VOLUME 12, ISSUE 3, MARCH 2025

Abstract: Railway accidents pose risks to passenger safety, infrastructure, and economic stability. Traditional accident prevention methods rely on rule-based systems and human intervention, often failing to address real-time risks effectively. This paper integrates Deep Learning (DL), Machine Learning (ML), and Global Positioning System (GPS) tracking to enhance railway accident prevention. By leveraging historical accident data and knowledge-based analysis, we propose an intelligent system capable of real-time anomaly detection, predictive maintenance, and automated decision-making.

Keywords: Artificial Intelligence, Data Processing, Deep Learning, GPS, Machine Learning

Abstract: Sign language is one of the oldest and most natural form of language for communication, hence we have come up with a real time method using neural networks for finger spelling based American sign language. Automatic human gesture recognition from camera images is an interesting topic for developing vision. We propose a convolution neural network (CNN) method to recognize hand gestures of human actions from an image captured by camera. The purpose is to recognize hand gestures of human task activities from a camera image. The position of hand and orientation are applied to obtain the training and testing data for the CNN. The hand is first passed through a filter and after the filter is applied where the hand is passed through a classifier which predicts the class of the hand gestures. Then the calibrated images are used to train CNN.

Keywords: Convolution Neural Network (CNN), American Sign Language (ASL), gesture recognition and deep learning technologies.

Abstract: This paper deals with the possibility of application of LLM (ChatGPT) on the coffee bean roast process. To study the possibility for application of LLM on coffee bean roast process, Firstly, this paper reviews several LLMs on how LLM gives an influence in many areas because its area is so wide such as normal editing works, speak, many advises, and code development by using huge data like coffee flavor. Secondly, this paper reviews several processes of coffee from the seeding to drinking. There are many possibilities in application of AI for coffee process. However, this paper focuses on the roasting process of coffee bean because its impact is so important on coffee flavor. Finally, this study focuses on how to control the effective roast process of coffee bean by ChatGPT. This paper also implements several questions to the ChatGPT (Prompts) to confirm the possibility application of ChatGPT on coffee roast process. The results show the very useful possibility to apply for coffee bean roast process and can have many ideas.

Keywords: Coffee roast, Coffee bean, ChatGPT, LLM, AI automatic control.

Abstract: This project introduces an AI-powered agricultural management system designed to support farmers with data-driven insights. The system features crop yield prediction using a Random Forest Regressor, considering key factors like rainfall, temperature, humidity, and soil properties. A crop recommendation module, built with a Random Forest Classifier, suggests the most suitable crops based on regional and climatic conditions. Additionally, a chatbot, powered by TF-IDF and cosine similarity techniques, provides quick responses to common agricultural questions on crop selection, fertilizer usage, and disease management. The inclusion of a weather API offers real-time weather updates, enabling farmers to stay informed about current environmental conditions. Deployed as a web application, the platform combines multiple tools into one accessible interface, aiming to improve farm productivity, enhance decision-making, and promote modern agricultural practices. This system aims to improve agricultural decision-making, enhance farm productivity, and support sustainable farming.

Keywords: Agricultural Decision Support, Crop Yield Forecasting, Crop Recommendation, AI in Farming, Machine Learning, Chatbot Integration, Weather Data, Smart Agriculture.

Abstract: Rice is a staple crop that plays a crucial role in global food security. However, its productivity is significantly affected by various diseases such as Bacterial Blight (BB), Brown Spot (BS), and Leaf Smut (LS). Early detection and classification of these diseases are essential for effective management and yield improvement. This paper presents an automated approach to rice plant disease detection using Neural Architecture Search (NAS), which optimizes convolutional neural network (CNN) architectures for high-accuracy classification. The system is trained on the Rice Life Disease Dataset, which contains extensive image data of diseased rice plants. NAS automates the model selection process, eliminating the need for manual experimentation while enhancing classification performance. The proposed model is evaluated using accuracy, precision, recall, and F1-score, demonstrating its effectiveness in disease identification. By integrating deep learning with automated model optimization, this research contributes to agricultural sustainability by providing farmers and agronomists with a reliable tool for early disease detection, thus reducing crop losses and improving productivity.

Keywords: Rice Plant Disease, Neural Architecture Search (NAS), Convolutional Neural Network (CNN), Bacterial Blight (BB), Deep Learning, Image Classification, Crop Monitoring.

Abstract: Calorie estimation is crucial for dietary tracking and health management. This work presents a novel method that combines depth prediction and feature fusion to improve estimation accuracy. In order to accurately estimate the volume of food items, deep learning algorithms are used to forecast depth information. To enhance calorie computation, the retrieved depth, RGB, and texture data are subsequently combined utilizing sophisticated aggregation techniques. The suggested technique gets over the drawbacks of conventional 2D-based methods by utilizing RGB-D pictures, which results in more precise calorie and portion size estimation. According to experimental results, this technology performs better than traditional approaches and achieves a higher level of precision in calorie measurement.In real-time applications, robustness and efficiency are guaranteed by the combination of machine learning and multimodal data fusion. Prior research has confirmed the benefits of using depth information for volume estimate and demonstrated the efficacy of depth-based methods in food analysis. This approach may find use in healthcare, automated nutrition monitoring, and customized diet management programs. This method helps create intelligent health-monitoring tools that help users maintain balanced eating habits by increasing the accuracy of calorie calculation.

Keywords: Deep learning, CNN,Graph Neural Networks, food calorie estimation, depth prediction, feature fusion, RGB-D pictures, nutritional analysis, multimodal data fusion, machine learning, food volume estimation, real-time applications, dietary tracking, healthcare, and AI-driven models

Abstract: This paper examines key factors for optimizing go-kart performance through practical adjustments. Maintaining a low center of gravity improves stability, while forward positioning increases front grip (but may cause understeer) and rearward placement enhances cornering (with possible oversteer). Balanced weight distribution across axles maximizes tire contact and traction. Testing shows softer tires provide better grip but wear faster, while harder tires last longer with reduced traction. Driver position adjustments and suspension tuning further refine performance. These modifications, combined with real-time track data analysis, enable racers to achieve optimal balance between speed, handling, and consistency.

Keywords: Go-kart performance, center of gravity, weight distribution, tire selection, suspension tuning, vehicle dynamics

Abstract: Honey has been linked to health benefits such as improved heart health, wound healing, and antioxidant status in the blood. Raw honey has been used throughout history as a medicine and has a wide variety of health & medicinal uses. Most honey that is found in grocery stores is pasteurized. The present study was aimed to know the preservation, adulteration, and perception of honey in households among homemakers. The information regarding the preservation, adulteration, and perception of honey in households among homemakers was collected using a questionnaire through an online platform, and around 100 responses were taken for analysis. It has been observed that honey was used occasionally in households (57%). The primary reasons for its use were its taste (53%) and quality (39%). Additionally, most respondents stored honey at room temperature (82%), as it had a shelf life of over six months (39%), as seen in the article.

