featuring a responsive dark/light theme toggle. A personalized dashboard tracks historical ATS scores and interview performance trends using Chart.js visualizations, enabling users to monitor their growth over time. ResuMentor demonstrates that integrating large language models into career development tools can significantly improve candidate preparedness and resume quality.

Al-Powered EBOM To MBOM Converter Optimized Manufacturing

Authors: N. Gokul Krishnan, M. Gokulnath, S.Manoj, Mrs.P.G.Gayathri

Abstract: In modern manufacturing, moving from an Engineering Bill of Materials (eBOM) to a Manufacturing Bill of Materials (mBOM) is still a manual, slow, and error-prone task. This problem often results in data inconsistencies, production delays, and higher manufacturing costs. To address these issues, we propose an AI-powered BOM Converter that automatically converts eBOM into improved mBOM for production workflows. The system uses a mix of machine learning and rule-based logic to examine eBOM structures, identify component connections, and produce an accurate mBOM, complete with manufacturing details like process steps, work centers, tooling, and procurement information. It integrates with existing ERP/PLM systems to ensure smooth data exchange and real-time updates with production planning. By automating the conversion from eBOM to mBOM, this system reduces manual labor, improves data consistency, cuts conversion time, and lowers operational costs. This intelligent converter seeks to transform the digital manufacturing workflow, allowing for quicker product launches and better overall production efficiency.

DOI: https://doi.org/10.5281/zenodo.18831453

Digital Governance And Financial Transparency In Municipal Administration: A Look At BRICS Countries

Authors: Abhinav Pandey, CO. Dr. Preeti Devi

Abstract: This research report provides an exhaustive analysis ofi the intersection between digital governance and financial transparency within the municipal administrations ofi the BRICS nations—Brazil, Russia, India, China, and South Afirica. Utilizing a robust comparative firamework, the study evaluates how digital platforms, legal mandates, and institutional capacity influence the disclosure ofi fiscal infiormation to the public. The findings demonstrate a complex landscape: while national-level digital maturity is high across the bloc (evidenced by Group A and B rankings in the World Bank’s GovTech Maturity Index 2025), the actual translation into municipal transparency is hindered by over-centralization in Brazil, restricted access in Russia, localized “refiorm islands” in India, selective disclosure in China, and severe capacity constraints in South Afirica. Through an examination ofi the Open Budget Survey 2023 data, the report identifies that while transparency has increased globally by 24% since 2008, significant gaps remain in public participation and legislative oversight. Recommendations fiocus on decentralizing digital implementation, institutionalizing public engagement modules, and bridging the skill gap at the local level to ensure that technological advancements yield tangible improvements in fiscal accountability.

DOI: https://doi.org/10.5281/zenodo.18835347

SMART PLATFORM FOR MANAGING NEARSHORE & HYBRID OUTSOURCING TEAMS

Authors: Thenmozhi P, Abarna M, Mahalakshmi D, Malini S

Abstract: Hybrid and nearshore outsourcing paradigms are becoming more popular in order to strike a balance between cost-effectiveness availability of talent and flexibility in operations nevertheless the problem of time- zone lack can affect geographically distributed teams and some of the issues include an uneven distribution of workload and infrequent monitoring of performance on the team the traditional project management tools use a static method of coordination and are not smart in terms of decision making in this project a smart platform to manage nearshore and hybrid outsourcing teams with an agentic ai based multi-agent architecture is introduced the platform automatically breaks down project goals into tasks and allocates them based on the knowledge availability time-zone coverage and historical outcomes specialized ai agents are involved in the organization of tasks time management forecasting performance and assessing risks the system developed based on an event-driven architecture with real time synchronization and continuous learning provides better accuracy in task allocation early risk identification and productivity in a distributed outsourcing setting.

Smart City Public Service Information Portal

Authors: R.B.Dhayanandhan, N.Naresh, Mrs.A.Gowri/Ap

Abstract: The Smart City Information Portal is a centralized backend application developed using Spring Boot and MongoDB to manage smart city information efficiently. It provides RESTful APIs for handling user accounts, public services such as hospitals and schools, and city administrative data. The system includes a complaint management and resolution module that allows citizens to register complaints, track their status, and receive updates from city authorities, improving transparency and participation. Secure access is ensured through a role-based access control system with roles such as regular users, city administrators, and super administrators. MongoDB supports scalable and flexible data storage, while Spring Boot ensures a secure, modular, and maintainable backend architecture. The system is extensible and can be integrated with web or mobile front-end applications, supporting digital governance and improved public service delivery in smart cities. The application reduces manual effort by automating administrative workflows and ensures consistent data handling across services. It also provides a reliable foundation for future enhancements and smart city integrations.

DOI: https://doi.org/10.5281/zenodo.18832025

Moisture Detection In Smart Irrigation System

Authors: Priyanshu Singh, Priyanshu Soni, Manmohan Singh Yadav

Abstract: One of the significant resources used in agriculture is water whose availability is extremely low. Most people use traditional irrigation techniques, which are manual, and these techniques tend to cause wastage of water. The current paper has introduced a smart irrigation system, which is founded on soil moisture detection to enhance water management. The type of system employed in the specified project in this paper is based on a soil moisture sensor and an ultrasonic sensor that are linked to an Arduino UNO microcontroller. It constantly measures soil conditions and regulates automatically a water pump with a relay module. When the soil gets dry the pump is switched ON and when the moisture potential is achieved accordingly the pump is switched OFF. The system also offers real time updates to the farmers via the mobile notification and also show the information on an LCD screen. The solution saves water, less human labour, and enhances crop output. It is also affordable, simple to operate and can be applied in small and medium-scale farmers hence is an effective solution to current day farming.

Moisture Detection In Smart Irrigation System

Authors: Priyanshu Singh, Priyanshu Soni, Manmohan Singh Yadav

Abstract: One of the significant resources used in agriculture is water whose availability is extremely low. Most people use traditional irrigation techniques, which are manual, and these techniques tend to cause wastage of water. The current paper has introduced a smart irrigation system, which is founded on soil moisture detection to enhance water management. The type of system employed in the specified project in this paper is based on a soil moisture sensor and an ultrasonic sensor that are linked to an Arduino UNO microcontroller. It constantly measures soil conditions and regulates automatically a water pump with a relay module. When the soil gets dry the pump is switched ON and when the moisture potential is achieved accordingly the pump is switched OFF. The system also offers real time updates to the farmers via the mobile notification and also show the information on an LCD screen. The solution saves water, less human labour, and enhances crop output. It is also affordable, simple to operate and can be applied in small and medium-scale farmers hence is an effective solution to current day farming.

Garbage Bin Fill-Level Monitor Using Ultrasonic Sensor with Route Optimization Mockup

Authors: Mr. K.Karthick, Pradeepkumar K, Prathap P, Surendar S

Abstract: Efficient waste collection plays a crucial role in maintaining clean urban environments while reducing operational costs. Traditional garbage collection systems follow fixed schedules, which often lead to unnecessary trips to halfempty bins or delayed pickups of overflowing bins. This project proposes a Smart Waste Monitoring and Collection System that uses ultrasonic sensors to continuously measure the fill level of garbage bins and transmit the data to a cloud platform. The collected data is visualized on a webbased dashboard that enables administrators to monitor the status of each bin in real time and assign optimized routes to garbage collection vehicles. The system includes an intelligent route optimization module that prioritizes bins requiring urgent attention, reducing fuel consumption and travel time. A key enhancement in this project is the integration of a predictive overflow feature. By analyzing historical filllevel patterns, the system forecasts when a bin is likely to reach its capacity. This prediction enables proactive scheduling of collection before overflow occurs, which improves cleanliness and resource utilization. The proposed solution enhances overall waste management efficiency through datadriven decision making. The system is scalable, costeffective, and suitable for implementation in smart city initiatives.

