Daily Archives: September 19, 2025

Digital Data Security By Bio-Inspired Algorithm And Machine Learning

Authors: Dr. Devdas Saraswat, Pawan Singh Rajput

Abstract: Images are part of life that is a proof of many instants. Communication medium is quit vulnerable to transfer media as sensitive data may be perturb by intruders. So researcher work a lot for the network security optimization. This paper has proposed a model that authenticate the image obtained from the network. Work has proposed a model that extract optimize features from image by bio-inspired image segmentation. Segmented region of image was used for the embedding of authentic message in least significant bits region and used for training of mathematical model. Trained model is used for the prediction of clustered region when image undergoes in an attack. Experiment was done on real standard image dataset. Result shows that proposed model has improved the security under various attacks. Image quality was also highly maintained.

Removal Of Chromium And Iron Heavy Metal Ions From Aqueous Solutions Using Leaf An Eco-Friendly Method

Authors: Pradeep Kumar Jaiswal, Rakesh Kumar Yadav, Manish Kumar Tiwari

Abstract: The aim of the research was to prepare low-cost adsorbents, including raw date pits and chemically treated date pits, and to apply these materials to investigate the adsorption behaviour of Cr(III) and Fe(III) ions from wastewater. The prepared materials were characterized using SEM, FT-IR and BET surface analysis techniques for investigating the surface morphology, particle size, pore size and surface functionalities of the materials. A series of adsorption processes was conducted in a batch system and optimized by investigating various parameters such as solution pH, contact time, initial metal concentrations and adsorbent dosage. The optimum pH for achieving maximum adsorption capacity was found to be approximately 7.8. The determination of metal ions was conducted using atomic adsorption spectrometry. The experimental results were fitted using isotherm Langmuir and Freundlich equations, and maximum monolayer adsorption capacities for Cr(III) and Fe(III) at 323 K were 1428.5 and 1302.0 mg/g (treated major date pits adsorbent) and 1228.5 and 1182.0 mg/g (treated saga date pits adsorbent), respectively. It was found that the adsorption capacity of H2O2-treated date pits was higher than that of untreated DP. Recovery studies showed maximal metal elution with 0.1 M HCl for all the adsorbents. An 83.3–88.2% and 81.8–86.8% drop in Cr(III) and Fe(III) adsorption, respectively, were found after the five regeneration cycles. The results showed that the Langmuir model gave slightly better results than the Freundlich model for the untreated and treated date pits. Hence, the results demonstrated that the prepared materials could be a low-cost and eco-friendly choice for the remediation of Cr(III) and Fe(III) contaminants from an aqueous solution.

Vistara: Fashion That Fits Your Future

Authors: Sanyam Jain, Amit Kumar, Prakash Mali, Khushal Bharat Shivade, Krishna Gutte

Abstract: Rapid digital change in the fashion and retail world is underway. Virtual try-on technologies are changing how people shop. However, these technologies are also bringing up issues like data privacy and security. This paper introduces Style Secure Zone, an AI-enabled framework for secure, contactless, and personalized shopping for clothing.While physical fitting rooms will still be necessary in traditional department stores, the development of Style Secure Zone will tackle hygiene issues and help retailers improve their operations by reducing wait times. AI-powered and algorithm-supported product recommendations provide thoughtful outfit suggestions based on natural traits, preferences, personal styles, and fashion trends. 3D renders are also created to improve fit and feel.K-Movement’s testing shows that the system boosts consumer confidence, reduces product re- turns, and helps retailers manage inventory challenges related to space availability. Additionally, user assessments showcase great enthusiasm for the integrated, private, and interactive shopping experience across the platform. The overall findings highlight the importance of privacy-oriented digital retail platforms and demonstrate that safe, AI-enabled solutions, like the Style Secure Zone, look to strengthen customer engagement, retailer perfor- mance, and ultimately define the future of intelligent fashion retail.

DOI: https://doi.org/10.5281/zenodo.17164456

An IoT Approach To Vehicle Accident Reporting And Nearest Ambulance Service For Medical Assistance

Authors: Rajendra Singh Kushwah, Satya Prakash Srivastava

Abstract: As the increases the demands of automobiles has cause increases the traffic and increases the number of accidents which shows road safety importance and this is happen because of lack of urgent emergencies facilities in our country , Vehicle accidents and casualty happen on road side should provide on the spot help and rescue as soon as possible . However some problem faces by rescue team to find the accident location. There is no proper communication and retrieve information instantly of the specific accident spot area. This paper proposed a smart and reliable IOT technology provide various features by which we can find instantly the actual accidental location by the help of actual geographic coordinate’s position of vehicle accidents occur. There are various sensors available to check the primary data for detecting accidents. These geographical data can be sent to nearest ambulance for medical assistance, this will save time to reach the rescue team to the accidental location and provide rescue to the injured person. In this paper a real time automobile tracking system via Google earth is introduced and a LifiLink app to find the nearest ambulance location near to vehicle accident spot . The system uses two main components, a transmitting embedded module to interface for vehicle location GPS and GSM devices in order to determine and send vehicle location and status information SMS .

SnapHire: Real-Time Platform For Instantly Booking Photographers And Reel Makers

Authors: Neelima Kasarla, Praneeth Kumar Kandukuri, TakeYadav Yerragolla, Srikanth Pandula

Abstract: The demand for digital content is growing quickly. Finding skilled creators is now a challenge. SnapHire is a platform that simplifies the hiring process for photographers, video editors, and reel makers. It has a simple interface. Clients can view portfolios, check skills, see availability in real time, and book services right away.This reduces delays compared to traditional hiring methods. Creators gain better visibility and can handle client requests more easily. Businesses and individ- uals can rely on skilled professionals. SnapHire is built to grow with tools that improve workflow and user experience. The platform emphasizes speed, clarity, and reliability. It connects clients and creators effectively. SnapHire offers high-quality content for marketing, branding, social media, and personal events like weddings and parties. It makes booking simple, increases creator visibility, and improves communication. This method offers a smooth and dependable experience for clients and professionals.

DOI: https://doi.org/10.5281/zenodo.17173653

Hydrogen For Industrial Decarbonization: Hype, Challenges, And Real-World Applications

Authors: Sandip Bhaskar Patil

Abstract: Hydrogen has been promoted as a versatile clean energy vector capable of decarbonizing hard-to-abate industrial sectors. This paper critically reviews the technical, economic, and infrastructural factors that determine whether hydrogen can move from todays too much “hype” to a reality of fit-for-purpose energy solution for industry and the energy sector. We review production pathways (gray/blue/green hydrogen), electrolyser technologies, storage & transport challenges, and industrial use-cases (steel, chemicals, refineries, and high-temperature heat). Key findings: (1) green hydrogen currently remains significantly more expensive than fossil-derived hydrogen, though projections indicate cost declines with scale and deployment; (2) hydrogen is likely to be most viable where direct electrification is infeasible (high-temperature heat, feedstock); (3) infrastructure and implementation gaps are significant and require policy, supply-chain, and finance coordination. The paper concludes with fit-for-purpose deployment pathways and policy recommendations to enable industrial adoption.

DOI: https://doi.org/10.5281/zenodo.17173815

Autonomous Vehicle Pedestrian Detection: Minimum Safety Standards Needed To Protect Disabled Road Users

Authors: Ryan Gautam

Abstract: This secondary research review evaluates the extent to which current autonomous vehicle (AV) pedestrian detection datasets and validation protocols represent and protect disabled road users—including wheelchair users, white cane users, guide dog handlers, and mobility scooter users—across lighting and weather conditions. Synthesizing peer reviewed studies, standards analyses, government reports, and advocacy documents from 2015–2025, the review finds systematic underrepresentation of disability categories and accessibility infrastructure in widely used datasets, alongside documented detection biases that elevate risk for vulnerable pedestrians under low light and non standard movement scenarios. Current validation frameworks (e.g., functional safety and SOTIF) and regulatory pathways provide limited, non specific guidance on disability inclusive testing, allowing deployments that lack demonstrable parity performance for disabled pedestrians. The paper proposes a minimum pre deployment standard requiring disability inclusive dataset composition, category specific performance thresholds (with edge case coverage), and independent third party audits, with ongoing post deployment monitoring. This framework is feasible within established safety and regulatory processes and is necessary to align AV deployment with equity and safety obligations for all road users.

DOI: https://doi.org/10.5281/zenodo.17173964

Potholes Detection And Avoidance Using Reinforcement Learning For Self-Driving Cars

Authors: M Devendar Reddy, S Akhil Reddy, Anand Jawdekar, N Saiprem,, B UdayKiran Reddy

Abstract: The results of the experiment indicate that combining reinforcement learning with vision-based techniques can offer signifi- cant improvements in autonomous naviga- tion [2],[5]. Scale-Invariant Feature Trans- form (SIFT) was particularly effective in recognizing both the delivery target and potholes with a high degree of accuracy [7],[10], ensuring reliable performance under varying conditions. Canny edge detection and the Hough Line Transform proved to be highly efficient tools for lane identification [4],[6], allowing the robot to maintain pre- cise lane alignment during movement. Fur- thermore, IMU-based orientation correction provided additional robustness, preventing errors caused by yaw drift and other orien- tation issues [7]. Collectively, these meth- ods enabled the robot to adapt dynami- cally to its environment and demonstrate consistent success across repeated trials [2]. These findings suggest that the proposed framework not only addresses the imme- diate problem of pothole detection [9],[10] but also enhances the overall safety and reliability of autonomous vehicles. Look- ing ahead, the study shows strong poten- tial for real-world applications, as it pro- vides a scalable and practical solution that can be integrated into future self-driving systems to improve passenger safety, vehi- cle durability, and overall traffic efficiency [5]. Autonomous driving continues to be one of the most promising innova- tions in intelligent transportation sys- tems, but real-world challenges such as potholes still pose serious risks to safety and efficiency [2],[5]. This study explores the application of rein- forcement learning for addressing the issue of pothole detection and avoid- ance in self-driving cars [2]. To evalu- ate the framework, a detailed robot simulation was built in the Webots environment, making use of Python programming and OpenCV for vision processing [8]. Within this setup, the robot was designed to complete three key tasks: it first identifies a delivery target symbolized by a gnome placed in the environment, then transitions into lane-following mode to maintain safe navigation, and finally responds appropriately by halting when a pot- hole is detected on its path [8]. Each of these components plays a crucial role in ensuring safe and reliable op- eration. The framework integrates several technologies, including real- time computer vision for object detec- tion, IMU sensor feedback for orien- tation correction, and motor control for smooth navigation [7]. These el- ements work together to enable the robot to perceive its surroundings, adapt to hazards, and make sequen- tial decisions that reduce the risk of accidents [2]. The results of the experiment in- dicate that combining reinforcement learning with vision-based techniques can offer significant improvements in autonomous navigation [2],[5]. Scale- Invariant Feature Transform (SIFT) was particularly effective in recogniz- ing both the delivery target and pot- holes with a high degree of accuracy [7],[10], ensuring reliable performance under varying conditions. Canny edge detection and the Hough Line Trans- form proved to be highly efficient tools for lane identification [4],[6], al- lowing the robot to maintain pre- cise lane alignment during movement. Furthermore, IMU-based orientation correction provided additional robust- ness, preventing errors caused by yaw drift and other orientation issues [7]. Collectively, these methods enabled the robot to adapt dynamically to its environment and demonstrate consis- tent success across repeated trials [2]. These findings suggest that the pro- posed framework not only addresses the immediate problem of pothole de- tection [9],[10] but also enhances the overall safety and reliability of au- tonomous vehicles. Looking ahead, the study shows strong potential for real-world applications, as it provides a scalable and practical solution that can be integrated into future self- driving systems to improve passenger safety, vehicle durability, and overall traffic efficiency [5].

DOI: https://doi.org/10.5281/zenodo.17174389

Comparative Evaluation Of Pre-Trained Models For Brain Tumor Identification Based On MRI And CT Image

Authors: Atharva Daga, Viraj Laddha, Prathmesh Jain, Tanmay Sharma

Abstract: Brain tumor detection is important in neuroimaging, affecting patient outcomes and prognosis. To improve detection capabilities, this study uses MRI & CT Scan Image to classify brain tumor while employing deep learning techniques. We test how well pre-trained models like VGG-19, DenseNet-121, and ResNet-50 perform by using detailed information from MRI and CT scans to improve the accuracy of detecting brain tumors and help identify them more clearly and precisely, facilitating swift diagnosis and informed treatment planning. This research utilizes image fusion and prediction algorithms to address challenges such as limited data diversity and difficulties in differentiating tumor boundaries from surrounding tissues, thereby improving model performance. By evaluating the results, we identified the most accurate model for brain tumor diagnosis and provided insights into its use and impact on diagnosis. This research advances technology and improves patient outcomes through more accurate and timely diagnoses. Analysis shows Resnet-50 achieving the highest accuracy among all other models is effective for tumor detection.

Artificial Intelligence And The Future Of Work: Lessons From The Industrial Revolutions

Authors: Praveen Lokanath

Abstract: The rise of artificial intelligence (AI) is reshaping the nature of work in ways that echo past industrial revolutions, yet with unprecedented speed and complexity. This paper explores how previous waves of technological transformation — from mechanization in the 18th century to the digital revolution of the late 20th century — can inform our understanding of AI’s current and future impact on employment, labor markets, and workforce dynamics. Drawing lessons from history, the study highlights patterns of job displacement, creation, and evolution, emphasizing the critical roles of policy, education, and social adaptation. It also examines the unique characteristics of AI that distinguish it from earlier innovations, particularly its capacity to automate cognitive tasks and decision-making processes. By synthesizing historical insights and contemporary developments, the paper offers a framework for anticipating the challenges and opportunities AI presents, aiming to guide stakeholders in shaping a more equitable and resilient future of work

FROM PIXELS TO SENTENCES: AUTOMATED IMAGE CAPTIONING WITH CNNs RNNs

Authors: Sangani Harshil, Kalariya Meet, Baraiya Ravi, Vasani Bhumil, Dr. Vikram B.Kaushik

Abstract: The ability to automatically describe visual content through natural language represents a compelling frontier in artificial intelligence research. Our work addresses this complex challenge by developing a sophisticated neural architecture that translates visual information into coherent textual descriptions. The methodology we employed centers on a two-stage approach: initially, we leverage the robust feature extraction capabilities of InceptionV3, a well-established convolutional neural network, to visual elements present in uploaded images. The extracted visual representations then feed into our custom language generation pipeline, built around a Gated Recurrent Unit (GRU) architec- ture. What distinguishes our implementation is the incorporation of a spatial attention module that enables selective focus across different image regions during the caption formation process. This attention-driven approach mirrors human visual processing, where we naturally emphasize certain areas while describing a scene. To validate the practical utility of our research, we constructed an intuitive web-based platform using Streamlit framework. This interactive system allows users to seamlessly up- load photographs and receive instantaneous caption generation, enhanced with audio narration capabilities through integrated speech synthesis technology.

Modular Monoliths In Large-Scale IOS Apps: Balancing Reusability And Performance

Authors: Abdullah Tariq

Abstract: The evolution of iOS application development has witnessed a significant shift from traditional monolithic architectures to more sophisticated patterns that balance modularity with performance. This paper examines the concept of modular monoliths in large-scale iOS applications, exploring how this architectural pattern addresses the dual challenges of code reusability and runtime performance. Through analysis of implementation strategies, performance metrics, and real-world case studies, we demonstrate that modular monoliths offer a pragmatic middle ground between rigid monoliths and complex microservices architectures. Our findings suggest that when properly implemented, modular monoliths can achieve up to 40% better build times, 25% improved memory efficiency, and significantly enhanced developer productivity while maintaining the deployment simplicity of monolithic applications.

DOI: https://zenodo.org/records/17183546

The Impact Of Artificial Intelligence On The Job Market

Authors: Bhavesh Vallepu

Abstract: Artificial Intelligence (AI) is transforming the global job market, reshaping industries, and redefining the nature of work. This paper explores the multifaceted impact of AI on employment, highlighting both the opportunities and challenges it presents. While AI drives efficiency and innovation, leading to the creation of new job categories and business models, it also poses a threat to certain traditional roles through automation and displacement. The analysis considers various sectors, skill levels, and geographical regions, emphasizing the need for adaptive education systems, upskilling, and policy intervention to manage the transition. By examining current trends and future projections, this study aims to provide a balanced perspective on how AI will influence employment dynamics in the years to come

Wi-Fi Controlled Personal Assistant Robot For Elderly People _797

Authors: Varshini J S, Praveen, Keshav Acharya. P, Rakesh, Rohit. S

Abstract: This work presents a personal assistant robot designed to minimize human labor in daily activities. Operated by voice command, it features a camera, robotic arm, object detection, and distance measurement, making it suitable for various applications, including chemical industries and healthcare. This scoping review sought to comprehend individuals’ experiences using humanoid robots to perform daily living activities. studies were studied, and nine robots to perform different tasks were identified. The majority of participants found the robots safe and convenient but didn’t like their size and slowness. Others found the robots fascinating but not appropriate for domestic use. The results indicate the necessity of tailored research to enhance the performance of humanoid robots in health care.

IOT Based Industrial Safety Using ESP8266 And Blynk

Authors: Mr Shaikh Aslam Amir, Ms Khalate Vaishanavi Suresh, Shaikh Karishma Mohammad, Malve Somnath Bhanudas

Abstract: This project introduces a smart environmental monitoring and alert system built using IoT technology. The system uses a NodeMCU (ESP8266) microcontroller connected to three important sensors: the DHT11 for measuring temperature and humidity, a Flame Sensor for detecting fire, and an MQ6 Gas Sensor for identifying dangerous gases like LPG and propane.Data from these sensors is collected in real time and sent to the Blynk IoT platform, where it can be viewed on a mobile app. The system also includes a Telegram Bot API that sends immediate alerts to the user’s Telegram account when dangerous situations occur, such as high temperature, gas leaks, or fire detection. This ensures that users are informed quickly, even if they are not physically present near the sensors. The system is affordable, easy to expand, and suitable for home safety, industrial use, and remote monitoring.Using IoT platforms and instant messaging services, this system provides a practical method for smart alerts and real-time detection of environmental dangers

DOI: http://doi.org/10.5281/zenodo.17191043

A Review Of Passive And Semi-Active Controls For Blast Protection In High-Rise Buildings

Authors: Dr. R Sridhar

Abstract: – It is increasingly important for high-rise buildings in urban environments to consider blast threats arising from explosions. When blast loads produce high-amplitude and short-duration pressure pulses, they primarily pose a threat to the facade systems, which are at risk from high-rise blowouts. It could also cause harmful reactions over the world and the gradual breakdown of weak systems. The state-of-the-art in semi-active structural control techniques (particularly magnetorheological (MR) dampers) and passive protective measures (glazing, cladding, sandwich panels, anchorage/retention systems) is summarised in this review. Hybrid design approaches that integrate adaptive damping and material resilience are also examined. In order to identify critical research gaps (scaling of semi-active devices, controller latency for impulsive loads, long-term durability, and standards integration), we survey empirical blast test programs, standards and guidance documents, numerical modelling approaches, controller algorithms, and device technologies. We then suggest a prioritised research agenda that includes system-level testing, predictive control research, and demonstration projects.

Analysis Of Cosmological Constant In Bianchi Type 1 With Cosmological Model

Authors: Dr. R.K. Dubey, Mohd. Wahid Mansury

Abstract: This study analyzes the effects of the cosmological constant  in the context of the Bianchi Type I cosmological model. The Bianchi Type I model represents an anisotropic but spatially that universe, where expansion rates can differ along three spatial directions. The cosmological, plays a significant role in the universe expansion. This work aims to understand how influences the expansion, energy density, and anisotropy of the universe. Einstein’s field equations with variable cosmological constant if considered in the presence of a perfect fluid for a Bianchi type I universe by assuming that the cosmological term is proportional to the square of the Hubble parameter. The variation law for vacum density was recently proposed by many researches on the basis of the quantum field estimation in a curved expanding background. The cosmological term tends asymptotically to a genuine cosmological constant and the model tends to a de- Sitter universe. More obtained some new results by using a slightly different method from that of other researchers obtained the result that the present universe is accelerating with a large fraction of cosmological density in the form of a cosmological term.

Multi-Functional Assistant For Task, Technology And Intelligent Support

Authors: Haritha A, Nithyasri P, Sandhiya M

Abstract: In our fast-moving digital age, the demand for automation and intelligent systems is on the rise. More and more people are turning to digital assistants to help them juggle tasks, enhance workflows, and minimize manual effort. However, many of the voice assistants available today struggle with multitasking, remembering context, and executing tasks effectively. This gap creates a mismatch between user expectations and the capabilities of current virtual assistants. Enter M.A.T.T.I.S (Multi-Functional Assistant for Task, Technology, and Intelligent Support). This innovative solution aims to bridge that gap by integrating cutting-edge automation, multi-language processing, and AI-driven conversational memory. Built on Python frameworks, M.A.T.T.I.S leverages advanced speech recognition and natural language processing to deliver highly accurate responses. Unlike traditional virtual assistants, M.A.T.T.I.S is crafted to manage multiple tasks simultaneously, minimizing delays and enhancing productivity. It also boasts smart automation features, allowing users to control system functions, access real-time data, and streamline their digital activities effortlessly. This paper takes a deep dive into M.A.T.T.I.S ’s system architecture, workflow, and implementation strategies. It also compares M.A.T.T.I.S [Multi-functional Assistant for Task, Technology and Intelligent Support] with leading commercial voice assistants, showcasing its superior efficiency in task execution and user-friendly design. Additionally, the study presents real-life scenarios where M.A.T.T.I.S significantly improves task management in both professional and personal settings. A series of tests were conducted to assess the assistant’s accuracy, efficiency, and performance in real-time situations. The results reveal an impressive 95% accuracy rate in speech recognition, an average response time of under a second, and the capability to seamlessly handle over 50 tasks simultaneously.

DOI: http://doi.org/10.5281/zenodo.17197525

Lab-Grown Meat: The Future Of Poultry On Your Plate

Authors: Mr. G. Vignesh, Mr. G. Karthikeyan, Ms. Kowsalya

Abstract: The increasing global demand for protein, together with environmental degradation, animal welfare concerns, and food safety risks associated with conventional poultry farming, has motivated research into lab‑grown (cultured) poultry. This review (or article) explores the state of the art in cultured‐meat technology applied to poultry: from cell sourcing and culture media, to scaffolding, bioreactor design, tissue vascularization, and challenges in mimicking texture and flavor. It discusses the potential advantages of lab‑grown poultry—reduced greenhouse gas emissions, lower land and water use, elimination of slaughter, and improved safety control—and weighs these against the technical, regulatory, economic, and consumer‑acceptance obstacles. The article concludes by projecting a pathway toward commercialization, outlining necessary innovations and policy frameworks, and evaluating the prospects for lab‑grown poultry becoming a mainstream component of the human diet.

DOI: https://doi.org/10.5281/zenodo.17198116

Growth And Morphological Changes Of Two-dimensional Carbon Nanostructures With Time Of Deposition By Radio Frequency Plasma Enhanced Chemical Vapor Deposition

Authors: V. Durga Prasadu, Dr. B Purna Chandra rao, R. Haribabu, Dr.K. Subbarao

Abstract: In this work, we report the growth of different types of two-dimensional(2D) carbon nanostructures and their change in the morphology, growth and size on Si substrate with deposition time. The 2D carbon nanostructure were grown in the presence of Argon plasma with Methane as a carbon source without any special pre-treatment of the substrate using Radio Frequency Plasma Enhanced Chemical Vapor Deposition (RF-PECVD). Field Emission Scanning Electron Microscopy (FE-SEM) and Atomic Force Microscopy (AFM) were proven the grown carbon nanostructures were hexagonal Islands, rod shaped and white patches like structures. We identified the changes in the diameters of the grown nanostructures from 35-130 nm with deposition times 30 to 120 minutes. The surface roughness of the samples was reported by the 3D analysis of AFM. The presence of D and G peaks was identified by Raman Spectroscopy. Raman spectroscopy showed that the ratio of I(D)/I(G) increases with increasing deposition time.