Keywords: Preservation, adulteration, honey, homemakers.

Abstract: Serverless architecture provides a modern, scalable, and cost-effective solution for hosting WordPress websites, eliminating the complexities associated with traditional server-based setups. By leveraging AWS services such as AWS Lambda for executing backend logic, Amazon API Gateway for routing HTTP requests, Amazon S3 for hosting static assets, and Amazon RDS for managing the relational database, this architecture supports dynamic scalability, high availability, and operational efficiency. This paper delves into the design and implementation of deploying WordPress in a serverless environment, offering insights into the technical workflow and integration of serverless components. The study also examines the benefits of automatic scaling, reduced infrastructure costs, and minimized maintenance efforts while addressing challenges such as cold starts, integration complexities, and latency issues. Through a detailed analysis, the paper highlights the potential of serverless architecture in transforming traditional WordPress deployments into highly efficient and resilient systems, offering significant value for developers, businesses, and end-users.

Keywords: Serverless Architecture, WordPress, AWS Lambda, API Gateway, Amazon S3, Amazon RDS, Scalability, Cost Optimization

Abstract: FunGamesHub is a web-based gaming platform designed to provide users with an engaging and accessible collection of online games. With the growing demand for instant and browser-based entertainment, this project aims to eliminate the need for downloads or installations, allowing users to play games seamlessly on various devices, including desktops, tablets, and smartphones. The platform features a diverse range of games, including puzzle, arcade, and strategy-based challenges, catering to different user preferences. Developed using HTML, CSS, JavaScript, and PHP, the website ensures smooth gameplay, responsive design, and optimized performance for an immersive user experience.

Keywords: FunGamesHub, seamless gaming, web-based, online gaming platform.

Abstract: As we know a world is now going towards a green energy, every sector is trying to adopt a green energy as soon as possible. A transportation sector has a big opportunity to implement a green energy so many of countries tries to make a vehicle which operate by green energy like Electric, NG (Natural Gas) etc. But every technology has limited operating capacity and also issues which have a complex solution. In electric vehicle has some issues which have a limited solution also an availability of electric sources like charging station available everywhere and also charging time is more than gasoline vehicle consequences vehicle manufacturing companies make a CNG two vehicle which is easy to available and on spot utilise, In this paper we compare both system as per different researcher and tries to understand which system is better for future green technology.

Keywords: Introduction, Basic Principle, Availability of Fuel, Hybrid Vehicle

Abstract: This research focuses at the ways that various friction stir welding (FSW) settings affect the mechanical characteristics of two different aluminum alloys, AA5085 and AA6061. To find the best welding settings for increasing hardness and tensile strength, we used regression modeling, analysis of variance (ANOVA), and the Taguchi L9 orthogonal array. Welding speed mainly impacts hardness, although feed rate greatly impacts tensile strength, according to the results. The peak tensile strength was 125 MPa and the hardness was 62.4 HV, with the optimal settings being a welding axial force of 5 kN, feed rate of 10 mm/min, and speed of 900 RPM. Findings from this study highlight the significance of feed rate and welding speed for achieving strong, defect-free joints for use in the aviation, marine, and automobile sectors. Additional factors that could be investigated in future studies to improve FSW performance include tool geometry, rotational direction, and microstructural features.

Keywords: Friction Stir Welding of Aluminum Alloys, Taguchi Method, ANOVA, Tensile Strength, Hardness

Abstract: Although much progress has been made, the safety of women continues to be a global concern, and deserves innovative and comprehensive solutions to address the emerging urban and rural challenges. To support women's safety, this paper presents a novel platform which integrates options on secure login, real time alerts, emergency panic systems, incident reporting, and route navigation alongside police station proximity. Using advanced technologies such as IoT, geolocation services, and real time data analytics, the platform offers a dynamic and user friendly interface for its users to proactively and reactively mitigate safety concerns. It features an SOS panic button that transmits real time location data to contacts and authorities and a community driven heatmap visualization allowing users to see hotspots. Personal and community safety as well as emergency response have been improved through the platform's capability to provide personalized safety recommendations and to facilitate rapid responses in emergencies, making the platform a dependable and scalable solution. This work captures the transformative potential of technology to strengthen the position of women and to enhance safety.

Keywords: Women's Safety, IoT, Real-Time Alerts, SOS System, Geolocation, Incident Reporting, Route Navigation, Police Proximity.

Abstract: This paper explores the feasibility and effectiveness of friction stir pinless spot welding (FSPSSW) for joining dissimilar aluminium alloys. The absence of a traditional pin in the tool design reduces material deformation, improves surface finish, and eliminates exit hole defects. Experimental trials were conducted on various aluminium alloy combinations, optimizing parameters such as rotational speed, plunge depth, and dwell time. Results indicated strong metallurgical bonding with refined microstructures and minimal intermetallic formation. Mechanical testing showed high tensile shear strength and ductility, validating the quality of the welds. The process proved advantageous in enhancing joint integrity while being cost-effective and energy-efficient. This study confirms the suitability of pinless FSSW for lightweight applications in automotive and aerospace industries, offering an innovative alternative to conventional welding techniques.

Keywords: Friction Stir Welding (FSW), Pinless Tool, Dissimilar Aluminium Alloys, Spot Welding, Solid-State Joining, Weld Strength, Microstructure, Heat-Affected Zone (HAZ), Lightweight Materials, Aerospace Applications.

Abstract: Energy harvesting, which transforms ambient energy sources including thermal and vibrational energy into usable power, is crucial for sustainable power generation in a variety of applications, such as industrial machinery, Internet of Things devices, and electric vehicles (EVs). This technology allows for self-sustaining systems in remote or off-grid areas, lowers the environmental impact, and lessens reliance on traditional energy sources. In order to maximize energy capture, this study investigates the combination of thermal and vibrational energy harvesting methods, particularly thermoelectric and piezoelectric devices. The potential of hybrid energy-harvesting systems to increase energy efficiency and lower operating costs is highlighted by a thorough assessment of the literature. This analysis focuses on improving power production through multi-source integration while examining important technologies, prospects, and limitations. Recent advancements in vibration-based systems and thermal energy harvesting hold promise for a number of uses, including electric cars, where mechanical vibrations and waste heat can be transformed into electrical power. Particularly in low-frequency and multidirectional vibration situations, this work highlights important areas for future research, such as advanced system designs, nonlinear dynamics, and hybrid systems to improve energy conversion efficiency. This study advances self-powered systems in the transportation and industrial sectors by providing insightful information on how thermal and vibrational energy harvesting can be combined to create sustainable energy solutions.

