IJSRET Volume 11 Issue 4, Jul-Aug-2025

Secure Edge Data Deduplication Under Uncertainties Using Aes-Based Robust Optimization

Authors: Dr. M.M. Janeela Therasa, Gayathri CL
Abstract: Mobile Edge Computing (MEC) enables low-latency data processing by bringing computation closer to users. However, it faces critical challenges such as limited storage capacity, unpredictable data patterns, network instability, and increasing volumes of duplicate data. These factors lead to performance degradation, increased retrieval latency, and inefficient resource utilization. To tackle these issues, a robust optimization-based deduplication framework is proposed. This system leverages two algorithms: UEDDE-C (Uncertainty-based Edge Data Deduplication with Column and Constraint Generation), which provides high accuracy in detecting exact duplicates, and UEDDE-A (Approximation-based), which is computationally lightweight and effective for identifying near duplicates. Furthermore, since data security is a pressing concern in edge environments, the proposed system integrates AES encryption to safeguard sensitive information before the deduplication process. This ensures not only confidentiality and integrity but also standardizes data into consistent formats for more efficient handling. The proposed framework significantly reduces redundant storage, lowers network traffic, speeds up data access, ensures security compliance, and enhances overall MEC system reliability under uncertain and dynamic conditions.

DOI: https://doi.org/10.5281/zenodo.15834266
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Experimental Analysis on Hybrid Technique for Traffic Flow Prediction with Missing Traffic Data

Authors: B.Dileep, Assistant Professor Mudigonda Harish Kumar

Abstract: This paper proposes hybrid traffic flow prediction techniques using Parametrical Doped Learning (PDL) and Truncated Dual Flow Optimization (TDFO) along with Adaptive Wildfire Optimization (AWO) and Spatial Pattern Super Learning (SPSL). These techniques are validated using datasets from LTPP and PeMS. Performance comparison with traditional algorithms like TrAdaBoost, KLT, and others shows superior outcomes in terms of accuracy, F1-score, sensitivity, and recall.

Analysis of Transport Network and Regional Development the State of the Art

Authors: M. Ramesh Reddy, Assistant Professor K.Abhiram

Abstract: Here we offer a study that falls under the umbrella of regional studies. While ideas like regional development and resources—with a focus on transportation infrastructure—have been around for a while, regional science is a more recent subfield of economics. Nearly everyone is more worried about regional economic planning now than they were a decade ago. A plethora of new concerns and challenges have recently come to light, stemming from the experiences of both industrialized and poor nations. The desirability of various sites for different activities has altered due to the integration of economic activity. Both regional planning on an individual level and coordinated regional planning at the system level have recently garnered increased attention from the governments of a number of nations. A big problem for regional economists has been the increased need for comprehensive information brought about by this interest in economic planning. A nation or area needed a specific amount of transportation infrastructure to make the most of its resources at any particular point in its economic growth. Adami (1987) 1. The effectiveness of the transportation network is a key component of the regional economic structure. In order to reach both local and foreign markets, a country’s production and distribution system relies on its transportation network, particularly its road network. This network must be both adequate and efficient. In addition to the particular function it provides, the transport network is important because of the integrating and uniting effect it has on society and the economy.

Analysis and Investigation towards Performance Enhancement of Traffic Light Control System

Authors: K.Ganesh, Assistant Professor K.Abhiram

Abstract: With these new tools, we can only speculate as to the level of intelligence that the next generation will achieve by modifying current methods. More and more, society benefits from increasingly sophisticated computing techniques. This article explores and discusses the smart road traffic system, which is one of the advantages. Using these state-of-the-art computational approaches will undoubtedly make the control system for road traffic lights easier and more efficient in the future. In order to optimize the overall trip time, the road traffic system takes into account several parameters, such as total travel time, wait time or delay time in the traffic line, vehicle speed, etc. There are a few issues with the road traffic flow, such as drivers not obeying the regulations of the road, accidents, and congestion. The real issue with traffic congestion is the lack of a better model for managing traffic on roads, which necessitates the dedication of researchers to finding a solution. The current traffic light control system has a few limitations, such as not being able to adjust the delay time based on the density of traffic, not being able to prioritize lane flow when emergency vehicles like fire trucks and ambulances are on the road, and so on. These studies aim to find solutions to the aforementioned traffic issues and, using the aforementioned factors, to optimize the overall travel time in a way that causes minimal delay or congestion. To address these issues, this study employs a probabilistic traffic queue analytical model to find a computationally efficient traffic model. An improved traffic light management system is developed by simulating a model that takes vehicle categorization into account and tests it with various traffic conditions. The model accounts for a variable time delay.

Cfd Simulation of an Industrial Dust Cyclone Separator on The Influence of Inlet Velocity and Particle Size Performance

Authors: Dyaga Suresh
Abstract: – Cyclone separators are widely used in industrial applications to remove particulate matter from gaseous streams. The performance of a cyclone is highly dependent on operational parameters such as inlet velocity and particle size. In this study, Computational Fluid Dynamics (CFD) is utilized to simulate and evaluate the performance of an industrial cyclone separator. Various inlet velocities (10 m/s to 25 m/s) and particle diameters (1 µm to 50 µm) were analyzed to determine their effects on separation efficiency and pressure drop. The simulations reveal that both inlet velocity and particle size significantly influence cyclone performance. This work helps optimize cyclone design parameters to improve dust removal efficiency while minimizing energy losses.

Analyzing Music Streaming Patterns Through Business Intelligence: A Power BI-Based Study Of Spotify Data

Authors: Dr. Suhas Mache, Bhagyashree Ingale
Abstract: Globally, music streaming services have completely changed how people listen to music. This study uses Power BI- based data analysis to investigate Spotify user behavior and listening habits. Using dynamic dashboards and visual aids, the study looks at regional preferences, artist performance, track patterns, and genre popularity. With the goal of assisting artists, marketers, and producers in making defensible decisions based on streaming data, business intelligence approaches provide actionable insights.

DOI: https://doi.org/10.5281/zenodo.15836070

Analysis on Knowledge Based Prediction System for Road Traffic Congestion

Authors: S.Likhitha Priya, Assistant Professor B.Narsimha

Abstract: The transportation networks, economy, and quality of life in cities throughout the world are all negatively impacted by the pervasive problem of traffic congestion. Congestion relief is of critical importance in India since the country’s fast urbanization and population increase are making the situation worse. By integrating user and expert perspectives and using a data-driven approach to prepare a knowledge base for traffic congestion, this thesis presents a holistic strategy to address road traffic congestion. The system is tailored to the lndian scenario in the context of inter-urban highways. One module of this thesis is based on user survey methodology, while the other is centered around applying deep learning techniques to analyze the dynamics of congestion. Several facets of congestion prediction are covered in this thesis’s objectives. Through observation and feedback, it seeks to understand the causes that contribute to congestion and identify its characteristics from the perspective of travelers. To achieve this goal, we analyzed data from thirteen separate sites along the undivided interurban highway that were selected based on their mixed land use pattern and where congestion is an issue. The survey questionnaire yielded 206 responses, which constitute the data set. The Mann Whitney U test was used to see if there is any heterogeneity between the two traveler groups, drivers and passengers. This goal assesses the features of congestion using the Relative Impoftancc Index (RII) and also scores their preference for measures to alleviate it, in addition to identifying the attributes of congestion. Additionally, it suggests legislative recommendations based on the opinions of travelers in an effort to enhance their traveling experience on interurban highways. The second aim of the thesis uses exploratory factor analysis on the characteristics derived from the first objective and empirical knowledge to investigate the interrelationships among the congestion factors. This objectivc has sampled data from 282 replies and 5 experts. The professionals at Thcsc come from a variety of backgrounds in the transportation industry. After conducting an exploratory factual analysis on the traffic congestion attributes identified in objective one using interpretive knowledge based on the experiments, the fiuzy based total interpretive structural modeling technique (Fwzy TISM) was used to model the interrelationships between the factors. This technique provides both direct and indirect links of cause and effect between the factors. Micmac analysis has been used to determine the independent, dependent, linkage, and autonomous components in the connection diagram that was derived via Fuzzy TISM. Eight important factors contributing to traffic jams on interurban highways were identified through exploratory factors analysis. These factors include inadequate road geometry (Cl), environmental factors (C2), external events (C3), inefficient public transportation (C5), driving behavior (C6), special events (C7), and regional economic dynamics (C8). Using the r- uzzy TISM technique, we may partition the variables into different levels of the hierarchical connection diagram. At the Level I factor level, we have C3, C4, and C6. The second Lcvel component is C5, the third is Cl and CT, and the fourth is C2 and C8. Based on the results of the fuzzy Micmac analysis, we can deduce that factors such as Regional Economic Dynamics (C8), Special Events (C7), and Environmental Factors (C2) are independent variables that impact most of the factors, despite having less driving power. The occurrence of these variables also affects the linkage factor. In this study, archaic traffic management (C4) is a linkage factor that has a high driving power and a high dependence power owing to its unstable proper. It impacts and is affected by other factors such as road geometry dynamics (C3), logistical events (C4), inefficient public transportation facilities (C5), and driving behavior (C6). This thesis’s first module is complete with the first two objectives. Objectives three and four are located in the second root of the thcsis plant. The third objective of the project is to automate the process of traffic volume determination utilizing modern dccp lcarning dctcctors and trackers. This would enable the collecting of real-time data for congestion prediction. This objective has been tested on a bespoke dataset consisting of 10,000 virtual reality images from India. The photos in this dataset depict a variety of highway situations with several automobiles. A model for vehicle categorization and traffic volume counting has been developed. It has been trained using YOI-O variants, a single stage object identification methodology, and Byte trackcr, a counting method. The results of the traffic volume counter are examined on the intersection scenes and mid blocks of the intcr urban highways on videos ranging from one minute to five minutes. The short videos achieve an accuracy of 98 degrees of freedom, while the long videos reach an accuracy of up to 87 degrees of freedom. In order to build a comprehensive knowledge base for predicting congestion states, the fourth objective of the study suggests a framework for a knowledge base prediction system regarding road congestion. This framework integrates the results of objectives one and three by combining the impedance factors identified in objective one with traffic flow variables obtained from objective three. The 84-hour data sample includes both weekdays and weekends, and this target has been executed on that basis. The ANFIS (Adaptive Network based fuzzy inferencc system) technique, which is based on several membership functions, has been used to analyze and generate the if-then rules for decision makers. It demonstrates that the existence of rnultiple impedance factors is responsible for the differences in congestion levels on weekdays and weekends. Finally, to improve the efficacy and precision of congestion prediction, the proposed knowledge-based basc road traffic system makes use of both uscr-drivcn insights and cxpert knowlcdgc. By combining user feedback with expert analysis and automated data collection techniques, the system provides a comprehensive view of traffic congestion and how to predict it. In the Indian context, where varied traffic situations and intricate urban landscapes call for precise prediction systems, this comprehensive methodology is of utmost importance. The system is able to produce strong and trustworthy predictions because it uses a combination of fuzzy logic and deep learning techniques, which allow it to deal with the inherent uncertainty and complexity of real-world traffic scenarios. Additionally, the system may enhance its predictive capabilities through the incorporation of user feedback and cxpcrt insights, guaranteeing its relevance and efficacy in ever-changing traffic scenarios. Finally, as a solution to the widespread issue of traffic congestion in India, the proposed knowledge-based road traffic congestion prediction system brings hope. The system offers a comprehensive framework for understanding, analysing, and forecasting congestion lcvcls by combining USCr perspectives, cxpcrt knowledge, and sophisticated technology. Improving traffic management strategies, reducing congestion, and making urban transportation systems in India more efficient might all be possible with the use of this system.

Analysis on Modelling of Traffic Pollution to Biomonitor Traffic Police

Authors: MD.Afroz, Assistant Professor B.Narsimha

Abstract: In Indian cities, air pollution is a major problem that affects both the environment and people’s health. The traffic, congestion, and pollution levels of Mumbai, one of the most densely populated metropolises in the world, have been steadily rising in recent years. When it comes to city traffic, the top 20 locations are well-known for their extreme congestion. These areas are known as “hotspots” because of the large concentration of vehicles at certain intersections around the city. At the morning’s peak hour (from 7:30 to 9:00 am) and the evening’s peak hour (from 6:30 to 7:30 pm), cars travel at an average speed of 10 kmph and 8 kmph, respectively. Records show that evenings are the most polluting and crowded times of day. Congestion in the roadways exposes the general population to air and noise pollution. Central Pollution Control Board- specified noise levels are exceeded at the intersections, according to recorded data. The noise levels at the intersections are over 90 dB on average, even though they should be below 75 dB for industrial and 65 dB for commercial settings. 55 decibels for homes and 50 decibels for quiet zones. Vehicle emissions include a wide range of contaminants, including suspended particle matter (SPM), oxides of sulfur (SOx), PAN, and nitrogen oxides (NOx). Any illness affecting the respiratory system is caused by SPM. The research here takes RSPM 2.5 (respirable suspended particulate matter) into account. At 20 different hotspots, we assessed noise levels, RSPM 2.5, number of cars, and congestion at different times of the day and season. The aforementioned parameters are averaged and their standard deviations are computed. In addition, CalRoads View Software is used for data analysis and modeling. Predicting pollution concentrations near intersections where cars idle in waiting for signals is the job of CALINE 3. This program was useful for figuring out how pollutants were distributed at the intersections. Congestion areas and intersections were the sites of the surveys. The poll was conducted via a questionnaire and was open to retailers, street vendors, and traffic police. Breathing and respiratory issues affected the majority of those who participated in the poll. Many of them have had asthma diagnoses at some point. The exposed individuals showed signs of both acute and chronic illnesses. Lots of people who took the poll reported having an illness of the respiratory system, such as sinusitis, tonsillitis, bronchitis, pneumonia, etc. Researchers believe that traffic officers are the best demographic to investigate because of their heightened sensitivity to the effects of vehicle emissions on human systems, particularly the respiratory system and hearing impairments. Participants in the study will be traffic officials tasked with administrative tasks, who will serve as the control group, while traffic officials stationed at different intersections will serve as the exposed group. Individuals working in traffic were tested for noise-induced hearing loss (NIHL). The results clearly show that traffic cops have a greater risk of hearing loss. Unbelievably, 57.14% of traffic cops who have been on the job for more than 25 years have had serious injuries from NIHL. The current investigation did not find any cases of genetic or familial deafness or ear sickness among the participants. The prevalence of asthma was also assessed in other traffic staff. Asthma prevalence among traffic workers can be diagnosed using spirometry analysis. To find the maximum expiratory flow rate in liters per minute, one uses the MINISPIR S/N TO 1795. Asthma diagnosis incidence among traffic police officers aged 18–58 years old (FVC). The ages of the diagnosed traffic professionals range from zero to five, with exposure years in the upper limit. The PEFR is measured in both the exposed and control groups of traffic cops. Approximately eight hours of each workday expose traffic workers directly to particulate matter. Diagnosis ranges from less than five years to over twenty-five years of experience for traffic police. Rejection from the analysis was based on the fact that the traffic police were actively pursuing a smoking habit. The exposed and control groups were both tested for diseases. More than 35% more people in this group have asthma than in the control group. According to the data, there is also an increase in the number of cases of asthma. The effects of air pollution on traffic workers can be mitigated by the development and testing of protective gear. Surgical masks and handkerchiefs are examples of compensatory activities people take in response to pollution threats in order to lower their actual exposures. The research set out to develop a study mask capable of preventing exposure to potentially harmful particles such as dust, allergies, and infectious aerosols. Gradually, further features are included to minimize noise and dust particles that might potentially harm the eyes. We created and tested stage-wise masks. Nose piece with inhaler, exhaust fan near the section of the mouth or nose, lightweight, simple to wear, adaptable to all face sizes and shapes, low maintenance, aesthetically pleasing, and aesthetically pleasing are all features and components that were combined. To determine how effective the study mask was, it was compared to other masks on the market and put through its paces. But how well current face masks protect is anybody’s guess. The purpose was to find out how well face masks protect the respiratory system against RSPM levels that might be hazardous. After subtracting the SPM particles from the total air volume inhaled in one minute, the readings on the muslin fabric are reported. Additional issues caused by RSPM are mitigated to a certain degree. The average number of particles collected in one minute is greater than 0.1 gm/min. Not only does the mask shield you from harmful RSPM levels and noise, but it also has an exhaust fan that draws carbon dioxide out of your mouth and keeps you well-ventilated. With this mask, you won’t have to worry about the usual issues of sweating or itching while wearing it. Stages 1 and 2 of the mask’s modification process were carried out utilizing 3D printer technology. The mask underwent both quantitative and qualitative testing in accordance with OSHA regulations, and test methods were prepared for submission to the ethics committee. All things considered, the outcomes are satisfactory. Additional improvements in terms of size variations, compatibility with additional safety gear like helmets, etc., will allow the device to be released to the public for usage.

Performance Analysis of Two Potential Indus Waste Materials Fa & Lsludge

Authors: B.Lavanya, Assistant Professor M.Harish Kumar

Abstract: This study stabilized two potentially hazardous industrial waste materials—Fly Ash (FA) and Lime Sludge (LS)—using Commercial Lime (CL) and Gypsum (G), respectively. The FA and LS come in large quantities from thermal power plants and water treatment plants, and they pose environmental hazards. The goal was to make the sludge and FA suitable for use in Civil Engineering construction applications. Tests for Unconfined Compressive Strength (UCS), California Bearing Ratio (CBR), and Split Tensile Strength Test (STS) were conducted on 39 different combinations of FA, LS, CL, and G. Using these metrics, we were able to determine that two composites stabilized with 12%CL and 1%G performed admirably: (optimum mix 2, 50%FA+50%LS) and (optimum mix 1,95%FA+5%LS). After 28 days of curing, the UCS for optimal mix 1 was 6.6 MPa, while for optimum mix 2 it was 5.8 MPa, and for STS it was 1.3 MPa, and for both mixes it was 1.1 MPa. After 28 days of curing, the optimal mix 1 had a soaked CBR value of 75% and an unsoaked value of 89%. When optimal mix 2 was cured for 28 days, the CBR values were 91% for the unsoaked condition and 82% for the soaked condition. When tested for durability according to both British and American standards, both composites passed with flying colors. For instance, following twelve cycles of wetting and drying, mix 2 samples lost 1.05% of their initial weight while mix 1 samples only lost 1.12% of their initial weight, both of which were well within the criteria that were recommended. Also, after 28 days of curing, the composites had almost little heavy metal content leaching out. Additionally, scanning electron microscopy (SEM) and X-ray diffraction (XRD) were used to study the development of cementitious compounds during curing. Having said that, the stabilized composites exhibited brittleness. The ductility and strength were examined after adding fibers to these composites in an effort to reduce their brittleness. The results of the trials with the various fiber percentages showed that adding 0.3% of fiber to both composites increased the mix’s strength and ductility. The strength index and deformability index were used to measure the improvement in ductility and strength, respectively. There is an 80% increase in failure strain and a 40% increase in strength when fibers are added. The findings indicated that using Composites have achieved the necessary strength, durability, and ductility to be used as base course layer materials in flexible pavements. Furthermore, it is recommended that the total pavement thickness be decided upon based on dependability, taking into account the uncertainty in the input data such as the design traffic load (measured in Million Standard Axle, MSA) and soil carrying capacity (measured in CBR value). In order to determine the total thickness of the flexible pavement that can guarantee a specific degree of dependability in the pavement’s performance, design charts were produced, taking into account the uncertainty in the input data. In addition, the reliability-based technique—which combines the Mechanistic- Empirical approach with Monte Carlo Simulation (MCS) and the First Order Reliability Method—was used to study the uncertainties in distress analysis of flexible pavement, specifically with regard to fatigue and rutting failure. In order to determine the pavement’s performance, the reliability index (β) was calculated and strain at crucial sites for fatigue and rutting failure was calculated using PLAXIS 2D coding software on a three-layered flexible pavement model. The specified strength, durability, and ductility requirements are met by the produced composites, namely Optimal Mix 1 [(95%FA+5%LS), 12% CL, 1% G and 0.3% F] and Optimal Mix 2 [(50%FA+50%LS), 12% CL, 1% G and 0.3% F]. The compressive strain value at the crucial point was found to be ɛc = 13.42 x 10-5 and the tensile strain value at the same region was found to be ɛt = 18.11 x 10-5 for a 450 mm thick foundation of optimal mix 2. For 10 MSA of design traffic load, the pavement performed admirably (βR and βF >5) according to the probabilistic based methodology. However, when subjected to larger traffic loads, the pavement exhibited above-average performance in terms of fatigue and rutting.