DOI: https://doi.org/10.5281/zenodo.17198747

Waste Management – An Urgent National Need Of India – A Comprehensive Analysis

Authors: Dr. Shubham Tayal

Abstract: India faces a critical challenge in managing its burgeoning waste generation due to its rapidly growing population and urbanization. This research paper explores the multifaceted dimensions of waste management in India, highlighting its urgent need and the complex interplay of factors contributing to the crisis. The paper analyzes the current state of waste management, including the types of waste generated, collection systems, and disposal methods. It delves into the environmental, health, and socioeconomic impacts of improper waste management. Furthermore, the paper examines various stakeholders involved in waste management, such as government agencies, urban local bodies, private sector, and civil society. It assesses the existing policies, regulations, and initiatives aimed at addressing the waste crisis. Finally, the paper proposes a comprehensive framework for sustainable waste management in India, emphasizing the need for integrated approaches, technological advancements, public awareness, and behavioral change.

DOI: http://doi.org/10.5281/zenodo.17199754

Advancing Compliance Maturity Through The Five Whys Methodology: A Strategic Framework For Root Cause Analysis And Continuous Improvement

Authors: Justin Paul Iacouzzi

Abstract: This article critically examines the Five Whys methodology within the context of regulatory compliance, emphasizing its role as a structured root cause analysis tool that transcends traditional reactive approaches. It situates the method historically and practically, corroborates its efficacy through empirical evidence, explores organizational adoption challenges, and illustrates its application through case studies in cybersecurity and logistics. The article concludes by highlighting the Five Whys’ strategic potential to enhance compliance maturity, operational performance, and ethical governance, particularly in the era of rapid technological advancement enabling more sophisticated risk management capabilities

Review On Case Studies Of Highway Pavement Crack Recognition Under Complex Environment.

Authors: Harsh Tomar, Professor Jitendra Chouhan

Abstract: Pavements are complex structures involving many variables, such as materials, construction methods, loads, environment, maintenance, and economics. Thus, various technical and economic factors must be well understood to design, build pavements, and to maintain better pavements. Moreover, the problems relating to pavement maintenance are still complex due to the dynamic nature of road pavements where elements of the pavement are constantly changing, being added or removed. These elements deteriorate with time and therefore to be maintained in good condition requires substantial expenditure

Improvement The Heat Transfer Rate Of Ac Evaporator By Optimizing Materials- Review

Authors: Ranu Parste, Deepak Solanki

Abstract: The enhancement of heat transfer in air conditioning (AC) evaporators is essential for improving system efficiency, reducing energy consumption, and meeting modern environmental standards. This review focuses on the role of material optimization in improving the heat transfer performance of AC evaporators. Various materials such as copper, aluminum, and composite structures are evaluated based on their thermal conductivity, corrosion resistance, weight, and cost-effectiveness. Advanced approaches, including nano-coatings, porous structures, and hybrid materials, are also discussed for their potential to enhance heat exchange rates. Furthermore, the use of computational techniques like Finite Element Analysis (FEA), Computational Fluid Dynamics (CFD), and optimization algorithms such as Genetic Algorithms (GA) plays a significant role in simulating and selecting optimal materials and designs. This review concludes that material optimization, combined with innovative surface treatments and smart design techniques, holds significant promise for the next generation of high-efficiency AC systems.

Digital Transformation In Tourism: How Technology Shapes Travel Experiences

Authors: Dr.Muhammed Anas .B, Dr. V. Basil Hans, Dr. N Subbukrishna Sastry

Abstract: Tourism has undergone a profound transformation in the digital era, reshaping how travellers plan, experience, and reflect upon their journeys. The integration of advanced technologies such as artificial intelligence, virtual reality, augmented reality, mobile applications, and digital payment systems has redefined the global tourism landscape. In India, iconic destinations like the Taj Mahal, Jaipur’s palaces, Kerala’s backwaters, and Varanasi’s spiritual centres are increasingly leveraging digital platforms for smart navigation, virtual tours, and cultural storytelling. Similarly, global tourist attractions such as the Eiffel Tower in France, the Great Wall of China, and the Grand Canyon in the United States are adopting immersive technologies and digital engagement strategies to enhance visitor experience and accessibility. This digital shift not only personalizes travel but also ensures sustainable tourism management by reducing overcrowding, providing real-time updates, and promoting local businesses. The aim of the researcher is to highlight the role of technology in facilitating awareness, enriching travel experiences, and ensuring inclusivity for both domestic and international tourists. Furthermore, this study envisions the future of tourism where continuous digital innovation—through AI-driven recommendations, virtual heritage preservation, and smart tourism ecosystems—will shape the way travellers connect with destinations. By exploring present practices in India and abroad, and by anticipating future advancements, this research aspires to provide a roadmap for policymakers, tourism boards, and industry stakeholders to maximize technology’s potential in promoting tourism and cultural heritage.

DOI: http://doi.org/10.5281/zenodo.17205715

Review On Novel Approach To Implementation Of Channel Estimation In 6g Spectrum By Using Noma And Artificial Intelligence Hybrid Technique

Authors: Ajay Damor, Dr Nidhi Tiwari, Professor Madhavi S Bhanwar

Abstract: With the surge of data demands, ultra-reliable low-latency communications (URLLC), and massive connectivity envisioned in 6G networks, accurate and efficient channel state information (CSI) acquisition becomes critically important. Traditional channel estimation techniques often struggle under high mobility, wide bandwidths, and dense multi-user environments—especially when Non-Orthogonal Multiple Access (NOMA) is employed to improve spectral efficiency. This review surveys recent advances in hybrid techniques combining NOMA and Artificial Intelligence (AI) for channel estimation in 6G spectrum, and proposes a novel framework that leverages their complementary strengths. First, we examine the challenges in channel estimation under NOMA-based systems in 6G, including pilot contamination, interference due to superposition coding, and dynamic channel variation in mmWave/THz bands. Next, we analyze state-of-the-art AI methods—such as deep neural networks (CNNs, LSTM), graph neural networks, and reinforcement learning—that have been applied either alone or in combination with conventional estimation algorithms. We pay particular attention to hybrid approaches that integrate AI with compressive sensing, sparse recovery, or signal processing‐based beamforming to reduce estimation error and computational overhead. We then propose a hybrid AI-NOMA channel estimation model tailored for 6G, which includes: (i) user clustering and power‐domain assignment to mitigate inter-user interference in NOMA; (ii) an AI estimator (e.g., a CNN or LSTM) that refines a coarse initial estimate; and (iii) dynamic adaptation between AI and conventional methods based on channel conditions. Simulation results (or theoretical analysis) show that this hybrid approach reduces mean squared error (MSE), improves spectral efficiency, and maintains robustness under imperfect CSI and high mobility, exceeding benchmarks set by LS, MMSE, or pure AI‐based estimators. Finally, we discuss implementation considerations: training data requirements, model complexity, latency, and compatibility with existing 6G architectures. Open research directions are identified, including transfer learning across channel environments, online learning to adapt to changing spectrum conditions, and integrating with other 6G technologies such as Reconfigurable Intelligent Surfaces (RIS) and ultra-massive MIMO.

SructaARLearn: An Augmented Reality Platform For Enhancing Structural Engineering Pedagogy.

Authors: Salihu Sarki Ubayi, Mahmud Danladi, Abbas Sani, Habibu Idris, Salisu Mannir Ubayi, Idris Zakariyya Ishaq, Umar Shehu Ibrahim

Abstract: Structural engineering education has traditionally relied on textbooks, classroom lectures, and two-dimensional diagrams. However, students often struggle to translate these abstract resources into an understanding of real-world structural behavior. This limitation hinders their ability to connect theory with practice. To address this challenge, this paper proposes StructARLearn, a novel software platform derived from Structure + AR (Augmented Reality) + Learning. StructARLearn is an Augmented Reality (AR)-based platform designed to provide immersive, interactive, and experiential learning opportunities in structural engineering. It integrates AR visualizations, real-time finite element simulations, and interactive modules that enable students to apply loads, visualize deformations, and observe structural responses in real-world contexts through mobile devices or AR glasses. By bridging theoretical knowledge with practice, the platform improves comprehension, retention, and engagement. This paper presents the conceptualization and development methodology of StructARLearn, reviews related literature on AR in engineering pedagogy, outlines the framework of the platform, and discusses its anticipated benefits, challenges, and implications for large-scale adoption.

DOI: http://doi.org/10.5281/zenodo.17222735

Adaptive Strategic Workforce Planning Through Reinforcement Learning: A Data Driven Approach

Authors: Anushree, Dr. P. Lalitha Associate Professor, Dr. S. Suja Assistant Professor

Abstract: With the rapidly evolving business environment, the traditional forms of workforce planning can no longer be used to manage uncertainty, skill upheaval and rapidly shifting talent requirements. This paper provides a new data-driven framework based on Reinforcement Learning (RL) to reach the objective of Adaptive Strategic Workforce Planning (ASWP). The approach proposed is the RL-based approach, unlike the rest of the statical models because it is not based on the forecast, and even the changing conditions in all the cases of optimizing the talent decisions, instead it constantly uses the data of the organization and the forecast, it makes the adjustment itself. The conceptual model illustrates the incorporation of the key workforce planning intent such as planning talent requirements, bridging skill shortages, succession planning, and workforce cost-efficiency into a Reinforcement Learning (RL) system. The agent is addressing such factors as skill profiles, role transition, and labor market in this stage. The method of reward functions measures the extent to which the actions, such as hiring, upskilling, redeployment, and promotion are aligned with the objective of cost-efficiency, business alignment, and workforce agility. The flexibility of the model is fundamentally by the one of the complex situations and ongoing feedbacks assist in learning the ideal policies. The reward maximization, speed of convergence, ability to generalize to workforce situations and ability to scale to various organizational situations are among the most important measurement factors. Strategic results are quantified with the help of better accuracy of forecasts, reduction of talent gaps, better use of resources and better alignment to long-term business objectives. By contributing to the dynamic, robust, and interpretable planning tool used in organizations that operate in volatile labor markets, this research paper improves the use of artificial intelligence in human resource management and the workforce analytics field. The proposed method helps HR leaders to make decisions based on data about the talent path of the future. This causes the workforce planning to be a proactive strategic asset instead of a responsive program.

StructaARLearn: An Augmented Reality Platform For Enhancing Structural Engineering Pedagogy.

Authors: Salihu Sarki Ubayi, Mahmud Danladi, Abbas Sani, Habibu Idris, Salisu Mannir Ubayi, Idris Zakariyya Ishaq, Umar Shehu Ibrahim

Abstract: Structural engineering education has traditionally relied on textbooks, classroom lectures, and two-dimensional diagrams. However, students often struggle to translate these abstract resources into an understanding of real-world structural behavior. This limitation hinders their ability to connect theory with practice. To address this challenge, this paper proposes StructARLearn, a novel software platform derived from Structure + AR (Augmented Reality) + Learning. StructARLearn is an Augmented Reality (AR)-based platform designed to provide immersive, interactive, and experiential learning opportunities in structural engineering. It integrates AR visualizations, real-time finite element simulations, and interactive modules that enable students to apply loads, visualize deformations, and observe structural responses in real-world contexts through mobile devices or AR glasses. By bridging theoretical knowledge with practice, the platform improves comprehension, retention, and engagement. This paper presents the conceptualization and development methodology of StructARLearn, reviews related literature on AR in engineering pedagogy, outlines the framework of the platform, and discusses its anticipated benefits, challenges, and implications for large-scale adoption.

DOI: http://doi.org/10.5281/zenodo.17222735

Role Of Startup Mentorship In Achieving SDG 4: Enhancing Quality Education Through Entrepreneurial Skill Development In India

Authors: CEng. Shreekant Patil

Abstract: Providing inclusive and equitable quality education and fostering lifelong learning opportunities for everyone is the essence of Sustainable Development Goal 4 (SDG 4). In India, where a large youth population is both challenge and opportunity, developing entrepreneurial skills through quality education is central to economic prosperity and social integration. This research analyzes the crucial function of startup mentorship in promoting SDG 4 through skill building among Indian youth and adults. Capitalizing on the deep mentorship pool within India’s dynamic startup ecosystem, this research investigates how mentorship initiatives enhance the quality of education by combining academic theory with experiential entrepreneurial skills, empowering excluded groups, and promoting innovation-based livelihoods. The paper also highlights the need for policy environment, ecosystem assistance, and inclusive mentorship to build scalable impact consistent with India’s economic and social goals.

DOI: http://doi.org/10.5281/zenodo.17225033

Synthesis And Characterization Of Zinc Oxide Nanowire: Applying Findings To Predict Its Uses

Authors: Umudi E.Q, Ekpenyong I.O, Sani M.I, Onwugbuta G C, Ikechukwu S.C, Uzoh R.D, Obruche E.K

Abstract: Zinc Oxide (ZnO) nanowires featuring a hexagonal configuration were successfully synthesized through the chemical bath deposition technique. The characterization of the nanowires was conducted using scanning electron microscopy (SEM), X-ray diffraction (XRD), energy dispersive X-ray analysis (EDX), and a spectrophotometer. The SEM images revealed that the diameters of the ZnO nanowires varied from 170.3 to 481 nm, indicating that a bath solution pH of 8.1 is optimal for the formation of hexagonal ZnO nanowires. The XRD patterns validated that the ZnO nanowires exhibit a hexagonal crystallite structure, with the crystallite size, determined via Scherrer’s equation, increasing with elevated annealing temperatures (0.536 nm, 0.541 nm, and 0.557 nm at 100°C, 150°C, and 200°C, respectively). EDX analysis yielded insights into the elemental composition of the samples, confirming the presence of Zn and O. Results from optical analysis demonstrated that ZnO nanowires possess high absorbance in the ultraviolet and infrared spectra while exhibiting significant transmittance in the visible spectrum. Furthermore, the absorbance of the nanowires was found to increase with higher annealing temperatures. Their notable absorbance in the ultraviolet range indicates potential applications as solar harvesters for capturing solar energy for photovoltaic panels, which can convert sunlight directly into electricity for commercial or industrial use.

DOI: http://doi.org/10.5281/zenodo.17225255

Web-Based Student Grievance And Conduct Management System

Authors: Dr. P. Preethi, M. Yuthika, Sadhana Kamaraj, S. Kaviya

Abstract: This web-based system offers five separate login portals designed for students, counsellors, Heads of Departments, Class Advisor and the Principal, ensuring role-specific access and responsibilities. Students can easily submit personal or academic grievances online and monitor their progress. Counsellors manage these grievances by updating their status, adding remarks, and sending email notifications to keep students informed. They also maintain academic records like attendance shortages and disciplinary actions. Unresolved issues are escalated to the HOD for further review and resolution. The Principal oversees the entire system, viewing reports across all departments to ensure transparency and compliance with accreditation standards. The platform is built using Next.js for frontend and backend development, MongoDB for database management, and Tailwind CSS for responsive and modern styling. Email notifications are handled via integration with email services such as EmailJS. This technology stack enables a secure, scalable, and user-friendly system that simplifies communication, enhances accountability, and supports institutional accreditation processes.

DOI: https://doi.org/10.5281/zenodo.17278602

Regenerative Design In Healthcare: Case Study Approach

Authors: Lalitha Bhai Jagadeesan

Abstract: – The term Regenerative encompasses a broad and profound area of study, especially when applied within the built environment. Building upon my prior understanding of regenerative design, this research explores the concepts for the healthcare sector—specifically focusing on their potential to reduce energy consumption and enhance the mental well-being of patients, staff, and medical professionals. The study will examine the conceptual design of a hypothetical 100 -bedded hospital and identify regenerative strategies that can be implemented during both the design and execution phases to minimize carbon footprint and operational energy demands. Key areas of investigation will include site selection and planning, energy efficiency strategies, water conservation techniques, indoor air quality improvement, and occupant wellness and comfort. The research will also address biophilic design approaches, smart building technologies, and sustainable healthcare waste management practices. Additionally, the study will incorporate insights from existing green-certified healthcare facilities, evaluating metrics such as staff burnout levels and patient outcomes within regenerative versus conventional hospital environments. A brief comparative analysis of regulatory frameworks and certification systems—such as LEED for Healthcare, WELL Building Standard, the Green Guide for Healthcare (GGHC), and relevant ASHRAE standards—will be used to contextualize and support the proposed strategies. Through this integrated approach, the paper aims to highlight the practical applicability of regenerative design in creating high-performance, healing-centred healthcare spaces.

Blockchain-based University Election System With Biometric Authentication And AI-driven Anomaly Detection

Authors: Mr. M. Santhanaraj, S.Dharshini, R.Pooja, B.Devaki

Abstract: This project presents a Blockchain-based University Election System enhanced with biometric authentication and AI-driven anomaly detection to address the challenges of security, transparency, and reliability in student elections. The proposed system verifies voter identity through fingerprint and facial recognition, ensuring that only eligible students participate and eliminating risks of impersonation, duplicate, or proxy voting. Each vote is encrypted and immutably recorded on the blockchain ledger, preventing tampering, deletion, or manipulation while creating a transparent and verifiable audit trail. Smart contracts govern the election process by automating voter eligibility checks, enforcing the one-student-one-vote policy, scheduling the election, and instantly counting and publishing results without human intervention. The integration of AI adds another layer of protection by continuously analyzing voting behaviors, biometric data, and transaction patterns to detect anomalies, suspicious trends, or fraudulent activities in real time. This holistic approach reduces manual errors, enhances accountability, and builds student confidence in the election process through verifiable and tamper-proof outcomes. Additionally, the system is designed to be user-friendly, scalable, and cost-effective, making it adaptable not only for universities but also for larger institutions and government-level elections in the future. By combining blockchain, biometrics, and AI, this project demonstrates a secure, intelligent, and modern framework for conducting elections with integrity and efficiency

DOI: http://doi.org/10.5281/zenodo.17283666

Movies For U

Authors: Emmadi Uday, Anand Jawdekar, Komati Hema, Lakshmikanth Vuyyuru, Tadikamalla Vinod kumar

Abstract: This paper presents MoviesForYou, a web-based movie booking platform that integrates an AI-powered con- versational chatbot for mood-based movie recommendations, automated seat suggestions, and a data analytics module for theater and revenue insights. The system allows users to describe their mood in natural language; the chatbot leverages language understanding and movie metadata to recommend titles that match the user’s affective state and viewing prefer- ences. The analytics module computes weekly booking trends, genre popularity, theater performance, rating-based summaries, and revenue trend analysis. The platform intentionally supports offline payment workflows (seat-on-hold with time-limited reser- vation) rather than online payment. We describe the system architecture, implementation details (based on the provided project codebase), evaluation methodology, key results, and future directions. The paper includes flowchart and system- architecture placeholders, experimental and analytic outputs, and an APA-style reference list.

DOI: https://doi.org/10.5281/zenodo.17234742

Intelligent Visitor Tracking System Based On Vehicle Plate Recognition

Authors: Rutuja Gavai, muniba Ali, zarah Ali, Astha Gulhane, Prof. Sanju D. Garle

Abstract: The effective management of visitors has become a critical aspect of institutional security, smart campus initiatives, and organizational operations. Traditional visitor tracking methods, which rely on manual record-keeping or identity cards, are often prone to errors, delays, and inefficiencies. To address these shortcomings, vehicle plate recognition has emerged as a promising technology for developing intelligent visitor tracking systems. By leveraging the uniqueness of license plates as identifiers, organizations can implement automated, contactless, and reliable mechanisms to verify and monitor visitor entries and exits. This review paper presents a comprehensive survey of existing research on visitor tracking systems that integrate vehicle plate recognition. Key enabling technologies such as image preprocessing, Optical Character Recognition (OCR), fuzzy string matching, and cloud-based services (e.g., Microsoft Azure Cognitive Services) are analyzed for their role in improving accuracy and scalability. The study also discusses the integration of data analytics and reporting frameworks, which transform raw recognition results into actionable insights, such as visitor frequency patterns, identification of unknown vehicles, and predictive analytics for enhanced security planning. In synthesizing current literature, this review identifies major challenges, including image quality variations, diverse license plate formats, and real-time adaptability under unconstrained conditions. It also outlines research gaps in the application of deep learning, edge-based processing, and multimodal verification techniques for intelligent visitor management. The findings highlight that the combination of vehicle plate recognition with intelligent data-driven analysis offers a scalable and efficient pathway toward next-generation visitor tracking systems, particularly in academic institutions, corporate environments, and smart city infrastructures.

Predicting Stock Market Trends With ARIMA: A Data-Centric Approach To The BSE Index

Authors: Dr.M.Sravani, Kalyan Kumar Bethu

Abstract: Stock market volatility makes accurate forecasting vital for informed trading decisions and profit maximization. Over the years, various models have been introduced to enhance the reliability of time series predictions. This study applies the ARIMA model to evaluate data stability and forecast movements in the BSE Index. Model selection was guided by statistical measures including SIGMASQ, Adjusted R², AIC, and BIC, with ARIMA (2,1,2) emerging as the most suitable specification. Using monthly data from January 2021 to January 2025 (49 observations), the model generated forecasts for February 2025 through December 2025, yielding 11 projected values. The results highlight ARIMA’s effectiveness as a short-term forecasting tool, offering actionable insights for informed investment decisions.

DOI: https://doi.org/10.5281/zenodo.17240410

Sustainable Warfare: A Research Framework Analyzing Environmental Initiatives And Critical Perspectives

Authors: Piyush Kumar

Abstract: This research paper examines the emerging paradigm of sustainable warfare, investigating the technological innovations and strategic approaches being developed to reduce the environmental footprint of military operations. Through a systematic literature review and critical policy analysis, this study explores the inherent tensions between military objectives and environmental sustainability. The research analyzes current initiatives by major military powers, including NATO’s Climate Change and Security Action Plan and various “green military” technologies, while also addressing the critical perspectives that challenge the very concept of environmentally sustainable warfare. Findings indicate that while technological advancements in renewable energy integration, biodegradable munitions, and resource efficiency can marginally reduce military environmental impacts, the concept of truly sustainable warfare faces substantive limitations. The study reveals that current sustainability initiatives primarily serve operational effectiveness and strategic advantage rather than representing genuine ecological commitment. Furthermore, the discourse of sustainable warfare risks legitimizing continued militarization through what critics term “green militarism”—the co-option of environmental concerns to justify military expansion. The paper concludes that approaches focusing on conflict prevention and peaceful resolution may offer greater environmental benefits than attempts to green military operations. This research contributes to understanding the complex relationships between security, sustainability, and justice in an era of ecological crisis, suggesting that genuine ecological sustainability requires a fundamental rethinking of security paradigms rather than technological fixes within existing military frameworks.

Ride Safe Intelligent Helmet For Emergency Detection And Response

Authors: Mr.R. Palanikumar, Ap / It, R.Nithya, M. Dharshika, N. Kanishka

Abstract: Ride Safe is an IoT-based smart helmet designed to improve two-wheeler safety by detecting accidents and sending real-time emergency alerts. It uses an ESP32 microcontroller along with an accelerometer and impact sensor to identify sudden falls or collisions. A GPS module (NEO-6M) captures the rider’s live location, while a GSM module (SIM800L) transmits SMS alerts to family members or emergency services. To ensure reliability, the system activates only when the helmet is worn using a helmet-wear detection switch, and a cancel switch allows the rider to stop alerts in case of false triggers. This low-cost, reliable, and portable solution not only reduces emergency response time but also increases survival chances, enforces helmet usage, and offers applications in personal safety, fleet monitoring, and smart city systems.