Keywords: renewable systems, electric cars, thermoelectric, piezoelectric, thermal, vibrational, and multisource integration

Abstract: The Tasar silk industry plays a vital role in rural employment, particularly among tribal and female populations. It is widely used for sarees, dress materials, and furnishings, with several GI-tagged varieties. Clothing comfort, influenced by tactile sensations, depends on the fabric's mechanical and thermal properties. Despite advancements, no research exists on the tactile comfort of Tasar silk. This study aims to fill that gap by evaluating the handle properties of plain, twill, and satin Tasar fabrics for women's apparel.This study evaluates the low-stress mechanical and thermal properties of power loom-woven Tasar silk fabrics with three weave structures-Plain (T-P), Twill (T-T), and Sateen (T-S)-using the Kawabata Evaluation System. The Kawabata Evaluation System provides objective analysis, distinguishing different silk weaves based on stiffness, softness, and flexibility.The T-S weave exhibits superior stiffness and compression resistance, making it suitable for rigid applications. T-P offers excellent shear properties and grip, while T-T demonstrates the highest tensile strength, ideal for high-stress applications. T-T also provides the best balance of softness, smoothness, and thermal insulation, whereas T-S enhances stiffness but reduces pliability. Thermal analysis shows T-P excels in heat dissipation, while T-T is best for warmth retention. These findings guide fabric selection for tailored textile applications, with future research focusing on advanced finishing techniques.

Keywords: Handle Value, Kawabata, Plain Weave, Sateen Weave, Tasar Silk, Twill Weave, Thermal Value

Abstract: Fossil fuel reserves draining at an alarming rate, and rising day by day environment-related problems made the world find green energy reserves. One among such a type of solution has been in view of abundant availability, stability of the solar power by which photovoltaic system was decreased in terms of cost. This paper describes the development of a solar energy management system using a Single-Ended Primary Inductor Converter - based DC-DC converter and a three - phase Inverter for grid - interfacing application. The ANN-based regulation will enable dynamic regulation of the real and reactive power course across the system to facilitate efficient energy transfer and maintain stability of the grid. Furthermore, the performance of the system can be validated from simulation studies about its capability toward efficiency, power factor correction and adaptability to change in solar intensity and grid.

Keywords: Solar Power Energy Management, SEPIC Converter, Three - Phase Inverter, Artificial Neural Network(ANN),Grid Stability

Abstract: Technological advancement has allowed the design of more complex structures consequently making the traditional method of construction inefficient. Additionally, the decline of productivity has called for the use of modern technology such as augmented reality to improved construction productivity. This study investigated the productivity of using Interactive Augmented Reality (IAR) method for the construction of complex brick wall designs. A total of 17 people with no previous bricklaying experience participated in experiments to construct three different types of brick walls with different complexities. The walls were constructed using the traditional method (no technology) and with IAR and productivities recorded. The Wilcoxon/Kruskal-Wallis non-parametric test indicated statistically significant difference in the mean productivity of the IAR (ranging from 2 to 2.5 minutes/brick) and the traditional method (ranging from 6.61 to 49.4 minutes/brick). The results indicate that the average productivity rate for placing bricks per minute using the IAR method was significantly higher than that of the traditional method.

Keywords: Interactive Augmented Reality; Microsoft HoloLens; Brick Wall; Complex Design;

Abstract: The increasing demand for sustainable and environmentally friendly transportation solutions has driven the development of electric utility vehicles. However, traditional plastic components used in these vehicles' bodywork pose significant environmental concerns. This study explores the fabrication of bodywork components using composite materials as a replacement for plastic components in electric utility vehicles. A hybrid composite material consisting of carbon fiber, glass fiber, and a biodegradable polymer matrix was developed and tested for its mechanical, thermal, and environmental properties. The results show that the composite material exhibits superior strength-to-weight ratio, improved thermal stability, and reduced environmental impact compared to traditional plastic components. A prototype bodywork component was fabricated using the developed composite material, demonstrating its feasibility and potential for mass production. This study provides a promising solution for the sustainable development of electric utility vehicles, reducing their environmental footprint while maintaining performance and structural integrity.

Keywords: Composite materials, Electric utility vehicles, Sustainable transportation, Bodywork components, Carbon fiber, Mechanical properties.

Abstract: This project focuses on the analysis of a micro combustor for hydrogen fuel, aiming to improve the efficiency and performance of hydrogen combustion in small-scale applications. A micro combustor is a compact device that enables controlled combustion, typically used in applications such as portable power generation or micro-turbine systems. The project investigates the behaviour of hydrogen as a fuel in such a system, examining key factors like temperature distribution, pressure, and fuel-air mixture. Computational simulations are employed to model combustion processes and optimize the design for maximum efficiency and minimal emissions. The study also explores challenges like flame stability and heat loss, providing insights into enhancing micro combustor designs for clean, sustainable energy production using hydrogen as an alternative fuel. The goal is to contribute to the development of energy-efficient, environmentally friendly microscale combustion systems for future hydrogen-based technologies.

Keywords: Heat transfer, Combustion analysis, CFD simulation, Fuel-air ratio, Emissions reduction, Microscale combustion, Combustion Efficiency.

Abstract: This study focuses on the development of a functional beverage combining blueberry juice with Clitoria ternatea (blue pea flower). The research evaluates its acceptability, shelf life, nutritional composition, and sensory characteristics. The blue pea flower, known for its high anthocyanin content and health benefits, was incorporated into blueberry juice at different concentrations to determine the most suitable formulation. Sensory parameters such as appearance, color, texture, flavor, and taste were analyzed by a semi-trained panel. Additionally, nutrient profiling, microbial stability assessment, and cost analysis were conducted to ensure the product's affordability and stability. The results indicate that the formulated Blue Pea Butterfly Drink is a nutritious and cost-effective functional beverage with potent antioxidant properties, making it a promising addition to the market. Sensory evaluation identified Sample D as the most preferred formulation in terms of overall acceptability.

