The performance of the model is evaluated using metrics such as accuracy, precision, recall, and F1-score to ensure reliable predictions. This approach not only aims to improve diagnostic accuracy but also provides a data-driven solution to assist healthcare professionals in making informed decisions. The outcome of this project can contribute to better management of CKD, ultimately helping to reduce the burden on healthcare systems and improving patient care.

The digitization of cadastral maps plays a vital role in modern land management, facilitating efficient land administration, property transactions, and urban planning. However, many existing cadastral maps are still in paper form or stored in outdated digital formats, which are often difficult to access, process, and update. This paper presents an innovative solution utilizing Artificial Intelligence (AI) to automate the digitization of cadastral maps. By applying machine learning, computer vision, and image processing techniques, the proposed system is designed to accurately (...) extract property boundaries, classify map features (such as plot numbers, ownership details, and administrative boundaries), and convert legacy map data into standardized digital formats compatible with Geographic Information Systems (GIS). The system addresses key challenges such as poor map quality, inconsistent boundary definitions, and varying map scales. The result is an AI-driven approach that significantly enhances the efficiency, accuracy, and scalability of cadastral data management, enabling real-time updates, minimizing human error, and improving the overall accessibility of cadastral information for land administration and policy-making. This paper presents an AIdriven approach for automating the digitization of cadastral maps, converting paper- based or outdated digital maps into accurate, standardized digital formats. Using machine learning and computer vision, the system extracts property boundaries, recognizes map features, and integrates them into modern Geographic Information Systems (GIS). The proposed solution addresses challenges such as poor image quality, irregular boundaries, and inconsistent annotations, streamlining land management, reducing human error, and enhancing the accessibility of cadastral data for urban planning and property administration. (shrink)

. The concept of Prakriti, derived from Ayurveda, is a unique blend of physical, physiological, and psychological attributes that determine an individual's constitution. Understanding one's Prakriti is essential for personalized health recommendations, lifestyle adjustments, and preventive healthcare strategies. This app helps us to know the food consumptions you need to take according to your body conditions for better health. In this app we used node js as backend and react js as our front end. We also used html css for (...) app user interface. Existing methods for Prakriti determination rely on questionnaires, direct consultations with Ayurvedic practitioners, and physical examinations. These methods can be time-consuming, subjective, and inaccessible to a wider audience. We propose a novel chatbot solution that leverages artificial intelligence to determine an individual's Prakriti. The chatbot, designed with natural language processing capabilities, interacts with users to gather information about their physical attributes, habits, and mental state. (shrink)

Additionally, privacy and security concerns surrounding medical data are addressed by ensuring compliance with stringent data protection standards such as HIPAA and GDPR, thereby ensuring users that their medical information is handled securely and confidentially. The solution aims to improve healthcare accessibility, reduce delays in diagnosis, and provide users with greater control over their health management. This report outlines the chatbot’s design, the integration of image processing techniques, the use of machine learning models for disease identification, and the importance of (...) data privacy in healthcare applications. (shrink)

Increased online use and allowing users to engage with groups such as digital networking have contributed to the growth of hacking. Online abuse is a new type of harassment that has lately become more prevalent as online communities have grown in popularity. It tends to send messages which included defamatory claims or vocally harassing someone while in the internet group. Only if modern civilization recognizes harassment as it truly is, countless of hidden sufferers may continue to suffer. There have been (...) several studies on cyberbullying, but none of them have been able to offer a solid remedy. By creating a model that can recognize and block bullyingrelated incoming and outgoing communications, we address this issue in our project. By employing supervised classification techniques on an opensource dataset that has been carefully annotated, we hope to provide lexical baselines for this job. We used machine learning algorithm of logistic regression. Our model classifies a message whether its bullying or not. (shrink)

Chronic Kidney Disease (CKD) is a significant global health issue, often leading to kidney failure and requiring costly medical treatments such as dialysis or transplants. Early detection of CKD is essential for timely intervention and improved patient outcomes. This project aims to develop a machine learning-based predictive model for diagnosing CKD at an early stage. By utilizing a range of clinical features such as age, blood pressure, blood sugar, and other relevant biomarkers, we employ machine learning algorithms, including Decision Trees, (...) Random Forests, and Support Vector Machines (SVM), to predict the likelihood of a patient developing CKD. The dataset used in this study includes medical records of patients with various kidney conditions, and preprocessing techniques such as normalization and missing data handling are applied to ensure the model’s robustness. The performance of the model is evaluated using metrics such as accuracy, precision, recall, and F1-score to ensure reliable predictions. (shrink)