DOI: https://doi.org/10.5281/zenodo.18832421

Livlihood Analyses In Rameswaram Island

Authors: Mrs.V.Maria Subaitha, Dr. R. Vijayalakshmi

Abstract: The fishing industry in Rameshwaram, a coastal town in Tamil Nadu, India, is facing significant challenges, including declining fish stocks, rising operational costs, and regulatory restrictions. This study aims to identify and analyze the existing livelihood options available to the fishermen community in Rameshwaram, with a focus on understanding the socio-economic implications of these options. A mixed-methods approach was employed, combining surveys, interviews, and focus group discussions with fishermen and other stakeholders. The study found that fishermen in Rameshwaram have diversified their livelihood options beyond traditional fishing, including fish processing and marketing, tourism-related activities, and alternative livelihoods such as agriculture and small-scale industries. However, these options are often characterized by low incomes, limited job security, and inadequate social protection. The study highlights the need for targeted interventions to promote sustainable livelihoods for fishermen in Rameshwaram, including vocational training, credit facilities, and social protection programs. The findings of this study have important implications for policymakers, development practitioners, and researchers working on livelihood promotion and poverty reduction initiatives in coastal communities.

Implementation Of An Automated Transformer Rewinding System

Authors: Mr.R. Goplakrishnan, S. Ajay, K. Manikandaprabu, S. Santhosh kumar

Abstract: Transformer rewinding is generally a manual process that depends on skilled operators and predefined winding data, which may result in calculation errors and increased rewinding time. This project presents a bobbin-based automated transformer rewinding system using a microcontroller to improve accuracy and efficiency. The system accepts basic electrical inputs such as input voltage, output voltage, required output current and bobbin dimensions. Based on these inputs, the required number of winding turns is calculated using empirical transformer winding rules and an appropriate copper wire gauge is selected using the current density method. The system also verifies whether the winding can physically fit within the given bobbin dimensions by calculating turns per layer and total winding thickness. An Arduino UNO R4 controls an induction motor through a relay module, while a rotary encoder provides accurate turn counting. The calculated parameters and winding status are displayed on an LCD. This system reduces human dependency, minimizes errors and provides a cost-effective solution for transformer rewinding applications.

DOI: https://doi.org/10.5281/zenodo.18833928

Experimental Study On Replacement Of Aggregate With Scrap Rubber Tyre In Cement Concrete

Authors: mr. P.Sudheer Kumar, C.Ashok, Ug Student, m.Premchand, m.S.Sulthan

Abstract: The increasing demand for sustainable construction materials and the growing environmental problems associated with waste tyre disposal have encouraged researchers to explore alternative materials in concrete production. This study presents an experimental investigation on the replacement of natural coarse aggregate with scrap rubber tyre particles in cement concrete. The primary objective is to evaluate the feasibility of utilizing waste rubber as a partial aggregate replacement while maintaining acceptable mechanical performance and promoting eco-friendly construction practices.Concrete mixes were prepared by replacing coarse aggregates with scrap rubber tyre particles at different percentages of 0%, 5%, 10%, and 15% by weight. The mix design was carried out in accordance with IS 10262 guidelines, and specimens were cast and cured under controlled laboratory conditions. Fresh concrete properties were evaluated using the slump test, while hardened concrete properties were assessed through compressive strength and split tensile strength tests at 7 and 28 days of curing.

Smart Poultry Waste Collection Using an Iot- Controlled Movable Conveyor System

Authors: Dr. S.Ragul, V.Sivakumar, C.Sudhan Aghash, G.Vishnu Kumar

Abstract: The Poultry farms generate Efficient management of poultry waste is essential for maintaining farm hygiene, reducing labor costs and minimizing environmental impact. This project presents a Smart Poultry Waste Collection System based on an IoT-controlled movable conveyor mechanism designed to automate the collection and monitoring of poultry waste. The proposed system employs sensors to detect waste accumulation levels and environmental conditions, while a microcontroller enabled conveyor system dynamically moves to collect waste from designated areas within the poultry shed. Allowing remote monitoring, system control and performance analysis through a mobile. Automation reduces manual intervention, improves sanitation and helps prevent the spread of disease among poultry. The system is energy-efficient, scalable and adaptable to different poultry farm sizes. Experimental results demonstrate improved waste collection efficiency, reduced labor dependency and enhanced overall farm management, making the solution a practical step toward smart and sustainable poultry farming.

DOI: https://doi.org/10.5281/zenodo.18834843

Retinaseg: Deep Learning-Based Segmentation Of Retinal

Authors: Ch.Srilakshmi, Nithish Kanth M, Rupesh J, Tharun CR

Abstract: Retinal vessel segmentation is essential for the early diagnosis of diseases such as diabetic retinopathy, hypertensive retinopathy, and age-related macular degeneration. Manual segmentation of fundus images is time-consuming and prone to variability, limiting large-scale screening. This paper presents RETINASEG, a deep learning-based system for automated pixel-level segmentation of retinal vessels from fundus images. The proposed framework combines image enhancement techniques such as contrast normalization, CLAHE, and noise reduction with an encoder–decoder architecture based on U-Net and transformer-enhanced models. To address challenges including thin vessel detection and class imbalance, data augmentation and class-balanced loss functions are employed during training. Experimental results on DRIVE and STARE datasets demonstrate strong performance, achieving high accuracy and robustness across datasets. A web-based interface with real-time visualization and explainable AI support further enhances clinical usability. RETINASEG enables scalable, reliable, and automated retinal analysis for early disease detection and tele-ophthalmology applications.

Helpreach – AI Tool For Early Detection Brain Related Diseases

Authors: Priti Birajdar, Ambika Kshirsagar, Shravani Raut, Harshada Raykar, Prajakta Bhadale

Abstract: This paper presents an Artificial Intelligence (AI) based system designed for the early detection of brain-related diseases such as Alzheimer's disease, Parkinson's disease, brain tumors, and stroke using medical imaging and machine learning techniques. Early diagnosis of neurological disorders is critical for effective treatment and improved patient outcomes. Traditional diagnostic approaches rely heavily on manual interpretation of MRI scans, which may lead to delayed detection and human error. The proposed system integrates Deep Learning models, particularly Convolutional Neural Networks (CNN), to analyze MRI images and detect abnormalities at an early stage. The architecture consists of image preprocessing, feature extraction, classification, and result visualization modules. The system aims to assist neurologists by providing accurate and fast predictions.