DOI: https://doi.org/10.5281/zenodo.17374588

A New Strategy For Power Management In Multisource Microgrid System With MPPT Algorithm

Authors: Manish Kumar, Ishan Sethi

Abstract: This work proposes a novel approach to enhance Grid- connected wind-solar PV charging stations face with challenges like fluctuating energy supply, inefficient resource usage, and the necessity for adaptive real-time control. Traditional control methods, like PI controllers, often fall short in optimizing system performance under these dynamic conditions, resulting in inadequate power supply for EV charging. To tackle these hurdles, this study proposes a pioneering approach employing neural network (NN) controllers to enhance grid-connected wind- solar PV charging stations’ operation. NN controllers dynamically adjust charging station operations based on real- time data inputs, offering superior adaptability and efficiency. By integrating wind and solar power generation with intelligent NN control mechanisms, the system adeptly responds to varying environmental conditions and grid demands, ensuring more effective utilization of renewable energy sources. The proposed NN controller-based system targets enhancing the reliability, sustainability, and economic feasibility of grid-connected charging stations. Simulations showcase the effectiveness and stability of this approach in integrating renewable energy into transportation infrastructure. Performance evaluation can be conducted using Matlab/Simulink Software

DOI: http://doi.org/10.5281/zenodo.17248231

Implementation Of Neural Network Control Mechanism For Grid Connected Wind-Solar PV Charging Station

Authors: Manish Kumar, Ishan Sethi2

Abstract: Distributed Generators (DG) embody a multi-source microgrid amalgamated within a unified framework. These DGs are meticulously designed to calibrate voltage, current, and frequency in accordance with the load terminal’s observed power demand. Constructing an optimal control paradigm for these systems amplifies their functional efficacy. This study simulates a DG control architecture within MATLAB/Simulink, integrating photovoltaic (PV) arrays, a proton-exchange membrane fuel cell (PEMFC), and an ultra-capacitor to ensure a steady and dependable output for the grid. The PV component within this configuration utilizes a Maximum Power Point Tracking (MPPT) mechanism, which optimizes power transmission to the grid. To address PV’s inherent variability, an ultra-capacitor and PEMFC are employed, ensuring stable output. Here, the ultra-capacitor counterbalances the PEMFC’s thermodynamic fluctuations, enhancing reliability. A power-electronics-based interfacing circuit, paired with advanced control configurations, upholds power quality by regulating the grid’s voltage and frequency within permissible thresholds.

DOI: http://doi.org/10.5281/zenodo.17248263

Artificial Intelligence In Healthcare: Transforming Medical Practice Through Technology Integration

Authors: Tarun Krishna Mahajan

Abstract: This comprehensive review examines the current state and future prospects of artificial intelligence AI) in healthcare, with particular emphasis on implementation strategies, challenges, and outcomes. The global AI healthcare market, valued at $26.57 billion in 2024, is projected to reach $187.69 billion by 2030, growing at a CAGR of 38.62% [^62]. This study analyzes AI applications across clinical decision support systems, predictive analytics, telemedicine, and population health management. Key findings indicate that 94% of healthcare providers currently use AI in some capacity, with clinical decision support systems demonstrating significant improvements in diagnostic accuracy and patient outcomes [^65]. Machine learning approaches, particularly random forest algorithms 42% of studies) and logistic regression 37% of studies), show greatest effectiveness in disease prediction and management 1 . However, implementation faces substantial barriers including data quality issues 47% of leaders cite this concern), regulatory compliance challenges 39% , and workflow integration difficulties [^73]. The review presents the HealthWise ecosystem as a case study of comprehensive AI integration, demonstrating potential for government-scale deployment across 130 crore Aadhaar cardholders in India. Privacy and security considerations under HIPAA and GDPR regulations require careful attention, with end-to-end encryption and privacy-by-design approaches being essential for compliance [^82]. This analysis concludes that successful AI implementation requires integrated approaches combining technological innovation, regulatory compliance, stakeholder engagement, and sustainable business models to realize the transformative potential of AI in healthcare delivery.

Security Issues in Platform as a Service (PaaS) Cloud Computing

Authors: Shikha Goel

Abstract: Cloud computing has transformed IT service delivery by offering scalable, on-demand resources over the internet. Among its service models—Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS)—PaaS provides a robust platform for developing, running, and managing applications without the complexity of maintaining the infrastructure. However, PaaS introduces a unique set of security concerns due to its multi-tenancy, abstraction layers, and reliance on third-party services. This paper explores the key security issues in PaaS environments, including data isolation, insecure APIs, platform vulnerabilities, insider threats, and compliance challenges. We also discuss mitigation strategies and emerging trends to enhance PaaS security.

DOI: https://doi.org/10.5281/zenodo.17249277

YOLO Based Real-Time Object Detection And Distance Prediction In Autonomous Ground Vehicle

Authors: Chung Hyok Pak, Un Sim Ri, Se Hyon Kim

Abstract: It is the important global trend to use the unmanned production lines in order to meet the demand of customers and improve the efficiency for industrial processes. The automated storage and delivery system (ASDS) is one of the main components of the unmanned production line. It consists of many shields and several automata and complex control systems for loading and unloading, so its cost is so high. For the tradeoff of the cost and performance of cargo handling, forklift is a best alternative to the lack of financial ability enterprises/factories. In this paper, we propose a pallet detection method to allow forklifts to engage the pallet autonomously using only a monocular vision on the forklift in the harsh industrial environment. To reduce the number of features and increases the detection efficiency, we describe the pallet features by combining the Haar-like features and multi-block local binary pattern (MBLBP). 8 sets of Haar-type encoding models make the LBP feature better to encode the local structure. Adaboost classifier that use distribution information of features in training set, allows to detect pallet candidates with high accuracy and efficiency in harsh industrial environments. In particular, improved feature to maximize the margin when pattern classes are projected onto the classification hyperplane is used to enhance the discriminate ability of classifier and reduce the computational cost. The analysis of the geometric features of the pallets using integral-sum-difference (ISD) excludes the wrong candidates with high efficiency. The experimental results demonstrate that our proposed algorithm could detect the pallet with average rate of more than 98% and is robust to environmental changes.

DOI: http://doi.org/10.5281/zenodo.17255035

AI-Supported Decision-Making In Educational Policy And Scientific Administration

Authors: Nahid Almasov

Abstract: This paper outlines how mathematical modeling and artificial intelligence can be used to support scientific administration and educational policy-making. By supplementing the algorithmic capabilities of AI and analytical capabilities of quantitative models, the research facilitates better strategic planning, performance evaluation, and resource management. The proposed approach promotes data-driven, open, and responsive management practices. The article also touches on urgent issues such as model interpretability, ethics, and human-AI collaboration. It underscores the need for responsible innovation to bring about good governance and sustainable development of science and education institutions

DOI: http://doi.org/10.5281/zenodo.17256654

Review on – E Gram Panchayat

Authors: Ms. Chanchal Sachin Bedse, Ms. Vaishnavi Santosh Thorat, Mr. Niranjan Yogesh Borse, Ms. Bhagyashri Vishnu Gosavi, Mr. A. P. Patil

Abstract: E-Gram Panchayat is a digital governance platform designed to modernize rural administration by providing villagers with seamless access to essential services, including property tax payment, certificate issuance, census management, emergency assistance, and government schemes. By digitizing records and workflows, the platform reduces paperwork, minimizes dependency on intermediaries, and enhances transparency, efficiency, and accountability. This initiative contributes toward building self-reliant, digitally empowered villages, aligning with the objectives of Digital India.

DOI: https://doi.org/10.5281/zenodo.17256909

Decentralized Solutions For Healthcare Using Blockchain

Authors: Samiksha R Hajare, Prof. S. V. Raut

Abstract: Integration of blockchain technology into healthcare systems, particularly within telehealth and telemedicine frameworks, represents a paradigmatic shift aimed at resolving persistent challenges in digital health infrastructures. These challenges primarily encompass the secure exchange of medical data, achieving interoperability between disparate health information systems, and empowering patients with greater control over their personal health information. Blockchain enabled paradigms in healthcare are presented as transformative frameworks designed to address persistent issues such as secure medical data exchange, interoperability, and patient-centric control. The theoretical discussions around blockchain in healthcare highlight its potential complexities, with influence its research and practical use. The growth of telehealth and telemedicine has changed how healthcare is delivered, allowing for remote consultations and better resource management. However, current telemedicine systems often use centralized architectures, making them vulnerable to security threats like data breaches and fraud. This paper suggests incorporating blockchain technology into telemedicine platforms to improve security, transparency, and data integrity. By using a decentralized and tamper-resistant ledger, the proposed system aims to protect patient records and increase trust among healthcare providers and patients. Key features include secure appointment scheduling and reliable management of electronic health records within a user-friendly interface. This research helps advance telemedicine by addressing key security challenges and proposing a scalable, secure platform, especially useful in areas with limited access to traditional healthcare.

DOI: http://doi.org/10.5281/zenodo.17304779

A Hybrid Bee Ant Colony Algorithm For Load Balancing In Cloud Computing

Authors: I.C Emeto, B.P Gbaranwi, A.A. Galadima, A.C Okoloegbo, S. Kwaghbee, E.C Ochuba

Abstract: Cloud computing has emerged as a dominant paradigm for delivering scalable, on-demand computing resources, yet efficient load balancing remains a critical challenge in modern data centers. This paper presents a novel Hybrid Bee Ant Colony (HBAC) Algorithm that synergistically combines Ant Colony Optimization (ACO) and Artificial Bee Colony (ABC) metaheuristics to address the inherent limitations of existing load-balancing approaches. The proposed HBAC algorithm leverages ABC’s robust exploration capabilities to identify underutilized virtual machines (VMs) and ACO’s pheromone-driven exploitation mechanism to optimize task allocation, thereby achieving superior performance in dynamic cloud environments. Through extensive simulations using CloudSim with Google Cluster Data traces, we demonstrate that HBAC significantly outperforms standalone ACO and ABC algorithms across key performance metrics. Experimental results show 15.7% reduction in makespan, 22.3% improvement in response time, and 18.9% better resource utilization compared to conventional approaches. The hybrid model particularly excels in maintaining balanced VM workloads (degree of imbalance reduced by 27.4%) while demonstrating exceptional scalability under varying workload conditions (from 1,000 to 10,000 tasks). The algorithm’s innovative two-phase architecture – where ABC scouts first identify high-potential VMs and ACO ants then optimize task placement – effectively overcomes the slow convergence of pure ACO and the excessive exploration of pure ABC. Energy efficiency analysis reveals 13.2% reduction in power consumption, making HBAC particularly suitable for sustainable cloud operations.

DOI: http://doi.org/10.5281/zenodo.17264412

Optimal Integration Of Renewable Energy Sources Into Smart Grids Using Ai-Based Forecasting And Optimization Techniques

Authors: Prof. Ankita Fouzdar, Prof. Mayanka Roy Mandal, Prof. Shraddha Tiwari

Abstract: The rapid growth of renewable energy sources (RES) such as solar and wind has created new opportunities for sustainable power generation, while also posing significant challenges due to their intermittent and unpredictable nature. Smart grids, equipped with advanced communication and control technologies, offer a promising platform for efficiently integrating these variable energy resources. This study explores the optimal integration of renewable energy into smart grids using artificial intelligence (AI)-based forecasting and optimization techniques. Machine learning and deep learning models are employed to accurately predict renewable generation and demand patterns, reducing uncertainty and enabling proactive grid management. Furthermore, advanced optimization algorithms such as genetic algorithms, particle swarm optimization, and reinforcement learning are applied to achieve optimal scheduling, load balancing, and energy storage utilization. The proposed framework enhances grid stability, minimizes energy losses, reduces reliance on fossil fuels, and ensures cost-effective and reliable power delivery. Simulation results validate the effectiveness of the AI-driven approach in improving renewable energy penetration and overall smart grid performance. This work highlights the potential of AI-enabled forecasting and optimization as key enablers for achieving sustainable, resilient, and intelligent energy systems

Power Electronic Interface For Grid-Connected Solar PV Systems With Maximum Power Point Tracking

Authors: Prof. Shraddha Tiwari, Prof. Mayanka Roy Mandal, Prof. Ankita Fouzdar

Abstract: The integration of solar photovoltaic (PV) systems into the electrical grid requires efficient power electronic interfaces to ensure reliable operation and maximum energy extraction. This study focuses on the design and performance analysis of a power electronic interface for grid-connected solar PV systems incorporating Maximum Power Point Tracking (MPPT) techniques. A DC–DC converter controlled by MPPT algorithms such as Perturb and Observe (P&O) and Incremental Conductance (INC) is employed to optimize the PV output under varying irradiance and temperature conditions. The conditioned DC power is subsequently converted into synchronized AC power through a voltage source inverter (VSI) with appropriate grid synchronization and control strategies. The proposed system enhances the efficiency, stability, and power quality of PV-grid integration while minimizing harmonic distortion and ensuring compliance with grid codes. Simulation and experimental results validate that the implementation of an optimized MPPT-based power electronic interface significantly improves energy harvesting capability and supports sustainable and reliable integration of renewable energy into the power grid.

Development Of High-Efficiency DC–DC Converters For Electric Vehicle Applications

Authors: Prof. Mayanka Roy Mandal, Prof. Shraddha Tiwari, Prof. Ankita Fouzdar

Abstract: The rapid growth of electric vehicles (EVs) has created a strong demand for compact, reliable, and high-efficiency DC–DC converters to ensure effective power management and extended driving range. This study focuses on the development of high-efficiency DC–DC converters specifically designed for EV applications, addressing challenges such as wide input voltage variations, high power density, and stringent thermal constraints. Advanced topologies including interleaved, resonant, and soft-switching techniques are explored to minimize switching losses and improve overall efficiency. Furthermore, integration of digital control strategies and advanced semiconductor devices such as SiC and GaN MOSFETs enhances performance while reducing converter size and weight. Simulation and experimental results demonstrate improved efficiency, voltage regulation, and transient response under dynamic load conditions. The proposed converters are shown to meet the critical requirements of modern EV powertrains, offering a sustainable solution for future electric mobility.

API Based Social Media Analytics: Bridging Platforms, People, Patterns With Python

Authors: Ayush Pravin Kudale

Abstract: This study offers a repeatable, Python-based framework for unified social media analytics that uses open APIs to connect disparate platforms like YouTube, Reddit, and Twitter. The strategy promotes transparency, explainability, and real-time engagement by emphasizing cross-platform integration, user-centric sentiment analysis, and graph-based pattern recognition for actionable insights. The framework’s adaptability solves the research problems of data heterogeneity, scalability, and ethical stewardship while opening up new possibilities in marketing, crisis management, public opinion tracking, and policy-making. The massive, dynamic, and diverse statistics generated by social media platforms offer enormous possibilities for examining sentiment, public opinion, trending patterns, and the spread of information

DOI: http://doi.org/10.5281/zenodo.17275671

Real Time Oil Leakage Detection And Localisation Based On Flow Rates And Echo Principle.

Authors: Nwonye Charles A, Ezema. E. E, Abba. M.O

Abstract: This project is primarily designed to ensure real time oil leakage detection and localization using flow rates and echo principles. Flow station A has a microcontroller as the central control unit with start button and flow meter as inputs while time domain reflectometer (TDR), Sound alarm and Pump as outputs. It also has a modem with which it communicates to other flow stations. Flow station B has the same arrangement as flow station A except that it has only sound alarm as its output. Also, the pipeline that links the two flow stations has copper cables aligned along its length so that any attempt to break the pipeline must first cut any of the copper cables which are the basis to detect exact point of leakage using the TDR. Here, fluid flow between two flow stations is considered and the mass flow rates at both ends are measured and compared to check for possible leakage. For a given length of pipeline (L), Cross sectional area (A) and fluid density (ρ) over a given time (t), there is a threshold maximum for mass flow rate difference between flow station A and flow station B. Once, the difference between the two mass flow rates at the two flow stations A and B becomes higher than the given threshold, leakage has occurred, alarm will be activated at both flow stations to alert the workers and the time domain reflectometer (TDR) will be activated at the flow station A. The TDR will send a pulse over the copper cables aligned along the length of the pipeline since for leakage to occur one of the copper cables aligned along the length of the pipeline must have been tampered with. When a pulse is sent across the copper cables, the pulse will be reflected back at a point where the cable is cut. Hence, the TDR measures the time interval between when the pulse is sent to when it was reflected back from the point of leakage as time (t). Then using Echo Equation V=2X/t, where V= Velocity of the sent pulse (3 x 108m/s), hence X=Vt/2 where X is the exact point of leakage as measured from flow station A. The developed system recorded a very low error rate of 0.22% with very high precision.

DOI: http://doi.org/10.5281/zenodo.17276591

Techno-Economic Framework For Extraterrestrial Architecture: An Integrated Approach To Viable Space Habitat Development

Authors: Azar Djamali

Abstract: Advances in AI, robotics and construction technology are making in‑situ extraterrestrial building practicable. Demonstrations such as Mars Dune Alpha (NASA/ICON), Lunar Habitat via Contour Crafting (NASA/ICON), and the Autonomous Self‑Growing Structures research (Jin et al. 2023) show technical feasibility, but economic barriers remain the principal threat to long‑term habitation. This paper proposes a concise techno‑economic framework that treats economic sustainability as a primary design parameter, integrating financial intelligence—data‑driven financial planning and decision support, typically aided by AI—with architectural and robotic development. The framework shows how coordinated design, automated fabrication and targeted financial analysis can reduce lifecycle costs and improve value creation. Drawing on a broad literature synthesis and analysis of over 20 government, commercial and student projects (estimates compiled from public sources and open repositories and used here as indicative rather than fully verified), the study offers practical guidance for architects, students and industry stakeholders to mitigate financial risk, structure resilient financing and move space architecture toward commercial viability.

DOI: http://doi.org/10.5281/zenodo.17277057

Eco Friendly Route Finder

Authors: S.Jagadesh, M.Ganesh Reddy, A.Durga Prasad, B.Prema Sai, Rahul Kumar(Assistant professor)

Abstract: The project entitled Eco Friendly Route Finder (Transit Buddy) focuses on developing a simple, interactive, and efficient navigation application that helps users find routes between two different locations with ease. The system is designed to allow users to register and log in with their credentials, manage their profile, and then enter the source and destination addresses. Once the inputs are provided, the application generates a polyline route that visually indicates the path to be followed and finally redirects the user to Google Maps for actual navigation support. The main objective of this project is to minimize the confusion faced by users in switching between input interfaces and map services by providing a clean, direct, and user-friendly flow. In the current digital environment, many applications are overloaded with features that often complicate the basic task of route finding. This project, therefore, attempts to simplify the process by focusing only on essential requirements such as location entry, route visualization, and map redirection. It ensures that even casual users, such as students or city travelers, can quickly access the information they need without unnecessary complexity. The system has been developed with the aim of enhancing usability, accessibility, and interactivity. Another important aspect of the project is its potential to expand in the future. Though the present version does not claim to replace advanced navigation systems, it creates a strong foundation for enhancements such as voice-based commands, route saving, integration with calendar events, and traffic-aware suggestions. It also provides opportunities for incorporating eco-friendly routing features by integrating real-time traffic and environmental data.

DOI: https://doi.org/10.5281/zenodo.17278537

The Similarity-Attraction Paradigm In Leadership: A Qualitative Exploration Of Leader-Member Relationships

Authors: John Mathew Iacouzzi, Justin Paul Iacouzzi

Abstract: This study examines the similarity-attraction paradigm and its vital role in enhancing leader-member relationships and organizational outcomes. It investigates how perceived similarity between leaders and employees—encompassing shared attitudes, values, and cognitive styles—increases interpersonal attraction, psychological safety, and trust, thereby improving Leader-Member Exchange (LMX) quality. Meta-analytic and experimental evidence show similarity-based matches significantly increase employee engagement, reduce turnover, and improve performance (e.g., γ = .41, p < .001; r = 0.45, p < .001). Importantly, these dynamics extend beyond leader-employee dyads to relationships where leaders mentor and train other leaders as well as employees, supporting leadership development pipelines and continuity in organizational culture. The study addresses ethical concerns related to algorithmic matching, including bias and privacy, and underscores the need for organizations to incorporate similarity awareness into diversity and inclusion training to mitigate affinity bias. Qualitative data were collected through semi-structured interviews and thematic analysis to uncover nuanced relational mechanisms. Limitations include reliance on self-reports and a focus on perceived rather than objective similarity. Practical implications recommend comprehensive similarity assessments and continuous feedback loops in leadership programs to foster trust, empathy, and open communication. Future research should further investigate moderating cultural, structural, and neuroscientific factors impacting similarity in hybrid and global workforces. Null hypotheses tested posit no significant relationships between similarity and LMX or organizational citizenship behaviors (OCBs), which were rejected. This research validates similarity’s foundational role in building sustainable, inclusive, and effective leadership relationships.

DOI: https://doi.org/10.5281/zenodo.17278833

Empowering Democracy: AI and ML Based Online Voting System

Authors: Amit Kumar, Ritesh Chaudhary, Mohammad Razique, Nabin Prasad Chaudhary, Prasant Sah

Abstract: This paper presents the design, architecture, and implementation of an AI- and ML-based Online Voting Sys- tem (OVS) that integrates face recognition, anomaly detection, and chatbot support to enhance authentication, security, and accessibility. We describe system requirements, architecture, and workflow; provide improved UML and flowcharts; and include screenshots of an implemented prototype. Security analysis in- dicates reductions in impersonation and fraud risk compared to traditional methods.

Trends and Challenges in Scalable Storage Architercture for Big Data Processing: A Review

Authors: Pardeep Mehta, Sudhakar Ranjan

Abstract: In today’s digital age, the amount of data being created is growing at an extraordinary pace. Sources like social media, online shopping platforms, IoT devices, mobile apps, and business systems all contribute to this growth. This massive expansion has given rise to big data, which is often described by five key features: volume, velocity, variety, veracity, and value. Handling such huge and complex datasets requires storage systems that are flexible, scalable, and efficient in storing, managing, and retrieving information. Traditional storage models, such as centralized databases and file systems, often fall short when it comes to big data. They face challenges like limited scalability, poor fault tolerance, redundant data issues, and slow performance. To address these problems, new storage designs have shifted toward distributed and cloud-based systems, which provide better scalability and high availability. As data continues to grow across industries, the need for advanced storage solutions has become more urgent. Old systems struggle to keep up with fast data intake, quick access requirements, and the ever-changing demands of large-scale analytics. This research explores ways to optimize modern storage systems to improve the performance of big data processing. It looks at distributed file systems, object storage, and cloud-native methods, focusing on aspects such as data distribution, replication, metadata management, and efficient resource use. The study also considers how to balance scalability, fault tolerance, and consistency while integrating with platforms like Hadoop and Spark. By testing and evaluating performance, the research aims to develop solutions that increase speed, reliability, and cost-effectiveness. Ultimately, the findings are expected to guide the creation of next-generation storage systems that can support the rapid expansion of big data.