The AI-Enabled Water Well Predictor is a machine learning-based solution aimed at accurately predicting optimal drilling locations for water wells. This project leverages artificial intelligence to analyze vast datasets, including geological, hydrological, environmental, and meteorological data, to pinpoint areas with the highest likelihood of accessible groundwater. By integrating multiple data sources, the AI model identifies patterns and correlations that are difficult to detect through traditional methods, significantly increasing the reliability of well placement predictions. In regions where water scarcity is prevalent, (...) especially in rural and underdeveloped areas, this solution can provide a cost-effective, data-driven approach to well drilling. The AI-Enabled Water Well Predictor reduces the risks and costs associated with drilling unproductive wells, enhancing the success rate of water access initiatives. The system empowers governments, NGOs, and local communities to make informed decisions, promoting sustainable water resource management and supporting efforts to improve public health, agriculture, and economic resilience in water-stressed regions. (shrink)

Phishing attacks are an increasingly common cyber threat that exploit the trust users place in genuine websites by mimicking their look and feel. This research proposes a novel AI-based detection system designed to identify phishing domains that imitate authentic websites. Our approach leverages machine learning algorithms to analyze visual similarities, domain patterns, and metadata between phishing and legitimate sites. Through this method, the system detects phishing attempts before users are deceived. Key results indicate a high detection accuracy and a reduction (...) in false positives, which demonstrate the potential of this method for enhancing cybersecurity. Future improvements in realtime detection and integration into security protocols are discussed. (shrink)

Early detection of CKD is essential for timely intervention and improved patient outcomes. This project aims to develop a machine learning-based predictive model for diagnosing CKD at an early stage. By utilizing a range of clinical features such as age, blood pressure, blood sugar, and other relevant biomarkers, we employ machine learning algorithms, including Decision Trees, Random Forests, and Support Vector Machines (SVM), to predict the likelihood of a patient developing CKD.

By utilizing a range of clinical features such as age, blood pressure, blood sugar, and other relevant biomarkers, we employ machine learning algorithms, including Decision Trees, Random Forests, and Support Vector Machines (SVM), to predict the likelihood of a patient developing CKD. The dataset used in this study includes medical records of patients with various kidney conditions, and preprocessing techniques such as normalization and missing data handling are applied to ensure the model’s robustness. T.

issues in real-time, allowing authorities to address concerns swiftly. Additionally, the integration of IoT-based monitoring, utilizing ESP32/ARDUINO devices, will enable the collection of real-time data, providing alerts on water supply status and system malfunctions. The technology stack comprises JavaScript, PHP, and PostgreSQL with PostGIS for spatial data management, emphasizing secure, scalable data handling. The platform is designed for continuous improvement through user feedback, ensuring it evolves to meet the dynamic needs of the Jal Jeevan Mission. Through innovative technology and a (...) user-centric approach, this project aims to support equitable access to safe water across rural areas. (shrink)

The project aims to address the critical need for a robust explosion risk assessment tool in the oil and gas industry, particularly in oil and gas refinery environments where safety is paramount. Traditional methods of threat assessment are often manual, subjective, and limited in their capacity to provide real-time insights, which poses significant challenges for industry professionals in managing potential hazards. Our project leverages real-time weather data and machine learning algorithms to develop an advanced web application designed to identify and (...) assess threat zones within oil and gas refineries. By utilizing refinery-specific datasets, real-time weather parameters, and machine learning models, the application enables users to upload or access refinery data, select industry-specific details, and receive accurate risk predictions along with stability class assessments. The application integrates the OpenWeatherMap API to provide up-to-date weather information, while a Random Forest classifier used for gas classification and some other parameters are calculated and trained on relevant features such as wind speed, cloud cover, and insolation levels to predict explosion risks. Designed with an intuitive, user-friendly interface, this tool will be invaluable for safety engineers, refinery managers, and environmental professionals, facilitating proactive risk management and informed decision-making. By addressing limitations in current risk assessment methods—such as limited access to real-time environmental data, variable risk levels, and lack of predictive analytics—our project aims to provide a comprehensive, data-driven solution for managing safety and mitigating risks in high-stakes industrial environments. (shrink)

Blended learning is a teaching approach that combines traditional in-person instruction with digital resources to create a more adaptable and accessible educational experience. This hybrid model blends the strengths of classroom teaching with the flexibility of online tools like virtual classrooms, e-books, and digital assessments, helping to overcome many of the challenges found in conventional education. Traditional education systems often face issues such as limited access to quality resources, outdated instructional materials, and inadequate teacher support, especially in remote or underserved (...) areas. These limitations became even more apparent during the COVID-19 pandemic, which disrupted in-person learning worldwide and emphasized the need for flexible and sustainable educational methods that can adapt to changing circumstances. (shrink)

The application of this system can lead to higher crop yields, sustainable farming practices, and reduced risks associated with poor crop choices. Through rigorous evaluation using standard classification metrics, the model's performance demonstrates its potential to revolutionize farming practices by aiding farmers in making informed decisions. The system has the potential to be an invaluable tool for agricultural consultants, farmers, and policymakers, ensuring long-term sustainability and improved productivity.