Design of Flexible Pavement with Maximum Utilization of Industrial Waste

Authors: G.Srikanth, Assistant Professor M.Harish Kumar

Abstract: Since the subgrade is the bedrock upon which the pavement model is built, its behaviour is crucial to the design of flexible road projects. Therefore, pavement performance and design analysis need a considerable deal of attention. The expansive soils include a high concentration of clay and silt, hence it is necessary to stabilise or compact the soil subgrade prior to building a flexible pavement. Subgrade soil replacement is the method of choice for stabilising certain types of soils. Fly ash, an industrial waste product, is currently being used in around 55% of its total capacity. Substituting bitumen-coated chicken mesh with fly ash in subgrade at varying percentages and layering the mesh appropriately will make good use of the fly ash. A better California bearing ratio can be achieved by using bitumen-coated chicken mesh as extra reinforcement. This study utilises the CBR mould to impact fly ash samples till they reach their maximum dry density and optimal moisture content. The samples are then used with or without bitumen coated bamboo meshes and chicken mesh layers. Dimensions of the chicken mesh match those of the CBR mould in plan view. It was thereafter covered with chicken mesh after being placed in the preparations of 1, 2, 3, and 4 layers. The laboratory tests show that the mixture with four layers of bitumen-coated chicken mesh and a 15% substitution of fly ash has the highest CBR value, indicating that it is very strong. The soil’s moisture content is lowered by using plastic garbage as a partial replacement. In this study, various percentages of plastic garbage are introduced to soil in order to quantify and compare the values of UCS, CBR, MDD, and OMC with the unmodified soil.

Enhancing Student Performance PredictIon Using Random Forest And Feature Engineering Algorithms In Machine Learning

Authors: Ms.T. Nandhini Supervisor Assistant Professor, M.Mugesh Kumar, M.Snekan, R.Tamilselvan

Abstract: This project presents a machine learning-based methodology for student performance prediction with Random Forest and Feature Engineering. Academic institutions are becoming more dependent on data-driven intelligence to enhance educational planning and student support. Conventional models usually do not capture various student characteristics like demographic, academic, and behavioral features, thus restricting predictive capabilities. In this paper, we overcome these limitations by suggesting an ensemble learning approach with sophisticated feature engineering to enhance the interpretability and flexibility of the prediction process. The Random Forest classifier is employed due to its high accuracy and stability, and the model is assessed using metrics like accuracy, precision, recall, and F1-score. Experimental results indicate that the new system surpasses conventional AFSA-based models in detecting at-risk students, allowing for early intervention approaches to improve academic performance.

Performance Analysis of Two Potential Indus Waste Materials Fa & Lsludge

Authors: B.Lavanya, Assistant Professor M.Harish Kumar

Abstract: This study stabilized two potentially hazardous industrial waste materials—Fly Ash (FA) and Lime Sludge (LS)—using Commercial Lime (CL) and Gypsum (G), respectively. The FA and LS come in large quantities from thermal power plants and water treatment plants, and they pose environmental hazards. The goal was to make the sludge and FA suitable for use in Civil Engineering construction applications. Tests for Unconfined Compressive Strength (UCS), California Bearing Ratio (CBR), and Split Tensile Strength Test (STS) were conducted on 39 different combinations of FA, LS, CL, and G. Using these metrics, we were able to determine that two composites stabilized with 12%CL and 1%G performed admirably: (optimum mix 2, 50%FA+50%LS) and (optimum mix 1,95%FA+5%LS). After 28 days of curing, the UCS for optimal mix 1 was 6.6 MPa, while for optimum mix 2 it was 5.8 MPa, and for STS it was 1.3 MPa, and for both mixes it was 1.1 MPa. After 28 days of curing, the optimal mix 1 had a soaked CBR value of 75% and an unsoaked value of 89%. When optimal mix 2 was cured for 28 days, the CBR values were 91% for the unsoaked condition and 82% for the soaked condition. When tested for durability according to both British and American standards, both composites passed with flying colors. For instance, following twelve cycles of wetting and drying, mix 2 samples lost 1.05% of their initial weight while mix 1 samples only lost 1.12% of their initial weight, both of which were well within the criteria that were recommended. Also, after 28 days of curing, the composites had almost little heavy metal content leaching out. Additionally, scanning electron microscopy (SEM) and X-ray diffraction (XRD) were used to study the development of cementitious compounds during curing. Having said that, the stabilized composites exhibited brittleness. The ductility and strength were examined after adding fibers to these composites in an effort to reduce their brittleness. The results of the trials with the various fiber percentages showed that adding 0.3% of fiber to both composites increased the mix’s strength and ductility. The strength index and deformability index were used to measure the improvement in ductility and strength, respectively. There is an 80% increase in failure strain and a 40% increase in strength when fibers are added. The findings indicated that using Composites have achieved the necessary strength, durability, and ductility to be used as base course layer materials in flexible pavements. Furthermore, it is recommended that the total pavement thickness be decided upon based on dependability, taking into account the uncertainty in the input data such as the design traffic load (measured in Million Standard Axle, MSA) and soil carrying capacity (measured in CBR value). In order to determine the total thickness of the flexible pavement that can guarantee a specific degree of dependability in the pavement’s performance, design charts were produced, taking into account the uncertainty in the input data. In addition, the reliability-based technique—which combines the Mechanistic- Empirical approach with Monte Carlo Simulation (MCS) and the First Order Reliability Method—was used to study the uncertainties in distress analysis of flexible pavement, specifically with regard to fatigue and rutting failure. In order to determine the pavement’s performance, the reliability index (β) was calculated and strain at crucial sites for fatigue and rutting failure was calculated using PLAXIS 2D coding software on a three-layered flexible pavement model. The specified strength, durability, and ductility requirements are met by the produced composites, namely Optimal Mix 1 [(95%FA+5%LS), 12% CL, 1% G and 0.3% F] and Optimal Mix 2 [(50%FA+50%LS), 12% CL, 1% G and 0.3% F]. The compressive strain value at the crucial point was found to be ɛc = 13.42 x 10-5 and the tensile strain value at the same region was found to be ɛt = 18.11 x 10-5 for a 450 mm thick foundation of optimal mix 2. For 10 MSA of design traffic load, the pavement performed admirably (βR and βF >5) according to the probabilistic based methodology. However, when subjected to larger traffic loads, the pavement exhibited above-average performance in terms of fatigue and rutting.

Intelligent Detection of Mobile SMS Spam via Machine Learning and Deep Learning

Authors: Megha Birthare, Neelesh Jain

Abstract: The global rise in social media usage has led to a surge in unwanted bulk SMS, necessitating the development of an effective system to filter out these messages. The most prevalent issue on the internet is spam text messages. Sending a spam-filled SMS is a straightforward task for spammers. Spammers are able to take valuable data, including contacts and files, from our devices. In recent years, several word embedding techniques leveraging deep learning have been developed. These advancements in word representation could offer a reliable remedy for these problems. This study will look at a technique that employs natural language processing to distinguish among spam and ham texts utilizing the SMS Spam Collection Dataset from the UCI Machine Learning Repository. We compared the accuracy and outcomes of using the Bi-LSTM and LSTM. The effectiveness of the dataset is assessed using measures like F1-score, recall, and accuracy. The study demonstrates that the dataset’s overall accuracy increases when Bi-LSTM classification is used. Python is used for all work, and a Jupyter notebook is used for implementation.

DOI: https://doi.org/10.61137/ijsret.vol.11.issue4.114

Designing Multi-Speciality Hospitals: Architectural Integration of Healing, Functionality, and Technology in Rural India

Authors: Anant Kumar, Professor Gulfam B. Shaikh, Professor Dilip L. Jade

Abstract: With the rapid urbanization of rural regions in India, the demand for healthcare infrastructure has surged dramatically. This research paper explores the architectural planning and design of a Multi-Speciality Hospital in Sikandarpur, Bihta, Patna (Bihar)—a region currently underserved in medical facilities. Emphasizing healing environments, sustainable strategies, patient-centered design, and technological integration, the paper outlines the necessity, process, and architectural responses to contemporary hospital design. A case study of Tata Medical Centre, Kolkata informs the practical and structural feasibility of the proposed design.

Autopapermine: Research Paper Information Extractor

Authors: Jinta Johnson, Assistant Professor Athira B, Professor Dr. Shine Raj G

Abstract: This paper presents a lightweight and intelligent system for the automatic extraction of structured information from academic research papers in PDF format. The proposed system leverages Natural Language Processing (NLP) techniques, TF-IDF-based summarization, and Sentence-BERT semantic similarity to extract and analyze metadata such as title, authors, organizations, keywords, and references. Built using Python and Streamlit, the tool allows users to upload PDF documents, parse academic content, and interactively review summarized metadata, references, and semantic relevance—all in real-time. This paper details the system architecture, implementation pipeline, challenges, and experimental results, demonstrating its effectiveness and scope for future enhancements

A Word Embedding Approach To Analyzing CEO Earnings Call Transcripts And Stock Market Reactions

Authors: Harsha Sammangi, Aditya Jagatha, Hari Gopal Maddireddy

Abstract: This study presents a sentiment-driven Decision Support System (DSS) that leverages advanced word embedding techniques—Word2Vec, GloVe, and BERT—to analyze CEO earnings call transcripts and predict stock market reactions. Tra- ditional lexicon-based sentiment models fail to capture the nuanced, contextual language used by executives. By employing pre-trained embeddings and machine learning classifiers, the study enhances the accuracy of sentiment classification. The proposed system integrates quantitative sentiment scores with event study method- ology to assess the impact of CEO tone on stock performance. Thematic analysis further enriches interpretability by identifying recurring patterns in executive com- munication. Results demonstrate that positive CEO sentiment generally correlates with stock appreciation, while negative sentiment aligns with declines. Among models tested, BERT outperformed others in classification accuracy. This research contributes to real-time financial analytics by embedding sentiment intelligence into DSS frameworks, supporting investors, analysts, and automated trading sys- tems with improved decision-making capabilities grounded in contextual linguistic analysis.

DOI: https://doi.org/10.5281/zenodo.15845150

Integrating Time Series Forecasting And Business Intelligence: A Power BI Dashboard Approach For Sales Prediction

Authors: Vaishnavi Kane, Assistant Professor Dr. Suhas Mache, Assistant Professor Dr. Arshiya Khan

Abstract: In the modern business environment, accurate sales forecasting is essential for effective decision-making. This paper explores the integration of time series forecasting techniques with Business Intelligence (BI) tools, specifically using Microsoft Power BI, to build an interactive dashboard for sales prediction. We present a model that combines statistical forecasting methods with data visualization to enhance decision-making in sales management. The system is designed to provide real-time insights, support strategic planning, and identify sales trends through dynamic dashboards. Our case study demonstrates that integrating forecasting models within BI platforms significantly improves sales predictability and operational efficiency. This study explores the integration of time series forecasting techniques with Microsoft Power BI to build a dashboard that predicts future sales. By combining forecasting models like ARIMA and Prophet with Power BI’s interactive features, the system enables better business decision-making. The dashboard allows users to visualize historical trends and forecasted sales data in real time.

Seismic Performance Analysis Of A G+11 Building Based On Live Earthquake Load Using ETABS Software

Authors: Keshav Kumar Ahirwar, Asssitant professor Mrs. Ankita singhai, Mr. Rahul Satbhaiya

Abstract: A building needs to be able to withstand considerable ground vibrations during construction or operation in order to be deemed earthquake-resistant. However, ground motions have a particular effect on structure reactions. Time-history analysis is effective for buildings that are subject to large ground vibrations. Stepwise integration of the pushover analysis of a multi-degree-of-freedom system (MDOF) in the time domain is used to illustrate a structure’s response. This method is advantageous even if it takes a lot of time. In order to speed up the design and assessment of seismic structures, the pushover analysis was developed.Pushover study indicates that during seismic events, structures vibrate mostly in the lower or early modes. The multi-DOF system is then reduced to a single-DOF system using the characteristics revealed by its nonlinear static analysis. After that, a response spectrum analysis is performed on the ESDOF system using either nonlinear time history analysis, damped analysis, or constant-ductility analysis. Modal links are used to translate ESDOF seismic needs into MDOF seismic requirements.A model was created to show the overall consequences of the RCC frame building. In seismic zone II, four G+11 concrete planar frames with four bays oriented in X and Y were built in compliance with Indian regulations. There are five loading scenarios for every frame. Pushover analysis evaluates frames with different elevational anomalies but the same loading conditions. Frame after frame, the results are different. Each frame’s capacity spectrum and pushover curve between base shear and displacement are calculated and evaluated. At seismic zone II stress, STAAD was utilized to analyze RCC frame non-linear response. To weight parameters, utilize Pro v8i. Infill walls and bare frames were compared

DOI: https://doi.org/10.5281/zenodo.15846885

Privacy-Preserving Collaborative Searchable Encryption Using Blake3 for Cloud-Based Group Data Sharing

Authors: Aatheni U, Dr. M. M. Janeela Theresa

Abstract: – Collaborative searchable encryption for group data sharing enables authorized users to jointly generate trapdoors and retrieve encrypted data without compromising privacy. However, existing solutions remain vulnerable to keyword guessing attacks (KGAs) by malicious insiders and subversion threats such as backdoors from untrusted hardware or software vendors. To overcome these security challenges, we propose a Privacy- Preserving Collaborative Searchable Encryption (PCSE) scheme using the BLAKE3 hash function. PCSE introduces a dedicated keyword server to enable server-derived keywords that resist insider KGAs, and employs cryptographic reverse firewalls to mitigate subversion risks. A distributed, multi-server keyword architecture is adopted to prevent single-point failures. The system also supports multi-keyword search, result verification, and includes a rate-limiting mechanism to restrict brute-force attempts. Formal analysis confirms resistance against KGAs and subversion attacks. Empirical evaluations demonstrate that PCSE achieves strong privacy, scalability, and efficient keyword-based search, making it suitable for secure cloud-based group data sharing

DOI: https://doi.org/10.5281/zenodo.15847524

Blockchain-Based Insurance Claim

Authors: Priyanka Gupta, Hardik Gupta, Anurag Tomar, Piyush Raghav

Abstract: This project aims to revolutionize the insurance claims process by leveraging blockchain technology and smart contracts to address inefficiencies such as fraud, human errors, security risks, and high administrative costs. Traditional insurance claim processing is complex, requiring extensive human intervention, multi-domain interactions, and data from multiple sources, making it time-consuming and labor-intensive. By utilizing a private Ethereum blockchain and the Solidity programming language for smart contract development, this framework automates claim verification and settlement, ensuring transactions occur only if all predefined conditions are met. The integration of the Proof of Authority (PoA) consensus algorithm enhances transaction validation, improving security and transparency throughout the process. Additionally, decentralized applications (DApps) facilitate seamless user interactions, while the InterPlanetary File System (IPFS) enables off-chain data storage to maintain accessibility and immutability without overloading the blockchain network. This decentralized system prioritizes trust, transparency, and scalability, allowing for efficient processing of health insurance claims, particularly for prescription drugs, while significantly reducing operational costs. By combining blockchain’s transparency with scalable off-chain storage, this solution transforms the insurance sector, offering a reliable, secure, and cost-effective approach to claim management.

Novel Approach to Load Analysis of Multistory Building with Its Bending

Authors: Research Scholar Aman Singh Bais, Professor Rajesh Chouhan

Abstract: A multi-storey is a building that has multiple floors above the ground. It can be a residential or commercial building. In this project the analysis and design of multi-storey building. In general, the analysis of multi-storey is elaborate and rigorous because those are statically indeterminate structures. Shears and moments due to different loading conditions are determined by many methods such as portal method, moment distribution method and matrix method. The present project deals with the analysis of a building. The dead load & live loads are applied and the design for beams, columns, the footing is obtained manually.

Enhancement of Load Bearing Capacity in Diagrid Multistory Building with Observed Torsion

Authors: Research Scholar Yawar Khan, Professor Sachin Sironiya

Abstract: The difference between conventional outer brake frame structures and current diagrid structures is that for diagrid structures, almost all conventional vertical columns are removed. Elimination of vertical columns is possible because diagonal elements in diagrid structural systems can carry gravitational loads as well as lateral forces, while diagonals in conventional elastic frame structures carry only lateral loads. The most normal and popular material in the process of building diagrids is steel. The incisions commonly used are rectangular, rounded and wide flanges. The weight and size of the sections are made to withstand high bending loads.

Developing A Multi-Modal Edge-AI Framework For Continuous Infant Monitoring: Predicting Mental Health Outcomes

Authors: Dr. Sanjeev Puri, Sandeep Keshav

Abstract: The evolution of Edge-AI technologies has created new opportunities in pediatric healthcare, allowing for real-time monitoring of infants while maintaining privacy. This research introduces an innovative multi-modal Edge-AI framework that combines video, audio, and physiological data to anticipate potential mental health issues in infants. The proposed system processes information locally on edge devices, minimizing latency, enhancing privacy, and enabling continuous monitoring in both clinical and home settings. By employing lightweight AI models for on-device processing, the system promotes early identification of neurodevelopmental challenges and encourages timely interventions. This approach aims to shift healthcare from a reactive stance to a preventive one, ultimately aiming to foster long-term enhancements in mental health. The paper outlines the system’s architecture, techniques for optimizing AI models, and prospective applications in pediatric healthcare environments.

DOI: https://doi.org/10.5281/zenodo.15867669

Developing A Multi-Modal Edge-AI Framework For Continuous Infant Monitoring: Predicting Mental Health Outcomes

Authors: Dr. Sanjeev Puri, Sandeep Keshav
Abstract: The evolution of Edge-AI technologies has created new opportunities in pediatric healthcare, allowing for real-time monitoring of infants while maintaining privacy. This research introduces an innovative multi-modal Edge-AI framework that combines video, audio, and physiological data to anticipate potential mental health issues in infants. The proposed system processes information locally on edge devices, minimizing latency, enhancing privacy, and enabling continuous monitoring in both clinical and home settings. By employing lightweight AI models for on-device processing, the system promotes early identification of neurodevelopmental challenges and encourages timely interventions. This approach aims to shift healthcare from a reactive stance to a preventive one, ultimately aiming to foster long-term enhancements in mental health. The paper outlines the system’s architecture, techniques for optimizing AI models, and prospective applications in pediatric healthcare environments.

DOI: https://doi.org/10.5281/zenodo.15867669

A Generalized Tipping Condition For Arbitrary Geometric Objects Based On Contact Area And Applied Energy Using Cross Products

Authors: Rupsa Sarkar
Abstract: This work introduces a new energy-based model based on cross product torque analysis for the generalization of the tipping condition of rigid bodies of general shape. Classical mechanics employs torque to find rotational balance, but my method introduces the percent contact area (PCA) to define the extent to which the object is supported on a surface and how this influences tipping. The article presents a formula for computing the minimum amount of external energy to cause tipping by considering torque through the cross product and accounting for geometric distribution and weight. The PyBullet simulations yield high correlation, affirming the model’s ability to make predictions.

DOI: https://doi.org/10.5281/zenodo.15867962

AI-Driven QA In Print Production: Real-Time Monitoring For Zero-Defect Printing

Authors: Amit Sharma

Abstract: As the printing industry transitions into the era of Industry 4.0, traditional quality assurance methods—centered on manual inspection and reactive defect handling—are increasingly inadequate for the speed, complexity, and customization demands of modern pressrooms. This paper explores the transformative potential of Artificial Intelligence (AI) and Machine Learning (ML) in real-time monitoring and quality assurance (QA) across print production workflows. Leveraging technologies such as computer vision, IoT sensor networks, and predictive analytics, AI-enabled systems enable proactive defect detection, automated correction, and dynamic process optimization. Applications include in-line visual inspection, root cause analysis, intelligent alerting, and traceable compliance logging. Case studies demonstrate significant gains in defect reduction, throughput, and client satisfaction. However, adoption remains hindered by challenges such as legacy equipment integration, data infrastructure gaps, workforce readiness, and cybersecurity concerns. Future directions emphasize the role of digital twins, federated learning, cloud-based QA hubs, and sustainability-aware defect prevention. Ultimately, AI transforms quality assurance from a reactive function into a strategic enabler—advancing efficiency, brand protection, and environmental responsibility in next-generation print operations.