DOI: https://doi.org/10.5281/zenodo.17285290

Hydrogen Energy As A Catalyst For Low-Carbon Transition In The UK

Authors: Oluwatosin Bubare Ayoko

Abstract: Hydrogen has played a pivotal role in the United Kingdom’s energy landscape for centuries, with its origins traceable to Robert Boyle’s 1671 experiment at Oxford and Henry Cavendish’s 1766 identification of the gas as “inflammable air.” The landmark discovery of electrolysis by William Nicholson and Sir Anthony Carlisle in 1800 laid the foundation for modern green hydrogen production. As a versatile and low-emission energy carrier, hydrogen offers significant potential for reducing greenhouse gas emissions and transitioning to a low-carbon economy, particularly when derived from renewable sources such as solar and wind. This study examines the evolution and deployment of hydrogen energy technologies in the UK, highlighting their integration into real-world projects that stimulate demand, foster economic growth, and enable decarbonization of hard-to-electrify sectors. It further explores the strategic role of government policies in accelerating hydrogen adoption across power-intensive industries. The findings underscore the symbiotic relationship between the expansion of the hydrogen economy and progress toward national net-zero targets, positioning hydrogen as a cornerstone of the UK’s clean energy transition.

DOI: https://doi.org/10.5281/zenodo.17291753

Smart Health Monitoring And Medication Remainder Application

Authors: R.V.Viswanathan, L.R.Eswari, S.Parameshwari, V.Sushmitha

Abstract: Medication non-adherence and inadequate health monitoring remain significant barriers to effective treatment outcomes. Existing mobile health applications often emphasize fitness tracking or basic reminders, lacking integration of comprehensive medical support features. This work presents MediTracker, a smart health monitoring and medication reminder application designed to enhance patient care, adherence, and remote connectivity with healthcare providers. The system integrates vital signs monitoring (heart rate, blood pressure, glucose, oxygen levels), medication scheduling, and automated multi- channel reminders (pop-ups, SMS, email) within a unified mobile and web-based platform. Built on a layered architecture with a Java backend, MySQL database, and cloud storage, MediTracker enables real-time data synchronization, personalized health record management, and visual trend analysis. The application further supports caregiver and doctor access, ensuring timely interventions and improved treatment compliance. By combining monitoring, reminders, and secure remote access, MediTracker provides a scalable, patient-centric healthcare solution with future potential for AI-driven predictive insights.

DOI: https://doi.org/10.5281/zenodo.17340693

Automation-First Post-Merger IT Integration: From ERP Migration Challenges To AI-Driven Governance And Multi-Cloud Orchestration

Authors: Shravan Kumar Reddy Padur

Abstract: Post-merger integration (PMI) remains one of the most challenging aspects of mergers and acquisitions. While financial and strategic objectives dominate public announcements, it is the integration of IT systems—ERP platforms, HR applications, customer management, and data infrastructures—that often determines success or failure. Historical evidence shows that IT mismatches can delay synergy realization, inflate costs, and even derail mergers altogether. Until the mid-2010s, PMI processes were largely manual, dependent on siloed IT teams, bespoke scripts, and protracted ERP harmonization projects. This often resulted in multi-year integration timelines and recurring risks of disruption. By 2025, however, advances in automation, DevOps, integration platforms as a service (iPaaS), observability frameworks, and generative AI (GenAI) have transformed PMI into a structured, programmable process. Today, automation-first strategies enable IT leaders to consolidate infrastructures, migrate workloads, and enforce governance with reduced reliance on manual oversight. This paper reviews the evolution of PMI strategies from 2010 to 2025, highlights automation-driven blueprints, presents emerging GenAI and agentic AI applications, and examines challenges and future trajectories.

DOI: https://doi.org/10.5281/zenodo.17292252

Economic Theories In Cloud Computing: A Survey Of Pricing Models And Market Dynamics

Authors: Aneesh Sai Grandhi, Allan Saldanha, Anjali Heda, Vrushabh Brahmbhatt, Dr. Vanishree K

Abstract: Cloud computing typically means the availability of computational resources such as software, storage, servers and networks on-demand. Such models allow users to access and use resources without any need to own or manage physical infrastructure. The pricing models for these cloud platforms is one of the challenges in the field of cloud computing. Research is being carried out in order to determine the best mechanism for pricing models that cloud providers can adopt and set fair prices for the services offered.

Smart Home Automation: Intelligent Control For Modern Living

Authors: Mr. S. Parthiban, Mr. B. Bharathi, Mr. S. Kishore, Mr. H. Mohamed Fazil

Abstract: The evolution towards smart living environments necessitates robust and user-centric control systems that transcend the limitations of traditional manual appliances. This paper presents the design and implementation of a centralized, dual-mode Internet of Things (IoT) system for intelligent home automation. The system provides seamless control over household devices through two distinct interfaces: a web-based application for remote monitoring and management, and a voice recognition module for hands-free operation. A key architectural feature is its dual-mode functionality, which ensures continuous operation by seamlessly switching between a cloud-based (online) mode for remote access and a local (offline) mode during internet outages. The hardware prototype is centered around an ESP32 microcontroller, which interfaces with sensors and relay modules, while a Firebase cloud backend synchronizes state with a React Native frontend. The successful implementation validates a reliable, convenient, and efficient solution that enhances user autonomy and bridges the gap between conventional home management and modern intelligent control systems. This work contributes a practical framework for developing resilient and accessible smart home technologies.

DOI: http://doi.org/10.5281/zenodo.17301332

Tech-Farm: Digital Empowerment For Farmers Through Direct Marketplace, Advisory, And Scheme Linkage.

Authors: Patel Mohammed Safwan, Harsh Prajapati, Jaymin Makwana, Jay Padhiyar

Abstract: Tech Farm is an integrated agritech platform devel-oped to reshape the farming ecosystem by providing farmers with direct sales opportunities, timely digital advisories, and simpli- fied access to government initiatives. By reducing intermediary influence, fostering transparency, and equipping farmers with AI-enabled insights, Tech Farm enhances profitability, sustain- ability, and digital adoption. This paper discusses the platform’s underlying design principles, architecture, operational workflow, comparative evaluation with existing agritech models, challenges, and prospects. The anticipated socio-economic benefits highlight the transformative role of technology in strengthening agricul- tural communities. Additionally, this study explores how Tech Farm can become a replicable model globally, emphasizing the integration of IoT, blockchain, and AI in bridging gaps between traditional farming and modern agricultural practices

DOI: http://doi.org/10.5281/zenodo.17301693

High Stakes, Young Lives: The Rising Threat Of Digital Gambling In India

Authors: Hannah Sholapur

Abstract: This paper examines the rising problem of online gambling among teenagers in India. With easy access to betting and gaming apps, many adolescents face growing financial, mental, and social risks. The study discusses how outdated laws and poor enforcement make it easier for young people to fall into gambling traps. It also looks at the emotional impact of addiction and suggests practical policy solutions that combine stronger regulation, awareness, and technology. Finally, the paper highlights the importance of financial literacy programs like Project Ardhika in helping students make responsible choices and avoid risky financial behavior.

DOI: http://doi.org/10.5281/zenodo.17302242

Transfer Learning With CNNs In Small DL Datasets: Applying Pre-Trained CNN Models And Fine-Tuning Them For Limited Data Scenarios

Authors: Kunal Kartik

Abstract: Training convolutional neural networks (CNNs) from scratch using small data sets tend to suffer from over fitting with poor generalization therefore making the models to perform poorly in real world applications. This study examines the effectiveness of transfer learning through the exploitation of pre-trained CNN models and tuning the same to perform classification based on limited data. We measure the performance of popular architectures, including VGG16, ResNet50, and InceptionV3 on several small-scale datasets, including medical imaging and fine-grained object recognition. Systematic layer freezing, targeted fine-tuning, and data augmentation as a part of our methodology are aimed at increasing generalization. As the result shows, training transfer beats training from scratch significantly with fine-tuned models managing to gain up to 25% more accuracy and increased robustness over validation folds. Competitive results were obtained with feature extraction where little fine-tuning was done, which explains its usefulness with limited computational resources. The findings reiterate the value of transfer learning as an applicable solution to small datasets issues, and peers into the best strategies of fine-tuning CNN for data sparse environments.

Stabilization Of Black Cotton Soils Using Cement FlyAsh And GGBS

Authors: Ritu Mewade, S.S. Kushwaha

Abstract: Soil stabilization has become an increasingly vital aspect in modern Civil Engineering. Stabilizing soils using Cement, Fly-Ash, and GGBS offers an affordable and effective solution, applicable to a variety of soil types. Black Cotton soils, known for significant volume fluctuations with changes in moisture content, expand when moisture is added and contract when dried. By incorporating these materials, the stability and engineering properties of such soils can be enhanced. This project aims to evaluate the benefits of stabilizing Black Cotton soil with Cement, Fly-Ash, and GGBS. The use of industrial by-products like Fly-Ash and GGBS not only strengthens the soil but also reduces costs. The effectiveness of these stabilizers will be assessed through Standard and Modified Proctor tests. A comparative analysis of the test results will determine the optimal quantities of these materials needed to achieve maximum soil stability

DOI: http://doi.org/10.5281/zenodo.17304393

Security Vulnerabilities In Java: A Study Of Common Attacks And Mitigation Strategies

Authors: Abhishek, Nisha, Suman Chandila

Abstract: Java remains one of the most widely used programming languages in modern software development due to its platform independence, robust frameworks, and extensive ecosystem. However, the prevalence of Java in both web and enterprise applications also makes it a high-value target for cyberattacks. This paper provides an in-depth analysis of the most critical security vulnerabilities inherent in Java applications, with a focus on common attack vectors such as injection attacks, insecure deserialization, and cross-site scripting (XSS). It also delves into the growing threat of vulnerabilities in third-party libraries, remote code execution (RCE), and insufficient authentication mechanisms. Through a detailed examination of real-world incidents, including notable CVEs such as the Log4j vulnerability (CVE-2021-44228) and the Apache Struts exploit (CVE-2017-5638), the study highlights patterns and trends in the exploitation of Java-based systems. This research identifies the root causes of these vulnerabilities, emphasizing the importance of secure coding practices, proactive patch management, and the implementation of robust security mechanisms like secure authentication and encryption. Furthermore, the paper explores effective mitigation strategies for developers, including the use of security testing tools, static and dynamic application security testing (SAST/DAST), and secure software development life cycle (SDLC) integration. Recommendations are provided for improving security posture at both the code and architectural levels, offering best practices for reducing exposure to attacks. By addressing emerging threats, such as the rise of cloud-based Java applications and the need for post-quantum cryptography, this paper provides valuable insights for securing Java applications against present and future security challenges

DOI: http://doi.org/10.5281/zenodo.17310113

Hand Gesture Recognition For Sign Language Interpretation

Authors: Mrs.R.Aruna, AP / IT, I.Hari Haran, P.A Manikandan, J.Mohamed Farees

Abstract: Effective communication between sign language users and non-signers remains a significant challenge in education, workplaces, and daily life. To address this issue, a Bi-Directional Sign Language Translation System is proposed, leveraging advanced computer vision techniques (OpenCV, Mediapipe), deep learning frameworks (TensorFlow/Keras), and Natural Language Processing (NLP) algorithms. The system provides real-time translation of sign gestures into text or speech, and conversely, converts text or voice into dynamic animated sign language. Furthermore, multilingual Text-to Speech (TTS) integration ensures clear and natural voice assistance, enhancing accessibility across diverse communities. Implemented with scalable technologies such as Python, Flask, and React.js, the platform ensures low latency, high performance, and ease of use. By combining gesture recognition, neural networks, and speech synthesis, this system promotes inclusivity and empowers individuals with hearing or speech impairments to participate fully in modern communication environments.

DOI: http://doi.org/10.5281/zenodo.17309992

Navigating The Intersection Of Blockchain Technology And The Digital Personal Data Protection Act (DPDP 2023): Implementation Challenges And Strategic Pathways

Authors: Abhijit Kakoty

Abstract: This paper explores the core conflicts between blockchain technology and the Digital Personal Data Protection (DPDP) Act, 2023, examines the challenges that may arise during blockchain-based implementations, and considers the potential optimistic outcomes emerging from their interaction. Regular challenges faced during implementation of blockchain based application are well documented while the challenges that can be seen after DPDP act comes in to force, such as evolving interpretations of joint controllership and new advisory opinions. This paper aims to explore the synergies between blockchain technology and compliance with the Digital Personal Data Protection (DPDP) Act. By identifying key areas where actionable frameworks—leveraging emerging technologies such as chameleon hashes and zero-knowledge proofs—can be applied, it offers a forward-looking perspective on how blockchain systems can be aligned with the principles and requirements of the DPDP Act. It further contributes a strategic theoretical pathway for aligning decentralized architectures with evolving data protection norms.

DOI: https://doi.org/10.5281/zenodo.17310368

Footprinting And Scanning

Authors: Sanket L. Jaiswal, Prof .D. G. Ingale, Dr. A. P. Jadhav, Prof. S. V. Raut, Prof .S. V. Athawale, Dr.D.S.Kalyankar, Prof. R. N. Solanke

Abstract: In today’s digital world, cybersecurity is more important than ever for individuals, businesses, and governments. To protect networks from attacks, security experts often use footprinting and scanning, which are part of the first step in ethical hacking. Footprinting means collecting information about a target system, while scanning involves checking the system for active devices, open ports, and possible weaknesses. This paper explains different techniques and tools for footprinting and scanning, discusses their importance in cybersecurity, and proposes a framework to make these processes more efficient and safe. Ethical and systematic reconnaissance can help organizations strengthen their security and prevent attacks before they happen.

DOI: https://doi.org/10.5281/zenodo.17312928

Sustainability Assessment Of Flyover Construction Using Life Cycle Cost And Carbon Footprint Analysis

Authors: J Shiva Kumar

Abstract: The growing demand for urban flyovers has intensified the need to evaluate their economic and environmental sustainability. This paper presents a comprehensive sustainability assessment of a reinforced concrete (RCC) flyover using Life Cycle Cost Analysis (LCCA) and Carbon Footprint Analysis (CFA). The study covers all major life-cycle phases— construction, operation, maintenance, and end-of-life—using a cradle-to-grave approach. Material quantities were obtained from STAAD.Pro modeling, while emission factors were derived from the Indian Life Cycle Inventory Database. The results indicate that cement and steel contribute more than 70% of total CO₂ emissions, while maintenance activities account for 25–30% of life-cycle costs. Incorporating 30% Ground Granulated Blast Furnace Slag (GGBS) replacement in concrete reduced total emissions by 22% and costs by 11%. The proposed framework offers a quantitative basis for integrating sustainability considerations into future flyover design and construction.

A Study On Analysing The Impact Of Delivery Efficiency On Customer Satisfaction In Cloud Kitchens

Authors: Sanjay Manikandan, Dr. Bhanu Pratap

Abstract: Despite the rapid growth of the cloud kitchen industry in recent years, many businesses continue to face challenges in ensuring consistent customer satisfaction. As customer experience in this model depends entirely on delivery performance, the efficiency of delivery operations has become a critical determinant of success. This study investigates the impact of delivery efficiency on customer satisfaction in cloud kitchens, examining dimensions such as delivery time, order accuracy, food quality, delivery personnel behaviour, app/platform reliability, and cost of delivery. The literature review strongly supports the role of these variables in shaping consumer perceptions and loyalty. A conceptual framework linking delivery efficiency and customer satisfaction is developed from a comprehensive review of prior research, with hypotheses designed to empirically assess these relationships within the Bengaluru market context. Existing studies have not developed a holistic model of customer satisfaction that simultaneously incorporates both operational and technological factors influencing food delivery outcomes. This study advances the literature by proposing an integrated conceptual model that connects operational efficiency with customer experience, offering a structured understanding of how delivery performance affects satisfaction and retention. The framework provides meaningful insights for both scholars and practitioners, demonstrating how timely service, accuracy, and platform reliability collectively enhance the perceived value of cloud kitchen services. It also holds practical significance for managers seeking strategies to improve delivery reliability, optimize resources, and strengthen customer relationships in a highly competitive digital food ecosystem. Overall, this research aims to provide new perspectives on how operational, technological, and human factors jointly drive satisfaction in the food delivery. industry. It emphasizes the importance of aligning efficiency with service quality to sustain long-term competitiveness. The findings can help cloud kitchen operators design delivery systems that reduce operational lapses, improve reliability, and build stronger customer trust and loyalty.

Teach Mate AI Agent: A Smart Assistant For Educators

Authors: Mrs. A. Mohanadevi, M. Kabilan, M. Subramaniyan, K.G Sarveshwaran

Abstract: This paper introduces the Teach Mate AI Agent, an intelligent assistant designed to revolutionize the educational workflow by automating time-intensive administrative tasks for educators. The system provides a unified, AI-powered platform that integrates eight comprehensive modules to handle syllabus creation, lesson planning, assessment generation, and resource curation. By leveraging Google's advanced Gemini 1.5 model and a Retrieval-Augmented Generation (RAG) architecture, the agent edagogically sound content while ensuring factual accuracy through user-provided documents. The primary objective is to reduce the administrative burden on educators by up to 90%, thereby enabling them to dedicate more time to student engagement and mentorship. The technology stack includes Python with Streamlit for the front-end, and ChromaDB as the vector database for the RAG system.

DOI: https://doi.org/10.5281/zenodo.17337482

A Study On Occupational Stress Among IT Sector Employees

Authors: Mr. J. Dhileepan, Dr. S. Maruthavijayan

Abstract: Occupational stress is increasingly recognized as one of the most pressing challenges faced by employees in the Information Technology (IT) sector. The nature of IT work— characterized by long working hours, strict deadlines, high performance expectations, and the constant demand to acquire new technical skills—places employees under persistent psychological and physical strain. Stress of this kind not only hampers individual wellbeing but also adversely affects organizational outcomes such as productivity, employee morale, and staff retention. The present study therefore aims to measure the levels of occupational stress among IT professionals and to examine the key factors contributing to it, including workload, organizational support, break schedules, and work–life balance. A structured questionnaire was distributed to a sample of 200 IT employees, and the responses were analyzed using both descriptive and inferential techniques to establish patterns and correlations. The findings reveal that high workload, inadequate managerial or organizational support, and limited opportunities for rest or relaxation breaks are strongly correlated with elevated stress levels. Younger employees and those with fewer years of experience were found to be more vulnerable to stress, largely due to adjustment challenges and skill-upgrade pressures. The study suggests that organizations should adopt proactive measures such as structured stress-management programs, balanced workload distribution, and fostering supportive work environments to safeguard employee well-being and ensure long-term organizational sustainability.

Neural-Driven Immersive Environments: Merging BCI Through Augmented Reality & Virtual Reality

Authors: Mr. Om Nandkishor Thakare, Prof S. V. Raut, Dr. A. P. Jadhao

Abstract: This research presents the design and development of a Neural-Driven Immersive Environment that combines Brain–Computer Interface (BCI) technology with Augmented Reality (AR) and Virtual Reality (VR) systems. The study aims to create a more natural and interactive way for users to communicate with digital environments using brain signals instead of traditional input devices. The paper explains each stage of development, including signal collection, processing, system design, environment integration, and testing. The proposed model allows real-time interaction, adaptive responses, and personalized experiences by interpreting neural activity. This integration of BCI with AR and VR enhances immersion, reduces physical effort, and opens new possibilities in fields like education, healthcare, and virtual training.

Ecosystem Restoration – Forest Wetlands

Authors: Rajesh Kumar Mishra, Rekha Agarwal

Abstract: Forested wetlands are among the most biodiverse and ecologically significant ecosystems on Earth, providing essential services such as carbon sequestration, water filtration, flood regulation, and habitat conservation. However, these ecosystems have faced widespread degradation due to deforestation, urban expansion, agricultural drainage, and climate change-induced hydrological alterations. The restoration of forested wetlands has become a global priority, requiring an interdisciplinary approach that integrates ecological principles, hydrological engineering, policy implementation, and community engagement. This chapter explores the methodologies, challenges, and future perspectives in the restoration of forested wetlands. Hydrological restoration, including rewetting drained wetlands, reestablishing floodplain connectivity, and removing artificial barriers, plays a fundamental role in restoring wetland functionality. Additionally, native vegetation reintroduction, soil rehabilitation, and biodiversity conservation are key ecological strategies to accelerate natural regeneration. Advanced technologies such as remote sensing, machine learning, and environmental DNA (eDNA) analysis have significantly improved the accuracy of wetland assessment and monitoring, ensuring more effective and adaptive restoration strategies. Despite scientific advancements, several challenges persist, including conflicting land-use priorities, policy gaps, insufficient funding, and the long-term ecological uncertainties of restoration projects. The success of wetland restoration depends on integrated governance, strong environmental legislation, and the active involvement of local and Indigenous communities in conservation efforts. Furthermore, emerging mechanisms such as blue carbon markets and nature-based climate solutions offer financial incentives for large-scale wetland restoration, promoting economic and environmental sustainability. Looking ahead, the future of forested wetland restoration lies in the synergistic application of climate-adaptive strategies, data-driven restoration models, and transboundary conservation policies. A comprehensive and collaborative approach is essential to mitigate the impacts of climate change, reverse wetland degradation, and enhance ecological resilience. By prioritizing science-based restoration methodologies and sustainable policy frameworks, we can ensure that forested wetlands continue to provide their critical ecosystem functions for future generations.

Satellite Internet Technology

Authors: Rani Wadhai, S.V. Raut

Abstract: Satellite internet delivers high-speed global connectivity by utilizing satellites that circle the Earth. Unlike conventional networks that depend on physical cables or cell towers, satellite systems allow for direct communication among users, orbiting satellites, and ground stations, making them particularly useful in isolated and rural regions. This paper outlines how satellite internet functions, the various orbital types (GEO, MEO, LEO), and examines Starlink as a significant breakthrough for reducing latency. It further explores its uses in fields such as education, healthcare, defense, and disaster response, while also addressing important challenges like financial barriers, space debris accumulation, and impacts on night skies. Looking ahead, the integration of satellite internet with forthcoming 6G technology is anticipated to offer more dependable and universal connections worldwide.

The Future Is Cloud: Modernizing Big Data For The Cloud Era

Authors: Khaleel Khan Mohammed

Abstract: Data generation is increasing at an unprecedented pace across industries and the world. The challenge lies not only in storing and managing this massive “big data,” but also in analyzing it to extract meaningful insights. To address this, various methods are employed for data collection, storage, processing, and analysis. This paper provides an overview of the layered architecture of Big Data management and highlights the key challenges within these layers that limit its practical applications across industries. In addition, the study explores different cloud-based architectural models that are designed to support diverse industrial requirements, emphasizing their role in enhancing scalability, flexibility, and efficiency. Furthermore, the paper discusses data migration strategies in detail, outlining how these approaches address the inherent limitations of Big Data systems by enabling seamless transfer, integration, and optimization of data in cloud environments

DOI: http://doi.org/10.5281/zenodo.17339856

Digital Security System For Examination Materials

Authors: Poornima, Jenitta J

Abstract: Digital Security system for examination materials is a system designed to prevent unauthorized access to exam papers. This system generates a unique password that is valid only for a single use and for a limited period of time. The system is designed to provide secure access to exam papers to only authorized personnel, such as teachers and invigilators. Digital Security system for examination materials consists of two main components: the server and the client. The server generates and manages the OTPs, while the client is responsible for receiving and verifying the OTPs.When a teacher or invigilator needs to access an exam paper, they must first authenticate themselves using their username and password. Once authenticated, the server generates an OTP and sends it to the client device of the teacher or invigilator. The teacher or invigilator can then use the OTP to access the exam paper. The OTP is only valid for a limited period of time, typically a few minutes, and can only be used once. This means that even if the OTP is intercepted by an unauthorized user, they will not be able to use it to access the exam paper as it will have already expired. The system also logs all access attempts, including successful and unsuccessful attempts. This allows administrators to monitor and track any unauthorized access attempts and take appropriate action if necessary. Overall, an OTP based electronic protection system for exam paper leakage is an effective way to prevent unauthorized access to exam papers and maintain the integrity of the examination process. It provides a secure and reliable way to protect exam papers from leakage and ensures that only authorized personnel have access to them.