Tech-Driven Autorickshaw Rental_110

Authors: Saukhya, Santhosh, Sai Surya, Abdul Rahman Sharikh, Abhijit raj N

Abstract: Auto Go is a tech-enabled autorickshaw rental and fleet management platform designed to address the growing urban transportation challenges in Indian metropolitan cities, with an initial focus on Bengaluru. The project’s core objective is to facilitate affordable and flexible autorickshaw access for independent drivers and small businesses, thereby reducing the financial barrier posed by vehicle ownership and promoting economic opportunity. The service will provide a digital platform — including a website and mobile app — to enable customers to book autorickshaws under daily, weekly, or monthly rental agreements. Additionally, the platform will integrate payment processing, customer support, and vehicle tracking, improving transparency and operational efficiency.

Automated Colour Sorting Machine Using Arduino Microcontroller And TCS3200 Optical Sensor

Authors: Kunal Vishwajit Uke, Shreyas Anil Sonwalkar, Abhishek Avinash Yadav, Krushna Ganesh Vibhute, Prof. Chetna Sharma

Abstract: Industrial automation demands efficient material handling and quality control mechanisms to enhance productivity and reduce operational costs. This paper presents the design and implementation of an automated colour-sorting machine using Arduino microcontroller technology integrated with optical colour sensors. The system employs a conveyor belt mechanism that transports objects through a detection zone where a TCS3200 colour sensor identifies the colour of each item. Based on the detected colour signature, the Arduino controller processes the sensor data and activates corresponding servo motors to divert items into designated collection bins. The proposed system achieves high-speed sorting with accuracy exceeding 95%, significantly reducing manual labour requirements and minimizing classification errors. Experimental results demonstrate the system's effectiveness in sorting multiple colours simultaneously with minimal delay. This automated solution finds applications in food processing, pharmaceutical packaging, recycling industries, and quality control operations where colour-based segregation is essential.

Automated Humidity Control System Using ESP32

Authors: Om Vichare, Aniket Nale, Soham Deolalikar, Siddhant Kamble

Abstract: Maintaining optimal indoor humidity is essential for human comfort, health, and the safety of electronic devices. Conventional humidifiers lack intelligent monitoring and safety mechanisms, and typically require manual operation. This paper presents the design and implementation of a Smart Room Humidifier using the ESP32 microcontroller. The system continuously monitors ambient humidity using a DHT11 sensor and automatically controls a 5 V ultrasonic mist maker through a transistor-driven switching circuit. A water level sensor prevents dry operation, and a buzzer alerts the user when the water level is low. The ESP32’s built-in Wi-Fi module enables web- based remote ON/OFF control, while a 0.96-inch OLED display provides real-time readings of humidity, temperature, water level status, and system state. The system is powered by a 5 V buck converter; regulated 3.3 V for sensor modules is supplied directly by the ESP32. Experimental testing confirms reliable performance, safe operation, and effective humidity control. The system is low-cost and well-suited for indoor domestic applications.

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Gsm Based Machine Industrial Protection System

Authors: Professor Yash Deshmukh, Aishwarya Bhalekar, Vaibhav Dhok, Atharva Gaikwad, Epshita Gaikwad

Abstract: Industrial machines require continuous monitoring to avoid damage due to overcurrent, overheating, or abnormal voltage conditions. This paper presents a GSM-based machine protection system that monitorsmachine parameters and sends real-time alerts to theoperator using SMS. A microcontroller continuously checks sensor values, and when unsafe conditions are detected, the system automatically shuts down the machine through a relay and informs the user remotely. This system improves safety, reduces maintenance cost, and enables remote monitoring of industrial equipment.

A CPW Fed Antenna With Elliptical Shaped Patch For ISM, WLAN, WiMAX And Wi-Fi Bands

Authors: Annie Threse Edwis

Abstract: A CPW antenna with overall dimensions of 30 mm × 25 mm × 2 mm is designed using FR4 as the substrate material. Two rectangular ground planes, each having dimensions of 12 mm × 9.5 mm, are printed on the top surface of the substrate. A signal strip of width 5 mm is used to excite the antenna, and the gap between the signal strip and the ground planes is maintained at 0.5 mm. The proposed antenna structure consists of an elliptical patch connected to the central signal strip along with two rectangular CPW ground planes on the top side of the FR4 substrate. The elliptical patch, which is united with the signal strip, resonates at 6.25 GHz. The designed antenna operates over several wireless communication bands, including 5.15–5.35 GHz (5.2 GHz WLAN), 5.725–5.825 GHz (5.8 GHz WLAN), 5.25–5.85 GHz (5.5 GHz WiMAX), 5.725–5.875 GHz (ISM band), and 5.15–5.85 GHz (5.5 GHz Wi-Fi). The impedance (Z) and VSWR characteristics demonstrate good radiation performance at the resonant frequency of 6.25 GHz. Furthermore, the gain plot of the CPW radiator indicates efficient radiation across various wireless bands. The co-polarization level is significantly higher than the cross-polarization level at 6.25 GHz, confirming that the proposed antenna is suitable for multiple wireless communication applications.

Tomfuel: Sustainable Bioelectricity Production from Tomato Through Microbial Fuel Cell

Authors: Glecemae Rubino, Adriann R. Dela Peña, Reymark M. Petecio, Allana C. Dimarao, Hearth B. Musketer

Abstract: This study investigated the potential of overripe tomato waste as a sustainable bioenergy source through the use of a Microbial Fuel Cell (MFC) system for electricity generation. An experimental quantitative research design was employed to evaluate the electrical performance of an overripe tomato-based MFC in comparison with a mud-based MFC used as a positive control. Key parameters measured included voltage output, current production, and power density, along with microbial activity indicators such as bacterial growth rate, electron transfer efficiency, and biofilm formation. A total of ten trials were conducted under controlled conditions to ensure data reliability and consistency. Statistical analyses, including frequency distribution, mean computation, and independent sample t-tests at a 0.05 level of significance, were used to determine differences in electricity generation between substrates. Results of the study aim to demonstrate the feasibility of converting organic tomato waste into bioelectricity, supporting sustainable energy development, waste reduction, and low-cost renewable energy solutions, particularly for urban communities such as Davao City.

DOI: https://doi.org/10.5281/zenodo.18875721

Automatic Power Factor Correction Monitiring System Using Sequential Load Balancing

Authors: Nagalakshmi.S, Kuvvarapu rasmitha, Anbarasan. P

Abstract: Electrical distribution conditions that feed inducting appliances like induction motors and responding industrial devices often have poor power factor due to slow response time to the supply voltage. These conditions of operation cause a high conduction loss, a low stability of the voltage, and a low capacity utilization and energy waste. To help avoid such inefficiencies, a Automatic Power Factor Monitoring and Correction System based on Sequential Load Balancing is introduced as an autonomous driven real-time compensation structure.The suggested research structure incorporates an embedded controller based on Arduino to monitor the acquisition of voltage and current under the control of calibrated sensing modules. Phase difference testing on sensed waves can be used to compute power factor correctly as well as to estimate the amount of reactive compensation that is necessary. Relay-controlled capacitor stages are switched in in sequence when it is detected that there is deviation from target operating limits and hence, the reactive power injection is in an incremental fashion but not abrupt transiently. These simulated balancing ensure that overcompensation is prevented and operational stability is maintained at different loading conditions of variable loads.The measured electrical quantities such as the magnitude of voltage, current level, and post-corrected power factor are displayed on a liquid crystal interface whereas remote monitoring is also facilitated via wireless communication modules which are connected to the serial protocols. This is proved by experimental validation of a correction accuracy of 96.8, and the values of power factor are kept in the range of 0.95 or more after implementation under dynamic load variation. Measured data prove the significant decrease of the line losses and the increase of the voltage regulation. The derived implementation confirms its appropriateness in the industrial and domestic power distribution setting that needs affordable, high quality power factor improvement.