The dataset used in this study includes medical records of patients with various kidney conditions, and preprocessing techniques such as normalization and missing data handling are applied to ensure the model’s robustness. The performance of the model is evaluated using metrics such as accuracy, precision, recall, and F1-score to ensure reliable predictions. This approach not only aims to improve diagnostic accuracy but also provides a data-driven solution to assist healthcare professionals in making informed decisions. The outcome of this project can (...) contribute to better management of CKD, ultimately helping to reduce the burden on healthcare systems and improving patient care. (shrink)

. The Road Safety Violation Reporting System is an innovative approach to improve road safety by enabling community members to report traffic rule violations. India faces over 150,000 traffic fatalities each year due to non-compliance with laws like wearing seat belts, helmets, and avoiding mobile phone use while driving. Our platform allows citizens to report violations using photographic evidence, which includes location and timestamp data. The system automates fine generation while maintaining the anonymity of the reporters, encouraging public participation in (...) traffic law enforcement. This project leverages community engagement and advanced technology to significantly reduce traffic violations and enhance road safety. (shrink)

MLNova is a structured platform designed to bridge the gap between theoretical knowledge and practical application in machine learning. Focused on enhancing student learning, the platform offers a path for beginners to explore key concepts in data preprocessing and model evaluation. Through interactive modules, MLNova delivers prerecorded lessons and real-world projects, allowing learners to experience hands-on engagement. This paper outlines the platform’s design, methodology, and the impact of interactive learning on improving comprehension of machine learning principles. Early feedback indicates a (...) significant improvement in user engagement and learning outcomes. (shrink)

Chronic Kidney Disease (CKD) is a significant global health issue, often leading to kidney failure and requiring costly medical treatments such as dialysis or transplants. Early detection of CKD is essential for timely intervention and improved patient outcomes. This project aims to develop a machine learning-based predictive model for diagnosing CKD at an early stage. By utilizing a range of clinical features such as age, blood pressure, blood sugar, and other relevant biomarkers, we employ machine learning algorithms, including Decision Trees, (...) Random Forests, and Support Vector Machines (SVM), to predict the likelihood of a patient developing CKD. (shrink)

The "Malware App Detection System" project enhances mobile security by detecting malicious apps through machine learning (ML) analysis of app behaviours. It examines behaviours like system calls (requests apps make to the operating system), permissions (levels of access apps have), and network connections (internet interactions). This focus on behaviour, rather than relying on specific malware signatures, enables the system to identify both known and new threats. The detection system is built using a dataset of both benign (harmless) and malicious apps, (...) with each app’s behaviour categorized and labelled. By analysing system calls, the system identifies unusual requests, which may indicate malicious intent. Permissions are also reviewed, as apps that seek abnormal access levels, like to contacts or storage, might have harmful intentions. Network connections are monitored to detect suspicious activities, such as connections to unauthorized servers, which could indicate data theft. To achieve this, ML models like Random Forest are trained on these behaviour patterns, learning to distinguish between safe and harmful apps. Once deployed, the system provides real-time detection, alerting users if it finds potentially dangerous activities. This proactive, behaviour-based approach protects users from unauthorized access and data leaks, adapting to new malware types as they arise. In doing so, the project offers a flexible and effective solution to the evolving challenges of mobile security. (shrink)

"Genius AI" is an innovative SaaS platform poised to revolutionize the landscape of artificial intelligence tools. This dynamic platform integrates a diverse array of AI functionalities, including conversation generation, code generation, music generation, and video generation, all in one centralized hub. At its core, Smart AI harnesses cutting-edge machine learning algorithms and neural networks to empower users with unparalleled capabilities. The conversation generation feature enables seamless interaction with users through natural language processing, facilitating engaging and lifelike conversations. At its core, (...) Smart AI harnesses cutting-edge machine learning algorithms and neural networks to empower users with unparalleled capabilities. The conversation generation feature enables seamless interaction with users through natural language processing, facilitating engaging and lifelike conversations. At its core, Smart AI harnesses cutting-edge machine learning algorithms and neural networks to empower users with unparalleled capabilities. The conversation generation feature enables seamless interaction with users through natural language processing, facilitating engaging and lifelike conversations. For developers and coders, Smart AI offers a ground breaking code generation tool, streamlining the process of software development by automatically generating code snippets tailored to specific requirements and programming languages. Moreover, music aficionados can leverage Smart AI's advanced algorithms to compose original melodies, harmonies, and rhythms, personalized to suit any style or genre. Meanwhile, the video generation functionality empowers content creators with the ability to effortlessly produce high-quality videos, complete with customizable elements and visual effects. With its user-friendly interface and scalable architecture, Smart AI caters to a broad spectrum of industries and applications, from e-commerce and marketing to entertainment and education. Whether enhancing customer engagement, accelerating software development, or fostering artistic expression, Smart AI unlocks endless possibilities for innovation and creativity. Join the forefront of AI-driven transformation with Smart AI and redefine the way we interact with technology. (shrink)