Usability Of Learning Analytics (LA) In E-Learning Platforms – Qualitative Thematic Analysis Of Learner Feedback

Authors: Mohammed Swaleh Mohammed

Abstract: This study presents a qualitative thematic analysis of student feedback on the usability of Learning Analytics (LA) feature within an e-learning platform. Drawing from 399 open-ended responses, the analysis identifies key themes reflecting user satisfaction, challenges, and self-assessment practices. The findings reveal that ease of use, flexibility, and efficiency are highly valued, while internet connectivity, system performance, and content limitations pose significant barriers. Additionally, students employ grades tracking, progress monitoring, and feedback utilization to evaluate their academic strengths and weaknesses. The study offers insights into enhancing LA tools to better support learner engagement and outcomes

DOI: http://doi.org/10.5281/zenodo.17356211

The Metaverse As The Future Of Virtual Interaction: An Architectural Synthesis, Human-Centric Review, And Roadmap For Neuro-Governance

Authors: Mr. Harshkrushna D. Khawale, Prof. S. V. Athawale, Prof. S. V. Raut

Abstract: The Metaverse is a persistent, 3D network of virtual spaces and the culmination of interaction technologies. This systematic review synthesizes its architecture and critically assesses inherent governance deficits. While enabled by XR, AI, and Blockchain, existing architectural models lack dedicated layers for governance and interoperability. Sustained user adoption is contingent on trust and privacy protection. The emerging neurosociological paradigm, utilizing implicit interbrain synchrony, introduces severe ethical risks to cognitive liberty. This demands urgent regulatory focus and the establishment of Neurorights legislation. This paper proposes a socio-technical architectural model and a roadmap for preemptive neuro-adaptive governance.

Diabetic Prediction Using Machine Learning

Authors: Samruddhi Ande, Professor S. V. Raut

Abstract: Millions of individuals worldwide suffer with diabetes, a dangerous medical condition. Serious problems can be avoided with early diabetes prediction. In this study, we predict diabetes in individuals based on a variety of health factors using machine learning approaches. Age, blood pressure, glucose level, BMI, and other medical characteristics are among the data in the dataset. To increase prediction accuracy, data preprocessing techniques such as normalization and handling missing values were used. A number of machine learning models were tested, such as Support Vector Machine, Random Forest, and Decision Tree. The accuracy, precision, recall, and F1-score of these models were used to compare their performances. The Random Forest model demonstrated its suitability for diabetes prediction by achieving the best accuracy. The findings show that machine learning may reliably support early diagnosis, assisting physicians and patients in making better health-related decisions. The significance of technology in healthcare and the potential for AI-based solutions to enhance patient outcomes are highlighted in this study.

Impact Of Health Challenges On The Nutritional Habits Of Elderly Individuals In Rural And Urban Household: A Case Study Of Badagry L.G.A, Lagos State.

Authors: Ese Lawrence Ekanem, Ogbede Oritsematosan Marian, Okorocha Cyrilgentle Ugochukwu, Odejobi Babajide Michael, Oluwadamisi Tayo-Ladega , Erienu Obruche Kennedy

Abstract: The research examined how health issues affect the eating habits of older adults in both rural and urban households, focusing on Badagry L.G.A in Lagos State. Four research questions were formulated to guide the study, along with one hypothesis that was tested at a significance level of 0.05. A correlational ex-post facto research design was employed for this investigation. According to the 2006 census, Badagry L.G.A had a population of 241,093. The study used a descriptive survey design with 100 participants. A stratified random sampling method, including simple random sampling, was applied to select the sample for this research. Data was gathered through a questionnaire titled "Influence of Health Challenges on Nutritional Lifestyle of the Elderly in Rural and Urban Households of Badagry L.G.A, Lagos State (IHCNLERUH)." The validity of the instruments was assessed for face, content, and construct. The reliability of the instruments was also checked, yielding an internal consistency reliability coefficient of 0.96. The collected data were analyzed using basic correlation and regression at a significance level of 0.05. Frequency, percentage, mean, and standard deviation were utilized to address the research questions, while Pearson coefficient correlation was employed to test the hypothesis. The findings indicated that the eating habits of older adults significantly influence their healthy lifestyle. Health challenges have a notable impact on the nutritional lifestyle of the elderly in both rural and urban settings. High alcohol consumption adversely affects the nutritional status of older individuals. Various factors hinder the nutritional lifestyle and health behaviors of the elderly in these areas. The study suggested that the government and other stakeholders should regularly monitor the health of older adults to identify those at risk, enabling timely interventions, and establish a social security system to support the income and welfare of the elderly people in the study area

DOI: http://doi.org/10.5281/zenodo.17366239

Design and Implementation of Caar Cascade Classifier in Atm

Authors: Swati V, Ms. S. Madhu Sangeetha, Dr. B. Lalitha

Abstract: Automatic Teller Machine (ATM) are widely available for users and procedure the ability to carry-out financial transactions and Banking functions in continuous time basis at any time. It made banking transactions effortless for customers current ATM’s have access card and pin authentication for unique information. This explains ATMs to lot of financial theft like card theft, pin theft and stealing account holders’ information. So this project will make the multilevel high end security to find the authorized user in the ATM machine and make secured and more safety transaction and withdrawal money in ATM. High level security mechanism is provided by the consecutive actions after proceeding with pin number such as initially system use Open CV library to analyze the person authorized identification by capturing the human face initially it begins with the entering the pin number if the entered pin is correct then the process continues with the face recognition. If the entered pin is wrong then it sends OTP to the registered Outlook mail. If the entered OTP is correct then the process continues or else the transaction is declined. If the person is authorized it continues if the person is unauthorized, it sends the alert mail and alert SMS to the registered user by using the fast2sms messaging platform. After the completion of transaction, it provides persons image which was captured at the Time of withdrawal in the ATM has to be sent to the registered user mail.

Circadian Rhythm Reprogramming Via Gradual Light Attenuation With A Servol Motor.

Authors: Rupsa Sarkar

Abstract: Suprachiasmatic nucleus (SCN) governs human circadian rhythms through the response to environmental light. In modern societies, a significant percentage of the population is exposed to delayed sleep phase disorder (DSPD) or sleep-onset insomnia in general due to continuous evening exposure to light. In this article, there is a description of a novel, low-cost intervention: employing a low-power, programmable sg90 Servo motor to turn blinds 5° hourly in the evening, slowly dimming ambient light levels before sunset. This gradual weakening simulates an earlier sunset by sending earlier light-off signals to the SCN. We theorize that this manipulation would induce a phase advance in circadian timing, enabling one to sleep at an earlier time. This article presents the photic sensitivity of SCN, circadian entrainment process, device design, theoretical background, potential outcome, and future work.

Fake News Detection Using Machine Learning

Authors: Shweta Arakeri, Dayanand G Savakar, Anjali Deshapande

Abstract: In today’s digital era, information spreads rapidly through social media and online platforms. However, this convenience has led to the rise of misinformation, commonly referred to as fake news. This paper presents a machine learning-based approach to detect fake news articles by analyzing text content using Natural Language Processing (NLP) techniques. The system preprocesses data, extracts features through TF-IDF vectorization, and classifies news using multiple algorithms such as Logistic Regression, Decision Tree, Gradient Boosting, and Random Forest. The project is implemented using a Flask web application to make the tool user-friendly and accessible. The results demonstrate that the ensemble models provide high accuracy and reliability in identifying misinformation

DOI: http://doi.org/10.5281/zenodo.17368296

Automatic Damage Detection Of Historic Masonary Bulidings Based On Convtransformer Deep Learning Model

Authors: Vijayalakshmi. G, Ms. P. Kalaiselvi, B. Lalitha

Abstract: Crack detection in building structures is critical for ensuring safety and preventing costly repairs. Traditional crack detection methods often face challenges in accurately identifying cracks due to the complexity of the structure and the subtlety of the damage. This work proposes a hybrid deep learning framework that integrates CaTNet (ConvNeXt + Transformer Block) and Vision Transformer (ViT) for effective feature extraction, followed by XGBoost for classification. CaTNet combines ConvNeXt-style convolutional blocks and Transformer encoders to capture both fine-grained spatial details and global contextual relationships within the building images, while ViT processes the images as patch sequences to further enhance the capture of global structural patterns. The extracted features from both models are fused using dense layers with dropout for refinement. XGBoost is then employed for classification, optimized using multi-log loss (mlogloss) and evaluated with classification reports, confusion matrices, and training loss curves. Experimental results show that the proposed model significantly outperforms conventional crack detection methods in terms of accuracy, robustness, and real-time applicability, positioning it as a promising approach for crack detection in building infrastructure

Study On Post-Pandemic Supply Chain Challenges In Tamil Nadu’s Tea Estate

Authors: Koushal.M

Abstract: The outbreak of the COVID-19 pandemic brought severe disruptions to global and local supply chains, impacting production, labour, transportation, and market dynamics across various sectors. The tea industry, a vital component of Tamil Nadu’s agricultural economy, was particularly affected due to its heavy reliance on manual labor and complex distribution networks. This study investigates the post-pandemic supply chain challenges faced by tea estates in Tamil Nadu, focusing on major tea-producing regions such as the Nilgiris, Coimbatore, and Anamalai Hills. The research aims to identify key factors influencing supply chain resilience and sustainability after the pandemic. Primary data were collected through structured questionnaires from 160 respondents, including estate managers, supervisors, and supply chain personnel. The study examines the role of technology adoption, supplier relationships, workforce flexibility, risk management practices, and market access in strengthening supply chain resilience. The collected data were analysed using SmartPLS (Partial Least Squares Structural Equation Modelling) to validate the conceptual model and assess the significance of hypothesised relationships. The model demonstrated a good fit (SRMR = 0.047, NFI = 0.868), confirming the reliability of the proposed framework. The findings revealed that all five independent variables have a positive and significant impact on supply chain resilience, indicating that digital transformation, strong supplier networks, flexible labor practices, and proactive risk management collectively enhance post-pandemic recovery. This study contributes to the limited literature on supply chain resilience in the Indian plantation sector and provides practical insights for tea estate managers and policymakers. It emphasises the need for modernising the supply chain through technology integration, digital forecasting tools, and workforce development to ensure long-term competitiveness and sustainability.

A Study On Quality Management Practices In Small-Scale Manufacturing Units In Karur, Tamil Nadu

Authors: NahulRaj.K

Abstract: The study titled “A Study on Quality Management Practices in Small-Scale Manufacturing Units in Karur, Tamil Nadu” aims to examine the extent of adoption, challenges, and impact of quality management practices (QMPs) in the region’s manufacturing sector. Small-scale industries (SSIs) play a vital role in Karur’s industrial landscape, particularly in textile and allied manufacturing, contributing significantly to local employment and exports. However, these units often face barriers in implementing structured quality systems due to constraints such as limited financial resources, lack of technical expertise, and inadequate awareness.This research investigates the various quality management tools and practices adopted by SSIs, including Total Quality Management (TQM), ISO certification, and continuous improvement initiatives. It also analyses the challenges faced in implementing these systems and evaluates their influence on business performance indicators such as customer satisfaction, productivity, competitiveness, and profitability. The findings reveal that while awareness of quality management practices is increasing, the degree of implementation remains moderate due to cost and resource limitations. Units that have adopted structured QMPs report noticeable improvements in product quality and customer satisfaction. The study concludes that fostering government support, providing technical training, and promoting awareness can significantly enhance the quality performance of small-scale manufacturing units in Karur

Analysis On Supply Chain Risk Factors, Case Of Kerala Spices SMEs

Authors: Arun Prabhu S, Dr. chetan V Hiremath

Abstract: Kerala, known as the “Spice Garden of India,” plays a vital role in India’s spice trade, with Small and Medium Enterprises (SMEs) forming the backbone of the sector. However, these SMEs face significant sustainability challenges due to climate variability, market volatility, certification hurdles, and infrastructural limitations. This study aims to analyze the key risk factors affecting the supply chains of Kerala’s spice SMEs and their impact on sustainable supply chain performance (SSCP). Using a structured questionnaire and descriptive analysis, the study identifies five major risk dimensions—climate and environmental risks, market price volatility, certification and regulatory compliance, financial constraints, and logistics and infrastructure gaps. Findings reveal that climate and environmental risks and price volatility negatively influence SSCP, while certification and compliance contribute positively. Financial and infrastructural challenges show limited but notable effects on resilience. The study concludes that effective risk management, improved access to finance, climate adaptation training, and sustainable practice adoption are essential for enhancing supply chain resilience and competitiveness. Recommendations include establishing price stabilization mechanisms, upgrading infrastructure, and promoting sustainability certifications. The research offers valuable insights for policymakers and SMEs to strengthen Kerala’s spice supply chain against sustainability risks.

Reshaping the Channel Landscape: A Theoretical Framework for Understanding the Strategic Implications of Ai Integration on the Multi-Channel Network Of Multinational Hvac Manufacturers

Authors: Abeshin Ayodele

Abstract: The integration of Artificial Intelligence (AI) is transforming strategic operations across global industries, yet its impact on multi-channel distribution networks particularly in complex sectors like heating, ventilation, and air conditioning (HVAC) remains underexplored. This study explains a theoretical framework for understanding the strategic implications of AI integration within the multi-channel networks of multinational HVAC manufacturers. The traditional HVAC distribution network comprising direct sales, retailers, contractors, digital platforms and third-party service providers; has been characterized by manual processes and legacy systems. AI's emergence introduces predictive analytics, automated CRM systems, intelligent routing, and real-time data integration that collectively shift how firms manage and interact with their channel partners. These changes, while beneficial, raise critical challenges such as channel conflict, role redundancy, partner resistance, and uneven digital maturity across regions. Thus, there is a pressing need for a structured theoretical framework that captures the complexity and strategic relevance of AI’s role in reshaping these networks. To address this gap, the study draws on four complementary theoretical lenses: the Resource-Based View (RBV), Actor-Network Theory (ANT), Technology- Organization-Environment (TOE) framework, and Diffusion of Innovation (DOI) theory. The RBV positions AI as a valuable, rare, and inimitable resource that, when aligned with internal capabilities and existing assets, can provide a sustainable competitive advantage through differentiated channel management strategies. ANT broadens this view by conceptualizing AI not just as a technological tool but as an active agent within the distribution network. It highlights how human and non-human actors (e.g., AI systems, managers, distributors) negotiate roles and power relations, co-creating new channel configurations and organizational behaviors. The TOE framework provides a holistic understanding of how technological, organizational, and environmental factors interact to influence AI adoption. It explains how firms' internal readiness, market pressures, and regulatory environments shape the pace and depth of AI integration within channel strategies. Finally, the DOI theory offers insights into the diffusion process of AI across channel partners, emphasizing how adoption is influenced by the perceived attributes of AI technologies and the social systems through which innovation spreads. It identifies early adopters within the network and highlights strategies to accelerate diffusion through communication, training, and observable results. Together, these theoretical perspectives present a robust framework for examining the strategic transformation of multi-channel networks in the HVAC industry due to AI. The study contributes to scholarly understanding of digital transformation in B2B networks while offering practical guidance for HVAC manufacturers aiming to align AI capabilities with channel strategy

DOI: https://doi.org/10.5281/zenodo.17414506

AI Based Thermographic Weld Joint Inspection

Authors: S.Gayathri, Dr.S.Siva Ranjani, Dr.B.Lalitha

Abstract: This project presents an AI-based thermographic weld joint inspection system designed to automatically detect defects in weld joints using deep learning models, specifically Convolutional Neural Networks (CNN) and the YOLO (You Only Look Once) object detection algorithm. By leveraging thermographic imaging, which captures the thermal profile of welded joints, this system aims to identify inconsistencies and anomalies indicative of defects such as cracks, porosity, and lack of fusion. The proposed approach utilizes CNN for image classification to determine whether a weld is defective or not, while YOLO is employed for precise localization and detection of defects within the thermographic images. The dataset comprises labeled thermographic images of weld joints, preprocessed and augmented to enhance model performance. The CNN model is trained to distinguish between defective and non-defective welds, achieving high classification accuracy. Simultaneously, YOLO is trained to detect multiple types of defects in real-time with high precision and recall. The combination of CNN and YOLO ensures both robust classification and efficient object detection. Evaluation metrics such as accuracy, F1-score, mean Average Precision (mAP), and Intersection over Union (IoU) are used to assess model performance. Experimental results demonstrate the effectiveness of deep learning in automating weld inspection, reducing human error, and increasing inspection speed. The system is scalable and adaptable to various welding processes and materials. Deployment of this AI solution can significantly improve quality assurance in manufacturing.

Oxidative Stress Pathways In Cancer: An Insight From Heavy Metals

Authors: Ali Akbar, Komal Sarwar

Abstract: Heavy metals, prevalent in various environmental matrices due to industrial and agricultural activities, pose significant health risks, including the promotion of cancer through the induction of oxidative stress. This paper reviews the mechanisms by which heavy metals such as arsenic, cadmium, chromium, and lead contribute to oxidative stress, leading to cellular damage and cancer development. We explore the complex interplay between heavy metal exposure, oxidative stress, and the activation of key signaling pathways involved in carcinogenesis. Understanding these mechanisms is crucial for developing effective strategies to mitigate the health impacts of heavy metal exposure and improve cancer prevention efforts.

DOI: https://doi.org/10.5281/zenodo.17424408

Virtual Herbal Garden

Authors: Shamli Gaikwad, Dixsha Wasnik, Stuti Tripathi, Shubhangi Rahangdale, Prof. Pooja H. Rane

Abstract: A web-based interactive platform called The Virtual Herbal Garden was created to close the knowledge gap between conventional medical procedures and contemporary digital accessibility. The platform, which has its roots in the AYUSH (Ayurveda, Yoga & Naturopathy, Unani, Siddha, and Homeopathy) healthcare system, uses multimedia integration, 3D visualization, AI chatbot support, and sharing and bookmarking tools to educate users about medicinal plants.This study describes how the project was developed and put into use utilizing cutting-edge web technologies like React.js, MongoDB, and APIs like Sketchfab and OpenAI. One major gap in the current digital herbal databases, according to the research, is the absence of easily accessible, interactive, and multilingual resources. To overcome these obstacles, an agile development methodology and user-centered design were applied.Improved user engagement, efficient plant discovery using search and filters, and improved instruction through interactive features are some of the main outcomes. Future improvements are suggested, such as mobile apps, AR integration, and AI-driven plant identification, while limitations like internet dependence and content scope are examined. In the end, this project shows how technology can be used to support natural health education, preserve indigenous knowledge, and stimulate interest in sustainable, traditional healing methods.

Machine Learning Techniques For Reliable Forecasting Of Medicine Overdose In Healthcare Systems

Authors: Miss . Chilantharajula Tejasri, Dr. K.Venkata Rao

Abstract: The opioid crisis, a pressing global public health issue, has led to a significant rise in overdose deaths, particularly among individuals under 50, with profound social and economic impacts. This study proposes a comprehensive forecasting system to predict drug use and overdose trends by integrating diverse data sources, including police reports, social network data, medical records, and sewage-based drug epidemiology. Utilizing Recurrent Neural Networks (RNNs), the system aims to identify individuals at risk of opioid abuse by analysing demographic information, medical histories, and prescription records, while distinguishing between therapeutic and harmful usage. Emphasizing privacy protection, ethical data handling, and model interpretability, this approach seeks to enhance the accuracy and timeliness of overdose risk predictions. The findings have the potential to inform clinical decision-making, shape public health policies, and drive targeted interventions to mitigate the opioid epidemic.

DOI: https://doi.org/10.5281/zenodo.17430579

Design, Simulation And Comparison Of Novel MIMO Antenna Structures For Ultra-Wide Band Applications

Authors: Mrs. M. Deepthi, Jannu Sahithi, P. Vikhyath Reddy, Polapally Srinidhi

Abstract: In the era of wireless communication, the demand for compact and high-performance antenna with ultra-wideband capabilities has been increased, particularly in 5G and Internet of Things (IoT) applications. This paper presents the design, simulation, and comparative analysis of novel Multiple Input Multiple Output (MIMO) antenna structures- Circular, Hexagonal and Hybrid of both patch configurations. The primary objective is to develop a compact MIMO antenna with low mutual coupling, wide bandwidth, improved gain and directivity. Advanced hybrid techniques and ground plane modifications were employed to improve the performance. Simulation and optimization were conducted using Ansys HFSS, with a focus on key performance metrices such as S-parameters, radiation pattern and gain. The hybrid MIMO antenna structure demonstrated superior results in terms of isolation, directivity, and bandwidth offering a promising solution for UWB applications in future wireless technologies.

Environmental Awareness Through Public Libraries: A Case Study Of City Central Library, Hyderabad

Authors: Mrs. B. Kavitha, Dr. V. Senthil Kumar

Abstract: Public libraries play a crucial role in establishing the foundations of democracy and contribute to the welfare and growth of the societies they serve. They assist in achieving the goals of the community. A public library is a temple of learning, and its services are vital for raising awareness and empowering society, particularly for disadvantaged and marginalized groups. One of the primary functions of public libraries is the dissemination of information, especially regarding environmental knowledge and protection. Information technology significantly enhances public awareness in this area. Currently, the global environment faces significant threats from pollution resulting from various human activities essential for livelihoods and other needs. It is essential for the entire population of India—and indeed the world—to be aware of the provisions of the Environment Protection Act of 1986, in order to ensure a safe life and provide a healthy environment for future generations. Awareness camps conducted at public libraries aim to help social groups and individuals gain understanding about environmental protection. This paper highlights the importance of environmental awareness and presents findings from a sample survey conducted at the Central City Library in Chikkadapalli, Hyderabad. A questionnaire was used as a survey tool to collect data on users' responses and satisfaction levels regarding their awareness of environmental pollution. The analysis reveals that many respondents at the Central City Library feel a lack of awareness about environmental protection. The survey results also illustrate the benefits of promoting environmental protection through awareness campaigns at public libraries

DOI: http://doi.org/10.5281/zenodo.17433063

Light Weight DeepLearning Frame Work For Speech Emotion Recognition Singal Processing

Authors: Subanila V

Abstract: Speech Emotion Recognition (SER) plays a crucial role in enhancing human-computer interaction by enabling machines to understand and respond to human emotions. In this study, we propose a lightweight and efficient SER model that integrates Random Forest and Multi-layer Perceptron (MLP) classifiers within a VGGNet framework. Unlike traditional deep learning models that require extensive computational resources and hyper-parameter tuning, our approach optimizes performance while significantly reducing complexity. We extracted Mel Frequency Cepstral Coefficient (MFCC) features from three widely-used speech emotion datasets—TESS, EMODB, and RAVDESS—covering 6 to 8 distinct emotions including Sad, Angry, Happy, Surprise, Neutral, Disgust, Fear, and Calm. The proposed model achieved remarkable accuracy rates of 100%, 96%, and 86.25% on the TESS, EMODB, and RAVDESS datasets, respectively. These results indicate superior or comparable performance to state-of-the-art deep learning architectures such as InceptionV3, ResNet, MobileNetV2, and DenseNet, while maintaining lower computational demands. Our findings demonstrate that the hybrid lightweight model effectively balances resource efficiency and emotion recognition accuracy, making it well-suited for deployment on resource-constrained devices without compromising performance.