DOI: https://doi.org/10.5281/zenodo.15868325

Trends In Adoption And Challenges Of Green Computing In Africa

Authors: Eric Sifuna Siunduh, Victor Mony Otieno

Abstract: Green computing has emerged as a critical strategy for achieving sustainable technological growth globally. In Africa, this approach offers an essential path to mitigating pressing issues such as energy scarcity, poor e-waste management, and environmental degradation. Green computing encompasses environmentally friendly practices in the design, usage, and disposal of information and communication technology (ICT) infrastructure. This paper explores trends, drivers, challenges, and future directions of green computing adoption in Africa, emphasizing the role of data centers, cloud computing, and policy enforcement. A mixed-methods approach was employed, utilizing secondary sources such as journal articles, case studies, and institutional reports published between 2019 and 2024. Key findings reveal that renewable energy integration in ICT operations, policy support, and international collaborations are driving adoption in countries like Kenya, South Africa, and Rwanda. However, persistent barriers—such as limited infrastructure, high initial costs, and a shortage of skilled professionals—continue to hinder widespread implementation. The study recommends coordinated stakeholder action, increased investment in education and renewable energy, and the development of regional green ICT hubs. This paper contributes to understanding how Africa can align digital transformation with sustainability, advancing both environmental goals and socio-economic development.

Cloud Computing And Web 3.0 Technologies For Effective Public Participation: The African Context

Authors: Eric Sifuna Siunduh, Zachary Mwangi, Dr. Alice Nambiro Wechuli,

Abstract: The increasing adoption of cloud computing and Web 3.0 technologies offers transformative potential for public governance in Africa, particularly in enhancing citizen participation. Despite various efforts to digitize public services, many governments still struggle to ensure inclusive, transparent, and interactive participation frameworks. This paper examines how cloud computing and Web 3.0 technologies can be harnessed to empower citizens and strengthen e-participation in the African context. It explores the integration of semantic web, blockchain, and machine learning to facilitate interactive e-governance platforms. By employing an ex post facto research design, the study synthesizes empirical and theoretical insights to develop a model for citizen empowerment. Findings show that cloud-based platforms significantly increase accessibility and engagement, while Web 3.0 tools foster real-time collaboration and personalization. The proposed empowerment model emphasizes decentralization, transparency, and inclusivity. The study concludes with policy recommendations to foster digital literacy, improve infrastructure, and safeguard data governance for sustainable civic engagement.

Fault-Tolerant Software Architecture: A Comprehensive Analysis Of Design Patterns, Implementation Strategies And Performance Evaluation

Authors: Mr. Eric Sifuna Siunduh, Mr. Victor Mony Otieno, Professor Samuel Mbugua
Abstract: Fault-tolerant software architecture has become increasingly critical in modern distributed systems, where system failures can result in significant economic losses and service disruptions. This research paper provides a comprehensive analysis of fault-tolerant software architecture design patterns, implementation strategies, and performance evaluation methodologies. Through a systematic literature review of 45 peer-reviewed articles published between 2020-2024, this study identifies key architectural patterns including redundancy-based approaches, checkpoint-restart mechanisms, and self-healing systems. The methodology employed includes comparative analysis of fault tolerance techniques, performance benchmarking, and case study evaluation of real-world implementations. Data analysis reveals that hybrid approaches combining multiple fault tolerance strategies achieve 99.99% system availability with 15-30% performance overhead. Results demonstrate that micro services architectures with circuit breaker patterns and service mesh implementations provide superior fault isolation compared to monolithic systems. The discussion includes detailed analysis of trade-offs between fault tolerance levels and system performance, supported by empirical data from 12 case studies. Key findings indicate that automated recovery mechanisms reduce mean time to recovery (MTTR) by 65% compared to manual intervention approaches. This research contributes to the field by providing a comprehensive framework for evaluating fault-tolerant architectures and offers practical guidelines for system architects. Future research directions include exploration of AI-driven fault prediction, quantum-resistant fault tolerance mechanisms, and edge computing fault tolerance strategies.

DOI: http://doi.org/10.5281/zenodo.15868925

Advance Construction Techniques In Modern Sports Complexes

Authors: Prof. Ar. Gulfam Shaikh, Vedant Mundiwale, Prof. Ar. Dilip Jade
Abstract: The evolution of sports complexes has undergone a significant transformation over the last few decades, driven by advances in construction technologies, materials, and design philosophies. Modern sports infrastructure demands multifunctionality, sustainability, spectator comfort, and technological integration. This study explores the advanced construction techniques employed in modern sports complexes, including long-span roofing systems, modular construction, precast technologies, tensile membrane structures, and smart building systems. These approaches contribute to faster construction, cost efficiency, structural integrity, and enhanced user experience. Case studies such as Jawaharlal Nehru Stadium, Delhi and Tottenham Hotspur Stadium contextualize these practices. Challenges like cost overruns, design complexity, and sustainability mandates are also examined. The study concludes by emphasizing innovation, interdisciplinary collaboration, and future-ready design strategies for sports architecture.

Evaluating Mechanistic Data Analysis Methods For Machine Learning On Effects Of Climate Change In Africa

Authors: Eric Sifuna Siunduh, Zachary Mwangi, Dr. Anselemo Peters Ikoha

Abstract: Climate change poses unprecedented challenges to African nations, necessitating sophisticated analytical approaches to understand and predict its multifaceted impacts. This study evaluates the effectiveness of mechanistic data analysis methods in machine learning applications for assessing climate change effects across Africa. Through a comprehensive analysis of temperature, precipitation, and socioeconomic data from 2020-2024, the study compared traditional statistical approaches with mechanistic machine learning models including physics-informed neural networks (PINNs), causal inference frameworks, and hybrid mechanistic-statistical models. The methodology integrated satellite data, ground-based observations, and socioeconomic indicators from 54 African countries, employing cross-validation techniques and mechanistic validation approaches. Results demonstrate that mechanistic methods significantly outperform traditional approaches in prediction accuracy (RMSE improved by 23-31%) and interpretability. Physics-informed models showed superior performance in temperature prediction (R² = 0.89) while causal inference frameworks excelled in understanding precipitation-agriculture relationships. The study reveals critical insights into drought patterns, agricultural vulnerability, and urban heat island effects across different African climatic zones. Key findings indicate that mechanistic approaches provide more robust predictions for policy-relevant scenarios, particularly in data-sparse regions common across Africa. However, computational complexity and data requirements present implementation challenges. The study recommends the integration of mechanistic methods with traditional approaches for comprehensive climate impact assessment, emphasizing the need for capacity building and infrastructure development to support widespread adoption of these advanced analytical techniques in African climate research

DOI: http://doi.org/10.5281/zenodo.15869058

Tribal Building Techniques

Authors: Ritesh Vinod Deshmukh

Abstract: Indigenous communities across India have evolved construction systems that respond beautifully to their surroundings, traditions, and material availability. This research explores the tribal building techniques of the Gond community in Maharashtra—techniques shaped by centuries of climate interaction, local materials, and symbolic customs. Through on-site documentation, literature studies, and direct interactions with tribal members, the study uncovers the relevance of these methods in sustainable and context-sensitive architecture. This paper emphasizes how integrating tribal techniques into modern design, like in the proposed Gond Tribal Cultural Centre near Tadoba, can restore ecological balance and cultural identity in rural development.

Aerodynamic Analysis Of Ucav Ghatak Using Cfd

Authors: Saraniyan M, Assistant professor Gowtham G

Abstract: The Unmanned Combat Aerial Vehicle (UCAV) Ghatak represents a pivotal advancement in stealth and autonomous aerial warfare technology. This project focuses on a comprehensive aerodynamic analysis of the Ghatak UCAV using Computational Fluid Dynamics (CFD) tools. By simulating realistic flight conditions, this study evaluates the aerodynamic performance parameters such as lift, drag, pressure distribution, and flow behavior across the stealth-designed airframe. The analysis takes into account various angles of attack and flight regimes to understand the aircraft’s stability and efficiency. The objective is to optimize the aerodynamic characteristics of Ghatak to ensure superior performance, maneuverability, and reduced radar signature. The insights gained from this CFD-based investigation will contribute to enhancing the combat readiness and operational effectiveness of next-generation UCAVs.

Sustainable Strategies In Public Transportation Hubs

Authors: Vivek Sonone, Prof. Malini Nathe, Prof. Radhika Raut, Prof. Saiyam.S. Chaturvedi, Dr. Sudhir V. Dhomane, Dr. P. V. Thorat Principal
Abstract: Public transportation hubs serve as critical nodes in urban mobility networks, significantly influencing environmental, economic, and social sustainability. This research explores sustainable strategies implemented in transportation hubs to enhance energy efficiency, reduce carbon emissions, and promote multimodal integration. The study examines case studies from global cities, highlighting innovative practices such as green infrastructure, renewable energy integration, smart mobility technologies, and inclusive design. It also assesses policy frameworks, stakeholder involvement, and the role of digitalization in optimizing operations. The findings emphasize the importance of systemic planning and cross-sector collaboration to transform transportation hubs into resilient and sustainable urban assets.

Aerodynamic Analysis And Improvising Of Noiseless UAV Propeller Designs Using CFD

Authors: Sridhar A S, Assistant professor Gowtham G

Abstract: Unmanned Aerial Vehicles (UAVs) have become integral to various applications, yet their operational efficiency and noise emission remain critical challenges. This project focuses on the aerodynamic analysis and optimization of noiseless UAV propeller designs using Computational Fluid Dynamics (CFD). By leveraging advanced CFD simulations, we assess the airflow characteristics, thrust generation, and acoustic behavior of different propeller configurations. The study aims to reduce aerodynamic noise while maintaining or enhancing propulsive efficiency. Design modifications, including blade geometry refinement and tip treatments, are evaluated to identify optimal configurations that balance performance with acoustic stealth. The results provide actionable insights into developing quieter UAV systems suitable for sensitive missions and urban environments, where noise reduction is essential. This research contributes to the growing demand for UAVs that are both aerodynamically efficient and environmentally compliant.

The Psychology Of Space In Museum Environment

Authors: Imran Khan, Ar Malini O Nathe, Ar Radhika Raut

Abstract: The spatial design of museums significantly influences how visitors perceive, experience, and emotionally connect with exhibits. This research explores the psychological dimensions of space within museum environments, examining how architectural elements—such as spatial layout, lighting, scale, materiality, circulation, and enclosure—affect cognitive engagement, emotional response, and visitor behavior. Drawing on environmental psychology, spatial theory, and museum studies, this study analyzes how spatial configurations can either enhance or inhibit interpretive experiences and memory retention. Case studies from contemporary and traditional museums are evaluated to understand how spatial strategies support narrative storytelling, accessibility, and user comfort. The findings highlight the critical role of psychologically responsive design in creating immersive and meaningful museum experiences, informing both curatorial strategies and architectural practices.

Performance Assessment and Environmental Benefits of Emulsion-Based Warm Mix Asphalt

Authors: Research Scholor Mr. Arun Kumar Pyasi, Assistant Professor Mr. Hariram Sahu

Abstract: The adoption of Warm Mix Asphalt (WMA) offers a sustainable alternative to conventional Hot Mix Asphalt (HMA), especially in developing countries like India where energy efficiency and cost-effectiveness are critical. This study investigates a simplified method of producing WMA using a medium-setting bitumen emulsion and VG 30 binder for Dense Bituminous Macadam (DBM) mixes. Marshall samples were prepared at three different mixing temperatures—110°C, 120°C, and 130°C—with varying bitumen-to-emulsion (B:E) ratios ranging from 50:50 to 100:0. Marshall Stability, flow value, unit weight, air voids, voids in mineral aggregate (VMA), and voids filled with bitumen (VFB) were evaluated to determine the optimum binder content (OBC). The results revealed that a B:E ratio of 70:30 at 120°C provided the highest Marshall Stability value of 11.6 kN with acceptable volumetric parameters, demonstrating the optimal balance between strength and workability. The study confirms that emulsion-based WMA can deliver comparable mechanical properties to HMA while enabling lower production temperatures, making it suitable for Indian climatic and economic conditions.

DOI: https://doi.org/10.5281/zenodo.15876538

Designing Inclusive Spaces

Authors: Prof. Gulfam Shaikh, Gauri Gajanan Mankar, Prof. Dilip Jade
Abstract: Art and cultural hubs have emerged as powerful instruments in reshaping urban identities and fostering community engagement. This study explores the role of such spaces in promoting inclusivity while catalyzing urban regeneration. As cities grapple with socio-economic disparities, cultural fragmentation, and deteriorating public spaces, art and cultural centers offer dynamic platforms for interaction, expression, and innovation. These hubs serve not only as venues for creative expression but also as vital nodes that bridge diverse communities, stimulate local economies, and rejuvenate underutilized urban fabric. Through a multidisciplinary lens combining urban design, social theory, and cultural policy, this paper investigates how thoughtfully designed art and cultural hubs can facilitate inclusivity—physically, socially, and economically. It also examines global and local case studies that highlight successful regeneration projects driven by cultural infrastructure. Emphasis is placed on accessibility, participatory design processes, adaptive reuse of heritage structures, and integration with public space networks. The study concludes that inclusive design in cultural hubs—rooted in context, community, and climate—can transform marginalized urban areas into vibrant, equitable, and resilient neighborhoods, ultimately reinforcing a city’s cultural capital and collective identity.

Co2 Emission Rating by Vehicles Using Data Science

Authors: Assistant Professor Mrs.G.Sangeetha Lakshmi, Ms.S.Devagi

Abstract: Amid growing concerns about climate change and environmental sustainability, accurately evaluating vehicle CO₂ emissions has become increasingly important. As transportation remains a major source of greenhouse gases, there is a need for advanced, data-driven solutions to monitor and assess emission levels effectively. This project introduces a deep learning model based on Convolutional Neural Networks (CNN) to classify and rate vehicle emissions by analyzing key attributes such as fuel type, engine capacity, mileage, and emission standards. Unlike traditional rule-based or statistical methods that often struggle with complex and large datasets, CNNs excel at automatically extracting meaningful features, leading to higher prediction accuracy and adaptability. Trained on a comprehensive dataset of vehicle emission records, the model classifies vehicles into various emission categories, offering valuable insights for regulators, manufacturers, and consumers. By combining deep learning with data science, this system provides a scalable and automated method for emissions evaluation, promoting the adoption of energy-efficient vehicles and stricter environmental regulations. Furthermore, the model has the potential for real-time emission monitoring, aiding in better air quality management and supporting the shift toward greener transportation technologies.

The Evolving Landscape Of Digital Currency: Exploring Cryptocurrency’s Role In The Global Economy”

Authors: Dr. Raju Ghanhyam Shrirame
Abstract: The rise of digital currency has transformed the global financial landscape, with crypto currency emerging as a disruptive force in traditional economic systems. Over the past decade, crypto currencies such as Bit coin, Ethereum, and a range of altcoins have challenged conventional banking structures, offering decentralized, borderless, and highly secure financial transactions. This paper explores the evolving role of crypto currency in the global economy, assessing its impact on financial inclusion, regulatory challenges, monetary policy, and the future of digital finance. Crypto currency, built on block chain technology, provides a decentralized method of exchange that eliminates the need for intermediaries like banks and financial institutions. This shift has introduced benefits such as increased financial autonomy, lower transaction costs, enhanced security, and the potential to provide banking services to unbanked populations. However, it also presents risks, including volatility, regulatory uncertainty, security concerns, and its association with illicit financial activities. The adoption of digital currencies by individuals, businesses, and governments reflects the growing recognition of its potential while also raising questions about its long-term sustainability and integration into the global financial system. One of the key drivers of crypto currency adoption is the pursuit of financial inclusion. In many developing regions where traditional banking infrastructure is lacking, crypto currencies provide an alternative means of financial participation. Block chain-based transactions do not require credit histories, bank accounts, or financial intermediaries, enabling people in underserved regions to access financial services. Stable coins, which are pegged to fiat currencies, have gained particular traction as they offer stability while retaining the benefits of digital assets. Cross-border transactions, which are traditionally costly and slow due to banking restrictions, can be executed more efficiently using crypto currencies, making them an attractive solution for remittances and international trade. Despite its advantages, crypto currency remains highly volatile, with price fluctuations influenced by speculation, regulatory developments, technological advancements, and macroeconomic trends. Bitcoin, for example, has experienced extreme price swings, making it an attractive asset for speculative investors but a risky store of value. The volatility of crypto currencies raises concerns about their suitability as a stable medium of exchange and unit of account. Governments and financial institutions remain divided on how to integrate digital currencies into existing financial frameworks without disrupting monetary stability. Regulatory challenges continue to shape the future of crypto currency. Governments and central banks are actively working to establish guidelines for digital assets, balancing innovation with consumer protection and financial stability. While some countries, such as El Salvador, have embraced Bit coin as legal tender, others have imposed strict restrictions or outright bans due to concerns about money laundering, fraud, and tax evasion. The lack of standardized global regulations creates uncertainty, leading to fluctuating adoption rates and investor sentiment. In response, many nations are exploring the development of central bank digital currencies (CBDCs), which offer the efficiency of digital assets while maintaining government control over monetary policy. Institutional adoption has played a pivotal role in legitimizing crypto currency within mainstream financial markets. Major corporations, payment processors, and investment funds have integrated digital assets into their portfolios, acknowledging their potential as a hedge against inflation and an alternative investment vehicle. Financial institutions such as JPMorgan, PayPal, and Tesla have embraced crypto currency, either through direct investment, payment facilitation, or the development of block chain-based services. The emergence of decentralized finance (DeFi) platforms has further expanded crypto currency’s use cases, enabling lending, borrowing, staking, and yield farming without traditional financial intermediaries. The impact of crypto currency extends beyond financial markets, influencing sectors such as supply chain management, digital identity verification, and smart contract automation. Block chain technology provides transparency, traceability, and security, making it a valuable tool for industries seeking to improve operational efficiency. Smart contracts, self-executing agreements powered by block chain, enable automated transactions and reduce reliance on third parties, lowering costs and increasing efficiency in legal and business processes. However, security concerns persist in the digital currency ecosystem. While block chain itself is highly secure due to its decentralized nature, crypto currency exchanges, wallets, and smart contracts are vulnerable to hacking, fraud, and cyber attacks. High-profile exchange breaches and decentralized finance exploits have resulted in significant financial losses, highlighting the need for stronger security measures and regulatory oversight. Additionally, the irreversible nature of crypto currency transactions poses challenges in cases of fraud or accidental transfers, necessitating the development of user-friendly dispute resolution mechanisms. The environmental impact of crypto currency mining has also raised concerns. Bit coin and other proof-of-work (PoW) crypto currencies require substantial energy consumption for transaction validation and network security. Critics argue that this energy-intensive process contributes to carbon emissions and environmental degradation. In response, some block chain networks are transitioning to more sustainable consensus mechanisms such as proof-of-stake (PoS), which significantly reduces energy consumption while maintaining network integrity. The push for eco-friendly block chain solutions aligns with broader global efforts to promote sustainability in digital finance. As the crypto currency landscape continues to evolve, its integration into the global economy will depend on technological advancements, regulatory developments, and mainstream adoption. Governments and financial institutions must collaborate to establish clear guidelines that support innovation while mitigating risks. The potential for crypto currency to reshape global finance is undeniable, but its long-term success hinges on addressing volatility, security, regulatory concerns, and environmental sustainability. In the coming years, we may witness greater convergence between traditional finance and digital assets. The rise of central bank digital currencies (CBDCs) could coexist with decentralized crypto currencies, creating a hybrid financial system that leverages the benefits of both models. Additionally, the adoption of block chain technology in sectors beyond finance, such as healthcare, education, and governance, could further demonstrate its transformative potential. In conclusion, crypto currency represents a paradigm shift in the way we perceive and interact with money. While challenges remain, its disruptive impact on traditional financial systems cannot be ignored. Whether it ultimately serves as a mainstream medium of exchange, a speculative investment, or a decentralized financial infrastructure, its role in the global economy will continue to evolve. Stakeholders—including governments, businesses, and consumers—must navigate this rapidly changing landscape to harness its benefits while mitigating its risks. The future of digital currency is unfolding, and its implications will shape the economic and financial structures of the 21st century.