This paper explores how technology can address challenges for undertrial prisoners in India, who often face prolonged detentions due to systemic delays and limited resources. Focusing on legal assistance, case management, and prisoner welfare, the research demonstrates how technology can improve access to justice, speed up case processing, and support inmate welfare. Secure communication tools enable prisoners to connect with lawyers, streamlining legal aid and providing timely case updates. Pilot projects show promising results, with a 30% reduction in case-processing times (...) and a 40% improvement in lawyer-prisoner communication. Despite infrastructure and training challenges, the report recommends scalable tech solutions across Indian prisons, urging government collaboration and policy reforms to digitally transform the criminal justice system. Ultimately, the findings underscore technology’s potential to create a more accessible, efficient, and equitable justice system for undertrial prisoners. (shrink)

The Road Safety Violation Reporting System is an innovative approach to improve road safety by enabling community members to report traffic rule violations. India faces over 150,000 traffic fatalities each year due to non-compliance with laws like wearing seat belts, helmets, and avoiding mobile phone use while driving. Our platform allows citizens to report violations using photographic evidence, which includes location and timestamp data. The system automates fine generation while maintaining the anonymity of the reporters, encouraging public participation in traffic (...) law enforcement. This project leverages community engagement and advanced technology to significantly reduce traffic violations and enhance road safety. (shrink)

The dataset used in this study includes medical records of patients with various kidney conditions, and preprocessing techniques such as normalization and missing data handling are applied to ensure the model’s robustness. The performance of the model is evaluated using metrics such as accuracy, precision, recall, and F1-score to ensure reliable predictions. This approach not only aims to improve diagnostic accuracy but also provides a data-driven solution to assist healthcare professionals in making informed decisions. The outcome of this project can (...) contribute to better management of CKD, ultimately helping to reduce the burden on healthcare systems and improving patient care. (shrink)

Wool production industries rely heavily on accurate demand forecasting to manage supply chains and production schedules effectively. Predicting wool demand allows companies to avoid overproduction and resource wastage while meeting market needs efficiently. Traditional forecasting models often struggle with the seasonality and variability of wool demand. The ARIMA (Auto Regressive Integrated Moving Average) model, a time series forecasting technique, is particularly suited for this task due to its ability to capture both trends and seasonal fluctuations in historical data. The data (...) for this research was collected from official records of wool demand across various Indian states for 2016-2023. After data pre-processing (cleaning, sorting, and organizing), the ARIMA model was implemented using Python for analysis and forecasting. Flask was employed to create a web-based interface, allowing users to interact with the predictive model easily. The model parameters (p, d, q) were fine-tuned to fit the data and generate accurate forecasts. The ARIMA model demonstrated a strong ability to predict wool demand, achieving a low Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). The model's forecasts were closely aligned with actual demand figures across different states, capturing both long-term trends and seasonal variations. Graphical representations showed minimal deviation between predicted and actual values, validating the accuracy of the model. The implementation of the ARIMA model for wool demand forecasting has significant practical implications for production planning in the textile industry. Companies can use the system to optimize production schedules, reduce costs, and minimize wastage. This methodology can be extended to other commodities where seasonal demand plays a critical role, offering a versatile tool for various sectors. (shrink)

Chronic Kidney Disease (CKD) is a significant global health issue, often leading to kidney failure and requiring costly medical treatments such as dialysis or transplants. Early detection of CKD is essential for timely intervention and improved patient outcomes. This project aims to develop a machine learning-based predictive model for diagnosing CKD at an early stage. By utilizing a range of clinical features such as age, blood pressure, blood sugar, and other relevant biomarkers, we employ machine learning algorithms, including Decision Trees, (...) Random Forests, and Support Vector Machines (SVM), to predict the likelihood of a patient developing CKD. The dataset used in this study includes medical records of patients with various kidney conditions, and preprocessing techniques such as normalization and missing data handling are applied to ensure the model’s robustness. The performance of the model is evaluated using metrics such as accuracy, precision, recall, and F1-score to ensure reliable predictions. This approach not only aims to improve diagnostic accuracy but also provides a data-driven solution to assist healthcare professionals in making informed decisions. The outcome of this project can contribute to better management of CKD, ultimately helping to reduce the burden on healthcare systems and improving patient care. (shrink)

The increasing demand for reliable power sources has made diesel generators essential in various industries. However, traditional monitoring methods often rely on manual inspections, resulting in inefficiencies, unplanned downtimes, and higher operational costs. This project proposes a digital monitoring system for diesel generators that leverages advanced sensor technologies and real-time data analytics to overcome these challenges. The system will utilize Internet of Things (IoT) devices to collect key operational parameters, such as fuel levels, temperature, vibration, and performance metrics, enabling comprehensive (...) monitoring of generator health. By integrating machine learning algorithms, the system will analyze historical and real-time data to predict potential failures and optimize maintenance schedules, shifting from reactive to proactive management. A user-friendly interface will allow operators to visualize generator performance and receive instant alerts for detected anomalies, enhancing decision- making processes, reducing downtime, and lowering maintenance costs—ultimately increasing the reliability and efficiency of power supply. Additionally, the system facilitates remote monitoring, providing continuous oversight regardless of location. This project not only aims to enhance the operational efficiency of diesel generators but also lays the groundwork for smarter energy management solutions in the future. The successful implementation of this digital monitoring system could serve as a model for similar applications across various sectors, contributing to sustainability and operational excellence in energy management. Through this innovative approach, we aim to ensure diesel generators operate at optimal performance levels while minimizing environmental impact. Keywords. Diesel Generators, Digital Monitoring, IoT Devices, Real-time Data Analytics, Machine Learning. (shrink)