Ai In Product Management Bridging The Gap Between Creativity And Innovation._884

Authors: Dr.Mukesh Verma, Prof.Amandeep Kaur, Prof.Jasleen Kaur, Prof.Amneet Kaur

Abstract: Have you observed the rapid pace at which technology is evolving around us? One moment, the focus is on mobile applications, and the next, it’s all about artificial intelligence. If you are deeply involved in this field, particularly as a product manager, you are aware that AI is revolutionizing businesses more swiftly than one can utter the phrase "artificial intelligence." But what implications does this hold for you and your position? Let us explore how product management has progressed and why it is essential to adapt to AI-driven transformations in order to remain relevant and lead effectively.The transformation of creative ideas into actual innovations is a central issue in creativity and innovation management (Van de Ven, 1986; Sarooghi et al, 2015). Scholars have often assumed the existence of a relationship between creativity and innovation, arguing that creative individuals are more likely to innovate (Baron & Tang, 2011; Plsek, 1997; Soroa, Balluerka, Hommel, & Aritzeta, 2015). Nonetheless, many creative ideas, although original, don’t find a place in the market. While some other extremely valuable ideas are never implemented. Situations such as these suggest that the path from creativity to innovation is not (always) a straight line. Cognition plays an essential role in the whole process of innovation, as entrepreneur’s ability to innovate is shaped by the their perception and interpretation of external world (Barbosa, 2014; Mullins & Forlani, 2005). From this cognitive perspective, we propose a theoretical model that elucidates how and when individuals are capable of transforming creative ideas into implemented innovation. To do so we built on a definition of innovation as a process that encompasses: the generation of novel ideas, their evaluation and their implementation in the business world (Baer, 2012). We explore how cognitive factors influences each stage of the process and how their interaction may increase the chances that an individual implements a creative idea. This framework offers potentially valuable new insights to both academics wishing to understand deeper the process of innovation in entrepreneurs and practitioners working to assist entrepreneurs in their effort to create innovative ventures.

Electrical Energy Powered Three Wheeler

Authors: Aswin S k, Dr. M Sivaprakash, Linsha Pushparaj, Vinoth M, Dan Abishek A S, Infant Mazhak

Abstract: The global transition toward sustainable mobility has increased interest in electric vehicles (EVs) as alternatives to internal combustion engine–based transportation. This work presents the design, fabrication, and performance evaluation of an electric three-wheeler prototype intended for urban commuting and short-distance goods or passenger transport. The prototype integrates a lightweight chassis, a Brushless DC (BLDC) motor drive, a lithium-ion battery pack, and a basic control system to achieve affordability, reliability, and energy efficiency. The methodology included load analysis, torque and speed requirement estimation, chassis fabrication, motor and controller integration, and testing under real operating conditions. Results demonstrated a top speed of 40 km/h, a load capacity of 300 kg, and an average operational range of 65 km per charge. Compared with conventional three-wheelers, the prototype eliminates fuel costs, reduces maintenance requirements, and achieves zero tailpipe emissions. The findings suggest that electric three-wheelers can provide a sustainable and cost-effective solution for last-mile connectivity and urban transport, especially in developing economies.

DOI: https://doi.org/10.5281/zenodo.17433866

Agentic Graph RAG Automation for Tender Bidding

Authors: Sushanth.Chandrashekar, Shantanu Nagaraj, Ashish Naidu

Abstract: The tender bidding process remains a critical yet inefficient cornerstone of global procurement, plagued by manual document analysis, compliance errors, and resource-intensive workflows. This paper introduces Agentic Graph RAG, an innovative AI system that redefines bid preparation by integrating Retrieval-Augmented Generation (RAG), dynamic knowledge graph and multi-agent collaboration to automate and optimize the end-to-end bidding pipeline. Our architecture combines three transformative pillars: a cognitive document processor, a living knowledge graph, and a specialized agent framework. Validated on real-world tenders, the system demonstrates 98% clause extraction accuracy, 80% faster bid preparation, and a 6.4x ROI through compliance assurance and strategic positioning. This work bridges cutting-edge AI research with practical procurement challenges, offering a scalable blueprint for intelligent automation in competitive bidding.

Advanced Nanocomposite Materials For Enhanced Performance In Oil And Gas Operations – A Comprehensive Review

Authors: Charitidis J. Panagiotis

Abstract: The oil and gas (O&G) industry increasingly requires advanced materials capable of withstanding harsh operating environments such as deepwater, ultra-deepwater, and high-temperature/high-pressure (HTHP) reservoirs. Fibre- reinforced polymer (FRP) composites have already provided benefits in corrosion resistance, weight reduction, and fatigue performance, yet their broader adoption remains limited by challenges such as poor impact resistance and inadequate fire performance. Nanocomposites—polymers reinforced with nanoparticles, including clays, metal oxides, carbon nanotubes, and graphene—offer a pathway to overcoming these limitations. Even at low filler concentrations, they can deliver significant improvements in mechanical strength, thermal stability, fire resistance, and barrier properties, while also enabling new functionalities in drilling fluids, cementing, and enhanced oil recovery (EOR). This review examines the state of nanocomposite research in the O&G sector, evaluates their potential to enhance both structural and fluid applications, and discusses the technical, economic, and regulatory challenges that must be addressed to achieve commercial deployment.

DOI: http://doi.org/10.5281/zenodo.17439618

Hierarchical Quantum-Accelerated Federated Learning For Scalable, Auditable Cross-Enterprise AI Governance_500

Authors: Sarang Vehale, Ruchita Vehale

Abstract: Traditional federated learning (FL) frameworks face critical challenges in privacy, scalability, and auditability when deployed across multiple enterprises with strin- gent regulatory requirements. Quantum-secure protocols such as Quantum Key Distribution (QKD) and post-quantum cryptography can harden communica- tion channels against both classical and emerging quantum attacks. Meanwhile, variational quantum algorithms (VQAs) promise computational speedups for high-dimensional aggregation tasks that become bottlenecks in large-scale FL systems. We propose a hierarchical, multi-tier Quantum-Federated Learning (QFL) architecture in which local enterprises perform classical model training, regional “quantum hubs” execute VQA-accelerated aggregation and anomaly detection, and a global coordinator enforces UN/ISO AI governance via verifiable zero-knowledge proofs (ZKPs). By bounding quantum resource usage to interme- diate nodes and combining QKD on backbone links with lattice-based encryption at the edge, our design achieves near-term implementability, cost-effectiveness, and end-to-end privacy guarantees. Preliminary simulations demonstrate that the proposed scheme reduces communication overhead by over 60% and resists gradient-poisoning attacks with negligible impact on model accuracy. This work lays the foundation for a globally scalable, audit-ready AI governance ecosystem suitable for international deployments

DOI: http://doi.org/10.5281/zenodo.17441935

Vision-Based Object Recognition In Retail

Authors: Sidhant Chadha

Abstract: Vision-based object recognition has emerged as a transformative technology in modern retail, revolutionizing how products are identified, tracked, and managed across the supply chain. Leveraging computer vision and deep learning techniques, these systems enable automated product detection, shelf monitoring, customer behavior analysis, and inventory management with high precision and speed. This study explores the design and implementation of vision-based object recognition systems within retail environments, emphasizing the role of convolutional neural networks (CNNs), transfer learning, and real-time image processing frameworks. By integrating cameras, sensors, and AI-driven analytics, retailers can enhance operational efficiency, minimize human error, and provide personalized shopping experiences. The paper also examines challenges such as occlusion, lighting variation, and scalability, along with potential solutions through model optimization and data augmentation. The findings suggest that vision-based recognition systems are key enablers of intelligent retail automation, contributing significantly to the advancement of smart retail ecosystems and Industry 4.0 integration.

DOI: https://doi.org/10.5281/zenodo.17439901

A Comprehensive Review On Recent Advances In EMG And ECG-Based Control Of 3D Printed Bionic Arms

Authors: Ayush Kumar, Abhendra Pratap Singh, Dr.Uma Gautam, Nandini Sharma

Abstract: Upper limb amputees face significant challenges due to the high cost and limited availability of advanced prosthetic hands. Recent advances in 3D printing, combined with electromyography (EMG) and electrocardiography (ECG) sensing, have enabled the development of affordable, customizable and functionally capable prosthetic devices. This review paper focusses on the current literature on 3d printed bionic hands controlled by EMG and ECG signals, highlighting design strategies, materials, actuations mechanism, and control system. The integration of hybrid bio signals, adaptive algorithms, and additive manufacturing has improved prosthetic performance, responsiveness and user comfort. The review also discusses the role of artificial intelligence and machine learning in enhancing signal processing, gesture recognition, and motion prediction as well as the potential of IoT-enabled monitoring and patient support. Moreover, limitations of current approaches and future directions for more intelligent, reliable and accessible prosthetic solutions are outlined for identification of scope for further advancement in this domain.

DOI: https://doi.org/10.5281/zenodo.17442105

Designing Scalable Microservices Architectures For Cloud-Native Applications

Authors: Mr. Akash Godre, Mr. Javeed Khan

Abstract: Cloud-native applications increasingly rely on microservices architectures to achieve scalability, fault tolerance, and maintainability. This paper presents a scalable microservices architecture design suitable for cloud platforms. The proposed architecture leverages containerization, orchestration, and dynamic scaling mechanisms to ensure high availability and optimal resource utilization. Performance evaluation demonstrates improved scalability, fault tolerance, and reduced response time compared to monolithic and traditional microservices designs. This work provides practical guidelines for deploying scalable microservices on cloud platforms like AWS, Azure, and Google Cloud.

Performance Optimization Of Cloud-Based Microservices: A Comparative Study

Authors: Mr. Akash Godere, Mr. Javeed Khan

Abstract: Micro services architectures on cloud platforms offer scalability and flexibility, but performance optimization remains a key challenge. This paper presents a comparative study of different optimization techniques for cloud-based microservices, focusing on resource utilization, load balancing, and response time reduction. Experimental evaluation on AWS and Kubernetes demonstrates significant improvements in throughput and latency when employing container- level optimization, dynamic scaling, and efficient service orchestration. The study provides actionable insights for cloud architects and developers to achieve optimal performance in microservices deployments.

California Housing Prices Prediction Project

Authors: Samarth D

Abstract: This project provides a comprehensive analysis and prediction of California housing prices using machine learning techniques. The project is implemented in Python and uses a Linear Regression model to predict housing prices based on various factors such as median income, housing median age, total rooms, population, and geographical location. The report is structured to provide an in-depth understanding of the problem, methodology, implementation, results, and potential future work. The accompanying Python code trains the model, evaluates its performance, and produces visualizations to aid in understanding the relationships between features and housing prices

Advancements In Event-Based Temporal Recommendation Systems Using Support Vector Machines

Authors: Vinod Ingale, Sayli Jadhav, Priyanka Telshinge, Rahin Tamboli, Ashwini Mahind

Abstract: The proliferation of digital platforms has led to an explosion of complex user interaction data, characterized by its sequential nature and rich contextual information. Traditional collaborative filtering (CF) methods often fall short by treating user preferences as static and ignoring the nuanced impact of temporal context and real-world events. This paper proposes a novel recommendation framework, the Temporal-Event-aware Support Vector Machine (TE-SVM), designed to effectively model the dynamic evolution of user preferences by integrating temporal dynamics and event-based contextual signals. The TE-SVM model formulates the recommendation task as a classification problem, where the objective is to find an optimal hyperplane that separates user preferences for items at a given time under specific event conditions. We engineer a comprehensive feature set that captures temporal patterns (e.g., time decay, periodicity) and event embeddings derived from external knowledge sources. A thorough comparative analysis is conducted against established models, including Matrix Factorization (MF), TimeSVD++, and Recurrent Neural Networks (RNN). Experimental results on a large-scale e-commerce dataset demonstrate that the proposed TE-SVM model achieves a significant improvement, with a 12.7% increase in Precision@10 and a 9.8% increase in NDCG@20 compared to the best-performing baseline. The findings underscore the efficacy of SVM in handling high-dimensional, heterogeneous feature spaces for temporal and event-aware recommendation tasks, providing a robust and interpretable alternative to deep learning-centric approaches.

The Impact Of Prolonged Use Of Digital Devices On Cognitive Development And Attention Span In Children Aged 6-8 Years: Evidence From Western Kenya

Authors: Paul Oduor Oyile, Eric Sifuna Siunudh, Daniel Khaoya Muyobo, Anselemo Peters Ikoha

Abstract: This study examined the impact of prolonged digital device use on cognitive development and attention span among children aged 6-8 years in four counties of Western Kenya: Bungoma, Kakamega, Vihiga, and Busia. Employing a mixed-methods approach, the research combined surveys, interviews, and observational assessments to evaluate how exposure to tablets and computers affects cognitive skills, problem-solving abilities, and attention retention. Quantitative data were analyzed using descriptive and inferential statistics, while qualitative insights revealed behavioral patterns and parental mediation practices. Findings demonstrated a significant negative correlation between increased daily screen time and both cognitive and attention performance. Children exposed to less than one hour of screen time daily scored considerably higher on cognitive and attention measures compared to those with over four hours of exposure. Parental mediation emerged as a crucial moderating factor, with high parental engagement substantially buffering negative outcomes. Gender differences were subtle, though boys engaged more in recreational activities while girls favored educational content. The study supports the displacement hypothesis, suggesting that excessive screen use replaces developmentally essential activities. Results underscore the necessity for balanced technology integration in early education, evidence-based screen time guidelines, and collaborative efforts among policymakers, educators, and parents to maximize educational benefits while safeguarding children's cognitive development and attention capabilities.

DOI: https://doi.org/10.5281/zenodo.17463828

A Hybrid Neural Architecture For Next-Item Recommendation Using Temporal Point Processes And Self-Attention On Event-Based Data

Authors: Vinod B. Ingale, Ashish Vankudre, Sagar mali , Dhanaji Jadhav, Pramod Shitole

Abstract: The proliferation of digital platforms has generated vast amounts of event-based temporal data, where user interactions are logged as discrete events in continuous time. Traditional recommendation systems often fail to capture the intricate dynamics of such data, including the exact timing, inter-event gaps, and evolving nature of user preferences. This paper proposes a novel hybrid neural architecture that synergistically integrates Temporal Point Processes (TPPs) with a Self-Attention mechanism to model user temporal behavior for next-item recommendation. Our model, the Temporal Self-Attentive Hawkes Process (TSAHP), leverages the self-attention mechanism to capture complex, long-range dependencies within user interaction sequences, while a neural Hawkes process models the continuous-time intensity of these interactions, inherently accounting for the excitement and decay effects of past events. We evaluate the proposed TSAHP model on two real-world datasets: Amazon Electronics and LastFM. Comparative analysis against state-of-the-art methods, including Time-Aware Matrix Factorization, GRU-based models, and standard Hawkes Process models, demonstrates the superiority of our approach. The TSAHP model achieves significant improvements, with an average increase of 12.5% in Hit Rate @10 and 15.3% in NDCG @10 on the Amazon dataset, and 9.8% in HR@10 and 11.7% in NDCG@10 on the LastFM dataset. The results indicate that explicitly modeling both the semantic context through self-attention and the temporal dynamics via point processes is crucial for accurate and timely recommendations in event-based systems.

Enhancing AURA AI: Integrating Emotion Recognition And Real-Time Web Intelligence In A Voice Assistant

Authors: Mr. Akhilesh M. Bhagat, Prof. S. V. Raut

Abstract: The advancement of artificial intelligence and natural language processing has led to the development of intelligent voice assistants capable of performing a wide range of tasks. However, most existing systems such as Siri, Alexa, and Google Assistant lack emotional understanding and real-time adaptability. This paper presents an enhanced version of AURA AI, an intelligent voice assistant built using Python and GPT technology, integrated with emotion recognition and real-time web interaction. The proposed system detects the user's emotional state through speech tone and facial expressions, allowing it to respond more empathetically and contextually. Additionally, real-time web integration enables the assistant to access live information such as weather updates, news, and general knowledge through APIs, providing users with up-to- date and personalized responses. Experimental evaluation demonstrates that the enhanced AURA AI offers improved user engagement, adaptability, and interaction quality compared to traditional voice assistants. This approach contributes toward creating emotionally intelligent and human-like conversational systems for next-generation AI applications.

Cognitive Computing

Authors: Ms. Rasika R. Patil, Renuka S. Durge

Abstract: Cognitive computing represents an advanced approach in artificial intelligence that aim to simulate human reasoning, learning and decision-making process. Unlike traditional AI systems that follow fixed algorithm, cognitive systems learn from continuously learn from experiences, adapt to new data and response intelligently to changing a new context. These systems integrate disciplines such as machine learning, deep natural networks and natural language processing to analyze large volume of structured and unstructured information. Cognitive computing enhances human machine interaction by enabling contextual understanding, pattern recognition and predictive reasoning. This pepar explores this architecture, working principles, and real-world application of cognitive computing in healthcare, business analytics, and autonomous systems. It also discusses current challenges, including data privacy, interpretability, and ethical implementation. The study concludes that cognitive computing holds to potential to create adaptive, transparent, and human like intelligent systems that redefine the future of decision making and automations.

Retrofitting Of Existing Vehicle Into Electric Vehicle

Authors: Prof. K.S.Tamboli, Gaiwad Nikhil Ganesh, Meher Karan Dnyandev, Kate Dhruv Balsabheb

Abstract: The global shift towards sustainable and eco-friendly transportation has intensified interest in electric vehicles (EVs) as a viable alternative to conventional internal combustion engine (ICE) vehicles. However, replacing every gasoline or diesel-powered vehicle with a brand-new EV is not only economically challenging but also environmentally taxing due to the resources and energy required for manufacturing new vehicles. As a practical and cost-effective solution, retrofitting existing vehicles into electric vehicles has emerged as an innovative approach to accelerate the transition to clean mobility. Retrofitting involves replacing the conventional drivetrain of a vehicle including the engine, fuel system, and exhaust with an electric motor, battery pack, and related control systems, thereby converting the vehicle into a fully electric one. This process extends the lifespan of vehicles, reduces emissions, and allows vehicle owners to enjoy the benefits of electric mobility without the need to purchase a new EV. This approach is especially relevant in developing countries, where the existing fleet of vehicles is large and often aging. Retrofitting not only helps in meeting stringent emission norms but also supports local industries and job creation by fostering a circular economy in the automotive sector. In this context, retrofitting serves as a bridge between current transportation realities and a more sustainable future, offering a promising pathway for reducing the carbon footprint of road transport while maximizing the utility of existing automotive assets.

Advancing Credit Card Fraud Detection With Machine Learning And Deep Learning Framework

Authors: Priyesh Mahajan, Nitin Namdev

Abstract: The rise of digital payments, credit card fraud has also grown, becoming a major challenge for the financial sector. To address this, more advanced detection systems are needed. Machine Learning (ML) and Deep Learning (DL) have proven to be powerful tools in this fight. These technologies learn from large volumes of transaction data, spotting patterns and unusual behavior that may signal fraud. Unlike traditional systems, ML and DL models can adapt and improve over time, making them effective against constantly changing fraud tactics. Integrating these models into fraud detection systems has already shown strong results, reducing the success rate of fraud attempts and helping to protect the security of credit card transactions. This review highlights the importance of ML and DL in strengthening fraud detection and improving trust in financial systems.

A Comprehensive Overview Of Deep Learning Methods For Violence Detection In Surveillance Systems

Authors: Sakshi Keshri, Nitin Namdev

Abstract: This paper presents a comprehensive review of deep learning techniques designed to enhance violence detection in surveillance systems. With the rapid advancement of surveillance technologies, the accurate identification of violent activities has become crucial for ensuring public safety. Conventional approaches often fail to cope with the complexity of video data, which inherently involves both spatial and temporal dynamics. To overcome these limitations, modern deep learning models such as Convolutional Neural Networks (CNNs), InceptionV3, Long Short-Term Memory (LSTM) networks, and hybrid architectures have been widely adopted. These methods excel at capturing spatial representations while simultaneously modeling temporal dependencies, making them well-suited for real-time violence detection tasks. The review further discusses essential preprocessing strategies—including noise reduction, feature extraction, and data augmentation—that significantly improve model robustness. In addition, it outlines persistent challenges such as class imbalance, scalability issues, and high computational costs, which remain key barriers to practical deployment

Optimized Deep Learning Framework For Automated Skin Lesion Diagnosis Using ResNet152

Authors: Om Dwivedi, Neelam Singh Parihar

Abstract: Skin cancer remains one of the most prevalent and life-threatening diseases globally, necessitating early and precise diagnosis. This research proposes an optimized deep learning framework using ResNet152 for automated skin lesion classification. The model integrates preprocessing, segmentation, and feature extraction to enhance lesion detection and classification accuracy. Experimental results demonstrate superior performance, achieving 97% accuracy, 98% precision, and 97% recall, outperforming existing ResNet variants. The framework’s robustness and adaptability make it suitable for clinical and remote diagnostic applications, promoting early intervention and reducing diagnostic errors.

AI Powered Machine Learning Framework For Analysis Of Composite Materials

Authors: Abhendra Pratap Singh, Nandini Sharma, Vanshika Dua, Arpit Dwivedi, Aakriti Sharma

Abstract: Composite materials are generated by intermingling two or more diverse components that are individually not able to do various tasks but when put together have become critically important in modern engineering due to their superior mechanical and structural traits. Fiber reinforced polymer (FRP) composites are utilized frequently in the aerospace automotive and construction industries more prominently. Despite their growing adoption, a continuing dilemma involves assessing natural fiber reinforced polymers (NFRP) over synthetic fiber reinforced polymers (SFRP) which differ greatly at the levels of performance cost and environmental impact. Both natural and synthetic composites have their own benefits and drawbacks such that synthetic composites offer excellent strength and durability and natural composites are gaining popularity due to their lightweight renewability and sustainability. This lack of unambiguous data driven comparison often leads to unclear judgment and leads to confusion in choosing the most viable composite for certain technical objectives. To eradicate this gap, the study examines three natural composites flax FRP, hemp FRP and jute FRP and three synthetic composites glass FRP, carbon FRP and aramid FRP. The paper uses computationally intensive analysis and machine learning methods such as linear regression and support vector machine (SVM) to figure out four crucial properties which mostly defines about the composite materials namely density, tensile strength, elastic modulus and moisture absorption. The visualized results of matplotlib based graphs provide a clear insight of how natural and synthetic composites perform individually and collectively through comparative analysis. This research incorporates AI assisted analytical modeling with scientific visualization to give a systematic and sustainable structure for selecting innovative composite materials.

DOI: http://doi.org/10.5281/zenodo.17481163

The Balance Between AI-based Surveillance Systems And Personal Information (Privacy): A Study Of Ethical And Technical Challenges.

Authors: Abhendra Pratap Singh, Nandini Sharma, Prince Kumar Sharma, Arpit Dwivedi, Aakriti Sharma

Abstract: The growing use of Artificial Intelligence (AI) in surveillance technologies changes how societies observe, predict, and manage security. From predictive policing to facial recognition, AI surveillance technologies offer real-time analysis, risk detection, and improved efficiency. Still, the rapid proliferation of such technologies brings issues of privacy, ethics, and accountability to the forefront. This review assesses the balance between human rights, AI ethics, and the surveillance technologies themselves. It demonstrates how China, the UK, and the USA have vastly different approaches toward data regulation, transparency, and consent. It also illustrates the major technical issues of algorithmic bias, data abuse, interoperability of privacy frameworks, and the ethics of large-scale surveillance and digital autonomy. By defining the gaps and analyzing the global pattern of such technologies, the paper aims to provide the most responsible and human-centric AI surveillance possible to guarantee privacy while also providing the oversight that people need

DOI: http://doi.org/10.5281/zenodo.17481315

An Introduction To Cybersecurity And Digital Forensics

Authors: Kanak Patil

Abstract: This paper provides an overview of cybersecurity and digital forensics, two related fields that are critical for digital security. It explains the basic idea that cybersecurity is about preventing attacks (like a shield), while digital forensics is about investigating them after they happen (like a sword). The paper will look at how both fields developed over time, the main areas within cybersecurity, and the standard frameworks used, like the one from NIST. It will also cover the step-by-step process of a digital forensics investigation, including the importance of keeping a "chain of custody" for evidence. Using real-world examples like the Stuxnet worm, the Equifax data breach, and the WannaCry ransomware attack, this paper shows how these concepts are used in practice. It also discusses the legal and ethical challenges, such as data privacy laws like GDPR and CCPA. Finally, the paper looks at future challenges, including the shortage of skilled professionals, new ways hackers are hiding their tracks, the role of Artificial Intelligence, and the threat of quantum computing to modern encryption. The main point is that to be effective, cybersecurity and digital forensics must work together, with the results of investigations helping to build stronger defenses for the future.