Performance Evaluation of PET and Glass Fiber Modified High Modulus Asphalt Concrete under Simulated Extreme Indian Climates

Authors: Research Scholar Mr. Satyaveer Dhakad, Assistant Professor Mr. Hariram Sahu

Abstract: This study evaluates the mechanical and durability performance of High Modulus Asphalt Concrete (HMAC) modified with Polyethylene Terephthalate (PET) and Glass Fibers using a PMB 70 binder, under temperature and moisture conditions that simulate extreme Indian climates. Fiber dosages ranging from 0.0% to 0.4% by total mix weight were incorporated into Dense Bituminous Macadam (DBM) grade mixes. A series of laboratory tests, including Marshall Stability, Indirect Tensile Strength (ITS), Dynamic Modulus, and Tensile Strength Ratio (TSR), were conducted on specimens prepared at Optimum Binder Content (OBC) and varying fiber contents. Results indicate that the inclusion of 0.3% Glass Fiber improved Marshall Stability by 21.5% and reduced flow values by 14.2%, suggesting enhanced rutting resistance under high-temperature loading. PET Fiber, at 0.3%, yielded a 27.4% increase in ITS at –10°C, indicating improved resistance to low-temperature cracking. Both fiber types enhanced volumetric parameters, with Void Filled with Bitumen (VFB) exceeding 76% in all modified mixes. Moisture susceptibility improved substantially, with TSR values exceeding 85% for both fiber-reinforced mixes, signifying strong adhesion in the presence of water. The Dynamic Modulus of Glass Fiber mixes increased by 17.8% at 25°C compared to the control mix, while PET-modified mixes exhibited better modulus performance at lower frequencies. These findings suggest that Glass Fiber is more effective in improving high-temperature performance and stiffness, whereas PET Fiber excels in enhancing flexibility, crack resistance, and sustainability. The study supports the use of both fibers in region-specific applications, offering improved pavement life and reduced environmental burden.

DOI: https://doi.org/10.5281/zenodo.15913918

Applications Of Radiology In Ophthalomology

Authors: Fiza Shabir Mir

Abstract: Radiology plays a crucial role in ophthalmology by providing non-invasive imaging techniques for diagnosis and managing ocular diseases. The advancements in imaging technologies, modalities such as MRI, CT, USG and Optical Coherence Tomography (OCT) have significantly improved the detection of retinal, orbital and neuro-ophthalmic conditions. This paper explores the applications of radiological imaging in ophthalmology, highlighting recent techniques, advancements and their clinical impact.

Improving Students Assessment in E-learning

Authors: Professor Sakshi M. Rahangdale, Ms. Aachal Harinkhede, Ms. Dolly Raghorte, Ms. Jyoti Patle

Abstract: With the increasing reliance on digital platforms for education, e-learning has emerged as a powerful alternative to traditional classroom instruction. However, assessing students effectively in online environments poses numerous challenges, including issues of academic integrity, student engagement, technological access, and diverse learning styles. This paper focuses on improving student assessment practices in e-learning by exploring both pedagogical and technological innovations. It examines the limitations of conventional assessments like timed exams and emphasizes the need for more adaptive, continuous, and formative evaluation techniques. Advanced tools such as learning analytics, AI-driven assessment systems, and gamified quizzes are discussed for their potential to enhance objectivity and provide real-time feedback. Furthermore, the paper highlights the importance of inclusive assessment strategies that consider learners with varied needs, ensuring accessibility and fairness. It also underscores the need for teacher training in digital pedagogy to effectively design and implement online assessments. Strategies such as peer assessment, project-based evaluation, open-book exams, and scenario-based learning are proposed to make assessments more reflective of real-world understanding. By addressing technological, pedagogical, and ethical dimensions of e-assessment, this study aims to provide a comprehensive framework for educators and policymakers to design more robust, student-centered assessment systems in virtual learning environments. The insights presented can contribute to improving learning outcomes, promoting academic honesty, and increasing student satisfaction in e-learning ecosystems.

Research On Quality Hospice Care Centre In India

Authors: Dr. P. V. Thorat, Prof. Malini Nathe, Prof. Radhika Raut, Prof. Saiyam.S. Chaturvedi, Dr. Sudhir V. Dhomane, Shrinivas Shivaji Pote

Abstract: India’s approach to hospice and palliative care has evolved significantly over the past two decades, driven by a growing need for compassionate end-of-life care amidst an aging population and rising burden of chronic illnesses. This research explores quality hospice care centers across India, highlighting leading institutions such as Karunashraya in Bengaluru, Sparsh Hospice in Hyderabad, the Institute of Palliative Medicine in Kozhikode, Aastha Hospice in Lucknow, and CanSupport in New Delhi. These centers exemplify best practices in hospice care, combining medical support, emotional counseling, and family engagement, often free of cost or heavily subsidized. National efforts, including the development of the Minimum Standards for Palliative Care Programs by Pallium India and NABH accreditation, aim to standardize and improve care quality across centers. Centers recognized by international organizations like the World Health Organization (WHO) and the International Association for Hospice and Palliative Care (IAHPC) demonstrate excellence through community outreach, home care, and professional training initiatives. Despite challenges in accessibility, awareness, and funding, the emergence of such institutions indicates progress toward equitable, patient-centered hospice care in India. Further investment in policy, public awareness, and medical training is essential to scale these models nationwide.

Comparative Efficacy of Diaphragmatic Breathing and Respiratory Muscle Stretch Gymnastics in Interstitial Lung Disease: A Randomized Controlled Trial

Authors: Associate Professor Dr. Vandana, Assistant Professor Dr. Sapna Shokeen

Abstract: Background: Interstitial Lung Disease (ILD) profoundly impacts respiratory function, leading to restrictive ventilatory impairment, diminished respiratory muscle efficiency, and reduced exercise capacity. Two rehabilitative approaches, Diaphragmatic Breathing (DB) and Respiratory Muscle Stretch Gymnastics (RMSG), are commonly employed to enhance respiratory mechanics in these patients. Objective: This study aimed to critically compare the distinct effects of DB and RMSG on crucial clinical parameters, including pulmonary function, severity of dyspnea, respiratory muscle strength, and overall quality of life in individuals diagnosed with ILD. Methods: A single-blind randomized controlled trial (RCT) was conducted involving 60 ILD patients. Participants were equally and randomly allocated to one of two intervention groups: Group A (n=30) received Diaphragmatic Breathing exercises, while Group B (n=30) underwent Respiratory Muscle Stretch Gymnastics. Both interventions were delivered consistently, three times per week for a duration of eight weeks. Key outcome measures included spirometry readings (Forced Vital Capacity [FVC], Forced Expiratory Volume in 1 second [FEV1]), Maximum Inspiratory Pressure (MIP), the modified Medical Research Council (mMRC) Dyspnea Scale, and the St. George’s Respiratory Questionnaire (SGRQ). Results: Post-intervention analysis revealed significant improvements in both groups across various parameters (p<0.05). Notably, RMSG led to more substantial gains in chest expansion, demonstrating a 17.5% increase compared to 9.8% in the DB group (p<0.01), and a greater reduction in dyspnea severity (mMRC score change of 1.4 vs. 0.9). Conversely, DB exhibited superior enhancements in Maximum Inspiratory Pressure, with an average increase of 12 cmH₂O compared to 7 cmH₂O in the RMSG group. Conclusion: The findings suggest that RMSG is particularly effective in improving thoracic mobility and alleviating symptoms of dyspnea, while DB plays a more prominent role in optimizing inspiratory muscle strength. The distinct benefits observed underscore the potential for a combined rehabilitative strategy to offer synergistic and comprehensive improvements for patients with ILD.

Holistic Design Process in Modular Integrated Construction Technology – A Structured Literature Review

Authors: Joachim Zwicky, Sandra Filipe, Fernanda Rodrigues

Abstract: The purpose of the current study is to examine a State-of-the-Art Research (SoTA) in Modular Integrated Construction (MiC) with the analysing focus on a holistic design process in Terms of (A) Design (B) BIM and Digitalization and (C) Sustainability. The identified findings and research gaps will be presented in a structured way and an approach will be outlined to describe a holistic Modular integrated Construction Model by considering all research fields. This paper provides also a framework for practitioners withing the construction industry but especially for start-up companies out of the construction technology segment. The current State of the Art Review was guided by a Systematic Literature Review. The search engines Web of Science was used to find the required literature using a set of key words. In the research and literature review, the relevance and actuality of papers was considered as selection criteria. A total of 54 articles were examined and analysed. The paper examines aspects concerning Modular Integrated Construction (MiC) and sustainable construction practices. It emphasizes the necessity of employing a comprehensive design approach in MiC to enhance sustainability performance through progressive technologies and tactics. The paper underscores the value of utilizing automated generative design systems and advanced simulation methods to choose optimal building layouts, components, and materials in MiC projects. Furthermore, the document highlights the importance of incorporating Design for Manufacture and Assembly (DfMA) principles in MiC to simplify construction processes and streamline work packages. Lastly, it discusses the development of Internet of Things (IoT)-enabled smart Building Information Modeling (BIM) platforms for on-site assembly services in MiC projects. The topic of Modular Integrated Construction Technology with components prefabricated in a factory has already been investigated in many scientific papers. As a rule, the criteria were examined separately or in combination with two criteria such as Design for Manufacturing and Design for Assembly or BIM and Sustainability. With this State-of-the-Art Research, a holistic approach is to be pursued in which the findings and research gaps are structured and provide an evidence-based knowledge in order to conceive an innovative and holistic Modular Integrated Construction Model combing the criteria of Design, BIM and Digitalization and Sustainability. Modular Construction Technology is being discussed at the scientific, political and economic level and often presented as the “game changer” to make housing affordable particularly in urban areas and larger cities around the globe. The new German government e.g., announced by end of 2021 the demand of yearly 400.000 new apartments, mainly in terms of Social Residential Housing; in California, the target is to build 3,5 million new apartments until 2025 and in Hong Kong, around 200,000 people live in shacks of less than two square meters and pay as much as for a room in a shared apartment in Germany. The housing shortage mentioned above is becoming the driver of a necessary transformation in the construction industry. Important cornerstones of this transformation are to make construction faster and less cost-intensive, to make the value creation processes more sustainable and to inter-connect the separate process steps from planning to recycling of building materials or entire construction modules at the end of a house cycle.

DOI: https://doi.org/10.5281/zenodo.16027668

Real Time Chat App

Authors: Sarvesh Kumar, Naresh Kumar Kurmi, Rahul Kumar, Suresh Kumar, Shivansh Mishra, Associate Professor Satyarth Tiwari

Abstract: This paper presents the design and implementation of a real-time chat application that facilitates instantaneous communication between users. Leveraging WebSocket technology and a modern web development stack (Node.js, Express.js, and Socket.IO), the system supports low- latency, bi-directional communication. Security, scalability, and user experience are considered in the system architecture. Performance tests indicate the system handles multiple concurrent users effectively with minimal latency.

Blockchain-Enabled Final Seal Verification for Tea Supply Chain Integrity

Authors: Abhijit Kakoty

Abstract: This paper presents a blockchain-based prototype for enhancing traceability and data integrity in the tea supply chain through a Final Seal Verification mechanism. The system integrates Ethereum blockchain (via Ganache and Truffle), Node.js, Solidity smart contracts, and Laravel to ensure the authenticity of critical supply chain events. By hashing and sealing traceability data onto an immutable blockchain, the system can detect tampering attempts and provide customers with a reliable method of batch verification via QR codes.

1.58 Bit Large Language Model(LLM)

Authors: Kumarswamy S, Vidya Laxman Gadekar, Manasi

Abstract: Large Language Models (LLMs) have changed the landscape of natural language processing (NLP) with state of the art performance across numerous applications. Nonetheless, the computational and memory requirements for deployment in resource constrained environments are still a barrier. In this paper we describe the development of a 1.58-bit LLM which utilizes various quality quantization aware tuning and training techniques, and low- rank adaptation (LoRA), with additional memory efficient techniques (e.g., Flash Attention). The LLM quantization methods provide significant savings in both memory and energy consumption and retains competitive accuracy. Our experimental benchmarking demonstrates that effective training and quantization of LLMs can be applied to edge computing and other resource limited deployment methods.The advancement of Large Language Models (LLMs) has significantly transformed natural language processing (NLP) by achieving state-of-the-art results in multiple domains. Nevertheless, the highly computationally and memory-intensive nature of these models makes their deployment in resource-limited settings challenging. In this paper, we introduce the design of a 1.58-bit precision LLM with the state-of-the-art quantization approach and memory-efficient techniques including low- rank adaptation (LoRA) and Flash Attention. The proposed model offers a substantial cut in memory footprint and energy consumption, while maintaining a competitive accuracy. Experimental evaluations on benchmark datasets validate the effectiveness of this approach, demonstrating its applicability in edge computing and other resource-sensitive deployments.

Autochef AI: Multi-Modal Attention For Visual Ingredient Recognition And Recipe Generation From Food Images

Authors: Bhaskara B, Vinith M, Kumarswamy S

Abstract: – Understanding food from images poses marvellous challenge in the region of recipe search, with impactful applications in smart kitchens, dietary monitoring, and automated cooking assistance. Traditional approaches typically handle ingredient recognition and instruction generation as separate tasks, often resulting in incoherent or disjointed outputs. Here, we bring Autochef AI, multi-modal attention toll which seamlessly join visual and textual information to accurately identify ingredients and generate step-bystep cooking instructions from food images. By incorporating attention mechanisms across both image and text modalities, our model captures fine-grained features essential for coherent and contextually grounded recipe generation. Experimental results demonstrate that our approach significantly improves both ingredient prediction accuracy and instruction quality across a wide variety of recipes and cuisines.

DOI: https://doi.org/10.5281/zenodo.16522342

Deep Neural Networks Architecture, Applications, and Challenges

Authors: Mayank Shakkerwal

Abstract: The Deep Neural Networks (DNNS) has revolutionized the field of artificial intelligence by enabling machines to learn from large amounts of data with human-level accuracy in tasks such as image recognition, Natural Language Processing (NLP), and game playing. This Research Paper represents a comprehensive observation of the structure and function of DNN, great advances in their development, popular architecture, real -world applications and major challenges that hinder their widespread adoption. We also highlight future instructions in DNN research, including interpretation, efficiency and ethical/moral implications.

The Effectiveness of Artificial Intelligence Methods in Software Testing: An In-Depth Review

Authors: Dr. Kumarswamy S, Darshith L, Aditya B N, Mohammed Waseem, Nagraj, Nitin Reddy

Abstract: This review explores the effectiveness of Artificial Intelligence (AI) methods in software testing, addressing the high costs and challenges of traditional approaches. It examines key AI and Machine Learning (ML) techniques, their applications in test suite optimization, test case generation, and test case prioritization, highlighting quantitative improvements. The paper also discusses current challenges like data availability and model bias, and outlines future research directions for more adaptable and scalable AI solutions in software engineering.

DOI: https://doi.org/10.5281/zenodo.16149448

Assessment Of Pollution Levels Using Biomarkers In Callinectes Sapidus From Estuaries In Rivers State, Nigeria_635

Authors: Doris Ugochi Obinna, Dike Henry Ogbuagu, Enos I. Emereibeole, Chris Chibuzor Ejiogu

Abstract: The increasing anthropogenic pollution in estuarine ecosystems poses a significant threat to aquatic life and ecosystem health. This study aims to assess the pollution levels in selected estuaries of Rivers State, Nigeria, using biomarkers in Callinectes sapidus (blue crab) as an indicator of environmental contamination. In situ measurements for some water quality variables were made at the sampling locations. 48 female crabs (weight 149.20 ± 0.02 g) harvested for the estimation of biomarker levels. Mean concentrations of Total Petroleum Hydrocarbons (TPHs), Polycyclic Aromatic Hydrocarbons (PAHs), Zn and Cr (Sig. values=0.000 each), Cd, Pb, and Fe (Sig. t-values=0.003, 0.019 & 0.009 respectively) were significantly higher at the impacted than reference locations, while that of Monocyclic Aromatic Hydrocarbons (MAHs) and Fe (Sig. t- values=0.032 & 0.014 respectively) differed seasonally at p<0.05. Though there was no significant difference in accumulations of the heavy metals and hydrocarbons in tissues of the heavy metals and hydrocarbons in tissues of the organism, numerical accumulations of Zn (5.73±2.60 µg/g) and TPHs (1.84±1.08 µg/g) were highest in the digestive than the other tissues sampled. Mean levels of Lactate Dehydrogenase (LDH), Alanine Aminotransferase (ALT), Aspartate Aminotransferase (AST), Alkaline Phosphatase (ALP) and Malondialdehyde (MDA) (sig=0.000 each) at the OSD locations, and that of total proteins (Sig. t- value=0.030) in the rainy season were all markedly higher in the organism (p<0.05). Elevated MAHs appeared to induce the production of less ALT (r=-0.584) and AST (r=-0.519), Cr induced the production of less AST (r=-0.513) (p<0.05), while MAHs induced the production of less MDA (r=-0.634) (p<0.01). Lead and PAHs recorded very high Pollution indices (240,000 & 790,000) in sediments, while Zn and TPHs recorded high toxicity quotients of 1.59 and 2.83 in the organism. Allochthonous input of pollutants from petroleum sources into the creek caused biological disruptions, including tissue bioaccumulation and other biochemical disruptions in proteins and enzyme activities of C. sapidus, and these disruptions could rightly infer pollution. Treatment of oily effluents before discharge into the creek is recommended.

DOI: http://doi.org/

Block Chain System _799

Authors: Yashaswini.P, Yathiraj M N

Abstract: This paper provides a comprehensive overview of blockchain technology and explores its application in developing a robust, transparent, and tamper-resistant system to combat the growing issue of counterfeit products in the pharmaceutical industry. Over the past decade, pharmaceutical companies around the world have been grappling with significant challenges in monitoring and tracking their products across the supply chain. These vulnerabilities have created opportunities for counterfeiters to infiltrate the market with fake or substandard medicines, posing serious risks to public health and causing substantial economic losses to legitimate manufacturers. Counterfeit drugs represent a critical global challenge, undermining the integrity of healthcare systems and endangering the lives of millions. These illegitimate products often contain incorrect dosages, harmful ingredients, or no active pharmaceutical ingredients at all, leading to ineffective treatment, prolonged illness, and in some cases, fatal consequences. According to industry statistics, counterfeit drugs are responsible for an estimated $200 billion in annual losses to pharmaceutical companies in the United States alone. Moreover, a World Health Organization (WHO) survey report reveals that in many underdeveloped countries, approximately one out of every ten medicines consumed by patients is counterfeit or of low quality—highlighting the urgent need for a reliable and tamper-proof solution. In response to this pressing issue, our research proposes and implements a blockchain-based drug supply chain management system that leverages the core features of blockchain technology—immutability, decentralization, transparency, and traceability. In conclusion, this research highlights the transformative potential of blockchain technology in securing pharmaceutical supply chains.

DOI:

The Interplay Between Digital Literacy And E-Pedagogy In Education

Authors: Dr. Debasis Ghosh

Abstract: In the evolving landscape of education, digital literacy and e-pedagogy have emerged as critical competencies for both educators and learners. Digital literacy refers to the ability to effectively and critically navigate, evaluate, and create information using a range of digital technologies. E-pedagogy, on the other hand, encompasses the theories and practices of teaching and learning through digital means, emphasizing interactive, student-centered, and technology-integrated approaches. This paper explores the interplay between digital literacy and e-pedagogy, highlighting their role in enhancing educational access, engagement, and outcomes. It examines how educators can develop digital competencies to design inclusive, flexible, and effective digital learning environments. Furthermore, the study investigates challenges such as digital divide, infrastructure limitations, and the need for ongoing professional development. Ultimately, the paper underscores the importance of institutional support, policy initiatives, and pedagogical innovation in fostering a digitally literate and pedagogically adept teaching community capable of meeting 21st-century educational demands.

DOI: https://doi.org/10.5281/zenodo.16257149

Advances In Application Of InP1-xAsx Semiconductor Alloy For Quantum Computing, Quantum Dot Technology, Quantum Photonics, And Spin-based Qubits

Authors: Dr. Alla Srivani Professor

Abstract: The InP₁₋ₓAsₓ (Indium Phosphide-Arsenide) ternary semiconductor alloy plays a significant role in quantum computing and optoelectronics, especially in the context of quantum communication, qubit systems, and quantum dot structures. An essential issue in developing semiconductor devices for photo-voltaics and thermo electric is to design materials with appropriate band gaps plus the proper positioning of do pant levels relative to the bands. Ternary Semiconductor alloys provide a natural means of tuning the magnitude of the forbidden gap for wide Application of Semiconductor devices. The need to provide materials for applications in the long-wavelength range for infrared detectors has led to the development of III-V Ternary alloys of InP₁₋ₓAsx Ternary Semiconductor. InP₁₋ₓAsx III-V Ternary semiconductor is very important as an x of a constituent in the semiconductor is going to have significant changes in calculating Physical Property like Band Energy Gap. These Ternary Compounds can be derived from binary compounds InAs and InP by replacing one half of the atoms in one sub lattice by lower valence atoms, the other half by higher valence atoms and maintaining average number of valence electrons per atom. The subscript X refers to the alloy content or concentration of the material, which describes proportion of the material added and replaced by alloy material. This paper represents the InP₁₋ₓAsx III-V Ternary Semiconductor Band Energy Gap values. Our results agree well with the Available data in the literature.