The Alumni Association platform is designed to connect alumni and current students of an educational institution. It provides tools for alumni networking, professional development, and community engagement. The primary goal is to facilitate meaningful communication between alumni and students, helping students access job opportunities, mentorship, and collaborative spaces for startup ideas. Alumni can be easily located through a search feature, enabling personalized networking. Event management tools support the organization of alumni events, reunions, and workshops, fostering ongoing involvement. Announcements and updates (...) keep users informed of key events and opportunities. This portal ultimately aims to build a dynamic and supportive ecosystem, enhancing the educational experience and creating lasting bonds within the alumni community. (shrink)

Cloudbursts pose a significant threat in India, especially during the South-West Monsoon season that commences in June. India's diverse climate regions, including the northern Himalayan region, Indo-Gangetic Plain, southern peninsula, and coastal areas, experience sporadic cloudbursts, with only 31 recorded instances, mainly in Himachal Pradesh, Uttarakhand, and Jammu and Kashmir. To address the lack of comprehensive Indian cloudburst data, we've curated a dataset, incorporating meteorological factors for cloudburst prediction. This dataset encompasses variables such as Temperature, Wind Gust, Wind Gust Speed, (...) Humidity, Monsoon patterns, Air Pressure, and Cloud Density. Our goal is to improve preparedness and mitigation strategies, safeguarding lives, and property in cloudburst prone areas. Employing optimized machine learning algorithms, our model analyzes these parameters alongside prevailing weather conditions, facilitating cloudburst event prediction. We evaluate the prediction performance of machine learning algorithms, including KNN. The KNN algorithm outperformed others with an accuracy of 86.18%. Moreover, we provide graphical insights into the correlation between humidity and cloudburst occurrence, emphasizing the importance of weather variables in prediction models. This research contributes to cloudburst forecasting, even with limited Indian data, and highlights the potential of utilizing diverse machine learning techniques for improved accuracy. (shrink)

This project aims to provide a comprehensive placement preparation platform designed for college campus placements, utilizing a modern tech stack to deliver a robust and efficient solution. Built with Node.js and integrated with MySQL Workbench for database management, this web application centralizes critical resources such as company-specific interview experiences, placement materials, preparation roadmaps, and eligibility criteria. Students can create an account and log in to access these resources, ensuring they have organized and up-to-date information to guide their preparation. With its (...) efficient backend powered by Node.js and structured data storage in MySQL, the platform delivers a seamless and responsive user experience. By addressing common challenges in placement preparation—fragmented information access, outdated resources, and lack of guided preparation paths—this application provides a centralized, user-friendly solution to enhance students' readiness and increase their chances of success in campus recruitment efforts. (shrink)

The lack of access to quality education in rural areas presents a critical challenge, with students in these regions facing significant barriers such as limited educational resources, a shortage of qualified teachers, and inadequate infrastructure. This disparity leads to a substantial gap in learning opportunities compared to urban counterparts. To address this issue, we propose the development of an edtech platform that provides accessible online courses, assessments, and educational materials tailored for rural students. The platform is designed to offer high-quality (...) learning resources, enhance teaching support, and enable students to overcome the constraints posed by their environment. By utilizing digital technologies and cloud-based delivery systems, the solution aims to improve educational outcomes, reduce the urban-rural divide, and empower students in rural areas to reach their full potential. The key results of the study suggest that the implementation of such an edtech platform can have a transformative impact on rural education by providing equitable learning opportunities. The platform's ability to deliver comprehensive, easily accessible content can significantly enhance the quality of education in underserved regions. This research highlights the importance of leveraging technology to bridge educational gaps and promote inclusivity in education across rural and urban landscapes. (shrink)

his project focuses on developing a community-based water monitoring and conservation tool. The system allows users to track daily water consumption, receive real-time feedback on usage, and view eco-friendly tips. It integrates data visualization and eco-points for promoting conservation behavior. Built using node js, JavaScript, and Chart.js, this platform encourages users to reduce water consumption and share their achievements. The goal is to foster community engagement and sustainable water usage practices, addressing the global concern of water scarcity.

his paper presents a Multilingual Translation Engine designed to automate public announcements in multiple languages. It leverages Google Translate for real-time translation and gTTS (Google Text-to-Speech) for speech synthesis. The system supports over 90 languages, enhancing accessibility in transportation hubs. The engine's architecture is built using Flask for the backend, integrated with APIs to handle translation and audio generation. Additionally, the system supports custom profiles for recurring announcements. Future enhancements could include speech recognition and advanced scheduling.