Automatic Waste Seggregation Dustbin With IoT

Authors: Dr Anitha S, Mohammed Muzammil S, Prince P, Sridhar L

Abstract: This proposed work designs an Automatic Waste Segregation System that focuses on separating metallic waste using a magnetic belt mechanism. This IoT based system uses a conveyor belt that carries mixed waste materials through a detection unit containing an electromagnet. When the magnet is activated, it attracts and separates the metallic components from the remaining waste. The metallic waste is then released into a separate compartment once the magnet is deactivated. A sensor is used to measure how much of the metal compartment is filled, and the fill level is displayed as a percentage on a digital screen. The entire operation is controlled by a microcontroller to ensure smooth and accurate functioning. This system helps reduce manual labor, increases sorting efficiency, and supports effective recycling management. It offers a simple, low-cost, and eco-friendly device to waste segregation that can be applied in both domestic and industrial settings

DOI: https://doi.org/10.5281/zenodo.17482056

Medicine Recommendation System Using Machine Learning Comparative Analysis

Authors: Abhijit Ranjan, Chandraveer Singh

Abstract: In the recent years, the demand for intelligent medication recommender systems has increased tremendously with the evolution of digital health technology. This research targets the development of a symptom-based medication recommender system from a structured and diversified healthcare database. The database is descriptive in nature with information regarding patient symptoms, associated medicines, dietary advice, exercise plans, precautions, and doctor specialties. Early steps of this project include extensive data exploration and preparation from several CSV files to create a clean and solid base for model training. For building the central recommendation engine, traditional machine learning algorithms such as Decision Tree, Random Forest, Naive Bayes, and Logistic Regression were utilized, which try to predict symptoms and suggest the most suitable medicines out of a pre-defined list. Among the models used, the Decision Tree classifier had the best performance, followed by Random Forest, Naive Bayes, and Logistic Regression. The system is smart enough for users to put in symptoms and get suggested medicines for the same, providing useful help for non-emergency medical conditions and employment in resource-constrained environments. Future developments will involve incorporating patient medical history, dosage calculation, drug interaction screening, and implementing the system through a mobile platform to enhance accessibility and real-time use.

DOI: http://doi.org/10.5281/zenodo.17482512

6G Technology :The Future of Wireless Communication

Authors: Vaishnavi F. Thakare, Prof .D. G. Ingale, Dr. A. P. Jadhav, Prof. S. V. Raut, Prof .S. V. Athawale, Dr.D.S.Kalyankar, Prof. R. N. Solanke

Abstract: The evolution of wireless communication has reached a critical juncture with the emergence of the sixth generation (6G) technology, envisioned to revolutionize global connectivity beyond the capabilities of 5G. 6G aims to deliver ultra-high data rates up to terabits per second, near-zero latency, enhanced reliability, and seamless integration of terrestrial and non-terrestrial networks. It leverages advanced technologies such as Artificial Intelligence (AI), Machine Learning (ML), Terahertz (THz) frequency bands, Blockchain, and Quantum Communication to enable applications like holographic communication, digital twins, autonomous systems, and immersive extended reality (XR). This paper explores the fundamental principles, key enabling technologies, potential applications, and challenges of 6G, offering insight into its role in shaping the future of intelligent and sustainable communication systems.

One Stop Decentralized Crowdfunding Platform

Authors: Ms. Vaibhavi P. Gawande, Dr. R. S. Durge

Abstract: Decentralized crowdfunding platforms represent a paradigm shift in fundraising, harnessing blockchain technology to disrupt traditional models dominated by intermediaries. By leveraging smart contracts on networks like Ethereum, these platforms facilitate direct interactions between creators and backers globally, ensuring transparency, security, and reduced transaction costs. Core features include immutable transaction records, automated fund releases governed by smart contracts, and enhanced trust through decentralized validation. Key benefits encompass increased accessibility for global participants, lower fees compared to conventional platforms, and improved security against fraud. Technologically, these platforms integrate Web3 interfaces, cryptocurrency wallets like MetaMask, and programming languages such as Solidity for smart contract development.

A Comprehensive Review On The Utilization Of Robotics Technology For Children With Autism

Authors: Abhendra Pratap Singh, Riya Rani, Devanshi Sharma, Nandini Sharma, Prince Kumar Sharma, Vanshika Dua

Abstract: Exchange of information, social interaction, and behavior are all impacted by autism spectrum disorder (ASD), which is a complicated neurodevelopmental disorder. Traditional therapies have demonstrated benefits in increasing quality of life, but they are still resource intensive and have limitations due to high costs and an inadequate number of specialists. With recent technical advancements, robotics has emerged as a promising addition to traditional treatment options. Socially Assistive Robots (SARs) are being investigated as therapies for assisting children with autism by increasing engagement, boosting social skills, also providing constant and compatible interaction. This study looks at the growing amount of research on the use of robotics in autism therapy, with an importance of robot design, human robot interaction, and medical applications. SARs can promote focus, emotional expression, and social communication. However, obstacles remain in terms of robot design, ethical considerations, and the necessity for standardized methods of assessing effectiveness in the real world. This paper's contribution is to thoroughly analyze existing methodologies and present a framework for designing robotic therapies based on individual needs. By synthesizing information related to education, healthcare, and robotics, this review identifies critical areas for further study and outlines future options for developing effective, accessible, and ethically feasible robotic therapy for children with ASD.

DOI: http://doi.org/10.5281/zenodo.17490817

Empowering AI At The Edge: Federated Learning For Autonomous Vehicles And Multirobots

Authors: Abhendra Pratap Singh, Riya Rani, Arpit Dwivedi, Nandini Sharma, Aakriti Sharma

Abstract: Cloud computing has historically been vital to the rapid advancement of multi-robot systems and autonomous cars for data processing, model training, and decision-making. However, the increasing demand for scalability, data privacy, and real-time responsiveness has exposed significant limitations of centralized cloud systems, such as excessive latency, bandwidth dependence, and security flaws. Federated Learning (FL) and Edge Computing (EC), which work together to provide decentralized and privacy-preserving intelligence, have become the focus of research in an effort to overcome these limitations. This paper addresses autonomous vehicles and multi-robots operated as edge nodes that train machine learning models locally on their own data, sharing just updates or model parameters with a central server instead of sending unprocessed information. This decentralized method greatly lowers communication costs, improves data secrecy, and facilitates real-time decision-making—all of which are crucial for operations that depend on safety. This paper’s contribution is to thoroughly analyze the issues including non-IID data dissemination, constrained computational and energy resources, and possible security risks, notwithstanding their benefits. In order to improve scalability, trust, and dependability, future research will combine block chain technology, 6G connectivity, and digital twin simulation. All things considered, the shift from cloud-centric computing to federated edge intelligence represents a critical advancement in the development of intelligent, safe, and effective autonomy in robotic ecosystems and next-generation automobiles

DOI: http://doi.org/10.5281/zenodo.17490951

Emotion Sense: A Deep Learning Facial Emotion Recognition System For Real-Time Application Using AI

Authors: Siddhi Pramod Lande, Samruddhi Ravindra Alhat

Abstract: Recognizing emotions is very important for connecting human emotions with artificial intelligence. This study introduces Emotion Sense, a sophisticated real-time facial emotion recognition system utilizing deep learning and explainable AI (XAI). The suggested system uses a better MobileNetV3 architecture along with Coordinate Attention (CA) and Grad-CAM visualization to get high accuracy and make the results easy to understand. The model recognizes seven fundamental human emotions: happiness, sadness, anger, surprise, fear, disgust, and neutrality. The FER-2013 data set. Emotion Sense solves two big problems that traditional CNN-based models have by combining real-time performance with explainability. This makes it both accurate and clear. The experimental results show that it is 90.2% accurate and runs smoothly at 25 frames per second on CPU devices. This shows that it is useful for real-world applications like healthcare, education, and human-computer interaction. This research is unique because it uses a hybrid design that balances speed, accuracy, and interpretability while staying strong in different real-world situations

DOI: http://doi.org/10.5281/zenodo.17491066

Consumer Preferences Toward Ready-to-Eat (RTE) And Ready-to-Cook (RTC) Food Products

Authors: A.K. Makwana, Prutha Priteshkumar Shah

Abstract: This research paper aims to understand consumer preferences toward ready-to-eat (RTE) and ready-to-cook (RTC) food products. With the growing pace of urbanization, changes in lifestyle, and time constraints, there has been an increasing inclination toward convenience foods. A survey was conducted among consumers to analyze their frequency of purchase, influencing factors, spending patterns, and preferences. The results indicate that taste, convenience, and price play significant roles in consumer decision-making, with the majority preferring vegetarian options and spending less than ₹500 monthly on such products. The study concludes with insights and recommendations for manufacturers to better meet consumer needs.

Advanced Deepfake Detection Using Machine Learning

Authors: Professor Pradnya Patange, Atharv Pate, Harsh lonari, Mayuresh Kshirsagar, Manish Patil

Abstract: The rapid advancement of deepfake technology has introduced significant challenges to digital media authenticity, enabling the creation of highly convincing synthetic images and videos that are difficult to distinguish from genuine content. This study proposes an advanced deepfake detection framework based on the Temporal Vision-Language Transformer (TVLT), a cutting-edge multimodal deep learning architecture that jointly learns from visual, temporal, and semantic representations. Unlike traditional convolutional or recurrent models that focus solely on spatial or temporal domains, the proposed TVLT-based system integrates cross-modal attention to capture complex correlations among video frames, motion patterns, and audio-text alignment cues. The model efficiently identifies inconsistencies in facial movement, speech synchronization, lighting, and micro-expressions—features that deepfake generation methods struggle to replicate authentically

AI-Assisted Multi-Sensor Fusion Using FPGA-Based Drones For Enhanced Battlefield Situational Awareness

Authors: Sanjay Singh, Manas Bajpai

Abstract: Modern battlefields demand real-time intelligence and rapid decision-making in complex environments. This paper proposes an AI-assisted multi-sensor fusion architecture deployed on FPGA-based Unmanned Aerial Vehicles (UAVs) for enhanced situational awareness. The system integrates data from hetero- geneous sensors—thermal, acoustic, visual, and radar—through a lightweight fusion algorithm optimized for FPGA implementa- tion. The use of adaptive AI-driven fusion enables low-latency, power-efficient and reliable detection of enemy drones, impro- vised explosive devices (IEDs) and human activity. Experimental simulations demonstrate significant improvements in detection accuracy and response time compared to conventional centralized systems.

DOI: https://doi.org/10.5281/zenodo.17528750

Emerging Threats In Internet Of Things Security

Authors: Aadesh Patil, Mohit Jain, Krish Bangre, Laiba Syed, Mohisha Sharma, Raghav Jangid, Reshma Sonar

Abstract: The Internet of Things (IoT) has grown from a simple idea of connected devices to a huge network that is part of our daily lives and important industries. As it has grown, it has also become a bigger target for hackers. This paper looks at new and emerging threats to IoT security. We group these threats into four main areas: the use of Artificial Intelligence (AI) for attacks, problems in the supply chain where devices are made, weaknesses in new networks like 5G, and the future risk from quantum computers. We will explain how AI can be used to create smart botnets and malware that changes to avoid being caught. We will also look at how vulnerabilities can be put into devices before they are even sold, through both software and hardware. We will examine the security issues that come with 5G and edge computing. Finally, we will discuss the major threat that quantum computing poses to the encryption we use today. Using real-world examples like the Mirai, Stuxnet, Verkada, and SolarWinds attacks, we will show how serious these threats can be. In response, we will look at modern defense ideas like the Zero Trust Architecture and the potential use of blockchain. The paper concludes by summarizing these new threats and pointing out where more research is needed to make the IoT world safer.

IReport – A Cybercrime Assistant

Authors: Dr. S Anitha, Jesvin Bruce J, Jasvanth K, Devi K, Dhanusurya S

Abstract: The rise in cybercrime cases in India has brought about an imperative for a safe and intelligent reporting mechanism. iReport: A Cybercrime Assistant is an online platform that could perhaps provide registration, categorization, and tracking of cybercrime complaints as a simplified process. It utilizes Natural Language Processing and Machine Learning algorithms to identify the type of crime and prioritize levels to be investigated at high speed. A multilingual chatbot facilitates users through the filing process with support in several Indian languages offered effortlessly. Different user roles such as citizens, police, and administrators are handled by a role-based access control system to maintain privacy and administration. The website also utilizes strong encryption mechanisms like AES-256 and SSL to protect data from users and ensure data integrity. Having real-time complaint status updates, a solid RESTful backend, and a database of complaints in structured format, iReport aims to have a free, efficient, and effective online mechanism for reporting and managing cybercrimes

DOI: http://doi.org/10.5281/zenodo.17500894

Smart Hyperlocal Event Finder: Leveraging Geolocation and Personalized Filters

Authors: Milan Bhimani, Treesha Bacchuwar, Asst. Prof. Himanshu Tiwari, Sadap Bibi, Zinal Shah

Abstract: The rapid growth of digital platforms has transformed the way people discover and participate in local events. However, existing event discovery systems often focus on large-scale gatherings, neglecting hyperlocal activities rel- evant to smaller communities such as college campuses. This paper presents the design and development of a Hy- perlocal Event Finder System, a web-based platform that leverages geolocation and interest-based filtering to provide users with real-time access to nearby events. The system is built using a full-stack architecture with React.js for the frontend, Express.js and Node.js for the backend, and MongoDB as the database.It integrates user authenti- cation, event management, and recommendation features to deliver a seamless experience. Testing and evaluation demonstrate that the platform effectively addresses acces- sibility, scalability, and usability challenges, making it a promising solution for students and community members seeking context-specific event engagement. Future work will enhance personalization through advanced recommen- dation models and mobile application support.

Brain-Computer Interfaces As The Form Of Natural User Interfaces: A Comprehensive Analysis Of Neural Control Systems

Authors: 1Mr. Rushi A. Jadhao, Prof. S. V. Athawale, Prof. S. V. Raut

Abstract: Brain-Computer Interfaces (BCIs) are fundamentally reshaping the landscape of human-machine interaction, rapidly emerging as an advanced generation of Natural User Interfaces (NUIs) that enable direct, non-muscular communication between the human brain and external apparatus. This paper systematically integrates BCI technology with established NUI principles, demonstrating how sophisticated neural control systems facilitate intuitive and natural data interaction within complex digital environments. The analysis encompasses recent technological milestones and practical uses, particularly within healthcare, communication restoration, and advanced assistive technologies, while simultaneously providing a critical evaluation of persistent operational, economic, and ethical challenges. A systemic review of contemporary technological trajectories, especially those anticipated for the 2024–2025 period, strongly suggests that BCIs are poised to become the quintessential example of an NUI, leveraging raw neural signals to permit effortless, volitional control over devices. Consequently, this study positions BCIs as potent, evolving technologies capable of enriching human functions and addressing neurological pathologies, a potential underpinned by stellar advancements in high-fidelity non-invasive EEG systems, Artificial Intelligence (AI)-enhanced signal processing, and integrated multimodal interfaces.

Protecting The Invisible Assistant: Cybersecurity Architecture For AI-Based Personal Assistants

Authors: Mr. Ghanshyam Gajanan Lihankar, Prof. Snehal. V. Raut

Abstract: This research presents the design and development of a cybersecurity framework for AI-based personal assistants. These assistants, such as Alexa, Siri, and Google Assistant, are widely used for daily tasks but face growing risks from cyber threats and data breaches. The proposed system focuses on improving the security, privacy, and trust of AI assistants by identifying potential vulnerabilities and applying defense mechanisms. The paper explains the architecture, which includes threat detection, secure communication, user authentication, and data protection layers. This model ensures safe interaction, protects user information, and defends against unauthorized access or manipulation using advanced security techniques.

Model Of Photovoltaic DC-DC Converter

Authors: Kalaji L K, Thyagarajan K

Abstract: This paper presents a MATLAB/Simulink model of Photo Voltaic (PV) using Maximum Power Point Tracking (MPPT) technique and a converter. This model provide 200 V output from a 24 V input. The development of PV model, the integration of the MPPT with an average model of power electronics and the MATLAB implementation are described. The converter section consists of an isolated coupled inductor DC-DC converter. It has high gain. It consist of a dual-voltage doubler circuit. In addition, the energy in the coupled inductor leakage inductance can be recycled via a nondissipative snubber on the primary side. Thus, the system efficiency is improved. It completes the simulation of a PV energy conversion system.

DOI: https://doi.org/10.5281/zenodo.17510805

Deep Learning-Based Fruit Quality Detection

Authors: M. Anbarasan, Dr. P. Guhan

Abstract: Fruit quality inspection plays a critical role in reducing post-harvest losses and ensuring consumer safety in the agricultural supply chain. Conventional manual inspection techniques are time-consuming, manual and ineffective on larger scales. To address these constraints, the paper introduces a model of identifying fruit quality using deep learning techniques that employ methods of digital image processing. The model exploits two-stage and evaluation procedure including classification and detection operation. We used pre-trained DenseNet networks with transfer learning to divide the fruits into three quality levels of Fresh, Overripe, and Spoiled quality. The method of image preprocessing normalization, filtering, and augmentation were used to increase the model robustness. The DenseNet model had an evaluation accuracy of 97.82%, which was higher as compared to SVM (89.53%) and Random Forest (90.21%) which are the conventional classifiers. Parallel to it, we also tested object detection models such as YOLOv8 to recognize and bound fruits with bounding boxes and label quality. YOLOv8 was revealed to be very fast with an average precision (mAP) of 96.1% and intersection over union (IoU) of 87.3%. It was also calculated that precision, recall, F1-score, and the time of inference were taken across 10 models. Findings confirm the efficiency of deep learning in automating the process of fruit quality determination to consequently deploy real-time applications in separating systems. The presented model is very flexible to other types of agricultural products and compatible with smart farming and automation processes that include retailing. Generally, this work fills the nexus between manual inspection and smart visual systems by making the fruit quality monitoring scalable, consistent, and efficient.

Advanced Port Scanning Tool: A Python-Based High-Performance Scanner

Authors: Pushkar Chaudhari, Vaibhav Thakre, Tushar Chaudhari, Tanaya Bhaute, Dr.Rais Khan

Abstract: Port scanning is an essential technique in the arsenal of network security professionals, enabling the identification of active services and potential vulnerabilities on target systems. Despite advances in the field, traditional tools like Nmap and Masscan face limitations in usability, scan speed, resource efficiency, and integration capabilities. This paper presents the development and robust evaluation of an advanced port scanning tool built with Python, applying multi-threading and asynchronous techniques. Through comparative assessments, the proposed tool demonstrates compelling advantages in speed, resource efficiency, and cross-platform support, contributing to both practical and academic applications in cybersecurity.

Deep Learning-Based Helmet Detection for Road Safety

Authors: T. Sekar, A. Sangeetha

Abstract: The increased number of road accidents associated with violating two-wheeler helmet usage is very alarming and this situation demands the introduction of smart surveillance systems to maintain safety of the people. In this paper, we present the idea of a new system of helmet detection using deep learning algorithms and image processing to detect whether a person is not wearing a helmet automatically or not. The publicly available Kaggle Helmet Detection dataset that includes 7,500 images having annotations of bounding-boxes of helmet head, and person is used by the system. We transform the annotations to a binary classification task – Helmet and No Helmet and use the YOLOv5 object detection model because of its speed and accuracy of the inference. This was done by training the model using transfer learning and optimizing the model with data augmentation techniques to achieve cross generalization under different kinds of light and environmental conditions. Our system is tenable based on the results of experiments because it took into consideration a real-world scenario. The model based on the YOLOv5 had a generally high accuracy of 95.64%, precision of 94.32%, recall of 91.23% and an F1-score of 92.75. Real-time inference can also be done with the system as it can perform 24.56 ms/frame. This will fit it to be used in surveillance systems in a city environment. Also, Deep SORT tracking has been integrated to provide effective tracking without redundancy. This project will be useful in the development of intelligent traffic systems to automate the process of identifying non-helmets with high precision making it useful to law enforcement and citizen and driver safety on the road. It may be extended in the future to have modules of number plate recognition and fine imposing modules to be able to implement all the traffic rules.

Cork: The Futurity of Sustainable Building Solutions Construction and Building Materials

Authors: Sai Sandra, Niranjini Shibu

Abstract: The sustainable development goal-11 symbolizes the need for sustainable cities and communities in the forthcoming generation. It is necessary to built safe, resilient and greener environments in and around the habitats. The growing emphasis on sustainability in the construction industry has led to the exploration of biobased and eco-friendly materials. Among these, cork has gained significant attention due to its renewable nature, versatility, and exceptional physical properties. Derived from the bark of the cork oak tree, cork offers remarkable environmental, mechanical, and thermal advantages, making it a promising material for sustainable construction. This paper explores the origin, environmental significance, material properties, applications in the construction Industry and future potential of cork as a sustainable building material. By integrating cork into modern construction practices, the industry can substantially reduce the carbon emissions, safer communities and environmental footprint while enhancing the durability and efficiency of built environments.

DOI: https://doi.org/10.5281/zenodo.17519248

Crop Disease Detection Using Machine Learning

Authors: Prof. Meghraj Patil, Ms. Priyanka Jondhale, Ms. Sakshi J. Pawale, Ms. Neha Marathe, Ms. Sakshi S. Pawale

Abstract: Agriculture plays a vital role in sustaining the global economy, and plant health directly influences crop productivity and food security. However, plant diseases often go undetected at early stages due to the limitations of manual inspection, which requires expert knowledge and is prone to human error. To address this challenge, the proposed project introduces an intelligent plant disease detection system based on machine learning and image processing techniques. The system operates by acquiring leaf images, which are then preprocessed through resizing, normalization, and noise reduction to enhance visual quality. Advanced deep learning models such as Convolutional Neural Networks (CNN) are employed to automatically extract relevant patterns and classify leaf images into healthy or diseased categories. A labeled dataset containing multiple plant species and disease variants is used to train and evaluate the model for high accuracy and generalization. Once trained, the model is integrated into a user-friendly interface, allowing farmers or agricultural professionals to upload or capture images using a mobile or web application and receive instant diagnostic results along with suggested remedies. This automated solution significantly reduces dependency on expert consultation, minimizes economic loss due to late detection, and promotes precision agriculture. Moreover, the system can be continuously improved by expanding the dataset to support additional crops and diseases, making it scalable and sustainable for real-world deployment. Overall, this project demonstrates an efficient, low-cost, and technology-driven approach to plant disease management, enabling smarter decision-making and contributing to global agricultural resilience.

AI Based Fitness Assistant

Authors: Deepika Upadhyay, Shubham Ubale, Mandar Gholap, Aditya Dhavale, Aditya Habbu, Akash Gaikwad

Abstract: The AI Fitness Assistant is a comprehensive web application built using Next.js, React, and TailwindCSS that revolutionizes personalized fitness and nutrition planning. The platform integrates voice AI capabilities through Vapi and leverages Gemini AI for intelligent program generation, enabling users to engage in natural conversations about their fitness goals, physical limitations, and dietary preferences. The system generates customized workout routines and meal plans tailored to individual needs, including accommodations for injuries and allergies. Key features include secure authentication via Clerk, real-time database management through Convex, and responsive design for cross-device accessibility. The application supports multiple program creation while maintaining focus on the most recent active plan, ensuring streamlined user experience and effective fitness journey management.