DOI: https://doi.org/10.5281/zenodo.16258617

Structural Performance Evaluation of a Tall Building with Bracings and Base Isolation Using ETABS Software: A Review

Authors: Vivek Choudhary, Rahul Kumar Satbhaiya

Abstract: The growing demand for resilient high-rise structures in seismic-prone regions has led to the widespread adoption of seismic isolation and bracing systems. This review paper presents a comprehensive evaluation of the structural performance of tall buildings equipped with bracing and base isolation techniques, focusing on their seismic response. Emphasis is placed on research conducted using ETABS software, which provides advanced modeling and analysis tools for evaluating high-rise buildings under seismic loads. Key parameters such as story drift, base shear, lateral displacement, and floor acceleration are examined in the context of various isolation and bracing configurations. The review integrates findings from multiple studies, highlighting the effectiveness of lead-rubber bearings, friction pendulum systems, and bracing systems (X, V, and Z-bracing) in enhancing the lateral stability and energy dissipation capacity of structures. Comparative analyses demonstrate that the combination of base isolation and bracing significantly improves performance compared to conventional fixed-base models. Moreover, the role of soil-structure interaction and building geometry is also discussed to understand their influence on the overall response. This paper concludes that selecting an appropriate seismic control system based on building height, seismic zone, and soil type is critical for optimizing performance. The findings support the continued use of ETABS as a powerful tool for analyzing and designing seismically resistant tall buildings. This review aims to guide engineers, researchers, and designers in selecting efficient seismic mitigation strategies for modern structural systems.

Structural Performance Evaluation Of A Tall Building With Bracings And Base Isolation Using ETABS Software

Authors: Vivek Choudhary, Rahul Kumar Satbhaiya

Abstract: The safety of people inside a building depends on its ability to withstand seismic waves and survive an earthquake with minimal damage and repairs, and without collapsing easily. Various systems are used to absorb seismic energy, including dampers, seismic isolation devices, earthquake-resistant walls, and underground water tanks. The effectiveness of these systems depends on their type and location. In this study, the seismic analysis of a 16-story G+ residential building is carried out based on the analysis of the dynamics of floor shear stress and overturning moment. The ground motion dynamics data are taken from the PEER database. Based on the maximum floor shear stress and maximum overturning moment, the performance of transverse bracing and seismic isolation structures is compared with that of conventional moment structures. By placing these elements at the corners of the building in different models, an efficient and adequate model is obtained.

DOI: https://doi.org/10.5281/zenodo.16410753

A Study on Real Time Monitoring of Carbon Emissions Using Building Information Modelling with Ai

Authors: Mr. Ankit Sethi, Abhishek

Abstract: The construction industry contributes approximately 39% of global CO₂ emissions, with embodied carbon—emissions from material extraction, manufacturing, and transportation—accounting for 11%. Traditional life cycle assessment (LCA) tools for estimating embodied carbon are often disconnected from Building Information Modeling (BIM) environments and require manual input, limiting their usability during early design stages. This study presents an AI-integrated BIM framework that enables real-time embodied carbon estimation directly within Autodesk Revit. Using Python-based machine learning models—Random Forest, Gradient Boosting, and Support Vector Regression—trained on structural data extracted via Dynamo, the system predicts carbon values and visualizes results through heatmaps in the Revit model. The Random Forest model achieved the highest accuracy (MAE: 5.4 kg CO₂, R²: 0.93) and outperformed traditional tools like One Click LCA in both speed and precision. The framework enhances decision-making during the design phase and demonstrates strong potential for scalable, automated, and sustainable design practices in the built environment

Integrating Customer Relationship Management (CRM) With Digital Marketing: A Computer Science Perspective

Authors: Ms. Neha Bhat, Mr. Amit Punia

Abstract: The convergence of Customer Relationship Management (CRM) systems with digital marketing techniques has significantly transformed how organizations interact with their customers. In today’s digital economy, data-driven decision-making is essential. By integrating CRM with technologies such as Artificial Intelligence (AI), Machine Learning (ML), and Cloud Computing, businesses can enhance personalization, accurately segment customers, and foster greater loyalty. This paper adopts a computer science-centric approach to examine the architecture, intelligent algorithms, and system integration techniques that enable CRM to serve as an effective digital marketing tool. Real-world case studies from Amazon, Salesforce, and Zoho demonstrate how CRM systems contribute to operational efficiency, improved conversion rates, and long-term customer engagement. A technical framework for AI-enhanced CRM in omnichannel environments is also proposed.

DOI:

Study of Dissimilar Welding Microstructure of Duplex Stainless Steel SFA 2205 with High Strength Low Alloy Steel A387-GR.11 Welded by TIG Process

Authors: Seyyed Moslem Mousavi Khademi, Ali Shafiee, Abbas Najafizadeh

Abstract: In this paper, the dissimilar welding microstructure of the duplex stainless steel SFA 2205 with the high strength low alloy A378 Gr.11 was studied.The microstructure investigations indicated that the weld obtained has a two-phase structure, including dendritic and interdendritic areas. A high hardness transition area was detected in the interface of the A378 low alloy steel and ER 309L metal filler. An unmixed area was observable at the melting boundary of SFA2205 duplex steel and both austenitic and duplex filler metals. The results showed that for joining the two-phase stainless steel SFA2205 with the high strength low alloy A378 Gr.11, using the metal filler ER2209 is more appropriate as a result of forming a more suitable properties microstructure.

DOI: https://doi.org/10.5281/zenodo.16266378

REVIEW ON NOMA BASED COMMUNICATION IN 5G SCHEME ON NONLINEAR REAL SIGNAL SVM OFDM SYSTEM.

Authors: Hemant Iklodiya, Madhvi singh Bhanwar

Abstract: Due to massive connectivity and increasing demands of various services and data hungry applications, a full-scale implementation of the fifth generation (5G) wireless systems requires more effective radio access techniques. In this regard, non-orthogonal multiple access (NOMA) has recently gained ever-growing attention from both academia and industry. Compared to orthogonal multiple access (OMA) techniques, NOMA is superior in terms of spectral efficiency and is thus appropriate for 5G and beyond. In this article, we provide an overview of NOMA principles and applications.

DOI:

Developing Explainable Machine Learning Models For Decision Transparency In Healthcare And Finance

Authors: Ms. Roshni Shailesh Gupta

Abstract: Machine learning (ML) models are being widely adopted in high-stakes sectors such as healthcare and finance due to their ability to uncover patterns in data and produce predictive insights. However, many of these models function as opaque “black boxes,” making it difficult for end-users and stakeholders to understand how specific decisions are derived. This lack of interpretability can erode trust, hinder adoption, and raise ethical and regulatory concerns, particularly when decisions affect individuals’ health or financial well-being. Explainable Machine Learning (XML) aims to mitigate these issues by introducing methods that make ML models more transparent and understandable. This paper presents a comprehensive examination of XML techniques, evaluates their implementation across healthcare and finance, and proposes a methodological framework to enhance both accuracy and interpretability in ML systems. The findings highlight that XML is not merely a technical enhancement but a critical enabler of trustworthy, fair, and responsible artificial intelligence (AI).

Agentic AI Systems for Software Development Automation

Authors: Professor Nikita Bante, Professor Uday Mahure, Professor Prajakta Helonde, Professor Radha Yete, Professor Aachal Aakre

Abstract: The advent of Agentic AI systems—AI entities that possess autonomy, contextual awareness, and adaptive learning capabilities—has revolutionized the landscape of software development. Unlike traditional rule-based automation tools, agentic AI can perform high-level cognitive functions, including code generation, optimization, debugging, and collaborative task execution without constant human oversight. This paper explores the role of agentic AI in automating various phases of the software development lifecycle (SDLC), from requirements gathering to deployment and maintenance. The research highlights the growing integration of Large Language Models (LLMs), multi-agent systems, and self-improving codebases. It discusses how these intelligent agents enhance developer productivity, reduce time-to-market, and minimize manual coding errors. Through a blend of empirical evidence, recent technological advancements, and case studies, the study showcases the operational and strategic implications of adopting agentic AI. It further identifies potential challenges, such as security risks, interpretability, over-reliance, and ethical dilemmas. The goal is to contribute to a better understanding of how agentic systems are reshaping software engineering practices and to offer practical recommendations for integrating these tools in development workflows responsibly and efficiently.

DOI: https://doi.org/10.5281/zenodo.16316781

Structural Analysis of Power Take-Off Shafts under Operational Loads Using Finite Element Method

Authors: Research Scholar Vaibhav Gajbhiye, Assistant Professor Mr.Praveen Patidar, Assistant Professor Mr.Saurabh Verma

Abstract: This study investigates the structural behavior of a Power Take-Off (PTO) shaft used in agricultural applications under operational loading conditions using Finite Element Analysis in ANSYS. A detailed CAD model of the PTO shaft, including critical geometric features such as splines and fillet transitions, was developed to represent real-world configurations accurately. Structural analysis was performed by applying combined torsional and bending loads to simulate typical field conditions experienced during power transmission to implements. The analysis evaluated equivalent (von Mises) stress distribution, total deformation, and equivalent elastic strain to identify critical stress concentration regions and assess the shaft’s structural integrity. Results indicated that maximum stresses and strains were concentrated near the spline and fillet regions, consistent with areas of geometric discontinuity, while deformation profiles reflected cantilever behavior with the highest displacement at the free end. The study confirmed that the stress, strain, and deformation levels in the PTO shaft made of SAE 4140 remained within permissible limits under applied loads, ensuring operational safety and reliability. These findings provide valuable insights for design validation and optimization of PTO shafts to enhance durability and performance in agricultural machinery.

Geometric Dimensioning and Tolerance (GD&T): Enhancing Precision and Clarity in Engineering

Authors: Ms. Amisha Malviya, Mr. Rahul Khobragade, Mr. Rahul Ghotka

Abstract: Geometric Dimensioning and Tolerance (GD&T) is a standardized symbolic language used on engineering drawings and models to describe the allowable variation in part geometry. Unlike traditional linear dimensioning, GD&T defines permissible limits of imperfection using feature-based tolerance that relates to functional performance. The primary aim is to ensure that components fit together and function reliably under defined operating conditions, regardless of minor variations during manufacturing. This paper presents a comprehensive analysis of GD&T from its historical evolution to practical industry application, its advantages, challenges, and role in modern digital manufacturing. The study also explores how GD&T contributes to precision engineering, reduces rework, enhances productivity, and ensures compliance with global quality standards. Extensive references from international journals, technical standards, and industrial case studies are provided to support the theoretical and practical insights

DOI: http://doi.org/

A Comprehensive Observation of Video Quality Enhancement Using Machine Learning Algorithm

Authors: Kalpana Chaurasia, Sachin chourasia

Abstract: Studies show lots of advanced research on various data types such as image, speech, and text using deep learning techniques, but nowadays, research on video processing is also an emerging field of computer vision. Several surveys are present on video processing using computer vision deep learning techniques, targeting specific functionality such as anomaly detection, crowd analysis, activity monitoring, etc. However, a combined study is still unexplored. This paper aims to present a Systematic Literature Review (SLR) on video processing using deep learning to investigate the applications, functionalities, techniques, datasets, issues, and challenges by formulating the relevant research questions (RQs). One option to enhance the video quality is to change camera lens, which is costly. Thus, an alternative is required. Hence we also proposed convolutional neural network (CNN) may give priority to distinct aspects in an video and differentiate between them. A clear review of the literature on a few CNN-based video enhancing techniques is carried out.

DOI: https://doi.org/10.5281/zenodo.16406507

Folding Algorithms Of Life: Mathematical Insights Into Protein Misfolding, Disorders, And Therapies

Authors: Ms. Meenal Maan, Er. Rajdeep Saharawat, Muskan,, Dr. Vinit Kumar Sharma

Abstract: Proteins must fold into specific three-dimensional structures to function correctly. Errors in protein folding—misfolding—can lead to aggregation and are associated with several degenerative diseases, including Alzheimer’s, Parkinson’s, and Huntington’s. This review explores the molecular mechanisms of protein folding and misfolding, the cellular quality control systems managing these processes, and the pathogenesis of misfolding-related disorders. We also discuss therapeutic approaches aimed at correcting misfolding or enhancing proteostasis.

DOI: https://doi.org/10.5281/zenodo.16407753

Geometric Dimensioning and Tolerance (GD&T): Enhancing Precision and Clarity in Engineering

Authors: Ms. Amisha Malviya, Mr. Rahul Khobragade, Mr. Rahul Ghotkar

Abstract: Geometric Dimensioning and Tolerance (GD&T) is a standardized symbolic language used on engineering drawings and models to describe the allowable variation in part geometry. Unlike traditional linear dimensioning, GD&T defines permissible limits of imperfection using feature-based tolerance that relates to functional performance. The primary aim is to ensure that components fit together and function reliably under defined operating conditions, regardless of minor variations during manufacturing. This paper presents a comprehensive analysis of GD&T from its historical evolution to practical industry application, its advantages, challenges, and role in modern digital manufacturing. The study also explores how GD&T contributes to precision engineering, reduces rework, enhances productivity, and ensures compliance with global quality standards. Extensive references from international journals, technical standards, and industrial case studies are provided to support the theoretical and practical insights

DOI: https://doi.org/10.5281/zenodo.16407012

Geometric Dimensioning and Tolerance (GD&T): Enhancing Precision and Clarity in Engineering

Authors: Mr. Rahul Khobragade, Mr. Rahul Ghotkar, Ms. Amisha Malviya

Abstract: Geometric Dimensioning and Tolerance (GD&T) is a standardized symbolic language used on engineering drawings and models to describe the allowable variation in part geometry. Unlike traditional linear dimensioning, GD&T defines permissible limits of imperfection using feature-based tolerance that relates to functional performance. The primary aim is to ensure that components fit together and function reliably under defined operating conditions, regardless of minor variations during manufacturing. This paper presents a comprehensive analysis of GD&T from its historical evolution to practical industry application, its advantages, challenges, and role in modern digital manufacturing. The study also explores how GD&T contributes to precision engineering, reduces rework, enhances productivity, and ensures compliance with global quality standards. Extensive references from international journals, technical standards, and industrial case studies are provided to support the theoretical and practical insights

DOI: https://doi.org/10.5281/zenodo.16407012

Geometric Dimensioning and Tolerance (GD&T): Enhancing Precision and Clarity in Engineering

Authors: Mr. Rahul Khobragade, Mr. Rahul Ghotkar, Ms. Amisha Malviya

Abstract: Geometric Dimensioning and Tolerance (GD&T) is a standardized symbolic language used on engineering drawings and models to describe the allowable variation in part geometry. Unlike traditional linear dimensioning, GD&T defines permissible limits of imperfection using feature-based tolerance that relates to functional performance. The primary aim is to ensure that components fit together and function reliably under defined operating conditions, regardless of minor variations during manufacturing. This paper presents a comprehensive analysis of GD&T from its historical evolution to practical industry application, its advantages, challenges, and role in modern digital manufacturing. The study also explores how GD&T contributes to precision engineering, reduces rework, enhances productivity, and ensures compliance with global quality standards. Extensive references from international journals, technical standards, and industrial case studies are provided to support the theoretical and practical insights

DOI: https://doi.org/10.5281/zenodo.16407012

Geometric Dimensioning and Tolerance (GD&T): Enhancing Precision and Clarity in Engineering

Authors: Mr. Rahul Khobragade, Mr. Rahul Ghotkar, Ms. Amisha Malviya

Abstract: Geometric Dimensioning and Tolerance (GD&T) is a standardized symbolic language used on engineering drawings and models to describe the allowable variation in part geometry. Unlike traditional linear dimensioning, GD&T defines permissible limits of imperfection using feature-based tolerance that relates to functional performance. The primary aim is to ensure that components fit together and function reliably under defined operating conditions, regardless of minor variations during manufacturing. This paper presents a comprehensive analysis of GD&T from its historical evolution to practical industry application, its advantages, challenges, and role in modern digital manufacturing. The study also explores how GD&T contributes to precision engineering, reduces rework, enhances productivity, and ensures compliance with global quality standards. Extensive references from international journals, technical standards, and industrial case studies are provided to support the theoretical and practical insights

DOI: https://doi.org/10.5281/zenodo.16408435

A Comparative Study on Application of Various Methods in Game Theory

Authors: Kushagra Sharma, Dr Vinit Kumar Sharma, Anjali Goyal

Abstract: In this paper, we have discussed application of various methods ([9],[10]) for solving the problem of a game as Dominance method, Graphical method, Algebraic method, Simplex method etc. Each method has its limitations and benefits, which depends upon the nature of problem. Students may learn about the uses of various methods by study this paper.

Prediction Of Compressive And Splitting Tensile Strengths In Steel Fiber-Reinforced Recycled Aggregate Concrete Using Machine Learning And PSO Optimization

Authors: Yassine Dahbi, Hamza Naciri, Hamza Zaouri, Ouahib Alaoui

Abstract: This study examines the use of GradientBoostingRegressor, StackingRegressor, and Gradient Boosting Regression with HistGradientBoosting in developing models that predict the compressive strength (fcu) and splitting tensile strength (fsp) of steel fiber-reinforced recycled aggregate concrete (SFR-RAC). The information comprises 465 compressive strength and 339 splitting tensile strength data of concrete mixes with varied ratios. Training and model testing were performed using 80/20 split with PSO for the hyperparameter optimization. The performance of the model was measured with four statistical metrics: coefficient of determination (R²), mean absolute error (MAE), root mean squared error (RMSE), and mean absolute percentage error (MAPE). Out of the models, Gradient Boosting Regression with HistGradientBoosting performed better in terms of prediction, with StackingRegressor taking the second rank. SHapley Additive exPlanations (SHAP) and feature importance were employed to determine the influence of input parameters on model predictions. From the results obtained, it was evident that the water content, cement content, and fiber ratio influence considerably the strength of SFR-RAC. The models give good insights regarding SFR-RAC mixture behavior, which is helpful in the production of environmentally friendly concrete with greater enhanced strength. Future research can enhance the data and use other predictor variables to further support these models.

DOI: https://doi.org/10.5281/zenodo.16420152

Representation of Nature in Indian Advertising Logos

Authors: Assoc. Prof. Swati Mehta, Dr. Ujjvala M. Tiwari

Abstract: For hundreds of years, creators and designers have looked to nature for inspiration. Sustainability and environmentalism have led to the widespread use of natural textures, shapes, and colours in many areas of design. More lately, graphic designers have been unable to ignore the beauty of mother nature as a source of inspiration, in contrast to paintings, sculpture, architecture, and textiles. According to a number of studies, logos that represent real-world objects—such as plants, animals, or locations—require less processing work than abstract ones because they are easier to recognize. Because they appeal to a particular target demographic and offer a personal touch, animals are a popular symbol for logos. For instance, the image of the lion, who rules the jungle, stands for power, strength, bravery, and justice. On the one hand, jewellery logos use the beauty and grace of a swan, while logos that use lions may symbolize a brand’s strength or authority within its industry. Certain companies’ logos use plants, trees, and flowers to symbolize life, growth, creativity, freedom, harmony, prosperity, value, and tranquillity. Unilever’s emblem features 25 natural symbols: a lion and palm tree representing the RBI, a galloping horse for TVS Motor Company, a soaring swan with the Konark Chakra for Air India, and a banyan tree for Dabur India Limited. The current study focuses on how different types of nature are portrayed in logos for Indian advertising firms.

DOI: http://doi.org/10.5281/zenodo.16408435

Synthesis and Characterization of Polymer-Metal Chelates Derived from Oxalic Acid and Thiosemicarbazide

Authors: Professor Fiza Pathan, Professor Hirkanya Bhole, Professor Nageshwari Sarade, Professor Sushma Borewar, Professor Bhavesh Thakre

Abstract: Polymeric materials have become increasingly significant due to their wide-ranging applications and adaptability to modern societal needs. These materials exhibit remarkable properties, including thermal stability, chemical resistance, conductivity, and ion-exchange capacity. The development of polymeric ligands and coordination polymers, particularly those containing donor atoms such as N, S, and O, has expanded their utility in various fields, including catalysis, electronics, surface coatings, and biomedical applications. Chelate polymers, formed by coordinating metal ions with organic ligands, exhibit both organic and inorganic characteristics, offering desirable magnetic, thermal, and electrical properties. Despite challenges such as poor solubility and plasticity, chelate polymers are utilized in the aerospace, automotive industries, and semiconductor sectors due to their thermal resilience and functional diversity. Recent research has focused on designing low-band-gap conducting polymers and synthesizing various metal complexes with Schiff bases, hydroxamic acids, and thiosemicarbazones, leading to advances in coordination chemistry and material science. These developments underline the transformative role of polymers in science, industry, and everyday life.