. The Computerized Cognitive Retraining Program is a web-based application designed to support home-based cognitive training for children with cognitive disabilities. Cognitive disabilities often present challenges in areas such as memory, attention, and problemsolving, which can impact a child's learning and daily functioning. This project aims to provide an engaging, accessible platform for children to practice and improve these skills from the comfort of their home, under the guidance of healthcare professionals. The application enables children to engage with a variety (...) of interactive games and exercises specifically developed to target cognitive functions. Each activity is designed to be visually stimulating and user-friendly, encouraging consistent participation and gradual cognitive improvement. The program also features a patient dashboard for doctors, allowing them to monitor individual progress, assess performance trends, and make informed adjustments to each child's training plan. Built using HTML, CSS, JS, VS code local storage, this application offers secure access and a reliable database structure for storing user data, game progress, and performance metrics. Through this project, we aim to bridge the gap in cognitive support services by making training resources available in a home setting, thus extending the impact of cognitive retraining beyond traditional clinical environments. (shrink)

The application tracks the key user behaviors, such as job searches, applications, and consumption of its content. Session length, engagement usage, and interaction trends are captured to derive actionable insights. These insights actually help with optimizing content delivery and improving the user experience. Ultimately, Job Search India is looking to help improve PGRKAM's platform by offering insight data that can help optimize content relevance and engagement among users.

he accessibility and quality of legal services in India face substantial challenges, primarily due to limited transparency, high costs, and a fragmented network of service providers. This study proposes an incentive-based e-marketplace that connects citizens with a wide array of qualified legal professionals. The platform's unique model rewards high-quality service through client feedback, referrals, and professional milestones, creating a transparent, efficient, and user-friendly digital marketplace. This research paper discusses the platform’s architecture, incentive mechanisms, user interface, and initial results from user (...) engagement analysis, illustrating how the proposed solution enhances service accessibility, quality, and engagement in the legal domain. (shrink)

The Government Land Information System (GLIS) dataset provides detailed data on land use patterns, population distribution, and demographic characteristics across all States and Union Territories. This dataset serves as an essential tool for making data-driven decisions in areas such as urban planning, infrastructure development, environmental protection, and socio-economic advancement. The goal of analysing this dataset is to uncover key insights into current land use trends and their effects on sustainable urban development. By studying land use changes, the analysis can highlight (...) regions experiencing significant population growth or demographic shifts, identifying areas where infrastructure development may need to be prioritized. Additionally, the dataset offers a valuable opportunity to explore the environmental impacts of land use changes. This includes examining the effects of urban expansion, agricultural growth, and other land use changes on natural resources, ecosystems, and climate resilience. Understanding these impacts is vital for creating policies that balance environmental protection with the demands of urban and economic growth. Furthermore, the relationship between land use and socio-economic factors will be analysed to see how the distribution of land affects issues like income inequality, access to essential services, and community wellbeing. The insights gained from this analysis can be used to inform land management strategies that balance economic development with environmental sustainability. The goal is to propose actionable recommendations for managing land in a way that supports long-term growth while preserving natural resources for future generations. By integrating these insights into policy-making, it is possible to create more sustainable and equitable land management practices across all States and Union Territories. Ultimately, this analysis aims to support better decision-making in land use planning, promoting sustainable development and improving the quality of life while ensuring environmental conservation. Keywords. Land utilization, Urban planning, Sustainable development, Infrastructure development, Environmental sustainability. (shrink)

. The quality of textbooks plays a pivotal role in shaping educational outcomes, influencing teaching effectiveness and student comprehension. This project, "Quality of Textbooks," aims to provide an accessible and reliable platform for educators, students, and institutions to evaluate, review, and select high-quality textbooks. Through a user-friendly web application, users can log in, access expert reviews, search for textbooks, and browse detailed evaluations based on content accuracy, relevance to curriculum standards, clarity of presentation, engagement level, and cultural sensitivity. The project (...) leverages technologies like Java, Java ServerPages, Servlets, HTML, CSS, JavaScript, JDBC, and MySQL to create an interactive and responsive platform where verified experts can contribute their insights, and users can make informed decisions. By centralizing textbook evaluations, this platform seeks to bridge the gap between content creators and end-users, ensuring that quality educational materials are easily accessible. The ultimate goal is to foster an environment where high-standard resources are prioritized, enabling better learning experiences and, in turn, contributing to educational advancement. This application serves as a valuable resource for teachers, students, and educational institutions striving for excellence in academic materials, aiming to standardize textbook quality and enhance educational effectiveness across various subjects and grade levels. (shrink)