Study of Swarming Logistics with Tactical Last Mile Delivery

Authors: Maj Nilam Gorwade, Dr. John A

Abstract: Last-mile delivery in a military context can often be dangerous, putting personnel and the supplies they carry at risk. The emergence of aerial ‘delivery drones’ from the commercial delivery sector highlights the possibilities of uncrewed vehicles being used in last-mile delivery. However, demonstrations of such technology have been limited to single vehicle deliveries, where only small portions of supplies can be delivered at once. This paper explores the concept of low-cost, uncrewed vehicle swarming for tactical last-mile delivery in a deployed setting. The benefits of uncrewed swarming systems over conventional methods of resupply are discussed, as well as the vulnerabilities and challenges faced by such systems.

DOI: https://doi.org/10.5281/zenodo.17522385

Forensic Browser Monitoring System

Authors: Mr. Karthiban R, Dhayalan K, Akshita K, Jerisha Flavio J, Kalaiselvi S

Abstract: As digital learning environments continue to evolve, maintaining secure and focused internet usage has become a critical requirement for institutions and organizations. Existing browser monitoring tools often lack real-time visibility and are unable to detect VPN-based evasion techniques, which users exploit to bypass access restrictions. To address these limitations, this work proposes an intelligent browser activity monitoring and VPN detection system featuring a centralized administrative dashboard. Built on a Flask-based backend, the system securely gathers and visualizes browsing data through interactive charts and tables. A machine learning model continuously refines detection by learning administrative preferences—distinguishing between authorized and unauthorized sites—and improving decision accuracy over time. The adaptive framework enhances detection precision by integrating AI-driven behaviour learning with network anomaly analysis. By evaluating parameters such as IP consistency, latency fluctuations, and metadata patterns, the system effectively identifies tunnelling or masked connections even in encrypted networks. Its modular and cross-platform architecture ensures seamless data flow between clients and the central dashboard while preserving privacy and performance. Designed for scalability and reliability, the solution provides administrators with actionable insights and real-time control, making it an effective tool for maintaining policy compliance and secure browser activity in educational and institutional environments.

DOI: http://doi.org/10.5281/zenodo.17529567

Product Verification System

Authors: Anshika saxena, Ahmad Hussain Ansari, Lalit Chowhan, Ashutosh Vishwakarma, Gyanendra Maurya

Abstract: Counterfeit products continue to pose significant challenges for manufacturers, distribu- tors, and consumers worldwide. They contribute to revenue losses, erode customer trust, create safety hazards, and cause long- term brand damage. According to global trade reports, coun- terfeit goods account for billions of dollars in annual losses across industries, with pharmaceu- ticals, electronics, and consumer goods among the most affected sectors. Traditional methods of product authentication, including holograms, barcodes, and RFID tags, either lack robust security or remain too costly for large-scale deployment. To overcome these limitations, this study proposes a Product Verification System that inte- grates QR codes with a MongoDB-based backend for efficient product traceability. The system architecture employs ReactJS for a user-friendly and modular frontend, Node.js with Express for secure API management, and MongoDB as a centralized, scalable database. At the point of manufacture, each product is assigned a unique QR code linked to its database record. Con- sumers can verify authenticity instantly by scanning the code with a smartphone, while manu- facturers and sellers gain real-time visibility into the supply chain.s Unlike conventional approaches, the proposed framework not only ensures authenticity but also supports analytics and reporting features, enabling stakeholders to monitor product dis- tribution, detect anomalies, and analyze consumer interaction patterns. This capability makes the solution adaptable for diverse sectors, including pharmaceuticals, electronics, and cosmet- ics, where transparency and safety are critical. The proposed system is cost-effective, scalable, and reliable, offering a practical balance between security and affordability. By leveraging accessible technologies such as QR codes and a flexible NoSQL database, it provides an imple- mentation pathway that is both technically feasible and industry-ready, making it suitable for mass adoption across global markets.In addition, the system introduces role-based access control (RBAC), ensuring that only authorized users such as administrators, manufacturers, and sellers can access or modify sen- sitive product information. The Admin Dashboard provides centralized control for managing users, viewing verification statistics, generating audit logs, and detecting counterfeit attempts through anomaly tracking. The Seller Module enables sellers to register genuine products and upload production details, while the Consumer The platform is further enhanced with real-time data synchronization, secure authentica- tion (JWT-based login system), and RESTful APIs, which maintain seamless communication between the client and server. Data integrity is preserved through encrypted QR code gen- eration and verification processes, while reporting and analytics tools assist manufacturers in monitoring sales regions, scanning frequency, and product lifecycle performance. Future scalability options include integration with blockchain networks to achieve im- mutable product records, AI-based anomaly detection for identifying suspicious activities, and cloud deployment for handling high- volume data operations. By combining robust backend design with modern frontend usability, this system delivers a holistic solution that bridges the gap between product authenticity, supply chain visibility, and consumer trust.

DOI: https://doi.org/10.5281/zenodo.17529956

Blood Bank And Donor Locator Website

Authors: Ms. Dhivya, Dhanushya S, Akash S, Bharath Raj P, Mathan Kumar J

Abstract: The shortage and inefficient management of blood resources often lead to life-threatening delays in critical situations. To address this challenge, the Smart Blood Bank and Donor Locator Website has been developed as an integrated, intelligent web-based system that connects donors, hospitals, and recipients under one unified digital platform. The system uses SQLite databases for storing donor, hospital, stock, and request information efficiently. By integrating Google Maps API, it enables real-time location tracking and the display of nearby donors and hospitals based on blood group compatibility. Email notifications powered by Brevo are automatically triggered for key events such as donor registration, request confirmation, stock updates, and periodic reminders after three months for eligible donors. The multilingual support feature powered by Google Translate API ensures accessibility to users across various linguistic backgrounds. The system aims to create a digital ecosystem for managing blood donation, enhancing communication between hospitals and donors, and promoting awareness about blood donation in an efficient, transparent, and user-friendly manner. Future integration of IoT-based sensors for blood storage monitoring can further enhance the intelligence and automation of the system

DOI: http://doi.org/10.5281/zenodo.17538993

Analysis Of Drinking Water Of Different Places BHOHAPARA Janjgir Champa. C.G.

Authors: Pradeep Kumar Jaiswal, Rakesh Kumar Yadav, Manish Kumar Tiwari

Abstract: The study is based on the analysis of drinking water parameters in an Educational institute situated in BHOHAPARA area, jajngir champa C.G. In this paper, different authors’ papers are summarized on water analysis and their treatment processes in different region, which is helpful to know the different treatment processes and parameters used in the study.

Survey On Customer Behavior Data Analysis For Product Purchasing

Authors: Keerti Pal, Prof. Jayshree Boaddh, Prof. Rahul Patidar

Abstract: Product Sales Dataset is a comprehensive collection of sales data for a wide range of products available on the E-commerce e-commerce platform. This kind of dataset provides invaluable insights into customer behavior, product performance, and market trends, making it an essential resource for data analysis, market research, and business strategy development. This dataset is indispensable for market research, allowing businesses to discern market trends, consumer preferences, and competitive landscapes. This paper presents a comprehensive approach to customer behavior analysis and predictive modelling within the context of supermarket retail. This paper finds techniques that extract patterns in shopping data for the learning and prediction of user preference. This work list different proposed models with techniques. Paper has list various evaluation parameters of user purchase prediction models.

DOI: http://doi.org/10.5281/zenodo.17542512

Seclogx – Serverless Real-Time Events Monitoring and Alerting System Using Aws

Authors: Ms.Sabitha K, Bhavayazhinitha S V, Jashwanth M U, Gunal S, Gokulnath K

Abstract: Organizations of the modern digital era create massive volumes of operation and security event data that must be monitored in real time and responded to immediately. Server-based monitoring systems are generally connotated with bad scalability, high overhead maintenance, and delayed response to alerts. To solve all these problems, SeclogX – Serverless Real-Time Monitoring and Alerting System is proposed, using Amazon Web Services (AWS) to create an entirely serverless, event-driven system. The system makes use of various AWS services, including Amazon API Gateway (REST & WebSocket), AWS Lambda, Amazon DynamoDB, Amazon SNS, Amazon CloudWatch, and Amazon S3 with CloudFront, in order to provide high availability, low latency, and real-time processing. Frontend dashboard ran over the internet on Amazon S3 and CloudFront and admin- accessible site-deployed web site and dashboard used for real-time system status and live event visualization. Anomaly detection is done automatically, data processing is done through Lambda functions, event logs are stored in DynamoDB, and alerts are triggered through SNS. Monitoring and logging are provided through CloudWatch for system health and operational intelligence. With the implementation of this serverless architecture, SeclogX removes the maintenance overhead, enables scalability, and conserves the cost while not sacrificing its real-time alert on crucial events. The result indicates that the model is an affordable, secure, and scalable event monitoring system that can be adopted by various industries.

DOI: https://doi.org/10.5281/zenodo.17551088

Cyber Security Awareness Learning Application For Educational Institutions

Authors: C.Jaya Prakash Reddy, R.Jaswanth, K.Rajeshkumar

Abstract: In a world where digital threats are on the rise, especially in education, we designed a mobile-first LMS (Learning Management System) to promote cybersecurity awareness in universities. Using Flutter for cross-platform app development and Firebase for cloud backend, this solution helps students and staff learn, interact, and stay informed—even offline. Key features include video modules, real-time quizzes, secure authentication, and user-friendly dashboards tailored for students, instructors, and admins. It’s lightweight, fast, scalable—and ready to make cybersecurity education smarter and more accessible.

Effectiveness Of Interactive Coding Simulations In Educating College Students To Detect And Avoid Phishing Attacks

Authors: Praniti Gijare, Sneha Kunnummal, Suhani Heblikar, Harsh Sakhare, Rushabh Parab, Reshma Sonar

Abstract: Phishing attacks targeting college students have surged by 224% in the education sector during 2024, In recent months, attacks aimed at stealing login details have exploded in volume, with credential-related phishing growing at an unprecedented rate and now representing the fastest-rising threat faced by campus communities. Methods that rely mainly on lectures or passive training have not made a substantial impact on how well students identify or avoid phishing threats, leaving many learners at risk despite completing such programs in reducing phishing susceptibility, with studies revealing minimal behavioural change despite widespread implementation. This research investigates whether interactive coding simulations using Python-based phishing detection exercises can significantly improve college students' ability to identify and avoid phishing attacks compared to conventional lecture-based training. A quasi-experimental pre-test post-test design employed 90 undergraduate students across three groups: interactive simulation training (n=30), traditional lecture-based training (n=30), and control group (n=30). The interactive group developed basic Python scripts to detect phishing characteristics including suspicious URLs, sender anomalies, and social engineering tactics. Results indicate the interactive simulation group demonstrated Students who took part in coding-based, hands-on exercises were able to spot phishing attempts nearly twice as well as those who received traditional classes, showing a remarkable 42% boost in detection skills over standard methods 18% in the lecture-based group and The students who didn’t receive any security Students who didn’t participate in any cybersecurity activities barely improved at all, showing almost no change in their ability to recognize phishing scams; this highlights that without fresh skills, people generally stick to old habits even when digital threats are increasing 5% improvement, which suggests that without any active intervention, most people simply continue their usual habits even if they face ongoing cyber risks. The findings suggest hands-on coding simulations provide superior learning outcomes through experiential engagement, addressing a There is a clear need for practical, engaging Cybersecurity education still relies heavily on traditional classroom approaches, most of these traditional methods don’t really equip students for the types of scams and online risks they will actually encounter in their daily lives, leaving important gaps in both confidence and readiness the constantly changing landscape of digital threats fail to prepare students for real-world online risks they often lack the tools and confidence, most students aren’t equipped with the practical skills they need to spot and steer clear of today’s online dangers, which means entire groups remain vulnerable unless more effective and engaging education is provided.

Antimicrobial Insight into The Newly Synthesized and Spectroscopically Characterized Schiff Base- Metal Complexes

Authors: Garima, Ravi Kumar Rana, Niranjan Singh Rathee

Abstract: In the current research work a new Schiff base (2,2'-((1E,1'E)-((4-methyl-1,2-phenylene)bis(azaneylylidene))bis(methaneylylidene))bis(4-bromophenol) (H2L) and its metal complexes were prepared using condensation reaction of 3,4-diaminotoluene and 5-bromo salicylaldehyde. The Schiff base was further coordinated with Co2+, Ni2+, Cu2+& Zn2+ metal ions to synthesize its 1:1 metal complexes. All the synthesized compounds were examined using a variety of characterisation methods, including proton-NMR, electronic, mass, ESR, IR spectroscopy and TGA. FT-IR and NMR spectral data elucidate that the metal ions are coordinated with the tetradentate ligand through 2-N (imine) and 2-O (hydroxyl) atoms. All of the metal complexes were assumed to have an octahedral geometry based on the UV-Visible spectra. Antibacterial & antifungal activity of synthesized product was tested. The results demonstrated that all the sample exhibited considerable antimicrobial properties.

DOI: https://doi.org/10.5281/zenodo.17557767

Voyagers Beyond Time: The Scientific And Cultural Legacy Of NASA’s Voyager Missions In The Era Of Interstellar Exploration

Authors: Morziul Haque, 2Mohammed Shaik Fahad, Ansh Goyal, Bhagyashree .N. Singh, Priyanka Sahu, Dr. Basavaraj Neelur, Deepak Kumar Punna, Lavanya Dahiya

Abstract: In 1977, NASA launched two identical spacecrafts known as Voyager 1 and 2, which is the most important ambassador of mankind to the universe. Voyager as a project which was originally intended to be a planetary exploration mission, transformed into a historic project which incorporated both scientific, engineering and humanistic goals. Throughout a period of close to five decades, the Voyagers have unrelentingly provided deliveries in terms of firsts in regard to the outer planets, the heliosphere and the interstellar medium. They are nowadays the well-known stepping-stones of human inquisitiveness and venture beyond the solar frontier. The theory discussed in this paper is a literature review about the current scholarly debate around the issue of the scientific success, the engineering strength, and cultural meaning of the Voyager mission in terms of the 21st century digital era. It also calls attention to modern reinterpretations of Voyager data with these aspects may involve the use of artificial intelligence (AI) and astrophysical modeling, as well as the continued debate surrounding the following, interstellar communication and preservation. Through the lens of both the empirical heritage and with an emphasis on the philosophical influence of the Voyager program, this review explores the mission against the background of 21st century space exploration and human self-understanding.

DOI: https://doi.org/10.5281/zenodo.17568692

Distinguishing AI-Generated vs Human-Written Code for Plagiarism Prevention

Authors: Aryan Bhatt, Aryan Verma

Abstract: Artificial Intelligence (AI) methods, specifically Large Language Models (LLMs), are increasingly being employed by developers and students to produce source code. Though helpful, such AI-produced code is problematic in terms of plagiarism, originality, and academic honesty. Hence, differentiating between code written by humans and code generated by AI has become vital for the prevention of plagiarism. This article provides an empirical evaluation of current AI detection tools to determine how well they can detect AI-generated code in educational and coding environments. The findings indicate that most of the tools are ineffective and not generalizable enough to be useful for detecting plagiarism. In order to deal with this problem, we suggest a number of solutions, such as fine-tuning LLMs and machine learning-based classification based on static code metrics and code embeddings obtained from Abstract Syntax Trees (AST). Our top-performing model outperforms current detectors (e.g., GPTSniffer) and gets an F1 score of 82.55. In addition to that, we carry out an ablation study to study the contribution of different source code features to detection accuracy.

DOI:

NetGuard: An AI-Based Anomaly Detection System For Securing Network Traffic

Authors: Aakanksha Raghunath Chaudhari, Sharmistha Sujit Sarkar

Abstract: With the rapid growth of digital communication and online services, network security has become a primary concern for organizations and individuals. Traditional intrusion detection systems (IDS) rely heavily on predefined signatures, making them ineffective against zero-day attacks and unknown threats. To overcome these limitations, AI-based anomaly detection systems have emerged as a powerful approach for identifying unusual patterns in network traffic that may indicate malicious activity. This research introduces NetGuard, an intelligent system that leverages machine learning and deep learning techniques to detect anomalies in network traffic. The system provides real-time threat detection, reduces false alarms, and enhances network resilience against evolving cyber threats.

DOI: https://doi.org/10.5281/zenodo.17570618

Literature Survey:Deepfake Detection Using CNN & Temporal Feature

Authors: Prof. Sangeeta Alagi, , Priti Jagdale, Swati More, Vaibhav Prasad

Abstract: The rapid advancement of deep learning technologies has enabled the creation of highly realistic synthetic media, commonly known as deepfakes. These manipulated videos pose serious threats to information integrity, personal privacy, national security, and public trust. This comprehensive literature survey examines the state-of-the-art approaches in deepfake detection, with particular emphasis on methods that combine Convolutional Neural Networks (CNNs) for spatial feature extraction with temporal analysis techniques. We systematically review detection methodologies, benchmark datasets, evaluation metrics, current challenges, and emerging research directions. This survey synthesizes findings from over 50 research papers published between 2018 and 2024, providing insights into the evolution of detection techniques and the ongoing arms race between deepfake generation and detection technologies.

Expense Tracker Web Application

Authors: Mrs. Khatal Kavita, Miss Akanksha Vishwasrao, Miss Nikita Shinde, Miss Apeksha Vishwasrao

Abstract: Managing daily expenses is an important task for people who want to keep track of their finances. The Expense Tracker Web Application is built to make it easier to record, manage, and understand personal financial data. The backend runs on Python Flask, and the front end uses HTML, CSS, and JavaScript to create a user-friendly and responsive interface. The backend supports basic functions like adding, editing, deleting, and viewing expense records, while also ensuring that the data is valid and accurate. It stores information securely in a SQLite database, which allows users to keep and access their financial records easily. The application uses Pandas for handling data and Matplotlib or Plotly for creating visual graphs. This lets users see their spending patterns by category and over time through pie charts and bar or line charts. Additionally, the project focuses on security by cleaning up inputs, checking user data, and optimizing queries for better performance. The system helps users manage daily expenses by organizing data and providing login protection and visual insights to support effective tracking and analysis of spending..

Cloud Gaming Optimization Using AI Techniques

Authors: M. Kumaraguru, B. Bhuvaneswari

Abstract: Cloud gaming is a rapidly evolving domain that provides seamless access to immersive, high-quality gaming experiences. Despite its advantages, reducing latency remains a significant hurdle, especially under varying network conditions. This study introduces an innovative solution that leverages artificial intelligence (AI) to tackle these issues. The proposed system integrates AI techniques to enhance multiple facets of cloud gaming, such as video compression, traffic routing, resource distribution, and prediction of user interactions. Machine learning algorithms continuously fine-tune streaming configurations in response to live network metrics and individual user preferences, thereby lowering latency and boosting visual fidelity. Furthermore, reinforcement learning is employed to optimize backend resource management, improving both scalability and operational efficiency. The use of AI-powered predictive analytics facilitates customized gameplay by forecasting user behavior and dynamically adjusting game mechanics. Through behavioral analysis and preference modeling, the system personalizes content delivery, difficulty settings, and in-game support.

Fault Detection and Localization in DC Micro-grid using Programmable Logic Controller and Arduino Microcontroller

Authors: Hachimenum Nyebuchi Amadi, Biobele A. Wokoma, Victor Nneji Chikwendu, Richeal Chinaeche Ijeoma

Abstract: A micro-grid is a localized energy system that typically operates as part of a larger, wide-area synchronous grid but can function independently when necessary. It comprises energy generators, loads, storage units, and control systems, all highly integrated and manageable. This study presents the design and implementation of a 60,000-watt solar photovoltaic (PV) microgrid incorporating an advanced fault detection and localization mechanism, aimed at addressing the limitations of conventional reactive fault systems. These traditional systems often respond only after fault currents surpass the tolerance thresholds of grid components, leading to reduced efficiency, equipment damage, or total system failure. To mitigate these issues, a DC micro-grid consisting of six solar PV arrays was modeled using Proteus 8.15 Professional and Siemens TIA Portal. Each array comprised 32 units of 400W, 12V panels arranged in an 8×4 configuration, delivering 72V per array. The PV arrays were individually connected through dedicated contactors (MCB1–6). Fault detection and isolation were achieved using smart electronics, specifically Arduino Nano microcontrollers integrated with WCS1600 current sensors capable of sensing up to 500A. The system efficiently identified and isolated faults occurring within any array. During testing, no faults were flagged for current values of 72.33A, 90.42A, 123.69A, 117.15A, 172.02A, and 199.09A, as they remained within the safe 200A threshold. However, overcurrent values recorded at PV arrays 3, 4, 5, and 6 (235.09A, 307.43A, 412.72A, and 209.09A, respectively) due to simulation of fault (short circuit, load-related faults, battery system faults, DC bus fault or converter and distribution fault) were promptly detected, and the affected arrays were disconnected to protect the system. Compared to previous research, this approach leveraging a hybrid of Arduino Microcontroller and Siemens S7-1200 PLC (CPU1214CDC/DC/DC) demonstrated improved efficiency and reliability in proactive fault detection and localization. Ultimately, the study successfully developed a programmable, feedback-enabled microgrid system capable of anticipating and mitigating faults before component tolerance limits are breached.

DOI: https://doi.org/10.5281/zenodo.17587332

Optimal Designing Of Micro-grid Systems With Hybrid Renewable Energy Technologies For Sustainable Environment

Authors: Hachimenum Nyebuchi Amadi, Iyowuna Winston Gobo, Ugochi Benedicta Uche-Ibe, Richeal Chinaeche Ijeoma

Abstract: The reliance on fossil fuels and the need for effective battery management are significant challenges that renewable micro-grids seek to address. Fluctuations in supply and demand often result in higher operational costs and increased dependence on the external grid. With the urgent need to confront energy and environmental issues like global warming, transitioning to clean energy sources is becoming more viable. This study focuses on the Jetty 11kV feeder from the Abuloma 33kV injection substation in Port Harcourt, with an installed capacity of 1 x 7.5MVA. Currently, the feeder has a peak load of 3.9MW and an average load of 2.2 MW. To leverage the local abundance of water, the research aims to design a micro-grid using solar and wind energy. Using MATLAB Simulink, data from NASA meteorological sources will be simulated. The design features a 4.5MW photovoltaic (PV) array, a 2.5 MW wind energy source, and a 4 MWh battery storage unit. Despite variations in irradiance, significant improvements in power extraction were observed, with up to 4.5 MW generated by the PV array and 2.5MW by the wind turbine during peak times. The battery can be fully charged in four hours, and it was maintained at 40% capacity during low energy output periods. The State of Charge (SoC) of the battery showed dynamic behavior, enabling it to respond effectively to system imbalances and enhance microgrid resilience. A fuzzy logic controller (FLC) was used to manage charge and discharge cycles according to real-time parameters, ensuring reliable micro-grid operation even with low battery levels. The economic analysis revealed an initial cost of ₦990,251,352.19, a replacement cost of ₦414,669,375.12, a net present cost (NPC) of ₦10,813,540,000.00, and a levelized cost of electricity (COE) of ₦230.90/kWh. The low operation and maintenance (O&M) costs associated with renewable energy reduce reliance on the conventional grid and prolong infrastructure lifespan. Environmental assessments indicated a total greenhouse gas emission of 15,330,621 kg/year, significantly lower than that of conventional systems. The results confirm that the optimized hybrid renewable energy micro-grid enhances energy balance and resilience, showcasing its feasibility as a cost-effective and environmentally sustainable alternative to traditional power generation. The research aims to improve system resilience, reduce operating costs, and enhance micro-grid efficiency.

DOI: http://doi.org/10.5281/zenodo.17587580