Keywords: Blue pea flower, antioxidant, anti-inflammatory, anti-carcinogenic

Abstract: The project is introduced based on traditional role-based access control environments are being challenged in a cyber threat era due to the static nature of their permission systems, which do not merit any flexibility for dynamic contextual factors. In this context, the paper proposes a novel method providing an additional degree of integration of context-aware artificial intelligence (AI) within a role-based authentication system for enhanced security and flexibility. In the proposed method, AI dynamically applies access permission changes based on real-time contextual data, utilizing information such as user behaviour, location, or device particulars. The research shows the promising role of context-aware AI in reducing the setbacks of static authentication systems while opening avenues for the future of dynamic access control systems. The findings emphasize integrating AI with contextual data for maximizing cybersecurity in a connected ecosystem.

Keywords: Cyber Security, Role Based Access Control, Context Aware Mechanism, Machine Learning, Artificial Intelligence, Anomaly Detection

Abstract: The integration of Artificial Intelligence (AI) in legal advisory services is transforming access to legal information and decision-making. This research paper presents an AI-driven Legal Assistance Chatbot designed to provide accessible, efficient, and accurate legal guidance based on Indian legal frameworks. By leveraging Natural Language Processing (NLP), Machine Learning (ML), and Term Frequency-Inverse Document Frequency (TF-IDF) algorithms, the chatbot can interpret complex legal queries, retrieve relevant legal precedents, and generate user-friendly legal advice. The system enhances legal research efficiency, reduces manual workload, and ensures ethical AI deployment through data privacy compliance. This study explores the technological, ethical, and regulatory considerations of AI-powered legal assistance, highlighting its potential to democratize legal knowledge and bridge the gap between legal professionals and the public.

Keywords: AI Legal Chatbot, Natural Language Processing, Legal Information Retrieval, Machine Learning, TF-IDF, Ethical AI.

Abstract: Swarm robotics leverages decentralized control and self-organizing principles to achieve collective intelligence, enabling autonomous robots to cooperate and adapt to dynamic environments. This paper explores emergent patterns in swarm robotics, focusing on self-organizing behaviors that arise from local interactions among individual agents. By analyzing bio-inspired algorithms, such as ant colony optimization and flocking behavior, we investigate how swarm intelligence facilitates robust, scalable, and flexible multi-robot coordination. The proposed framework integrates distributed decision-making and adaptive communication strategies to enhance swarm performance in complex tasks such as exploration, object clustering, and path optimization. Through extensive simulations and real-world experiments, we demonstrate how emergent behaviors contribute to efficient problem-solving without centralized control. The findings highlight the advantages of self-organization in swarm robotics, emphasizing its applications in search and rescue, environmental monitoring, and industrial automation.

Keywords: Swarm Robotics, Self-Organization, Emergent Behavior, Multi-Robot Systems, Bio-Inspired Algorithms, Decentralized Control, Collective Intelligence, Distributed Robotics.

Abstract: Deepor Beel is a wetland of great biodiversity, situated in the southwest part of the Guwahati city of Assam. The Rani and Garbhanga Reserved Forests are adjacent to the wetland, which altogether stands as a complete ecosystem providing environmental solutions, food security and different types of biodiversity to the city. With the rapid advancement of urban development in the city of Guwahati, the wetland is under constant threat of diminishing area of the wetland, extinction of biodiversity, as well as transformation of land use pattern of the entire area and its surroundings. The study aims to assess the changes in the lake water area from the surface area using the Modified Normalized Difference Water Index(MNDWI) and the changes in the vegetation and human habitation area using the Land Use Land Cover(LULC) classification with the help of the Arc GIS software. Time- series Landsat 8 images of the years of 2014, 2016, 2018, 2020 and 2021 were used to extract the MNDWI in GIS domain. The analysis showed a declining nature of the area of the wetland with the years and an increase in the built-up area near the wetland. The decline in the lake water area is a serious concern in the age of rapid urbanization of big cities like Guwahati. The study reveals the potential of Landsat data and GIS study in mapping the change in the wetland ecosystem. It is hoped that the study will have its utility in the preparation of proper management plan for the conservation of the ecosystem of the Deepor Beel wetland.

Keywords: Surface area, Modified Normalized Difference Water Index, Land Use Land Cover, Landsat.

Abstract: Diabetes is a chronic health condition that affects millions of individuals worldwide, posing significant risks to their well-being. Early detection and accurate prediction of diabetes are crucial for effective management and prevention. In recent years, deep learning techniques have emerged as a powerful tool for medical diagnosis, particularly in predicting diabetes. However, the inherent complexity and uncertainties in medical data often challenge the performance and interpretability of these models. This project aims to enhance deep learning techniques for diabetes prediction by incorporating fuzzy logic, a computational framework that can handle uncertainty and imprecision in data. The proposed approach integrates fuzzy logic with deep learning models to improve prediction accuracy and provide more interpretable results. Fuzzy logic is utilized to handle the vagueness and ambiguity associated with medical data, such as variations in test results, patient symptoms, and risk factors. By combining fuzzy logic with neural networks, the model learns to handle fuzzy inputs effectively, offering more robust predictions. The project focuses on implementing hybrid models that leverage fuzzy inference systems (FIS) and deep neural networks (DNN) to predict the likelihood of diabetes in individuals based on medical data such as blood glucose levels, BMI, age, and family history. A significant advantage of this approach is the interpretability of the model. Unlike traditional deep learning methods that operate as black-box systems, the hybrid model with fuzzy logic offers transparency in decision-making, allowing healthcare professionals to better understand the reasoning behind the predictions. This can lead to more informed decision-making and improved patient care. The project involves training and evaluating the hybrid model on a diabetes dataset, comparing its performance with conventional deep learning models and other machine learning algorithms. The results are expected to demonstrate enhanced accuracy, interpretability, and reliability, making the model a valuable tool for early diabetes prediction in clinical settings. Ultimately, this research aims to contribute to the development of intelligent healthcare systems that can improve patient outcomes and reduce the burden of diabetes.

Keywords: Diabetes prediction, deep learning, fuzzy logic, neural networks, fuzzy inference systems, healthcare, interpretability, machine learning, medical data, early detection.