This project centers on developing a geo-tagging and monitoring system for plantations within the catchment area of a hydro project. Designed for the forest department, it provides real-time insights into environmental conditions to support the sustainable management of forest resources. The system comprises an Arduino Uno microcontroller that integrates GPS, DHT11 (temperature and humidity sensor), and LoRa modules to track and communicate critical data, such as the geographic location, temperature, and humidity around each plantation site. Powered by a 7.4V Li-ion (...) battery and regulated through an LM7805 voltage regulator for consistent 5V output, the system is optimized for low power consumption to ensure longevity in remote settings. Using the LoRa module, the system transmits data over long distances to a central receiver, which could be stationed at a monitoring hub. This data transmission operates independently of internet connectivity, making it ideal for isolated forest areas where network coverage is limited or non-existent. Each plant or designated plantation area can be geo-tagged and monitored through data collected by the sensors and sent through LoRa communication. The data is then processed and can be visualized on a server or application interface, giving forest officials an overview of the environmental conditions in each monitored area. This innovative system offers a scalable solution for remote environmental monitoring, allowing for expansion with additional sensors or wider deployment across forest areas. Furthermore, it lays a foundation for enhanced forest management through features like environmental alerts and trend monitoring, potentially aiding in conservation efforts by ensuring optimal conditions for plantation growth. By leveraging IoT technology in an eco-friendly, power-efficient setup, this project presents a modern approach to sustainable forestry and plantation monitoring. (shrink)

This project addresses the critical need for improved compliance, governance, and conduct in the educational ecosystem for specially-abled students. While educational institutions have made progress toward inclusivity, many still lack effective frameworks to fully support these students' needs. This study assesses the current landscape by reviewing policies, analyzing institutional practices, and gathering insights from educators, administrators, and advocacy groups. Key findings indicate gaps in policy adherence, insufficient training for staff, and inconsistencies in governance models that prevent effective support for specially-abled (...) students. To address these challenges, the project proposes a structured governance framework, periodic compliance audits, and mandatory training programs focused on inclusivity and accessibility. The proposed framework outlines actionable steps for educational institutions to standardize support measures, ensuring equitable access to resources, learning tools, and physical spaces. Pilot implementations of these recommendations demonstrate improvements in institutional readiness and responsiveness to specially-abled students' needs. The results highlight that a unified approach, with well-defined standards and accountability mechanisms, significantly enhances the educational experience for specially-abled students, laying the groundwork for a more inclusive and equitable education system. (shrink)

This project explores the design and implementation of an Automated Street Lighting System using the Internet of Things (IoT). The system aims to enhance urban lighting efficiency, reduce energy consumption, and improve public safety by employing smart sensors and IoT technology. The system's primary components include motion sensors, ambient light sensors, and IoT controllers, enabling dynamic lighting adjustments based on real-time environmental data. The proposed system is designed to operate autonomously, detecting pedestrian and vehicular movement to activate streetlights only when (...) needed. The project demonstrates significant energy savings and operational efficiency compared to traditional street lighting systems. By utilizing energy-efficient LED lights and integrating renewable energy sources, the system further reduces the carbon footprint, contributing to sustainability goals. This study illustrates the potential of IoT-enabled street lighting to transform urban environments, offering a scalable and cost-effective solution for modern cities striving for smarter, safer, and more sustainable infrastructure. (shrink)

Given the great significance of institutions in the journey towards sustainability, most of them lack the ability to measure and improve their ESG activities. Based on this argument, this work presents the development of a web-based application called ESGConnect, intended to assist institutions in the evaluation process pertaining to their sustainability practices through an interactive structured submission and feedback mechanism. In ESGConnect, institutions provide ESG-related documents, which administrators verify and score with recommendations on how to enhance the documents based on (...) the Environmental, Social, and Governance criteria. User-specific dashboards are present on the tool: administrators monitor submissions, whereas users see scores and comments associated with the submissions. In addition to these items, ESGConnect also supports collaboration because students and various stakeholders are allowed to have a dedicated online community for discussion and sharing information on sustainability practices. The architecture of the platform relies on Java Servlets, JSP, and database integration for easy handling of user management and data. Preliminary testing does indeed suggest that ESGConnect might be able to facilitate better communications and actionable insights for institutions interested in improving their sustainability efforts. Expansion of the platform features and improvement in scalability to service a much larger number of educational institutions are part of the future work. (shrink)

en the increasing importance of human target detection in various security applications, many existing systems face challenges related to cost, accuracy, and adaptability. This work presents the development of a machine learning-based system called Human Target Detection and Acquisition, designed to assist security personnel in the detection and tracking of human targets through an adaptive and cost-effective approach. In this, standard CCTV cameras provide visual data, which intelligent algorithms process to identify and track human targets, even in challenging conditions. The (...) system offers user-specific dashboards: administrators can monitor detections and adjust system parameters, while security personnel receive real-time alerts and tracking information. Additionally, it supports continuous improvement through its machine learning capabilities, allowing for enhanced performance over time without the need for hardware upgrades. The architecture of the platform relies on TensorFlow, OpenCV, and database integration for efficient handling of video processing and data management. Initial testing suggests that it can provide accurate and reliable human target detection while significantly reducing costs compared to hardwareintensive solutions. Future work includes expanding the system's capabilities to handle multiple camera feeds simultaneously and implementing more advanced tracking algorithms. (shrink)