DOI: https://doi.org/10.5281/zenodo.16420178

Phishing Website Detection Using Machine Learning

Authors: Udith A, Harsha H S, Jayanth B R, Prathibhavani P M

Abstract: – In today’s fast-changing digital environment, phishing attacks are a major cybersecurity concern. These attacks use deceptive messages to trick users into revealing sensitive information or installing harmful software. Historically, such attacks have involved widespread spam campaigns that target many users with malicious URLs or files designed to bypass standard security measures. To address the increasing so- phistication of these threats, this research introduces an intelligent, real-time framework for detecting phish- ing URLs using machine learning. A gradient boosting classifier was specifically chosen to systematically examine and distinguish phishing URLs from legitimate ones. The approach relies on a broad suite of lexical, structural, and host-based feature extraction. The classifier outperforms traditional methods—including support vector machines, decision trees, random forests, and neural networks—demonstrating both higher accuracy and lower false positive rates. These results validate the system’s capacity for timely and effective phishing detection. The work underscores the promise of sophisticated machine learning methods for enhancing digital trust and reinforcing cyber defense architectures.

DOI: http://doi.org/10.5281/zenodo.16408435

Ai Powered Medikit An AI System With Miscellaneous Medical Applications

Authors: Praveen, Bhaskarm

Abstract: Healthcare is the foundation of a well-functioning society, yet billions especially in rural, remote, or economically disadvantaged regions continue to face limited or delayed access to qualified medical support. In such contexts, Artificial Intelligence (AI) is not just a technological advancement but a transformative tool for democratizing healthcare services. The AI-Powered MediKit is designed as a comprehensive, intelligent, and accessible digital healthcare assistant that empowers users with preliminary diagnostics, wellness support, and actionable medical guidance from the comfort of their homes. Rather than being a single-purpose tool, MediKit is a modular, multi-functional platform that integrates advanced AI technologies such as image processing, speech and audio analysis, natural language understanding, and knowledge-based recommendation systems. Each module is carefully developed for scalability, usability, and inclusivity ensuring that the system is effective across diverse user groups. MediKit bridges the healthcare gap by making intelligent, early-stage diagnostics available on everyday devices, thus contributing to more equitable and proactive health management

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Humour vs. Knowledge: The Impact of Memes on Public Opinion in the Context of Truth

Authors: Arina Das

Abstract: In today’s digital ecosystem, memes have become influential toolsIof communication, especially in the realm of celebrity culture. While their humour and virality attract widespread engagement, this study investigates how such content can distort, simplify, or even erase factual understanding. Focusing on a three month period (October to December 2024), the research examines high engagement celebrity memes on Instagram and Facebook using a Critical Discourse Analysis (CDA) framework. By analyzing both the visual textual elements of the memes and audience responses in comment sections, the study uncovers how humour frequently overshadows truth, reducing complex public narratives to easily consumable jokes. Findings reveal recurring patterns of objectification, ageism, slut shaming, and misinformation, often masked as harmless entertainment. Audience engagement further reinforces these distorted portrayals, indicating a cultural shift where emotional appeal takes precedence over informed discourse. The research also highlights how platform algorithms favour humorous, sensational content, amplifying its reach regardless of its accuracy or ethical implications. While humour can be powerful medium for critique and resistance, its overuse, particularly when misapplied, poses significant risks to public understanding. This study emphasizes the urgent need for critical media literacy and ethical content creation in order to preserve the integrity of information in an era whereIhumour increasingly dominates digital storytelling.

DOI: https://doi.org/10.5281/zenodo.16444282

Neuroarchitecture in Incubation Centers: Designing Spaces That Think

Authors: Danish Khan, Professor Ar. Dilip Jade, Professor Ar. Gulfam Shaikh

Abstract: In the ever-evolving landscape of innovation, architecture is no longer just a backdrop; it is an active participant in shaping human thought, behavior, and performance. Neuroarchitecture—a discipline at the intersection of neuroscience and architectural design—explores how spatial environments influence brain function and cognition. As startup culture thrives and the demand for incubation centers increases, understanding the neurological impact of spatial design becomes vital. This research investigates the principles of neuroarchitecture and their application in designing incubation centers that foster creativity, productivity, collaboration, and psychological well-being. The paper highlights the science behind neuroarchitectural strategies and presents design guidelines and case studies that illustrate how spaces can be programmed to “think” with their users.

TruthLens: A System For Stock Market News Analysis And Fake News Detection Using BERT

Authors: Shreyash Akole, Mansi Rakhonde, Saish Desai

Abstract: The rapid growth of online news and digital media has significantly impacted financial markets, where even a single headline can influence investor behavior. With this increasing dependence on news, ensuring the authenticity and sentiment of financial information has become more important than ever. This research presents a dual-purpose system that combines stock market news sentiment analysis with fake news detection. Our model aims to solve this by using Natural Language Processing (NLP) techniques and Machine Learning (ML) algorithms to analyze financial news, detect fake information, and suggest investment actions such as Buy, Sell, or Hold. This system uses Bidirectional Encoder Representations from Transformers (BERT) and Long Short-Term Memory (LSTM) models for sentiment and authenticity analysis, ensuring reliability and accuracy. The framework empowers traders, investors, and institutions to make smarter, safer financial decisions by combining sentiment analysis and fake news detection into a single platform. Most systems only do either sentiment analysis or fake news detection. Our system does both in one place, making it more reliable. It reached 93% accuracy in sentiment analysis and 96% in fake news detection, helping users make better and safer financial decisions.

DOI:

Adaptive Design Of Overwater Villas For Rising Sea Levels

Authors: Sharyu Chinchole

Abstract: As climate change continues to reshape coastlines and alter ecosystems, architecture stands at a crossroads—between crisis and creativity. This research explores the architectural response to rising sea levels through the lens of adaptive overwater villas. Traditionally associated with luxury, overwater villas are reimagined here as resilient, climate-responsive habitats that float, adapt, investigates and endure. The study floating systems, modular strategies, sustainable design tools, and precedents from across the globe, aiming to develop a contextual, feasible solution for future-ready living. The final proposition blends engineering and environmentalism into a design that is not only functional but poetic—an architecture that floats with the planet, not against it.

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GREEN BUILDING

Authors: Sakshi Gajanan Jayale, Jayant Ingole, Saiyam.S. Chaturvedi, Saiyam.S. Chaturvedi, Dr. Sudhir V. Dhomane, Dr. P. V. Thorat

Abstract: In the wake of escalating climate change and environmental degradation, green building technologies have emerged as essential strategies in sustainable development. This seminar explores the relationship between built environments and the natural ecosystem, emphasizing the pivotal role of buildings in energy consumption, carbon emissions, and global warming. The study categorizes green technologies that enhance energy efficiency, occupant comfort, and environmental performance through passive and active systems such as thermal mass utilization, natural ventilation, stack effect, daylighting, and high-performance glazing. It also underlines adaptive strategies to respond to climate projections using mitigation and resilience-based approaches. The report introduces a structured methodology for integrating these technologies into new and existing buildings, highlighting key principles like integrated design, responsive planning, performance benchmarking, and climate adaptation. By bridging theoretical frameworks with practical design applications, this research reinforces the potential of green building strategies to minimize ecological footprints while ensuring productivity, health, and comfort within the built environment.

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Healing Through Nature: A Study Of Biophilic Elements In Rehabilitation Environments

Authors: Aliya Fulara

Abstract: Biophilic design is a powerful architectural and psychological approach that reconnects humans with nature. This study investigates the impact of biophilic elements in rehabilitation environments, particularly in centers aimed at treating various forms of addiction and psychological stress. Through the integration of natural materials, daylight, vegetation, water features, and views of nature, biophilic design can significantly improve patient recovery rates, reduce stress, and enhance emotional well-being. The paper explores how spatial design rooted in biophilic principles—such as natural ventilation, organic forms, and immersive landscapes—can promote healing. It also examines case studies and concludes with recommendations for designing restorative spaces that prioritize the human-nature connection. This research aims to guide architects, planners, and healthcare professionals in creating rehabilitation spaces that truly support holistic recovery. It explores global examples and proposes strategies to integrate these principles in future rehabilitation architecture. The results affirm the positive impact of nature on healing, mental restoration, and long-term patient engagement.

Predicting Food Wastage In Nepal Using Linear Regression: An Empirical Assessment Towards Achieving SDG 12.3

Authors: Dr. Saumendra Mohanty

Abstract: – Food wastage contributes significantly to global environmental degradation and economic inefficiencies, particularly in developing countries. This study investigates the trend and forecast of per capita food wastage in Nepal using a simple Linear Regression model applied to UNEP and FAO datasets (2015–2021). The findings assess the nation’s trajectory against the Sustainable Development Goal (SDG) 12.3, which aims to halve food waste by 2030. The analysis reveals that Nepal is not on track to meet this target unless intervention strategies are urgently adopted. We also discuss the potential of data-driven policymaking to guide national sustainability

Custom Gpt System

Authors: Prathibhavani P M, Pragathi S

Abstract: We propose a Custom GPT System that allows domain-specific adaptation of a large language model through a flexible, modular pipeline. The system uses a Python Flask REST API as the backend server, facilitating a microservices-style deployment of core components. A high performance Groq API endpoint executes inference on Meta’s LLaMA 3 model (available in 8B and 70B parameter sizes) to generate responses. Crucially, our pipeline integrates a Retrieval Augmented Generation (RAG) stage: incoming queries trigger semantic retrieval from a vector store of domain documents, and the returned context is combined with the user prompt to guide generation. Prompts and model settings are parameterized via human-readable YAML configuration files, enabling easy customization of system behaviour and personality. This architecture can be applied to sectors like healthcare, education, and customer support by supplying relevant documents and prompt templates. We describe the system architecture (see Fig. 1), implementation details, and an API-based deployment strategy. In evaluation, the Groq-accelerated LLaMA 3 achieves up to 18× throughput improvements versus a GPU baseline, and the RAG component markedly reduces hallucinations by grounding output in up-to-date knowledge.

DOI: http://doi.org/10.5281/zenodo.16444282

Design Considerations for Schools for Children with Autism Spectrum Disorder

Authors: Vaidehi Rajkumar, Ar. Dilip Jade, Ar. Gulfam Shaikh

Abstract: Children with Autism Spectrum Disorder (ASD) face a wide range of sensory, behavioural, and social challenges that deeply influence how they experience school. Most traditional learning environments do not meet their specific needs, which can lead to anxiety, sensory overload, and difficulties with learning and socialization. This paper discusses practical design strategies that can make schools more inclusive and supportive for children with ASD. By focusing on sensory-friendly spaces, clear layouts, flexible areas, and safe opportunities for social interaction, we can create environments that help students feel more comfortable and engaged. Drawing from research and global case studies, this paper highlights the importance of thoughtful, adaptable, and human-cantered design in supporting the development of autistic students

DOI: http://doi.org/10.5281/zenodo.16444282

Structural Evaluation Of Tall Buildings Using Ferroconcrete And Steel-Timber Hybrid Systems Under Lateral Loads In ETABS: A Review_655

Authors: Pavan Patel, Rahul Kumar Satbhaiya

Abstract: This paper reviews the structural evaluation of tall buildings using Ferro concrete (reinforced concrete) and steel-timber hybrid systems under lateral loads, emphasizing seismic and wind forces. With the growing need for sustainable and high-performance building materials, steel-timber hybrids present a viable alternative to conventional RCC systems. Using insights from past research and simulation tools like ETABS, the study analyses lateral load performance, energy dissipation, environmental benefits. The review highlights key developments, identifies research gaps, and suggests future directions for adopting hybrid systems in Indian structural design

Structural Evaluation Of Tall Buildings Using Ferroconcrete And Steel-Timber Hybrid Systems Under Lateral Loads In ETABS

Authors: Pavan Patel, Rahul Kumar Satbhaiya

Abstract: In a densely populated and rapidly urbanizing country like India, the construction of multistorey buildings is essential to meet increasing housing and infrastructure demands. These structures are often subjected to lateral forces, which act horizontally and can significantly affect their performance. Such forces primarily include wind loads and seismic activity, both of which can cause structural deflection, instability, or even failure if not adequately addressed. To ensure buildings can withstand these lateral effects, advanced structural analysis tools like ETABS are employed. ETABS is a comprehensive software used for modeling, analysis, and design of buildings in compliance with national and international codes. It provides accurate simulations of how structures respond to various loading conditions, making it ideal for evaluating complex buildings. Hybrid structural systems, which combine different construction materials, are gaining popularity for enhancing structural performance. One common and effective hybrid combination is steel and concrete, which leverages the tensile strength of steel and the compressive strength of concrete. Steel is especially favoured for its high load-carrying capacity and fast construction capabilities, and when integrated with other materials such as timber or ferrocement, it helps overcome limitations inherent to individual materials. This study investigates a G+7 storey steel-timber hybrid building reinforced with ferroconcrete (wire mesh embedded in concrete). A three-dimensional static analysis is performed using ETABS to assess the building’s structural behavior. The study focuses on key parameters such as maximum nodal displacement, bending moment, shear force, axial force, and storey drift to evaluate the effectiveness of the hybrid system under lateral loading conditions

DOI: https://doi.org/10.5281/zenodo.16595886

Hybrid Cnn-Gru Model with Residual Connections for Multi-Class Fault Detection In Industrial Systems

Authors: S.Radha Krishnan, Assistant Professor,M.Aishwarya, Dr.R.Natarajan

Abstract: Fault detection in industrial systems is crucial for ensuring operational safety, minimizing downtime, and reducing maintenance costs. This work proposes a hybrid deep learning model combining Convolutional Neural Networks (CNN) and Gated Recurrent Units (GRU) to detect and classify machine faults from time-series data. The CNN layers extract spatial features, while GRU layers model temporal depen,dencies in the data. The architecture incorporates residual connections to enhance gradient flow and improve learning efficiency. The model is evaluated on multi­ class fault detection datasets, achieving robust performance with high accuracy, precision, recall, and F1-score. Advanced metrics, including ROC-AUC, logarithmic loss, Cohen’s Kappa, and Matthews Correlation Coefficient, demonstrate the model’s reliability. Visualization of confusion matrices and detailed performance metrics validates its effectiveness in detecting anomalies and classifying fault types. This approach can be generalized for real-time monitoring systems in various industrial applications, ensuring predictive maintenance and operational excellence.

Loading Analysis of Two Different Light Weight Steel Structure and Observations It’s Loading

Authors: Rajnandanikoshti, Assistant professor Anubhav Rai

Abstract: The Calibration and testing of light weight structure are very important part for building design. However proposed work is belongs for a different types of steel structure analysis. In this proposed steel structures main focus to reduce of total weight and easily design. Now these research work identification of best light weight structures using staad pro software. The main object of this work design a steel frames and compare both structures

Assessment Of Combined Antibacterial Activity Of Tridax Procumbens And Lantana Camara Leaf Extracts.

Authors: Maurya Kiran M, Gadekar Mayuri N, Davkhar Kalyani U, Kadbhane Ashwini S

Abstract: This study investigated antibacterial activity of two medicinal plants: Tridax procumbens, and Lantana camara; they are common weeds often found in cultivated areas and wastelands, and hold significant importance in traditional Indian medicine. These plants are rich in a diverse array of secondary metabolites, including tannins, flavonoids, alkaloids, saponins, phenols, steroids, anthocyanins, proteins, and carbohydrates. These secondary metabolites are extracted using Soxhlet’s method and ethanol as a solvent. Screening of plants’ phytochemicals is done as per standard protocol. In vitro, antimicrobial activity was assessed using the well diffusion method. The antibacterial activity of Ethanol extracts of these two plant leaves was evaluated against disease-causing bacteria, including Staphylococcus aureus., Escherichia coli, Pseudomonas aeruginosa, Streptococcus sp., and Klebsiella pneumoniae Combined ethanolic plant extracts exhibited maximum antibacterial activity compared to the individual plant extracts.

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Review On Stabilization Of Clayey Soil Using Industrial Waste Products

Authors: Neha Dongre, Dr.SunilSugandhi

Abstract: Soil stabilization can be explained as the alteration of the soil properties by chemical or physical means in order to enhance the engineering quality of the soil. The main objectives of the soil stabilization is to increase the bearing capacity of the clay soil, it’s resistance to weathering process and soil permeability. The long-term performance of any construction project depends on the soundness of the underlying soils. Unstable clay soils can create significant problems for pavements or structures, Therefore soil stabilization techniques are necessary to ensure the good stability of clay soil so that it can successfully sustain the load of the superstructure especially in case of clay soil which are highly active, also it saves a lot of time and millions of money when compared to the method of cutting out and replacing the unstable soil. This paper deals with the complete analysis of the improvement of clay soil properties and its stabilization using industrial waste sand and lime. The experimentation is carried out keeping 20% of lime as constant and industrial waste sand 10%, 20%and 30%. Disposal of these waste materials is essential as these are causing hazardous effects on the environment. With the same intention literature review is undertaken on utilization of solid waste materials for the stabilization of soils and their performance is discussed

The Future of Cybersecurity and the Challenges It Presents

Authors: Minal Jain, Mr.Aaron Dlima

Abstract: In recent years, the Internet has become an important part of daily life for people all over the world. On the other hand, with the increase in online activities, cyber crimes are also increasing. Cybersecurity has made great strides in recent years to keep up with the rapid changes in cyberspace. Cybersecurity refers to the procedures a country or organization can use to protect its assets and information in cyberspace. Twenty years ago, the term “cybersecurity” was not recognized by the public. Cybersecurity affects not only individuals but also organizations or governments. In recent years, everything has become digital using various technologies such as cybernetics, cloud computing, smartphones and Internet of Things technology. Cyber attacks raise concerns about privacy, security and financial compensation. Cybersecurity is a set of technologies, processes and practices designed to prevent attacks, crimes and unauthorized access to networks, computers, processes and system information. The main purpose of this article is to provide an overview of cyber security types, why cyber security is important, cyber security methods, cyber security tools and cyber security issues. Cybersecurity protects the data and integrity of the organization’s network or computing assets to protect them from threats throughout the life of a cyber attack.

Topic:Integration Of IoT With AI And ML

Authors: Sm Hassab Qais

Abstract: In recent years, the Internet has become an important part of daily life for people all over the world. On the other hand, with the increase in online activities, cyber crimes are also increasing. Cybersecurity has made great strides in recent years to keep up with the rapid changes in cyberspace. Cybersecurity refers to the procedures a country or organization can use to protect its assets and information in cyberspace. Twenty years ago, the term “cybersecurity” was not recognized by the public. Cybersecurity affects not only individuals but also organizations or governments. In recent years, everything has become digital using various technologies such as cybernetics, cloud computing, smartphones and Internet of Things technology. Cyber attacks raise concerns about privacy, security and financial compensation. Cybersecurity is a set of technologies, processes and practices designed to prevent attacks, crimes and unauthorized access to networks, computers, processes and system information. The main purpose of this article is to provide an overview of cyber security types, why cyber security is important, cyber security methods, cyber security tools and cyber security issues. Cybersecurity protects the data and integrity of the organization’s network or computing assets to protect them from threats throughout the life of a cyber attack.

Learning Analytics Usability Factors Towards Learner Performance Assessment – A Review Of Literature

Authors: Mohammed Swaleh Mohammed, Alice Nambiro, Bostley Muyembe Asenahabi, Eric Sifuna

Abstract: The Covid-19 pandemic significantly accelerated the adoption of e-learning, resulting in increased investment in digital education platforms and the generation of vast amounts of learner data, commonly referred to as big data. This shift has propelled the development of learning analytics, a field focused on the systematic collection, analysis, and reporting of data to better understand learner behavior and enhance educational outcomes. Despite growing interest in learning analytics, there remains limited understanding of its usability, particularly in assessing student performance within virtual learning environments. This paper presents a critical literature review examining the usability factors of learning analytics in the context of Kenyan universities. Using a desktop research approach, the study synthesizes existing research to identify key gaps and highlight usability dimensions relevant to performance assessment. The review identifies four primary factors influencing the usability of learning analytics tools: perceived usefulness, perceived ease of use, user satisfaction, and learnability. These findings provide a foundation for future empirical studies aimed at evaluating the impact of these factors on learner performance in virtual settings.