Abstract: Natural dyes from renewable sources such as Plants, Fruits, and Minerals have gained popularity as environmentally friendly alternatives to synthetic dyes. This study focuses on the extraction and application of natural dyes derived from coconut husk and henna, using eco-friendly mordants such as vinegar (acetic acid) and lemon juice (citric acid) to dye Kid -garments. With an increasing awareness of the environmental and health risks connected with synthetic dyes, this study emphasizes the necessity of natural dyeing methods in the textile sector, particularly for sensitive applications such as children's clothes. Coconut husk, a readily available agricultural byproduct, contains tannins and polyphenolic chemicals that give fabrics a warm brown tone. Henna, best known for its natural reddish orange pigment (lawsone), is also renowned for its colouring properties. Pigments from these sources are aqueously extracted and then applied to pre-treated cotton fabric. Vinegar (acetic acid) and lemon juice (citric acid) are used as mordants to absorb and fix dyes. Temperature, pH, and mordanting were all parameters considered while dyeing to promote colour fastness and vibrancy. The dyed samples are tested to determine whether they are suitable for practical use. The tests includes perspiration resistance, colour fastness to light, rubbing, and Washing. These tests ensure that the dyed cloth can endure wear and tear, making it suitable for kids' garments. The findings revealed that both dyes had a high affinity for natural textiles like cotton, and that applying mordants like vinegar (acetic acid) and lemon (citric acid) improved colour retention. The colouring procedure has no negative impact on the environment, resulting in composting biodegradable residues. This study not only highlights the potential of natural resources and also emphasizes the role of eco-friendly products in sustainable textile products. By combining traditional dye with modern techniques, the natural dyes can provide a viable and environmental consciousness.

Keywords: Cocos nucifera, Lawsonia inermis, Cotton, Acetic acid, Citric acid, biodegradable

Abstract: The design and production of infant shoes with Tencel twill, a fabric known for its eco-friendliness, breathability, and suppleness, is the primary emphasis of this project. It's suitable for a baby's first step. It is impossible to overestimate the significance of comfortable footwear in the fast-paced world of today, where kids are always on the go. It might be difficult for parents of children with broader feet to choose the appropriate shoes. Babies who wear wide shoes benefit greatly from increased stability and balance, less chance of calluses and blisters, and plenty of space for healthy foot development. Tencel twill provides durability and improves general comfort, making the product long-lasting and kind to the baby's skin. To satisfy the needs of sustainability and functionality, the project entails fabric selection, pattern alteration, and testing. This project is meeting the growing need for baby goods that put comfort, safety, and environmental awareness first. The initiative intends to provide baby shoes that satisfy the demands of contemporary parents while supporting environmentally responsible fashion industry practices by fusing sustainable materials with smart design. In the end, this study shows how cutting-edge materials and designs can improve the quality of a baby's footwear and aid in a child's early developmental milestones.

Keywords: Baby shoes, Tencel twill, walking development, eco-friendliness, comfort.

Abstract: This paper investigates the effect of nonlinear load variation on power factor and harmonic distortion using MATLAB/Simulink. A three-phase system with a step-down transformer and a diode bridge rectifier feeding a resistive-inductive load was modelled. The inductance was varied in steps to analyse its impact on Total Harmonic Distortion (THD) and power factor (PF). FFT analysis was used to extract the harmonic spectrum, while scope blocks were used to observe voltage-current waveforms and calculate PF. Results show that increasing the inductive component of the nonlinear load significantly raises THD and reduces power factor. This highlights the importance of harmonic analysis and the role of simulation in designing power-efficient and power-quality-compliant industrial systems.

Keywords: Power Factor, Harmonics, THD, MATLAB Simulation, Nonlinear Loads.

Abstract: The aim of the study was to evaluate the performance of dyes extracted from plant leaves in cotton dyeing. The current textile dyeing business employs much too many synthetic dyes to suit the color requirements of the world's textile consumption since they are less expensive, offer a greater variety of vibrant hues, and have significantly better fastness qualities than natural dyes. These dyes pose major health risks and have a detrimental impact on the natural equilibrium. Natural dyes are made from natural resources, as the name suggested. Different textile fabrics were colored using coloring ingredients derived from natural resources of plant, animal, mineral, and microbiological origins. The dyeing industry was most sophisticated and scientific level today. There are many different methods for coloring. The natural dyes are extracted and the fabric dyeing process is examined in this article. The processes of extraction and purification are essential in the handling of natural dyes.. First extract the dye from Mangifera indica, Annona squamosa L. Eclipta by using aqueous solution method. At a boiling point of 100 °C, these compounds have a specific vapor pressure and are either insoluble in water. After completed boiling green & red shade dyes are obtained. Natural mordrant like alum, salt, vinegar & coffee powder was used as a mordant in the extracted dye coloration process for achieving different colours. Cotton fabric is tie & dyed with this three different solution and dried. After the process physical tests are like Colour fastness to rubbing, colour fastness to sunlight and abrasion testing to know the fabric quality. Finally the sustainable products are developed

Keywords: Natural dyes, Mangifera indica, Annona squamosa L. Eclipta Alba, natural mordrants, colour fastness, sustainable products.

Abstract: This study looks at the antibacterial finishing of bamboo fabric using a natural Aloe barbadensis miller extract and methanol as a solvent. Bamboo's softness and breath-ability make it a popular cloth for infant swaddling wraps. Strengthening its antibacterial properties can further improve its safety and sanitary attributes.Aloe Vera's antimicrobial and skin-benefiting properties are well known. In this work, Aloe barbadensis miller extract is made and applied to bamboo fabric using a methanol-based finishing technique. The antibacterial effectiveness of the treated cloth is evaluated using common bacterial strains. Additionally, the fabric's durability, softness, and safety are assessed to ensure that the treatment doesn't take away from its natural qualities.The project's outcomes are meant to support the development of ecologically friendly and sustainable antibacterial textiles specifically for products used for infant care.

Keywords: Aloe barbadensis miller , antibacterial finish , bamboo fabric , methanol - swaddle wrapper , sustainability.

Abstract: This paper presents a comprehensive MATLAB/Simulink model for simulating a regenerative braking system in an electric vehicle (EV). Regenerative braking offers a significant advantage in EVs by converting the kinetic energy of the vehicle during deceleration into electrical energy, which is then fed back to the battery, thereby improving energy efficiency and extending the driving range. The developed simulation model incorporates key components of the regenerative braking system, including the vehicle dynamics, electric motor/generator, battery model, and the control logic that governs the transition between regenerative and mechanical braking. Different control strategies for distributing the braking torque between the regenerative and friction brakes are implemented and analyzed. The simulation results demonstrate the effectiveness of the proposed model in capturing the energy regeneration process under various driving conditions, including different deceleration rates and vehicle speeds. Furthermore, the impact of different regenerative braking control strategies on the energy recovery and overall braking performance is evaluated and compared. This study provides valuable insights into the design and optimization of regenerative braking systems in EVs, contributing to the development of more energy-efficient and sustainable transportation solutions.

Keywords: Electric Vehicle (EV), Regenerative Braking, MATLAB/Simulink, Simulation, Energy Efficiency, Braking Control Strategy.