India is grappling with a serious challenge in managing electronic waste, producing over 1.71 million metric tons annually. When improperly handled, this waste contributes to significant environmental damage and public health hazards. Existing disposal methods lack comprehensive integration, making it difficult for both individuals and organizations to locate trustworthy e-waste recycling facilities. This paper proposes a solution in the form of the E-Waste Facility Locator, a web-based platform that aggregates e-waste recycling centers across India, enabling users to conveniently find and (...) access these facilities. Beyond locating services, the platform also provides educational resources on responsible e-waste disposal practices and a centralized repository of regulatory information encouraging compliance. This paper details the development, testing, and deployment processes, underscoring the platform’s potential to facilitate sustainable e-waste management and safeguard public health. (shrink)

. In the high-stakes environments of mining and construction, the efficiency of loading operations is crucial for maximizing productivity and ensuring safety. Shovel operators often rely on estimates or indirect cues to gauge the load status of haul trucks, leading to challenges such as overloading, underloading , and increased operational costs. Introducing real-time visibility for dumper load status can significantly transform this process. By equipping haul trucks with sensors and data transmission technology, operators gain immediate access to accurate load information, (...) allowing them to make informed decisions on the fly. This capability ensures that trucks are filled to capacity without exceeding weight limits, effectively reducing wait times and streamlining operations. Furthermore, real-time visibility enhances communication between shovel operators and dumper drivers, fostering collaboration and minimizing miscommunication. The operational improvements also lead to substantial cost savings by reducing wear and tear on equipment and improving fuel efficiency. Ultimately, the integration of real-time visibility not only boosts productivity and safety but also represents a modern advancement that paves the way for a more efficient and cooperative future in the mining and construction industries. (shrink)

Taste is an important aspect for the assessment of medicinal plants as such assessment helps to determine the therapeutic property and application of medicinal plants. Traditionally, plant taste has been assessed based on human sensory perception. This project aims at developing a machine learning (ML) model that will quantify and predict plant taste in terms of their chemical composition. Given the dataset of chemical compounds, the model will relate a specific compound to the known taste types: sweet, bitter, pungent, sour, (...) salty. The methodology in this encompasses data preprocessing, feature extraction, and supervised learning techniques over the preparation of the inference model. Major steps include training over labeled data and validating the accuracy over cross-validation. In addition, techniques of web scraping and API integration are used in order to expand the dataset with the properties of medicinal plants from a chemical standpoint. The last model will be a tool for researchers and practitioners within the field of pharmacology and herbal medicine so as to have a more rapid and more objective evaluation of plant taste, which could thus be useful in identifying new applications of medicinal interest. (shrink)

This paper presents a Human Emotion Detection system utilizing Convolutional Neural Networks (CNN). The model is trained on facial expression data to classify various human emotions such as happiness, sadness, anger, and surprise. The CNN approach allows the system to automatically learn features that distinguish different emotions. We describe the model architecture, data preprocessing, and training process in detail. Key results demonstrate the system's high accuracy in detecting emotions in real-time applications. This work highlights the potential of CNNs in emotion (...) recognition and its implications for applications in human-computer interaction, security, and behavioral analysis. (shrink)

Abstract. The chemical and petrochemical industries are vital to the global economy, yet they face significant challenges in strategic decision-making due to fragmented, inconsistent, and outdated data. Existing Methods Existing methods for data collection and analysis in the chemical and petrochemical industries often involve using fragmented datasets from individual sources like industry reports, regulatory filings, and market research. These methods typically lack integration, leading to inconsistencies and gaps in the data. Proposed Solution It involves systematic data collection, cleansing, and integration (...) processes, followed by advanced analytics to generate actionable insights. By creating a unified data repository and utilizing predictive and prescriptive analytics, this approach aims to enhance decision-making. -/- . (shrink)

An efficient SMS spam detection system is developed using the Multinomial Naive Bayes (MNB) algorithm. It employs a labeled dataset and extracts features with the term frequency-inverse document frequency (TF- IDF) method. The MNB algorithm classifies messages by modeling term probability distributions. Parameter tuning and pre-processing techniques like text normalization and stop-word removal enhance feature quality. Experimental results show high accuracy, precision, recall, and F1-score, making MNB suitable for real-time applications. The system provides a practical solution for SMS spam detection (...) and enhances mobile communication security. (shrink)

The crop recommendation system employing machine learning methods will be covered in this study. For sustainable agricultural practices to be followed and to increase crop yields, crop advice is crucial. Based on several factors, including nitrogen (N), phosphorus (P), potassium (K), and humidity, we will advise the best crop for the given site. We analyzed various algorithms like KNN, Decision Tree, Random Forest, SVM etc. But based on various accuracy levels we committed to random forest implementation. Means in this paper (...) we are going to implement a crop recommendation system using random forest algorithm. The model is allowed to train upon a large dataset and the Performance of the recommendation system is measured using accuracy score. Finally, Using the trained model we are going to predict suitable crops for land according to the given parameters. Our proposed approach can be helpful for farmers, researchers, and policymakers in making informed decisions regarding crop management and planning. -/- . (shrink)