DOI: http://doi.org/10.5281/zenodo.17158417

Microplastic Pollution In Oceans: Detection And Removal Methods

Authors: Nanthini.N.L, Sheeja.B

Abstract: Microplastics are tiny plastic pieces that are less than 5 mm in size. These small plastics enter the oceans through the breakdown of larger plastic waste or through products like face wash and toothpaste. They pose threats to marine life and potentially human health. This paper explains how microplastics are detected and removed, including advanced technologies such as spectroscopy and AI, as well as the challenges involved and the need for improved solutions.

Ai and Ai Robotics in Terrestrial and Extraterrestrial Architecture

Authors: Azar Djamali

Abstract: This study reviews the application of Artificial Intelligence (AI) and robotic tools in terrestrial and extraterrestrial architecture. The rapid advancement of AI and robotics is transforming the architectural profession, enhancing creative exploration through data aggregation and analytics while complicating decision-making due to the vast datasets involved. Architects must balance technical proficiency with strategic discernment to harness AI’s potential while addressing operational challenges. AI significantly impacts architectural design throughout the building lifecycle, influencing predictive analytics, construction supervision, and facility maintenance (Yangluxi Li et al.). As humanity moves toward interplanetary colonisation, architects are designing habitable structures for extreme environments. Notable projects include the Mars Habitat Concept by Foster + Partners and NASA (2015–2016), Mars Science City by Bjarke Ingels Group (BIG) for the UAE (2017–present), and Skidmore, Owings C Merrill’s (SOM) Moon Village concept, featuring inflatable modules, showcased at the Venice Architecture Biennale 2025. AI and AI-assisted robots, such as the Perseverance rover, enable adaptive and efficient design for harsh environments. This article synthesises insights from over 100 research papers, exploring how machine learning (ML), generative design, and autonomous systems will redefine extraterrestrial construction. By optimising structural integrity and resource utilisation, AI facilitates spatial optimisation and energy-efficient designs (Yangluxi Li et al., 2025). This study highlights the roles of AI, robotics, and automation in optimising terrestrial and extraterrestrial architecture, presenting scalable solutions for future colonies and providing actionable insights for space agencies and architects. By leveraging AI and robotic technologies, architecture can adapt to extreme environments and advance innovative designs for future inhabitants.

Review Role of Ai in Early Detection and Treatment of Cardiovascular Diseases

Authors: Kiran Kumar, V. Narmada, Sagarika Kulkarni

Abstract: Cardiovascular diseases cause morbidity and mortality. Early detection is critical, as it can detect and the use of AI plays a significant role in and impacts cardiovascular diseases. Since cardiovascular diseases (CVDs) continue to be the world’s leading cause of death, improvements in early diagnosis, treatment, and management techniques are vital. The integration of artificial intelligence (AI), machine learning, and deep learning into cardiovascular medicine offers promising avenues to improve patient outcomes. This review explores recent progress in AI applications for CVDs, including automated electrocardiogram (ECG) analysis, medical imaging, wearable sensor technologies, and telemedicine. AI-driven systems have demonstrated potential in enhancing diagnostic accuracy, enabling remote monitoring, predicting disease risk, and supporting clinical decision-making. Despite significant advancements, challenges such as data bias, algorithmic fairness, and the need for rigorous clinical validation remain. Continued research and the responsible deployment of AI technologies can help address the global burden of CVDs through more precise, efficient, and personalized care

DOI: https://doi.org/10.5281/zenodo.16719088

Comparative Seismic Performance Analysis Of Rectangular And Circular Columns: Effects Of Replacing Rectangular Columns With Circular Columns In RC Structures

Authors: Rahul Solanki, Murlidhar Chourasia, Rahul Kumar Satbhaiya

Abstract: Designing earthquake-resistant structures necessitates ensuring their safety throughout both the construction phase and their operational lifespan, regardless of the intensity or frequency of seismic events. Ground motion effects are particularly complex due to their dynamic and multifaceted impact on structural behaviour. Among the various analytical techniques available, pushover analysis is considered one of the most dependable for evaluating a structure’s response to intense seismic forces. This approach is based on the assumption that during seismic events, buildings predominantly respond in their fundamental or lower vibration modes. Therefore, a multi-degree-of-freedom (MDOF) system can be effectively transformed into an equivalent single-degree-of-freedom (ESDOF) model. This ESDOF model is derived using nonlinear static analysis and subsequently subjected to nonlinear time history or response spectrum analysis utilizing constant-ductility or damped response spectra. The seismic demand parameters obtained from the ESDOF model are then mapped back to the MDOF system through modal transformation techniques. In this research, the seismic behaviour of a moment-resisting RC frame structure is analyzed using the pushover method. The study focuses on the effect of changing column shapes and sizes, specifically replacing rectangular columns with circular ones, to assess variations in seismic performance. The static pushover approach incorporates both constant gravity loads and incrementally applied lateral loads at each story level. Capacity curves, illustrating the relationship between base shear and total story drift, are generated using ETABS 2015 to extract critical seismic response parameters. Throughout the simulation, the overall plan dimensions of the building are maintained constant, while only the column dimensions are varied. Three sets of rectangular column sizes are examined for their nonlinear seismic response and subsequently compared with equivalent circular columns. All structural models are designed following the provisions of IS 456:2000 for concrete design and IS 1893:2002 for seismic loading conditions..

DOI: http://doi.org/

Comparative Seismic Performance Analysis Of Rectangular And Circular Columns: Effects Of Replacing Rectangular Columns With Circular Columns In RC Structures: A Review

Authors: Rahul Solanki, Murlidhar Chourasia, Rahul Kumar Satbhaiya

Abstract: The seismic performance of reinforced concrete (RC) structures is significantly influenced by the geometry and detailing of their columns, which act as the primary load-bearing and energy-dissipating elements during ground motion. Among the available column cross-sections, rectangular shapes have traditionally been used due to ease of construction and integration with architectural plans. However, in high-seismic zones, circular columns are gaining attention for their superior ductility, confinement efficiency, and uniform stress distribution.This study presents a comparative analysis of the seismic behavior of RC frames with rectangular and circular columns, focusing on their performance under lateral loading conditions. An extensive literature review was conducted, highlighting the influence of column shape on key seismic performance parameters such as base shear capacity, lateral drift, energy dissipation, plastic hinge formation, and failure mechanisms. Analytical studies, experimental investigations, and code-based assessments consistently indicate that circular columns outperform rectangular ones in terms of ductility and post-yield behavior. Their symmetrical geometry allows better confinement of core concrete, resulting in enhanced resilience during strong ground shaking.Additionally, the review underscores the limitations of conventional force-based design methods in accurately predicting the inelastic behavior of structures, especially those with irregular geometries. Nonlinear static (pushover) analysis, displacement-based approaches, and accurate modeling of infill-wall interaction emerge as essential tools for realistic seismic performance evaluation.The findings of this study support the strategic replacement or incorporation of circular columns in RC frames to improve seismic resistance, particularly in retrofitting and performance-based design scenarios. While practical challenges such as increased formwork complexity exist, the benefits in structural safety and energy absorption justify their use. This paper serves as a reference for engineers and researchers aiming to optimize RC structures for improved seismic resilience through column geometry selection.

DOI: https://doi.org/10.5281/zenodo.16728432

Comparative Seismic Performance Analysis Of Rectangular And Circular Columns: Effects Of Replacing Rectangular Columns With Circular Columns In RC Structures: A Review

Authors: Rahul Solanki, Murlidhar Chourasia, Rahul Kumar Satbhaiya

Abstract: The seismic performance of reinforced concrete (RC) structures is significantly influenced by the geometry and detailing of their columns, which act as the primary load-bearing and energy-dissipating elements during ground motion. Among the available column cross-sections, rectangular shapes have traditionally been used due to ease of construction and integration with architectural plans. However, in high-seismic zones, circular columns are gaining attention for their superior ductility, confinement efficiency, and uniform stress distribution.This study presents a comparative analysis of the seismic behavior of RC frames with rectangular and circular columns, focusing on their performance under lateral loading conditions. An extensive literature review was conducted, highlighting the influence of column shape on key seismic performance parameters such as base shear capacity, lateral drift, energy dissipation, plastic hinge formation, and failure mechanisms. Analytical studies, experimental investigations, and code-based assessments consistently indicate that circular columns outperform rectangular ones in terms of ductility and post-yield behavior. Their symmetrical geometry allows better confinement of core concrete, resulting in enhanced resilience during strong ground shaking.Additionally, the review underscores the limitations of conventional force-based design methods in accurately predicting the inelastic behavior of structures, especially those with irregular geometries. Nonlinear static (pushover) analysis, displacement-based approaches, and accurate modeling of infill-wall interaction emerge as essential tools for realistic seismic performance evaluation.The findings of this study support the strategic replacement or incorporation of circular columns in RC frames to improve seismic resistance, particularly in retrofitting and performance-based design scenarios. While practical challenges such as increased formwork complexity exist, the benefits in structural safety and energy absorption justify their use. This paper serves as a reference for engineers and researchers aiming to optimize RC structures for improved seismic resilience through column geometry selection.

DOI: https://doi.org/10.5281/zenodo.16728432

Farm-to-Film: Turning Wheat And Rice Straw Into Sustainable Bioplastics_905

Authors: Tushar Sharma, Dr. Rakesh Kumar

Abstract: Plastic pollution continues to pose a severe environmental threat worldwide. Simultaneously, the common practice of burning wheat and rice straw in agricultural fields leads to hazardous air quality and soil degradation. This study introduces a novel, sustainable alternative: transforming crop residues into biodegradable nanocellulose bioplastics. Through enzyme-assisted extraction, cellulose from straw can be processed into high-quality packaging material. This innovation not only curbs plastic pollution but also reduces air pollution and provides farmers with an alternative income stream. The paper emphasizes the need for policy support, industry collaboration, and rural engagement to scale this solution.

Evaluating Learning Analytics Usability Factors Towards Learner Performance Assessment In Virtual Environment In Kenyan Universities

Authors: Mohammed Swaleh Mohammed, Bostley Muyembe Asenahabi, Alice Nambiro, Eric Sifuna

Abstract: The purpose of the study was to evaluate the learning analytics usability factors towards e-learning learner performance assessment in Kenyan Universities. The study used quantitative methodology toward achieving the purpose of the study. Quantitative approach was attained through using five- point Likert scale distributed through random sampling to eight universities in Kenya. A focus on those students using e-learning whether blended or virtual learning. The findings revealed two factors: Perceived Usefulness and Perceived Ease of Use.

DOI: http://doi.org/10.5281/zenodo.16737143

The Evolution And Strategic Imperative Of HR: Toward A New Model Of Organizational Impact Measurement

Authors: Assistant Professor Dr. Atul Kumar, Professor Dr. Vinit Kumar Sharma

Abstract: Over the course of the last hundred years, the Human Resources (HR) profession has undergone a profound evolution. What was once regarded primarily as an administrative and compliance-driven function has steadily developed into a critical strategic component within modern organizations. Initially tasked with duties such as payroll management, employee record-keeping, and enforcement of labor laws, HR has now assumed a broader and more influential role—one that encompasses talent management, leadership development, organizational culture, and workforce planning aligned with business objectives. Despite this strategic repositioning, a significant challenge continues to limit the credibility and impact of the HR function: the absence of comprehensive and reliable mechanisms for measuring its true contribution to organizational success. Traditional HR metrics, while useful for tracking operational efficiency (e.g., turnover rates, time-to-hire, or training hours), are often insufficient in demonstrating the extent to which HR initiatives support or drive strategic outcomes. As businesses face increasing pressure to quantify return on investment across all departments, HR must develop more sophisticated tools to validate its role as a value-adding partner. This paper addresses this critical issue by examining the limitations of conventional HR measurement systems and emphasizing the need for a more integrative and multidimensional approach. Drawing upon both theoretical frameworks and empirical evidence, the study proposes a comprehensive model designed to assess the influence of HR on organizational performance. This model goes beyond input-output measures and includes three core dimensions: people-related outcomes (such as employee engagement and leadership effectiveness), process efficiencies (such as the agility of HR interventions and innovation in talent practices), and performance metrics (including business growth, productivity, and customer impact). By capturing the complex interplay between human capital and business execution, this proposed framework aims to provide organizations with a practical and evidence-based method to assess the strategic value of their HR functions. Ultimately, the goal is to support HR’s continued evolution into a fully integrated and analytically grounded contributor to sustainable business success.

Music Experience Center

Authors: Harshita Bissa, Malini Nathe, Ar. Radhika Raut

Abstract: The Music Experience Centre is a pioneering cultural and educational initiative designed to serve as a vibrant hub where individuals can explore, understand, and engage with music in all its forms. Blending tradition with innovation, the center offers an immersive environment where the physical, emotional, historical, and technological dimensions of music intersect to inspire creativity, foster learning, and promote community engagement. At its core, the center functions as an interactive space that demystifies the world of music for both musicians and non-musicians alike. It features state-of-the-art sound installations, digital composition labs, instrument galleries, and acoustic exploration zones that allow visitors to physically and sonically engage with music. Visitors can experiment with traditional and modern instruments, understand the physics of sound, compose music using AI-assisted software, and explore diverse musical cultures through multimedia exhibits. In addition to its interactive exhibits, the center will offer a wide range of programs, including music therapy sessions, artist residencies, live performances, educational workshops, and school outreach programs. These initiatives aim to make music accessible and meaningful across age groups and cultural backgrounds, with special attention to inclusivity, neurodiversity, and mental well-being. The Music Experience Centre also serves as a research and innovation platform, encouraging collaboration between musicians, educators, sound engineers, and technologists. Through its partnerships with academic institutions, cultural organizations, and the creative industries, it fosters dialogue and experimentation that push the boundaries of how music is created, experienced, and understood. Ultimately, the Music Experience Centre envisions a world where music is not just heard but felt—a tool for expression, connection, education, and healing. It stands as a landmark destination for music lovers, innovators, and communities, offering a transformative experience that resonates long after each visit.

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Comparative Structural Performance Analysis Of Residential Buildings With Constant Floor Area And Varying Plan Shapes Using ETAB

Authors:

Abstract: Earthquakes are among the most destructive natural hazards, often leading to substantial structural damage, human casualties, and economic disruption. These events are caused by sudden energy release within the Earth’s crust, generating seismic waves that impact large geographic regions. Past incidents, such as the 2015 Nepal earthquake, have underscored the importance of seismic-resistant design, especially in vulnerable areas. This study presents a comparative structural performance analysis of high-rise residential buildings (G+20) with constant floor area but varying plan shapes, evaluated using ETABS software. The analysis focuses on four distinct geometric configurations and is conducted as per the seismic design provisions outlined in IS 1893 (Part 1): 2002, considering Zone III conditions specific to the Betul region of India. Key parameters assessed include, Seismic force distribution, Bending moments, Lateral displacements, Construction cost implications The objective is to determine the most efficient plan configuration that offers an optimal balance between structural performance and economic viability under seismic loading. The findings aim to guide architects and engineers in adopting geometry-driven solutions that improve seismic resilience without increasing the building footprint.

DOI: http://doi.org/10.5281/zenodo.16737144

Comparative Structural Performance Analysis Of Residential Buildings With Constant Floor Area And Varying Plan Shapes Using ETABS: A Review

Authors: RAMMOHAN SAINI, ANKITA SINGHAI, RAHUL KUMAR SATBHAIYA

Abstract: The geometric configuration of a building plays a pivotal role in determining its seismic response. As urban environments demand both architectural creativity and structural safety, understanding how plan shape affects performance under seismic loading is increasingly critical. This review paper synthesizes existing research focused on the comparative structural behavior of residential buildings with identical floor areas but varying plan geometries—including Rectangular, Square, Triangular, and Circular configurations. Analytical studies using ETABS, a widely adopted structural analysis and design software, form the basis of this comparison, with performance metrics such as base shear and lateral displacement under seismic loads examined. Findings across multiple studies suggest that geometry significantly influences seismic resilience, with triangular plans often demonstrating better base shear resistance, while regular shapes such as squares perform more uniformly in terms of displacement control. These insights reinforce the necessity of integrating geometric considerations into the early stages of structural design. The review also highlights the limitations of current linear static analyses and emphasizes the need for future research involving nonlinear dynamic methods, soil-structure interaction, and irregular high-rise forms. Ultimately, this paper contributes to the evolving discourse on shape-based seismic optimization and supports the development of safer, code-compliant residential structures in seismically active regions.

DOI: https://zenodo.org/uploads/16750644

Economic Growth & Digital Payment Transactions In India

Authors: Dr Ramesh

Abstract: One of the fastest-growing industries in India is digital payments, such as UPI Payments, which banks are concentrating on for financial inclusion. Digital payments have the potential to guarantee rapid economic growth. By ensuring that bank account transactions are thoroughly examined, becoming cashless will reduce the bribery system. By following the proper processes, this replacement ails gradually eliminate paper money. The study also looks at how digital payment transactions affect e-commerce, retail, banking, and other areas of the Indian economy.

DOI: http://doi.org/

Study On Use Of Recycled Construction And Demolition Waste In Structural Applications: A Life-Cycle And Performance-Based Evaluation

Authors: Dr Balaji Shivaji Pasare

Abstract: Background: Rural areas such as Osmanabad are plagued by sustained degradation of infrastructure driven by climate variation, material fatigue, and restricted maintenance capabilities. Structural concrete, while strong, is susceptible to microcracking that can expedite deterioration and impede long-term resilience. Fostered by the recent advent of self-healing technologies, namely bio-concrete and polymer-fused conduit systems, these represent adequate solutions for low or no-maintenance, climate-responsive, and adaptable buildings in impoverished areas. Objectives: The current study is set forth with the objectives of field performance, healing potential, and stakeholders’ acceptance for the bio-concrete and polymer-based self-healing concrete (SHC) under semi-arid conditions in Osmanabad. It aims at the transition from laboratory innovation to rural deployment conditions, highlighting humanised engineering and participatory validation. Methods: A mixed-method design of experimental trials in 3-gram panchayats was implemented with stakeholder involvement. Quantitative data were compressive strengths, crack closure rates, and environmental (humidity, temperature) correlation variables. Qualitative information was obtained through interviews, focus groups, and participatory observation. The analytic techniques used were ANOVA, regression modelling, and thematic coding of the data. Results: Bio-concrete exhibited enhanced crack healing (94% recovery) and strength increases (~20% compared to control mixes), especially in high-humidity zones. Positive correlations were found between healing rates and environmental humidity. The highest level of trust from the stakeholders was observed from the farmers (avg. rating: 9.1/10), noting that it helped minimize maintenance and seemed to heal. The polymer SHC had milder performances, though lower photo-hysteresis. Conclusion: Bio-concrete is found to be a socially and technically acceptable, climate change resilient alternative for rural infrastructure. The research confirms its relevance in Osmanabad and supports community-scale scaling. Through the convergence of high-performance materials and ethical, participatory adoption, this research configures a new model of resilient, humanised infrastructure for the disenfranchised.

DOI: https://doi.org/10.5281/zenodo.16753496

Economic Growth & Digital Payment Transactions In India

Authors: Dr Ramesh

Abstract: One of the fastest-growing industries in India is digital payments, such as UPI Payments, which banks are concentrating on for financial inclusion. Digital payments have the potential to guarantee rapid economic growth. By ensuring that bank account transactions are thoroughly examined, becoming cashless will reduce the bribery system. By following the proper processes, this replacement ails gradually eliminate paper money. The study also looks at how digital payment transactions affect e-commerce, retail, banking, and other areas of the Indian economy.

DOI: https://zenodo.org/uploads/16755158

Aquatic Trash Collector Bot

Authors: Ishan Deshpande, Mohit Jagtap, Akash Satras, Mrs. Shreyasi Watve

Abstract: india’s vibrant cultural traditions involve many water-based religious rituals, which unintentionally contribute to environmental pollution. During festivals such as Ganesh Visarjan and Kumbh Mela, water bodies like the Godavari River in Nashik often become heavily contaminated due to the disposal of idols, flowers, and plastic items. These pollutants accumulate on the water surface, disturbing the aquatic balance. To address this issue, we propose an eco- conscious solution—an automated bot system designed for surface waste collection. This bot uses renewable energy to operate and effectively removes plastic, debris, and water hyacinth from still water bodies, supporting a cleaner and more sustainable environment.