The Dawn Of AGI: Syrup And Sword — How Artificial General Intelligence Could Deepen Human Closeness While Posing Existential Risks

Authors: Dr. Snehal Godse, Mr. Prathameshsingh U. Rajput, Prof. Apurva Shende

Abstract: Artificial Intelligence (AI) has evolved from task specific systems to increasingly adaptive and socially responsive agents. As research advances toward Artificial General Intelligence (AGI), a transformative shift is anticipated not only in computational capability but also in human–machine relationships. This study explores the dual nature of AGI—conceptualized as “Syrup and Sword”—wherein emotionally intelligent systems may deepen human closeness while simultaneously introducing existential and psychological risks. Drawing upon attachment theory, alignment research, and human–computer interaction (HCI) scholarship, this paper develops an integrative conceptual framework linking AI capability progression with attachment intensity and societal outcomes. A mixed-method research design is proposed, combining qualitative thematic analysis and quantitative experimental surveys to examine emotional scaling from Narrow AI to AGI-level systems. The study identifies key drivers of attachment such as empathy simulation, memory continuity, adaptive responsiveness, and perceived moral agency. It further analyzes risks including emotional dependency, anthropomorphic projection, manipulation, and value misalignment. The proposed “Syrup vs. Sword Framework” offers a structured lens to evaluate how increasing cognitive and affective sophistication in AGI could produce both enhanced well-being and destabilizing consequences. The findings contribute to interdisciplinary discourse by bridging psychological, ethical, and technical perspectives, emphasizing the necessity of emotionally aware governance in future AGI development. Index Terms—Artificial General Intelligence (AGI), Human–AI Attachment, AI Ethics, Alignment Problem, Emotional AI, Existential Risk, Human–Computer Interaction.

IMPACT OF ANIME CONSUMPTION ON ACADEMIC PRODUCTIVITY AMONG COLLEGE STUDENTS IN PUNE: A SURVEY-BASED STUDY

Authors: Dipak Kadve, Vaishali Suryawanshi, Aditi Choure, Pratibha Ghodake

Abstract: In recent years, digital streaming platforms have become an integral part of students’ daily routines. Among various forms of online entertainment, anime has gained significant popularity among college students in urban Indian cities, including Pune. While entertainment media can provide relaxation and emotional engagement, concerns are often raised regarding excessive viewing habits and their potential academic implications. The present study explores the relationship between anime consumption patterns and academic productivity among college students in Pune. A structured online questionnaire was administered to 150 respondents across different academic disciplines. The study examined variables such as daily viewing duration, binge-watching behavior, late-night streaming habits, sleep duration, study hours, and self-reported academic performance. The findings suggest that moderate anime consumption does not significantly affect academic productivity. However, extended late-night viewing and frequent binge-watching were associated with reduced study hours and irregular sleep patterns. The study highlights the importance of balanced digital engagement and responsible time management among students.

AI-Driven Zero Trust Security Architecture For Protecting U.S. Critical Infrastructure

Authors: Nagaraju Goshikonda

Abstract: The digitalization of critical infrastructure sectors of the U.S. economy such as energy, transportation, healthcare, and defense has expanded the cyber-attack surface at a rapid pace. The old models of perimeter-based security are no longer effective against complex attacks, like advanced persistent attacks (APTs), insider attacks and AI-assisted cyber-attacks. This paper will suggest AI-based Zero Trust Security Architecture (ZTSA) adapted to secure the critical infrastructure in the United States. The framework incorporates behavioral analytics, federated learning, and adaptive risk scoring, that allow one to continue verification and intelligent response to threats. The predictive and generative AI models are utilized to simulate the attack scenario, improve anomaly detection, and automate policy enforcement. Experimental assessment based on simulated critical infrastructure datasets is shown to have a higher detection rate of 95.8 and a 30% lower rate of false positives than traditional zero-trust systems. The outcomes show that AI-enhanced zero-trust models will be capable of mitigating critical infrastructure in the US to a considerably greater extent in terms of resilience, scalability, and mitigation of threats in real-time.

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Research On The Application Of Artificial Intelligence Technology In The Development Of Computer Vision

Authors: Ms. Dipti Rathod

Abstract: Artificial Intelligence (AI) has significantly transformed the field of computer vision by enabling machines to interpret and analyze visual data with high accuracy and efficiency. Computer vision, a major branch of AI, focuses on developing systems that can acquire, process, and understand images and videos in a manner similar to human vision. With the advancement of machine learning and deep learning techniques—particularly Convolutional Neural Networks (CNNs)—computer vision systems have achieved remarkable improvements in tasks such as image classification, object detection, facial recognition, and image segmentation. The integration of AI into computer vision has led to widespread applications across various industries, including healthcare, autonomous transportation, security and surveillance, retail, agriculture, and manufacturing. In healthcare, AI assists in medical image analysis and disease detection; in transportation, it powers self-driving vehicles; and in industrial sectors, it enhances quality inspection and automation. Despite these advancements, challenges such as the need for large labeled datasets, high computational costs, security risks, bias in algorithms, and ethical concerns remain significant issues in the computer industry. This research examines the role of artificial intelligence in the development of computer vision technology, explores its major applications, and highlights the key problems that need to be addressed. The study concludes that while AI-driven computer vision has revolutionized modern computing, continued research, ethical governance, and technological innovation are essential to fully realize its potential and ensure responsible implementation.

An AI-Enabled Low-Code CRM Architecture For Intelligent Fuel Booking And Predictive Inventory Management

Authors: Akhilash Pennam

Abstract: This paper proposes an Artificial Intelligence (AI)–enabled cloud-based CRM architecture developed on the Salesforce platform to modernize gas station operations through intelligent automation and predictive analytics. The system integrates fuel booking, inventory management, supplier coordination, and customer interaction into a unified digital platform. AI techniques including time-series forecasting, anomaly detection, and customer behavior analytics are embedded to transform operational data into predictive insights. Machine learning models analyze historical transactions to forecast fuel demand, optimize inventory levels, and detect abnormal operational patterns. Salesforce automation tools such as Flows and Apex triggers enforce business rules, while AI-driven dashboards provide real-time predictive decision support. Experimental evaluation demonstrates improved forecasting accuracy, reduced operational errors, faster transaction processing, and enhanced managerial decision-making. The proposed architecture demonstrates how AI can elevate traditional CRM systems into intelligent, scalable, and proactive operational platforms suitable for multi-branch fuel retail environments.

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“Importance Of Soil Testing & Analysis”

Authors: Dr. L. N. Malviya

Abstract: To improve the main shortcomings of insufficient nutrients, high salinity and low productivity of soils, soil testing and analysis are crucial processes. Understanding the composition, quality, and characteristics of soil is essential for making informed decisions related to land use, crop management, building foundation design, and environmental remediation. Soil testing and analysis is very important in agriculture, construction, environmental science, and various other fields.