Abstract: The rapid advancements in Artificial Intelligence (AI) and automation are revolutionizing Human Resources (HR) practices, transforming how organizations manage talent, streamline operations, and enhance employee experiences. This paper explores the impact of AI and automation on various HR functions, including recruitment, performance management, employee engagement, and learning & development. By leveraging AI technologies such as machine learning algorithms, natural language processing, and robotic process automation, HR departments can optimize their processes, reduce biases, improve decision-making, and drive greater efficiency. The integration of AI-powered chatbots for candidate screening, predictive analytics for employee performance, and automated feedback systems are reshaping the traditional HR landscape. However, the adoption of these technologies also raises concerns around data privacy, ethical implications, and the potential for job displacement. This study examines both the opportunities and challenges of implementing AI in HR, providing insights into how organizations can responsibly leverage these technologies to create more efficient, equitable, and employee-centred HR practices while maintaining a balance between automation and human expertise.

Keywords: Artificial Intelligence, Human Resources Management, Automation, Talent Acquisition, Employee Onboarding, Ethical AI, Machine Learning, HR Technology, Workforce Analytics, Predictive Analytics.

Abstract: Electric motors play a crucial role in industrial applications, but their efficiency is often limited to 75%-85% due to excessive heat generation. Overheating leads to increased energy losses, reduced lifespan, and higher maintenance costs. To address this issue, this study proposes an innovative Phase Change Material (PCM)-based Thermal Energy Storage (TES) system combined with IoT-based real-time temperature monitoring to enhance motor cooling performance and efficiency. The system incorporates paraffin wax (PCM) within a specially designed Al6061 aluminum casing that encloses the motor. The casing is fabricated using sheet rolling, laser cutting, and welding to create a hollow chamber for PCM storage. Paraffin wax, with its high latent heat capacity, absorbs excess heat and regulates temperature fluctuations effectively. Additionally, an Arduino Uno-based IoT system is integrated, featuring a temperature sensor and a GSM module for real-time temperature tracking and remote alerts. A 75W, 900 RPM AC motor is tested under two conditions: without PCM (conventional cooling) and with PCM (TES-based cooling + IoT monitoring). Performance is evaluated based on temperature variations, motor efficiency, and thermal stability, with data transmitted via GSM for analysis. The expected results indicate that the PCM-based system significantly reduces motor temperature, thereby enhancing efficiency, reducing energy losses, and extending motor lifespan. The IoT integration enables remote monitoring and predictive maintenance, making this approach a cost-effective, scalable solution for industrial motor cooling applications.

Keywords: Phase Change Material, Thermal Energy Storage, Paraffin Wax, Motor Cooling, IoT Monitoring, Efficiency Enhancement

Abstract: Aims The objective of the study was to examining the effects of Treadmill exercise on psychological well-being , Two groups were targeted as an experimental group and control group. The 34 male participated in the study and their age ranged between 19-28 years. The all students are sedentary and not participation any sporting or physical activities. Treadmill exercise Experimental group participated in Treadmill exercise Training program which was conducted for four-week, four days in a week and 15 minutes in a day. After the pre-test was over, the entire selected subjects were exposed to four-week Treadmill exercise. Findings and Conclusions The result reveals significant difference of Psychological well- being was found between pre and post test in experimental group sedentary students ; the four week of treadmill exercise significantly improve the psychological well- being to the sedentary students Recommendation The findings of the study will be proposing a new conceptual model that may assist the policy makers in framing new policies and strategies to manage the stress problem

Keywords: Distress, Treadmill, experimental, group

Abstract: The accelerating demand for software development has catalyzed the exploration of AI-driven solutions that can automate programming tasks. This paper presents a comprehensive study on the application of transformer-based models for code generation, examining their ability to translate natural language descriptions and formal specifications into executable code. Leveraging leading benchmarks such as HumanEval, MBPP, CodeXGLUE, and CONCODE, we evaluate models across diverse tasks, including code summarization, translation, completion, clone detection, and defect prediction. Our findings reveal that transformer-based models demonstrate strong capabilities in capturing programming intent, generating context-aware code, and adapting to multiple programming languages. However, challenges persist in ensuring syntactic correctness, semantic alignment, and real-world usability of AI-generated code. We further discuss integration strategies for incorporating these models into existing software engineering workflows, emphasizing the need for human oversight, rigorous evaluation metrics, and security considerations. By synthesizing current advancements and limitations, this work contributes to the evolving field of code intelligence and highlights future directions for developing more robust, generalizable, and trustworthy AI systems for software development.

Keywords: Code Generation, Transformer Networks, Artificial Intelligence, Software Automation, Natural Language Processing, Deep Learning.

Abstract: The objective of this project is to design and implement a mobile application for the Computer Engineering Department that helps students improve their academic skillsets. Developed using Flutter and Dart, the application provides year-wise access to academic resources such as notes, assignments, timetables, faculty details, events, quizzes, and CGPA calculators. Additionally, it offers a profile management system and an in/out notification feature. The proposed system provides a centralized, user-friendly platform for students and faculty, encouraging interactive learning and timely communication.

Keywords: Flutter, Dart, Academic Mobile App, Quiz Module, Notification System, CGPA Calculator, Notes Management.

Abstract: This project investigates the impact of varying electrical loads on the efficiency of power transformers through MATLAB-based simulation. Power transformers play a vital role in electrical power systems, and their efficiency is significantly influenced by the nature and magnitude of the connected load. Using transformer equivalent circuit modeling, the study incorporates essential parameters such as winding resistance, leakage reactance, and core magnetizing inductance. Simulations are conducted by varying both resistive and inductive loads to analyse how these changes affect input and output power, ultimately determining the transformer's efficiency under different operating conditions. The results demonstrate that transformer efficiency varies with load magnitude, reaching peak performance under optimal loading conditions while declining under light or excessive loads. Inductive loads, which introduce reactive power components, were also shown to reduce overall efficiency compared to purely resistive loads. These findings underscore the importance of effective load management for achieving energy savings, extending the operational lifespan of transformers, and ensuring efficient power distribution. The project concludes by suggesting future advancements involving real-time monitoring and adaptive control systems that could dynamically regulate transformer parameters in response to load variations, thereby maintaining optimal performance across diverse electrical

Keywords: Transformer, Efficiency, MATLAB Simulation, Loads variation.