DOI: https://zenodo.org/uploads/16755368

The Algorithmic Republic: The Social Impacts Of AI In Singapore

Authors: Lionel Seah

Abstract: Artificial Intelligence (AI) is the field of study and development of computer systems capable of performing tasks that would typically require human intelligence. These tasks include reasoning, problem-solving, learning, understanding natural language, and perceiving the environment. As defined by John McCarthy, one of the pioneers of AI, “Artificial intelligence is the science and engineering of making intelligent machines, especially intelligent computer programs” (McCarthy, 2007). AI aims to replicate or simulate human cognitive functions in machines to enhance or automate decision-making, pattern recognition, and interactions. According to Stuart Russell and Peter Norvig, in their foundational textbook Artificial Intelligence: A Modern Approach, “AI is concerned with intelligent behaviour in artifacts” (Norvig, 2021). They categorise AI systems based on their capabilities to act and think rationally or humanly.

DOI: http://doi.org/10.5281/zenodo.16757750

Comprehensive Review Of Structural Analysis Techniques For Gravity Dams Using STAAD.Pro Under Diverse Reservoir Conditions

Authors: Avdesh Kumar Ahirwar, Assistant Professor Murlidhar Chourasia, Rahul Kumar Satbhaiya

Abstract: Gravity dams, typically constructed using concrete or masonry, are massive hydraulic structures designed to resist external loads primarily through their own weight. These dams usually exhibit a triangular cross-sectional profile, with a wide base and a narrow crest, ensuring inherent stability against hydrostatic and seismic forces. Accurate analysis of such structures is essential for ensuring their safety and performance under varying reservoir conditions.This study presents a detailed evaluation of gravity dam behavior using STAAD.Pro, widely adopted structural analysis software. While traditionally employed for designing framed structures such as buildings, STAAD.Pro is also capable of modeling complex elements including plates, shells, and solid components. This flexibility makes it suitable for simulating gravity dams under different loading scenarios.In this analysis, the dam is modeled using solid elements to accurately represent its mass and geometry. The effects of hydrostatic pressure, uplift pressure, and other reservoir-induced forces are incorporated to simulate real-world conditions. By leveraging the computational capabilities of STAAD.Pro, stress distribution, deformation profiles, and stability parameters of the dam are systematically investigated. This approach eliminates the limitations of manual analysis, offering a time-efficient and precise method to assess dam safety under diverse operational conditions.

DOI: https://doi.org/10.5281/zenodo.16759714

Assessment Of Climate Change Impacts On Water Resources In Arid And Semi-Arid Regions: A Case Study Of Hargeisa, Somaliland

Authors: Ahmed Abshir Ahmed

Abstract: This study investigates the impacts of climate change on water resources in arid and semi-arid regions, with a focused case study on Hargeisa, Somaliland. As climate change intensifies hydrological variability, regions already facing environmental and demographic pressures experience exacerbated water scarcity. Analyzing climatic data (1991-2020) from SWALIM and regional groundwater studies, we identify three critical trends: (1) decreasing rainfall reliability (annual averages of 250-400mm with high interannual variability), (2) increasing evapotranspiration rates (up to 2100mm annually), and (3) progressive groundwater quality deterioration (TDS >1g/L in 90% of sampled wells). Our findings demonstrate particular vulnerability in shallow aquifers – the primary water source for most communities – which face both seasonal depletion and contamination risks. The research reveals a dual stressor system where climate variability interacts with rapid population growth to threaten water security. We propose four key interventions: (i) enhanced hydro-climatic monitoring networks, (ii) integrated climate adaptation planning, (iii) targeted aquifer recharge strategies, and (iv) policy reforms for sustainable groundwater governance. These recommendations provide actionable pathways for building resilience in water-stressed regions facing escalating climate risks

DOI: https://doi.org/10.5281/zenodo.16791946

Comprehensive Structural Analysis Of Gravity Dams: Evaluating Performance Under Full, Empty, And Partial Reservoir Conditions Via STAAD.Pro

Authors: Avdesh Kumar Ahirwar, Murlidhar Chourasia, Rahul Kumar Satbhaiya

Abstract: Gravity dams are essential infrastructural elements used for water storage, flood control, and hydropower generation. Their structural safety and performance under varying loading and reservoir conditions are critical due to the potential risks associated with failure. This paper presents a comprehensive review of structural analysis techniques for gravity dams, with a specific focus on modeling through STAAD.Pro software. The study highlights the application of finite element methods (FEM) in simulating dam behavior under different forces, including hydrostatic pressure, uplift pressure, seismic effects, and self-weight. The role of STAAD.Pro as a versatile tool capable of handling complex geometries and material properties is explored in detail. Emphasis is placed on its ability to model solid elements and perform static and dynamic analyses in accordance with IS 6512:1984 and IS 875 standards. The review synthesizes results from multiple case studies and simulations, examining factors such as stress distribution, displacement, sliding resistance, overturning moments, and shear friction parameters. Findings indicate that STAAD.Pro provides accurate and reliable predictions for assessing dam safety under full and partial reservoir conditions. Furthermore, the study identifies common stress concentration zones—particularly at the heel—and discusses reinforcement strategies to mitigate structural vulnerability. It also evaluates the factor of safety under various loading combinations and confirms compliance with national safety codes. This paper contributes to the evolving methodology of dam design and evaluation, offering valuable insights for engineers, researchers, and policymakers aiming to enhance the resilience of gravity dams through advanced numerical modeling.

DOI: http://doi.org/10.5281/zenodo.16875326

Linking E-Learning Effectiveness With Employee Performance Metrics

Authors: Ms. Shruti Rawat, Manasvee Jain

Abstract: – In today’s fast-paced and digitally driven work environment, organizations are increasingly relying on e-learning platforms for employee training and development. While digital training offers flexibility, scalability, and cost-efficiency, its actual impact on employee performance remains a critical area of inquiry. This study explores the relationship between e-learning effectiveness and employee performance metrics, aiming to bridge the gap between training delivery and measurable workplace outcomes. By analyzing data from employees across multiple departments in a mid-sized technology company, the research examines how engagement with online training modules correlates with key performance indicators such as task accuracy, productivity levels, customer satisfaction scores, and overall goal completion rates. A combination of pre-training and post-training performance data, user feedback, and system usage logs were used to assess the tangible benefits of e-learning initiatives. The findings suggest a positive link between well-structured e-learning programs and improved employee performance, particularly when courses are interactive, aligned with job roles, and supported by timely feedback. However, the study also highlights that the effectiveness of digital training is influenced by factors such as learner motivation, management support, and course design quality. This research underscores the importance of integrating performance metrics into e-learning evaluations to ensure learning investments translate into real-world results. The insights provided can help HR professionals, training managers, and organizational leaders refine their digital learning strategies for maximum impact.

DOI: http://doi.org/10.5281/zenodo.16792612

A Review of Role Of Machine Learning in Designing of Proposed Ransomware Detection Technique

Authors: Mr. Kartik, Dr. Bijendra Singh, Dr. Kavita

Abstract: This research aims to analyze and design an effective ransomware detection technique using machine learning algorithms. The study explores various ML approaches—such as classification, anomaly detection, and clustering—and evaluates their performance in identifying ransomware from normal and benign system behavior. Key features, such as file access patterns, process activities, and network communication, are extracted and analyzed to train and test ML models capable of early detection with high accuracy and low false positives. The primary aims of this study are to understand the behavioral characteristics of ransomware attacks; Identify and select relevant features for effective detection; Evaluate different machine learning models based on precision, recall, F1-score, and accuracy; and Propose a novel or improved ML-based detection framework tailored for real-time ransomware threat identification. This research contributes to the ongoing efforts to fortify cybersecurity by presenting a data-driven, machine learning-powered methodology that enhances early detection capabilities, thereby reducing potential damage and enabling quicker incident response.

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A Review On IoT-Integrated Artificial Intelligence For Smart Irrigation Systems: Trends, Technologies, And Challenges

Authors: Nitish Sharma, Dr Komal Garg

Abstract: The rising demand for food worldwide and declining freshwater resources have intensified the need for efficient agricultural practices. Smart irrigation, powered by the integration of the Internet of Things (IoT) and Artificial Intelligence (AI), offers a transformative solution for precise and sustainable water management. This review examines current advancements in smart irrigation technologies, concentrating on IoT-enabled sensor networks, real-time environmental monitoring, and AI-powered judgment models such as machine learning and predictive analytics. It examines the evolution among these technologies, their application in farming with accuracy, and the synergetic benefits of combining IoT and AI. Moreover, the review highlights implementation challenges, including high costs, energy constraints, data security, and region-specific limitations. The paper concludes with future research directions aimed at enhancing system efficiency, adaptability, and accessibility, especially in water-scarce and developing regions.

DOI: http://doi.org/10.5281/zenodo.16810519

Next-Generation Materials For High-Performance Flexible Antennas: A Comprehensive Review

Authors: Dhrisya S. Anil1, Dr Abhilash S. Vasu2

Abstract: Flexible antennas are key components in modern wireless systems, valued for their lightweight design, adaptability, and integration with non-planar surfaces. They are categorized into four main types: conformal antennas, which adapt to curved structures for aerodynamic and stealth uses; wearable or textile antennas, integrated into clothing or body-worn devices for healthcare, military, and sports; reconfigurable antennas, which dynamically adjust frequency, radiation pattern, or polarization; and fluidic or movable architectures, utilizing liquid metals or mechanical actuation for tunability and shape adaptability. Material selection significantly influences both mechanical flexibility and electromagnetic performance. Traditional conductors like copper and silver offer high conductivity but require special techniques for flexibility. Conductive polymers and composites combine electrical performance with mechanical compliance and environmental resistance. Textile-based conductors integrate antennas directly into fabrics for comfort and durability. Advanced flexible substrates such as polyimide, PDMS, LCP, and TPU provide low dielectric loss and resilience under stress. This review outlines classification, materials, and fabrication advances, emphasizing their role in enabling next-generation communication technologies like 5G/6G, IoT devices, aerospace systems, and wearable healthcare solutions. Flexible antennas promise compact, unobtrusive, and high-performance wireless connectivity for future applications.

DOI: https://doi.org/10.5281/zenodo.16869714

Student-Alumni Platform

Authors: Moushami D, Rachna V, Srinidhi B

Abstract: The Student-Alumni Platform is an educational community designed to develop connections between students alumni of an esteemed institution. It involves the current students and passed out alumni of the institution. This platform is helpful to make the students network, seek mentorship and have access to development resources which help the student to grow and learn. The alumni can offer guidance,share job opportunities,and contribute to their institution and students.By using this platform, the gap between student and alumni is solved and provides a seamless and interactive interface. The necessary features that are included in the platform are creating user profile, login, student dashboard, alumni dashboard with sending connection requests and messages. The platform is built by using HTML,CSS and JavaScript.As the platform evolves, future enhancements will further enrich user engagement by using more enhanced features like advanced messaging and group chats, linkedin integration,mobile access,and more features to help make the application more dynamic and dependant to the users.It also helps in building life long relationships by promoting the connections between student and alumni.

Strategic Challenges And Solutions In Implementing AI And IoT For Green Tech Adoption In The IT Supply Chain Ecosystem

Authors: Viraj P. Tathavadekar

Abstract: Combining Internet of Things (IoT) and artificial intelligence (AI) technologies in green technology adoption within IT supply chain ecosystems presents both unprecedented opportunities and complex challenges. This research investigates the strategic barriers, implementation solutions, and performance outcomes of AI-IoT integration for sustainable IT supply chain management. Through quantitative analysis of 350 IT companies across different maturity levels, this study examines the relationships between technological readiness, implementation challenges, and green technology adoption success. The findings reveal significant correlations between AI-IoT integration levels and sustainability performance metrics, while identifying critical success factors for overcoming implementation barriers. The research is one of the efforts to provide a body of literature on digital transformation in sustainable supply chain management, and offers empirical advice to practitioners and policy makers.

Fuzzy Modelling Techniques For Real Life Applications Via Data Analytics

Authors: Dr. V.Vijayalakshmi, Dr. D. Sridevi, L. Mohan, N.Sundarakannan

Abstract: In Genomic analysis, a comprehensive theoretical study is the need of the hour. Genomic analysis is an elaborate network that comprises various, expendable data. Such data has to be organized leading to comprehensible, practical data which is used to extricate critical information. A feasible procedure called SILE (Search, Identification, Load and Exploitation), is applied to assimilated genomic data with appropriate alterations that is associated with a certain disease. The principal aim is to propose natural drugs for treatment to those patients diagnosed with the disease and this is achieved by substantiating genes that have mutated. In this research, triangular fuzzy numbers are incorporated by considering three symptoms for examining the genes.

Trends And Techniques In Recommendation Systems : A Survey

Authors: Labdhi Jain, Rajesh Dhakad Associate Professor

Abstract: In the current digital era, the volume of data produced every second is staggering, making it challenging for users to find relevant information. Recommendation systems utilize extensive data and data mining techniques to analyze large amounts of data and provide accurate, personalized sug- gestions. Recommendation systems are information filter- ing systems that provide particular suggestions for items that are most pertinent to a particular user or a group of users. The algorithms and methods used for recommender systems are Content-Based Filtering, Collaborative Filtering, and Hybrid Methods. Recommendation systems include diverse applications and domains such as books, e-commerce ser- vices, social network services, movies, and tourism services. Key evaluation metrics of different recommender systems are discussed to provide insights into the assessment of mod- els and the optimization of their performance. Globally, recommendation systems have become important. The pur- pose of this paper is to include and give knowledge of each method, from a traditional-based recommendation system to a deep learning-based recommendation system. By synthe- sizing current trends, challenges, and future research direc- tions, this paper offers a comprehensive understanding of the recommendation system for both researchers and industry professionals.

DOI: http://doi.org/

Smart Iot Driven Wearable Safety Band.

Authors: Assistant professor R .Saranya , Naviya Ka, Brindhavini P, Nisha L

Abstract: – she-shield Designed to enhance personal safety for women, the wearable device offers a dual solution by combining a device integrates several critical sensors to enhance personal security and wearable IoT device developed on a Raspberry Pi platform, integrating a range of sensors ,The temperature and heartbeat sensors monitor physiological changes that may indicate distress or health issues .In active mode, the touch sensor allows the user to manually activate the device during an emergency,initiating a loud buzzer and alarm to deter attackers. The device is powered by a rechargeable lithium battery, ensuring long-lasting performance and reliability , The voice detection sensor uses a Support Vector Machine (SVM) algorithm to identify both general vocal patterns and distress screams, ensuring that the device can accurately recognize a woman’s voice in various contexts.the built-in GPS provides real-time location tracking,Combined with real-time streaming, video storage, and alert messaging, the device employs a dualalarm system: one alarm is emitted from the device itself second alert is sent via i

Artificial Intelligence In Education – Transforming Higher Education In India

Authors: Pratik Nikam, Dr. Priyanka Singh

Abstract: Artificial intelligence (AI) is transforming higher education in India by enabling personalized learning, enhancing student engagement, and providing educators with data-driven tools to optimize teaching. This paper explores AI’s potential to create adaptive learning environments, improve accessibility, and foster holistic student development. Through AI-powered platforms, virtual tutors, and analytics, education is becoming more inclusive and efficient. However, challenges like ethical concerns, data privacy, and equitable access must be addressed to ensure responsible adoption. This study advocates for a future where AI enhances learning outcomes while maintaining fairness and inclusivity, preparing students for a dynamic world.

Machine Learning In Prediction Of Fuel Efficiency In The Automotive Industry

Authors: Aviichal Sharma

Abstract: This study explores how machine learning algorithms can help to increase fuel efficiency. The implemented model is trained based on a dataset that consists of many features and attributes affecting a vehicle’s fuel efficiency such as MPG, Number of cylinders, Horsepower, Vehicle weight, and many more. For training the model, many machine learning models that fit the dataset variables were studied and implemented. It was found that the Random Forest Regression technique performed better than other algorithms in predicting fuel economy after extensive testing and analysis. It was the most appropriate algorithm for my research goal because of its capacity to manage intricate interactions between the input variables and accurately anticipate fuel usage. Random Forest Regression was demonstrated to be a potent approach to improving fuel economy prediction accuracy by utilizing the ensemble of decision trees and feature unpredictability.This study’s conclusion emphasizes the enormous potential of machine learning for enhancing fuel efficiency in the automotive sector. It was determined that Random Forest Regression is the best technique for forecasting fuel efficiency after investigation. It paved the path for improvements in resource optimization and environmental sustainability by taking into account several important criteria and investigating alternative algorithms. The objective is to encourage industry leaders to use machine learning as a catalyst for change, advancing the automobile industry toward a greener and more effective future.

DOI: https://doi.org/10.5281/zenodo.16885367

Megawatt Level Electric Vehicle Charging Station

Authors: Allagadapa Bharath, Deshaboina Sumanth

Abstract: This paper presents a comprehensive strategy for the efficient management of electric vehicle charging stations (EVCSs) integrated with a grid-side Modular Multilevel Converter (MMC) interface. The MMC topology is selected for its ability to directly connect to medium voltage grids without transformers, offering benefits such as reduced space and material usage. A key challenge addressed is the unbalanced load distribution caused by varying charging demands and random EV arrival-departure patterns, which can lead to significant disparities among MMC arms and internal modules. To ensure balanced and sinusoidal grid currents as well as stable module voltages, the proposed method integrates a Load Management (LM) algorithm with a Power Flow Management (PFM) algorithm. The LM algorithm schedules and allocates EV charging sessions to reduce phase and arm-level load variations, while the PFM regulates circulating currents to mitigate any remaining unbalances. Simulation results on a realistic scenario—such as a shopping mall EVCS—demonstrate that the proposed LM-PFM approach significantly enhances energy delivery and system stability, outperforming conventional rule-based methods. Real-time simulations further validate the feasibility of the proposed solution for practical deployment.

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F-HSRP: A Federated, Trust-Aware, And Energy-Efficient Secure Routing Protocol For Scalable And Privacy-Preserving IoT Networks

Authors: Piyali Ghosh , Dr. Dhirendra Kumar Tripathi

Abstract: With the accelerated growth of the Internet of Things (IoT), providing secure, scalable, and privacy-preserving communication has become a serious issue. Current routing protocols such as AODV, DSR, and HSRP are incapable of addressing the complex needs of today’s IoT systems, particularly in large-scale, heterogeneous, and energy-constrained systems. This paper introduces the Federated Hybrid Secure Routing Protocol (F-HSRP)—a new paradigm that combines federated learning, trust-based routing, AES-256 encryption, and blockchain-aided route verification to address these issues holistically. F-HSRP utilizes a light-weight Convolutional Neural Network (CNN) at the edge of IoT networks. With federated learning, local anomaly detection models are trained at the nodes, maintaining data privacy and facilitating real-time accurate threat detection. Routing decisions are informed through a composite trust score based on node behavior, residual energy, and anomaly scores. Secure data transfer is enabled by AES-256 encryption and a lightweight Proof-of-Authority (PoA) blockchain process that guarantees tamper-proof route verification without imposing substantial overhead. The protocol is tested with the Bot-IoT dataset and a hybrid simulation platform that integrates NS-3 and TensorFlow Federated. The results indicate F-HSRP outperforms conventional protocols with 96.3% anomaly detection rate, 27% energy efficiency improvement, and better packet delivery and delay metrics. It also successfully fights blackhole, replay, and Sybil attacks. By integrating federated intelligence, cryptographic security, and blockchain consensus, F-HSRP offers a strong, energy-efficient, and privacy-enhanced routing solution for real-time IoT applications in smart cities, industrial control, healthcare monitoring, and military systems.

The Impact Of AI On Back-Office Logistic Operations And Logistic Shared Service Operations: 6 Key Impacts In 2025

Authors: Sandipan Chakraborty

Abstract: Back-office logistics and shared services are being re-architected by AI in 2025. Beyond warehouse robots and route optimizers, the largest productivity lift is happening in the “paperwork and pixels” of logistics: order capture, document processing, freight audit & pay, customer service, and compliance. Drawing on current India-market data and public programs (ULIP, GST e-invoicing, ONDC) and global benchmarks (World Bank LPI), this journal synthesizes six concrete AI impacts that leaders can deploy now: (1) intelligent document processing and touchless workflows; (2) predictive ETA and exception control towers; (3) dynamic rating, tendering, and contract optimization; (4) forecasting for capacity, working capital, and SLA staffing; (5) AI copilots for shared-services agents; and (6) digital compliance across GST/e-invoicing and trade. We quantify the opportunity, map enabling Indian rails/APIs, list risks and controls, and close with a practical upskilling and tools roadmap tailored to India..

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