Blockchain-Enabled Architectures For Safeguarding Academic Data Integrity In Higher Education

Authors: Deepak Tomar, Kismat Chhillar, Dhruv Srivastava

Abstract: This paper examines the potential of blockchain technology in strengthening of academic data integrity within institutions of higher education by addressing persistent challenges of credential fraud, limited traceability, record falsification and fragmented oversight in traditional centralized systems. Grounded in contemporary research on decentralized architectures and verifiable credentials, the study analyzes weaknesses in existing platforms of student management and proposes a conceptual model that is integrating key blockchain principles of distributed consensus, immutability and smart contracts, with requirements of integrity such as auditability, verifiability, non-repudiation and selective disclosure. The proposed model provides an outline of a consortium-based platform for management of transcripts, qualifications, assessments and co-curricular records, designed to interoperate with existing databases of institutions while complying with regulations of data protection and sovereignty regulations such as FERPA and GDPR. Scenario-based evaluations suggest improvements in verification efficiency, inter-institutional trust and provenance tracking, along with reductions in administrative overhead and faster dissemination of academic records to external stakeholders. The study also critically considers practical challenges that are related to jurisdictional interoperability, scalability, institutional resistance and governance.

Machine Learning-Based Prediction Of Mortality Risk In Type 2 Diabetes Patients Using Multi-Organ Biomarkers

Authors: Krishna Prisad Bajgai, Dr. Saroj Khanal, Dr. Bhoj Raj Ghimire

Abstract: Type 2 Diabetes Mellitus (T2DM) remains a major global health burden and a leading contributor to cardiovascular, renal, and hepatic mortality. Traditional risk assessment models rely on limited clinical parameters and fail to capture complex nonlinear interactions among multi-organ biomarkers. This study proposes a comprehensive machine learning (ML) and deep learning (DL)-based survival modeling framework to predict mortality risk in T2DM patients using multi-organ biomarkers, including fasting blood glucose, HbA1c, serum creatinine, triglycerides (TG), total cholesterol, LDL, HDL, liver enzymes (ALT, AST), and fatty liver indicators. Using the National Health and Nutrition Examination Survey (NHANES) linked mortality dataset, we compare Cox Proportional Hazards, Random Survival Forest (RSF), Gradient Boosting Survival (GBM), DeepSurv, and Long Short-Term Memory (LSTM) models. Performance was evaluated using Concordance Index (C-index), time-dependent Area Under Curve (AUC), Hazard Ratio (HR), and Brier score. Results show DeepSurv achieved the highest C-index (0.82), followed by RSF (0.79), outperforming traditional Cox regression (0.72). SHAP-based feature importance revealed HbA1c, creatinine, triglycerides, and ALT as dominant mortality predictors. Risk stratification analysis demonstrated clear separation between low-, medium-, and high-risk groups (log-rank p < 0.001). The findings highlight the superiority of nonlinear survival models for mortality prediction in T2DM and provide clinically interpretable insights for personalized risk management.

DOI: https://doi.org/10.5281/zenodo.18886211

EV Charger Sharing Platform

Authors: Megha Garud, Lalit Gaikwad, Prakash Mane, Ranjit Misal, Amey Phatak, Premraj Takawale

Abstract: The increasing adoption of Electric Vehicles (EVs) has brought significant attention to the availability and efficiency of charging infrastructure. Although governments and private organizations are actively deploying public charging stations, their limited number and uneven distribution continue to pose challenges for EV users. In many cases, users experience long waiting times or difficulty locating nearby charging facilities. This survey paper presents an EV Charger Sharing Platform that encourages the utilization of privately owned EV chargers through a web-based system. The platform enables charger owners to list their chargers and EV users to search, view, and book available charging slots based on location and availability. Developed using standard web technologies such as HTML, CSS, and JavaScript, the system aims to improve charger accessibility, reduce waiting time, and promote sustainable transportation. By adopting a sharing-economy approach, the proposed solution offers a cost-effective and scalable alternative to traditional public charging infrastructure.

Deepfake And AI-Scam Protection

Authors: Siddhi Ekawade, Apurva Jate, Arya Kamble, Sharvari Kate, Prof.Pradnya Satpute

Abstract: Artificial Intelligence has made it easy to create realistic images, videos, and texts. These technologies have been misused to create deepfakes and online scams, which can lead to the spread of misinformation, financial scams, and cybersecurity attacks. It is hard to detect such content by human beings, as it is time-consuming. Hence, there is a need to develop an automated detection system for AI-generated content. The proposed project aims to develop a multimodal AI-generated content detection system that can analyze images, videos, and texts to detect potentially fake or scam content. The system can detect deepfakes in images and videos using a Convolutional Neural Network (CNN) model, and it can also analyze the text messages sent by the user to detect scams using a machine learning-based approach. The application has been developed as a web application using the Flask framework in Python. This processed media is analyzed, and the important features are identified, providing a probability score on whether the media is real or fake. The output is given in percentage probability, making it easier for the user to interpret the results. All analysis results are stored in a SQLite database, which is used for monitoring and administrative purposes. This proposed system has shown how deep learning and machine learning can be combined into a single framework to detect AI-generated content. This type of system can be used to enhance digital security, helping users identify fake media and possibly scam messages.

Smart Classroom System Using IoT

Authors: Aaditya Duche, Raviraj Deore, Sanskar Dalvi, Swapnil Paik, Prof. R. B. Shinde

Abstract: The Smart Classroom System using IoT modernizes the conventional education environment by integrating automation, sensing, and communication technologies. The system implements automatic student attendance using face recognition, smart control of lighting and fans, environmental monitoring, and remote data access through cloud platforms. A Raspberry Pi 3B single board computer and Pi camera module continuously monitor the classroom. During enrollment, facial images of each student are captured and stored in a database. During lecture hours, the system detects faces from live video frames and compares them with the stored dataset using OpenCV and the face_recognition library. When a match is confirmed, attendance is recorded with date and time. A 16×2 LCD display shows confirmation and a buzzer provides audio indication. The system eliminates proxy attendance, reduces manual effort, and improves accuracy, demonstrating the practical value of embedded systems and computer vision in smart educational infrastructure

Federated Learning Based Energy Management Techniques For Distributed Green Computing In IoT Networks

Authors: Deepak Tomar, Kismat Chhillar, Sanchit Agarwal

Abstract: This paper addresses the critical challenge of energy efficiency in distributed Internet of Things (IoT) networks through the application of federated learning-based energy management techniques tailored for green computing. With the exponential growth of connected devices, traditional centralized processing poses significant privacy, communication and energy consumption issues. Federated learning offers a decentralized paradigm that preserves user privacy while enabling collective model training across heterogeneous IoT nodes. This work proposes novel energy-aware federated learning algorithms that optimize communication and computation costs by leveraging techniques such as adaptive model updates, quantization, and device participation scheduling. The proposed framework integrates trust mechanisms to ensure secure and reliable cooperation among devices, thereby enhancing sustainability and network longevity. Experimental evaluations demonstrate significant reductions in energy consumption without compromising learning accuracy, highlighting the potential for real-world implementation in diverse IoT environments. The findings underscore the importance of leveraging collaborative intelligence for sustainable, green computing infrastructures, paving the way for future research in scalable, energy-efficient federated learning applications within IoT networks.