Abstract: One of the major issues facing by human is plastic pollution, which results by plastic's impact on every part of our environment. Research on the rate of plastic in land use has just currently began, in comparison with freshwater and aquatic areas, just it is contributing a lot to global plastic pollution (14%). Due to its difficulty of biodegrade, plastic waste has turned into an important environmental issue in recent years. The growth of plastic garbage in land might harm ecosystem function, soil health, and wildlife. In agricultural area, plastic waste might pollute the soil, stopping plants from proper absorption of nutrients and slowing their growth. Also, the presence of plastic materials might block irrigation systems, that may result in water stagnation and yield reduction. In addition to the effects, microplastic growth in soils from plastic materials in agriculture may result in a harmful impact on the fertility of the soil and structure. Sustainability aims to reduce a product's impact on the environment at each phase of its life cycle, from production to disposal Using biodegradable materials might reduce the quantity of plastic waste produced and the harmful effects it has on ecosystems. In order to cut down plastic waste in agriculture, various methods are being tested in addition to sustainable product.

Keywords: Eco-friendly, Decompose, Sustainable, Micro-plastic reduction, Soil Health

Abstract: The rapid adoption of artificial intelligence (AI) across industries has catalyzed a paradigm shift from generic, one-size-fits-all AI models to highly customized solutions tailored to enterprise-specific needs. IndoAI, with its advanced AI camera hardware and robust AI model development capabilities, is strategically positioned to dominate this emerging market. This paper investigates the growing demand for bespoke AI solutions, particularly among Fortune 500 companies, and evaluates how IndoAI can leverage its AI camera ecosystem to foster a developer-driven economy akin to the mobile app market. By analyzing market trends, technological advancements, strategic implications, and IndoAI's competitive positioning, we highlight its potential to become a powerhouse in the AI development landscape. The paper concludes with an exploration of challenges and future directions for IndoAI in shaping the next frontier of enterprise AI innovation.

Keywords: AI, AI Camera, AI Models, Indoai, edge computing.

Abstract: Nylon 6 and Glass fibre is a thermoplastic polymer widely recognized for its excellent impact resistance, toughness, and dimensional stability. However, its relatively moderate mechanical strength and thermal resistance limit its application in highperformance systems such as polymer composite gears. This study explores the potential of as a base material for gear manufacturing, focusing on improving its properties through reinforcement with materials like glass fibers, carbon fibers, PTFE, graphite, and nano-fillers. These reinforcements aim to address the stringent requirements of gears, including high wear resistance, low friction, and durability under operational loads. The investigation adopts a multi-faceted approach, including experimental testing and computational simulations. Mechanical testing, such as tensile strength, fatigue life, and impact resistance, evaluates the material's structural integrity, while thermal testing measures heat deflection temperature and thermal expansion properties. Tribological analysis assesses wear rate and friction behavior under operational conditions. Additionally, Finite Element Analysis (FEA) simulations provide insights into stress distribution, thermal effects, and dynamic performance, complementing experimental findings. Results from these analyses are benchmarked against traditional gear materials like Nylon and POM to identify composites' strengths and weaknesses. The incorporation of fillers significantly improves the mechanical and thermal properties of making it a strong candidate for light- and medium-duty gear applications. The study also evaluates the cost-effectiveness of -based composites, highlighting their potential to provide a balanced solution between performance and economic feasibility. This research demonstrates the viability of composites for use in lightweight, highperformance gears, particularly in cost-sensitive markets. By optimizing the formulation of composites, the study contributes to advancing gear material technology and offers a sustainable alternative to conventional materials. These findings pave the way for further exploration of polymer composites in advanced engineering applications.

Abstract: Using Finite Element Method (FEM)-based simulation In ANSYS Workbench, the present work demonstrates an extensive thermal and structural analysis of the grey cast iron disc brake. In particular, the work focuses on the numerical investigation of the residual mechanical behaviour of the composite brake disc through steady state and transient braking conditions to study temperature distribution, thermal stress and deformation behaviour. Grey cast iron was chosen due to its high thermal conductivity, wear resistance, and vibration absorbing characteristics. A mode of the disc brake system was created using a CAD program and placed under a range of braking loads, speeds, and thermal conditions. Full transient thermal analysis showed the maximum temperatures at the outer radius of the disc, and the steady-state simulation indicated thermal hotspots during extended periods of braking. The structural analysis presented critical stress concentration areas around the inner hub, with a maximum von Mises stress of 2661.8 MPa. The X and Z axis directional deformations revealed considerable dislocation at the outer rims and movement to the respective axis near the hub. This analysis showed that both peripheral and central region were more susceptible to elastic deformation. The results underline the importance of utilizing dynamic simulations in design validation. These results furnish crucial information needed for the optimization of disc geometry layout, material choices, and structural reinforcements for an improved braking performance, longevity and reliability for automotive applications.

Keywords: Grey Cast Iron, Disc Brakes, Thermal Analysis, Structural Analysis, ANSYS, Steady-State, Transient, FEM

Abstract: Disc brakes are vital for automotive safety, and they work by transforming kinetic energy into heat via friction to slow or halt vehicles. Here, we review disc brake research, with regards to thermal performance, structural integrity, material selection, noise-vibration-harshness (NVH) behaviour, etc. They focus on thermal management methods, like ventilated disc designs and materials that help dissipate heat. A structural analysis, mainly finite element analysis (FEA), is presented to quantify stress distribution and fatigue due to braking loads. Conventional and advanced materials are examined, including their mechanical durability, thermal characteristics, and environmental sustainability - including grey cast iron, stainless steel, carbon ceramic composites and aluminium alloys. The study also addresses NVH aspects and reasons of squeal and judder besides discussing numerical methods for prediction and prevention of these annoyances. It provides final suggestions for combined simulations, green materials and the new designs to enhance overall capabilities of the disc brake. Future research directions underscore enhancements in performance, cost-effective innovative approaches, and environmental sustainability in next generation braking systems.

Abstract: In the current energy scenario, solar photovoltaic (PV) systems have emerged as a key solution to sustainable power generation. However, the efficiency of these systems is highly influenced by environmental factors such as solar irradiance and temperature. This paper presents a detailed MATLAB/Simulink-based simulation of a Maximum Power Point Tracking (MPPT) system using the Perturb and Observe (P&O) algorithm integrated with a buck converter. The PV array parameters were modeled considering variable irradiance levels from 1000 W/m² to 500 W/m² and a constant temperature of 25°C. The buck converter was designed to step down the voltage from the maximum power point value of 30.7V to a desired load voltage of 15V, with switching frequency set to 10 kHz and a maximum power output of 250 W. Inductor (L), capacitor (C), and load resistance (R) values were calculated using MATLAB code to optimize converter performance. The proposed system effectively tracks the maximum power point in varying environmental conditions and enhances the overall efficiency of the PV system. The results validate the accuracy and stability of the P&O algorithm, making this approach suitable for practical deployment in residential, industrial, and off-grid solar applications.