Securing Data During Transmission And Storage

Authors: Surbhi Sahu

Abstract: In modern digital environments, sensitive information is constantly transmitted across networks and stored in distributed systems such as databases, cloud infrastructures, and storage devices. The increasing number of cyber threats such as data breaches, interception attacks, and unauthorized access has made data security a major concern for organizations and individuals. This research paper examines techniques used to secure data during transmission and storage, including encryption algorithms, secure communication protocols, and access control mechanisms. Symmetric and asymmetric cryptographic methods such as AES, DES, RSA, and ECC are analyzed to understand their effectiveness in protecting data confidentiality and integrity. Additionally, modern security approaches such as homomorphic encryption, blockchain-based storage, and quantum‑resistant cryptography are discussed. The paper concludes that a combination of encryption techniques, secure protocols, and strong authentication systems is essential for protecting sensitive information in modern computing systems.

DOI: https://doi.org/10.5281/zenodo.18888623

Digital Transformation Of Local Commerce: The Role Of Local Business Directories In Enhancing MSME Visibility – A Case Study Of IndiaBusinessTree

Authors: Sagar Kumar

Abstract: The rapid digitalization of commerce has significantly transformed how local businesses connect with customers. Small and medium enterprises (SMEs), particularly in developing economies like India, face challenges related to visibility, discoverability, and digital presence. Local business directories have emerged as cost-effective digital tools that bridge the gap between consumers and businesses. This research examines the role of online local business directories in improving market accessibility and digital inclusion, with a case study of IndiaBusinessTree (IBT), a free business listing and local directory platform in India. The study evaluates how structured business listings, search optimization, and location-based categorization enhance business exposure and customer engagement. Using qualitative analysis and platform-based observations, the paper highlights the impact of digital directories on customer acquisition, search engine visibility, and trust-building. The findings suggest that local directories significantly contribute to MSME growth by enabling affordable digital marketing, improving local search rankings, and fostering regional economic development. The study concludes that digital business directories are critical components of the modern digital ecosystem, especially in emerging markets.

DOI: https://doi.org/10.5281/zenodo.18888909

Accessibility And Usability Evaluation Of E-Governance Portals: Identifying Gaps For Inclusive Design

Authors: Prof. Atish Shriniwar, Ms.Vaishnavi Tikone, Ms.Akanksha Sawant, Prof. Badrinath Bulepatil

Abstract: The rapid expansion of India’s digital governance ecosystem under the Digital India initiative has positioned e- governance portals as critical platforms for delivering public services. However, the effectiveness of these platforms depends not only on service availability but also on ensuring accessibility, usability, and reliable performance. This study evaluates selected Indian e-governance portals to identify gaps affecting inclusive digital access. A mixed-method approach was adopted, combining automated accessibility testing based on WCAG 2.1 Level AA standards with a user perception survey conducted among 44 participants. The findings reveal a significant “Accessibility–Usability Gap.” Although most respondents were digitally proficient young adults, only 13.6% reported being very satisfied with their overall experience. Approximately 50% identified technical glitches and poor system performance as primary barriers, while 45.5% reported inadequate mobile compatibility. Furthermore, 27.3% indicated that accessibility support features, such as screen reader compatibility, were insufficient. The study concludes that digital accessibility must extend beyond technical compliance to incorporate mobile-first design principles, improved system performance, and user-centered interface development. Implementing these improvements can foster a more inclusive, efficient, and equitable digital governance framework in India.

DOI: https://doi.org/10.5281/zenodo.18898514

College Sport Management System

Authors: Pratik Bhosale, Vaishnavi Rathod, Kajal Zore, Parshuram Baile, Prof.Savita Biradar

Abstract: College Sports Management System is a modern web application designed to manage and organize sports activities within a college efficiently. In many educational institutions, sports events such as tournaments, team registrations, and match schedules are still managed manually using paper records or spreadsheets. This traditional approach can lead to errors, data loss, and difficulties in managing large amounts of information. To overcome these challenges, the College Sports Management System provides a digital platform that automates and simplifies the management of sports-related activities. The system is developed using React JS for the frontend and Python for the backend, providing a fast, interactive, and scalable web application. React JS helps in creating a dynamic and user-friendly interface where users can easily navigate through different sections such as sports events, match schedules, and team details. The backend developed using Python handles the application logic, data processing, and communication between the frontend and the database. This architecture ensures better performance, maintainability, and scalability of the system. The main objective of this system is to provide a centralized platform where administrators can efficiently manage players, teams, tournaments, and match schedules. The system includes several modules such as Admin Management, Player Registration, Team Management, Tournament Scheduling, and Match Result Management. Through the admin panel, administrators can add and manage player information, create teams, organize tournaments, schedule matches, and update match results. On the other hand, students and users can view sports event details, match schedules, and tournament results through the web interface. By implementing the College Sports Management System, the process of organizing and managing sports activities becomes more efficient and structured. The system reduces manual workload, improves data accuracy, and ensures that sports information is easily accessible. The use of modern web technologies like React JS and Python allows the application to provide better user experience, faster data handling, and improved system performance. In conclusion, the College Sports Management System offers an effective digital solution for managing sports events in colleges. It enhances the overall organization of sports activities and provides a convenient platform for administrators and students to access sports-related information. The system can also be extended in the future with additional features such as online player registration, live match score updates, and mobile application integration.

ARDUINO BASE ATOMATIC WATER DISTRIBUTION SYSTEM

Authors: Pranali Gavhane, Sapna Gaikwad, Sumit Ghogare, Abhay Ghalke

Abstract: This project proposes an Arduino-based automatic water distribution system designed to optimize water supply, minimize wastage, and reduce manual intervention in domestic or municipal contexts. Utilizing an Arduino microcontroller (such as Uno or Mega) as the central processing unit, the system integrates sensors to monitor water levels, flow rates, or soil moisture.

Advancing Drug Discovery Through Artificial Intelligence: Opportunities, Challenges, And Future Perspectives

Authors: Jose Gnana Babu, Lata Khani Bisht, Visaga Perumal, Vineeth Chandy

Abstract: In recent years, artificial intelligence (AI) has emerged as a strategic catalyst in the field of drug discovery, revolutionizing one of the most complex and resource-intensive areas of the pharmaceutical industry. AI introduces innovative methodologies that enhance efficiency and precision across multiple stages of drug discovery and development, including—though not limited to—virtual screening, target identification, lead optimization, and clinical trials. This review provides an in-depth examination of current AI-driven tools, programs, and platforms that are reshaping modern drug discovery. Beyond presenting the present state of AI applications in this domain, it also explores future directions, existing challenges, and emerging opportunities. The traditional drug discovery process is often constrained by its high cost, long timelines, and substantial attrition rates. However, the integration of AI and machine learning (ML) has introduced transformative solutions, making drug development more rapid, cost-effective, and data-driven. Leveraging vast biological and chemical datasets, AI and ML employ advanced computational techniques—such as neural networks, natural language processing (NLP), and reinforcement learning—to enhance prediction accuracy and streamline decision-making throughout the drug discovery pipeline. These technologies facilitate the identification of novel therapeutic targets, accurate efficacy and safety predictions, and the optimization of clinical trial design, thereby significantly shortening development cycles and reducing overall expenditures. Real-world case studies further illustrate AI’s contribution to groundbreaking therapies in fields such as oncology, neurodegenerative disorders, and rare genetic diseases. Despite these remarkable advancements, notable challenges remain. Concerns surrounding data quality, model transparency, algorithmic bias, and regulatory compliance continue to pose barriers to widespread adoption. Moreover, ethical issues related to data privacy, accountability, and the interpretability of AI-driven decisions demand critical attention. Looking ahead, emerging paradigms such as multi-omics data integration, quantum computing, and precision medicine are expected to redefine the landscape of AI-assisted drug discovery. Achieving this vision will require interdisciplinary collaboration, technological innovation, and the establishment of robust ethical and regulatory frameworks. Collectively, these efforts will pave the way for a new era of patient-centric, precision-driven pharmaceutical development, fully harnessing the transformative potential of AI and ML in drug discovery.

DOI: https://doi.org/10.5281/zenodo.18899332

Effect Of Recent Solar Events On High-energy Cosmic Ray Particles

Authors: Rekha Agarwal, Rajesh Kumar Mishra, Divyansh Mishra

Abstract: Recent solar cycles, particularly the ascending and peak phases of Solar Cycle 25 (2020–2025), have been characterized by heightened solar activity, including X-class flares, fast coronal mass ejections (CMEs), and complex interplanetary shocks. These transient events strongly modulate galactic cosmic rays (GCRs) and produce solar energetic particles (SEPs), thereby altering the flux, energy spectrum, and anisotropy of high-energy charged particles in near-Earth space. This paper synthesizes observational and theoretical advances concerning the effect of recent solar events on high-energy cosmic ray particles (>100 MeV to multi-GeV), with emphasis on Forbush decreases, shock acceleration, magnetic cloud interactions, and ground level enhancements (GLEs). We discuss observations from neutron monitor networks and space-based detectors such as Parker Solar Probe, Solar Orbiter, ACE, and GOES, highlighting case studies from 2021–2024. Quantitative comparisons reveal cosmic ray depressions of 3–20% during major CME passages and episodic enhancements up to GeV energies during extreme SEP events. The broader implications for space weather, atmospheric ionization, and radiation risk are examined.

Embedded Smart System For Automatic Speed Regulation In Sensitive Areas

Authors: Mr. Prathmesh M. Sadafale, Mr. Pratik S. Date, Mr. Raj S. Kharate, Prof. Ravindra R. Solanke

Abstract: Abstract – This research presents the design and development of an Embedded Intelligent System for Automatic Speed Regulation in sensitive areas such as school zones, hospitals, residential areas, and accident-prone locations. The main objective of the system is to improve road safety by automatically controlling vehicle speed without relying only on driver awareness. The proposed system uses embedded technology, sensors, and wireless communication to detect designated speed-control zones. When a vehicle enters a sensitive area, the system automatically limits its speed to a predefined safe level. Once the vehicle exits the zone, normal speed control is restored. The system operates in real time and reduces the risk of over-speeding. By minimizing human error and ensuring consistent speed regulation, the system enhances road safety, reduces accidents, and supports smarter transportation infrastructure.

Transforming Cancer Care With Artificial Intelligence: Advances, Applications, And Future Directions

Authors: Mr. Meenakshi, Dr. Brij Mohan Goel

Abstract: Artificial Intelligence (AI) and Machine Learning (ML) have significantly transformed the field of healthcare, particularly in cancer detection, diagnosis, and treatment. With the advancement of digital pathology, large clinical datasets, and powerful computational techniques, AI has become a crucial tool in oncology research and clinical practice. Deep learning algorithms can analyse high-resolution histopathology images, genomic data, and electronic health records to detect patterns that may not be visible to human experts. These technologies enable early cancer detection, risk prediction, accurate diagnosis, and personalized treatment planning. Additionally, AI-based approaches such as Natural Language Processing (NLP), radiomics, and biomarker discovery have enhanced the analysis of complex medical data. Cloud-based AI platforms further facilitate large-scale data processing and collaborative research. Despite these benefits, the integration of AI into cancer care also faces technical and ethical challenges, including data privacy concerns, lack of standardized datasets, algorithm bias, and interpretability issues. This paper explores the applications of AI in cancer prediction, diagnosis, and treatment while discussing the technical and ethical challenges associated with its implementation. The study highlights the future potential of AI-driven precision medicine and its role in improving cancer care outcomes.

DOI: https://doi.org/10.5281/zenodo.18919879

SkinAI : A Multi-Model Framework For Skin Analysis And Product Recommendation

Authors: Shravani Mali, Yukta Koli, Mayuri Mohite, Nilam Patil

Abstract: This study presents an AI-driven allergy checker designed explicitly for skincare. It reviews the user's allergies, skin type, and skin conditions and suggests a product and skincare routine based on those factors. The random forest model is used to classify skin type while the Light GBM model evaluates the skincare routine recommendations. Then a K-Nearest Neighbors (KNN) algorithm uses the allergy information the user provides to make the recommendations. A YOLOv8 model also analyzes the image the user provides and determines if there are skin conditions visible to the naked eye. In review, the system developed is able to provide appropriate personalized data-driven recommendations for skincare products and routines with a lower likelihood of allergic body complications, while also allowing for informed selections of skincare according to allergenic history.

Integrated Electronic Health Record System For Hospital

Authors: Ms.S.Kanimozhi, P.Guruprasaath, S.Lingaraj, S.Kanagaraj

Abstract: In modern healthcare, patient medical records are often distributed across multiple hospitals, resulting in repeated medical tests, delayed diagnosis, and poor continuity of care. This project proposes a Reference Electronic Health Record (EHR) System that provides secure and centralized access to patient records across hospitals. The system includes two login modules: users (patients) can view their medical history, disease descriptions, and prescriptions, while hospitals manage multiple doctors who select their own name to add new records for specific users. All medical records are maintained in a reference based manner, ensuring that previous information remains preserved, while doctors from different hospitals can view earlier records as reference to support accurate diagnosis and treatment. The system promotes better coordination among healthcare providers by maintaining a consistent and complete patient medical history. By offering structured storage and role-based access to sensitive medical information, the system enhances continuity of care, minimizes redundancy in medical testing, and improves overall efficiency within the healthcare process.

DOI: https://doi.org/10.5281/zenodo.18920808

Graphical Password Authentication

Authors: Shruti Dhage, Heena Barach, Sanakruti Jadhav, Vaishnavi Shivsharan, Shravani Pichake, Suchita Barkund

Abstract: Authentication is a critical component of digital systems, ensuring that only authorized users gain access to sensitive information and services. Traditional text-based password mechanisms, while widely used, suffer from vulnerabilities such as weak password selection, reuse across platforms, and susceptibility to brute-force and phishing attacks. To address these issues, this research presents the Graphical Password Authentication System, a web-based platform designed to enhance security by combining conventional password hashing with graphical pattern verification. The proposed system is developed using Java Server Pages (JSP), Servlets, MySQL database, HTML, CSS, and JavaScript, and deployed on the Apache Tomcat server. It includes features such as secure user registration, SHA-256 password hashing, graphical password setup and validation, OTP-based password recovery, and session management with duplicate login prevention. By introducing a dual-layer authentication mechanism, the system reduces risks of impersonation and unauthorized access while providing a user-friendly interface. The implementation demonstrates how graphical authentication can strengthen digital identity management and improve usability in academic, corporate, and community environments.