Keywords: MPPT, Perturb and Observe, Buck Converter, MATLAB Simulation, Solar PV System, Renewable Energy, Maximum Power Point, Irradiance Variation, Power Electronics, Photovoltaic Efficiency.

Abstract: Background: Menopause brings about major physical, mental, and psychological changes in women, making it an important time to pay attention to their health throughout this phase of life. Women may encounter various symptoms and health issues when their oestrogen levels drop, which may have an adverse effect on their quality of life. Objective: The study aims to find the impact of holistic health education on managing the menopausal symptoms severity. Methods: The experimental study conducted on 88 premenopausal women in rural area above the age 35 years at Coimbatore. In the experimental group women receive holistic health education which includes nutrition, physical activity, stress management and yoga in managing the menopausal symptoms. The pre-intervention and post-intervention was developed and data were collected with 6 month duration. Results: The result reveals that the economic factor of the participants had association (Regression t=-2.15, P=<0.05) with their menopausal symptoms score. In addition, there was a non-significant difference (P>0.05) in the pre-assessment data between the experimental group and the control group. The study shown that holistic approaches towards menopause symptoms shown a significant (P<0.001) improvement in BMI, nutrient intake, haemoglobin level by practicing wellness enhancing habits and significantly (P<0.01) lower the stress score, physiological and psychological symptoms score in the experimental group. Conclusion: The study's findings suggest that the lifestyle-focused, holistic health education successfully improved menopausal symptoms and promoted healthier lifestyle habits among menopausal women.

Keywords: Holistic health education, Menopausal symptoms, Nutrition, Premenopausal women

Abstract: Ground Reaction Force (GRF) is a critical biomechanical variable that directly influences athletic performance and injury risk. GRF represents the force exerted by the ground on the body during physical activity and is a key factor in movements such as jumping, sprinting, landing, and changing direction. Excessive or improperly absorbed GRFs have been associated with a wide range of sports-related injuries, particularly in the lower extremities. This review paper aims to explore the relationship between GRF and sports injuries by analyzing current research across different sports disciplines, including running, soccer, basketball, gymnastics, and volleyball. Research indicates that high vertical and horizontal GRF magnitudes, rapid loading rates, and asymmetrical force distribution are strongly correlated with the development of stress fractures, anterior cruciate ligament (ACL) injuries, patellofemoral pain syndrome (PFPS), and Achilles tendinopathy. For example, abrupt landings or deceleration movements often expose athletes to peak GRFs of 2.5 to 6 times body weight, significantly increasing tissue loading and injury potential. Moreover, poor neuromuscular control, fatigued muscles, and inadequate footwear or playing surfaces exacerbate the effects of high GRFs. Studies utilizing force plates, motion capture, and musculoskeletal modeling have provided deeper insights into how GRF contributes to both acute and overuse injuries. Preventive interventions, such as strength training, proprioceptive exercises, plyometric drills, and footwear design, have shown promising results in modifying GRF patterns and reducing injury incidence. This review emphasizes the importance of understanding GRF dynamics in sports biomechanics, injury surveillance, and prevention strategies. Monitoring and optimizing GRF through training, technique modification, and equipment can significantly mitigate injury risk while enhancing athletic performance. Continued interdisciplinary research combining biomechanics, sports medicine, and rehabilitation sciences is essential for developing effective interventions targeting GRF-related injury mechanisms.

Keywords: Ground Reaction Force, Sports injuries, Biomechanics, Injury prevention, Lower extremity injuries, Force plate analysis, Loading rate

Abstract: The study has been emphasized on the critical role of tax planning as an essential component offinancial strategy for individuals, particularly salaried employees and taxpayers in India. Across multiple studies, a consistent theme emerges: the strategic utilization of deductions, exemptions, rebates, and reliefs-primarily under Section 80C of the Income Tax Act, 1961-is crucial for minimizing tax liabilities and maximizing after-tax income. Research consistently explores the awareness levels of individuals regarding various tax-saving schemes and instruments, such as Life Insurance policies, Provident Funds, PPF, and NPS. The objective of these studies is to understand investment patterns, identify the most popular and suitable tax-saving instruments, and assess how effective tax planning influences income generation and wealth accumulation. Findings generally reveal that while awareness of traditional tax- saving options like 80C is high, there is a continuous effort to understand how strategic tax decisions can lead to reduced tax burdens and foster income growth over time.

Keywords: Tax Saving Strategies, Optimizing Your Taxes, Tax Advantages & Tax liability

Abstract: This study explores the influence of major macro-economic factors on the performance of Exchange-Traded Funds (ETFs), which are increasingly favored by investors for their diversification, affordability, and market accessibility. Adopting a quantitative research methodology, the analysis covers historical data from 2015 to 2025, focusing on key economic indicators such as GDP growth, inflation rate, and the repo rate. The primary objective is to assess how these variables affect the returns of ETFs, with particular emphasis on Nifty 50 and Gold ETFs. Statistical techniques like correlation and regression are employed to evaluate the strength and direction of these relationships. The results indicate that macro-economic variables-especially inflation and interest rates-have a significant impact on ETF performance. By offering insights into how economic shifts affect market behavior, this study aims to assist investors in making informed, data-driven investment decisions in an ever-evolving financial landscape.

Keywords: ETFs, Macro-Economic Factors, GDP, Inflation, Repo Rate, Nifty 50, Gold ETF

Abstract: This paper offers an in-depth review of recent progress in blockchain-enabled electronic voting systems, emphasizing voter authentication and data security. Traditional voting methods are prone to issues such as fraud, tampering, and limited transparency, while electronic voting systems often grapple with centralization and security risks. Blockchain technology provides a decentralized and tamper-resistant framework, effectively addressing these concerns and supporting secure and transparent electoral processes. The study reviews various approaches that integrate blockchain with modern authentication techniques, including deep learning-based facial recognition and OTP verification. Tools such as Ethereumblockchain, Ganache, and Metamask are analyzed for their roles in ensuring tamper-proof vote recording and secure transaction handling. Additionally, the system incorporates Python for backend development, PHP and SQL for database management, and WAMP server for efficient operations. Through this survey, we identify key trends, challenges, and research gaps in existing systems, such as scalability, computational demands, and user adoption. Finally, we propose an enhanced voting framework that leverages blockchain and biometric technologies to provide a robust, scalable, and user-friendly solution, paving the way for secure and transparent elections.

Keywords: Blockchain, E-Voting, Smart Contracts, Biometric Authentication, Ethereum, Deep Learning, Multi-Factor Authentication, Tamper-Proof, Security, Privacy, Decentralization, OTP Verification.