Malware Detection Using Machine Learning & Performance Evaluation

Authors: I.Sravani, D. Lakshmi, M.Ushaswini, L.Aswini, C. Subramanyam

Abstract: Malware is any type of program that is intended to wreak havoc to the computer system and network. Examples of malware are bot, ransomware, adware, keyloggers, viruses, trojan horses, worms and others. The exponential growth of malware is posing a great danger to the security of confidential information. The problem with many of the existing classification algorithms is their low performance in term of their ability to detect and prevent malware from infecting the computer system. There is an urgent need to evaluate the performance of the existing Machine Learning classification algorithms used for malware detection. This will help in creating more robust and efficient algorithms that have the capacity to overcome the weaknesses of the existing algorithms. This study did the performance evaluation of some classification algorithms such as J45, LMT, Naïve Bayes, Random Forest, MLP Classifier, Random Tree, REP Tree, Bagging, AdaBoost, KStar, SimpleLogistic, IBK, LWL, SVM, and RBF Network. The performance of the algorithms was evaluated in terms of Accuracy, Precision, Recall, Kappa Statistics, F-Measure, Matthew Correlation Coefficient, Receiver Operator Characteristics Area and Root Mean Squared Error using WEKA machine learning and data mining simulation tool. Our experimental results showed that Random Forest algorithm produced the best accuracy of 99.2%. This positively indicates that the Random Forest algorithm achieves good accuracy rates in detecting malware.

DOI: https://doi.org/10.5281/zenodo.18921650

KhetSetGo- Empowering Farmers And Machine Owners

Authors: Himanshu Kaspate, Tejas More, Atharv Sanas, Pruthviraj Sarade, Prof.Vijay Mohite

Abstract: Agriculture remains a primary source of livelihood in many developing regions, yet many farmers face challenges in accessing modern agricultural machinery due to high purchasing costs and limited availability. Small and medium-scale farmers often cannot afford expensive equipment such as tractors, harvesters, and other farming tools, which affects productivity and efficiency. To address this issue, this research presents KhetSetGo – Empowering Farmers and Machine Owners, a web-based platform designed to connect farmers who require agricultural machinery with machine owners willing to rent their equipment. The platform enables machine owners to post available machinery with relevant details, while farmers can easily browse, view machine information, and place booking requests according to their agricultural needs. The proposed system is developed using Java Server Pages (JSP), Servlets, MySQL database, HTML, CSS, and JavaScript, and is deployed on the Apache Tomcat server. The platform includes features such as user authentication, OTP-based password recovery, machine listing with media support, and booking management between farmers and machine owners. By enabling an online rental marketplace for agricultural equipment, the system helps reduce machinery costs for farmers while improving equipment utilization for owners. The implementation of KhetSetGo demonstrates how digital platforms can support smart farming practices and improve accessibility to agricultural resources through an efficient and user-friendly system.

Reducing Workload In Using AI-based API REST Test Generation

Authors: GowriDurga A, Kavin S, Thanush S, Elzin Selva M

Abstract: For communication between distributed services, modern software systems mainly rely on REST APIs, especially in microservices architectures. Preventing vulnerabilities and preserving system stability depend on these APIs' dependability and security. Nevertheless, manual API testing is laborious, prone to mistakes, and frequently leads to insufficient test coverage. In order to automate the development and administration of REST API test cases, this article suggests an AI-Based API REST Test Generation framework. The system uses organized test sequences to perform automatic vulnerability detection, intelligently analyze API endpoints, and confirm application reachability. Automated endpoint discovery, authentication validation, parameter testing, and security vulnerability detection techniques like BOLA vulnerabilities and RBAC violations are all included in the framework. The proposed system is implemented as a web-based platform that visually represents the security posture of APIs through a step-wise roadmap. Experimental evaluation demonstrates that the automated framework significantly reduces manual workload while improving the efficiency and accuracy of API testing.

Research Paper On UniTasker: A Web-Based Role-Oriented Academic Task Management System

Authors: Praful Madne, Agrim Khanna, Aary Mahadik, Yash Nanwatkar

Abstract: With the rapid growth of digital education systems, academic institutions require structured task management solutions to manage assignments, grading, and communication effectively. UniTasker is a lightweight, role-based web application designed for diploma-level institutions. The system enables faculty members to create assignments, set deadlines, review submissions, and assign grades, while students can submit tasks and receive automated reminders. This paper surveys existing Learning Management Systems (LMS) and generic task management tools, analyzes their limitations, and evaluates how UniTasker bridges these gaps through a modular three-tier architecture. The system is developed using JSP, JDBC, MySQL, and Apache Tomcat, ensuring low infrastructure requirements and high usability. Future enhancements include attendance tracking.

Ocular Disease Recognition Using VGG-19 Deep Learning With Multi-Class Classification On Retinal Images

Authors: Anuja Shinde

Abstract: This paper presents a deep learning-based framework for the automated recognition of ocular diseases using retinal fundus imaging data. Leveraging the VGG-19 convolutional neural network (CNN) architecture with transfer learning, the proposed system performs multi-class classification of retinal images to distinguish between seven ocular conditions: Myopia (M), Hypertension (H), Diabetes (D), Cataract (C), Glaucoma (G), Age-related Macular Degeneration (A), and other abnormalities (O). The input images are preprocessed using computer vision techniques including normalization, contrast enhancement, and texture and shape-based feature extraction. Unlike prior binary classification approaches, our system enables simultaneous prediction of multiple diseases within a single retinal image using the Ocular Disease Intelligent Recognition (ODIR) dataset of 10,000 images. Experimental results demonstrate high classification accuracy, with the model achieving competitive precision, recall, and F1-scores. The proposed system has significant implications for clinical ophthalmology, particularly in enabling early, accurate, and scalable eye disease diagnosis in resource-limited environments.

Fluid Dynamics-Inspired Cloud Management: The AI-Cloud-Navier-Stokes Framework

Authors: Uma Perumal, Vasantharajan Renganathan

Abstract: In this paper, we introduce a novel mathematical approach to connect fluid dynamics with artificial intelligence and cloud computing optimization. We introduce the AI-Cloud-Navier- Stokes (ACNS) System, a new framework which treats cloud infrastructure as continuum fluid system and allows us to predictably optimize resource deployments, load balance and failure tolerant. By correspondence typical cloud computing variables and fluid dynamic quantities – workload being mapped to velocity, resource availability to pressure, as the network latency to viscosity – we obtain a full set of partial differential equations enriched with neural network operators. The main technical contributions of our work are: (1) the formulation of a new set of cloud-specific Navier-Stokes equations, derived through rigorous mathematical theory; (2) a proof on AI-enhanced singularity prevention for stable systems; and (3) an applied case study which shows 23-28% reduction in cost and 85% accuracy in failure prediction from real-world cloud data. Beyond theoretical novelty, we evaluate the system empirically for large-scale computational problems based on production cloud data from AWS EC2 instances, and find that it significantly outperforms traditional optimization techniques. This work paves the way for a new direction of physics-informed AI for distributed systems optimization, with applications ranging from edge computing to IoT networks and large-scale datacentre orchestration.