Web Application Projects – ElysiumPro

The CarGo Rental System is a modern, full-featured web application developed with the Python-Flask framework, designed to streamline the vehicle rental process for both users and administrators. The platform supports a dual-user architecture, providing distinct interfaces for public users and site administrators. Users can register, log in, and browse a comprehensive catalog of vehicles, which are filterable by name and price range. The system uniquely categorizes vehicles into "Single Car" and "Bulk Booking" options, with the latter enforcing a minimum rental count. The user dashboard features a "My Orders" page where bookings can be tracked through their lifecycle—from Pending to Confirmed and Completed—and offers convenience features such as one-click rebooking and the ability to "pin" favourite rental configurations. Post-trip, an admin-prompted feedback system allows users to rate their experience, which contributes to a driver rating system and earns the user loyalty points, gamifying the rental experience.The administrator's backend is a robust management hub, featuring a dynamic dashboard that visualizes key performance indicators like total revenue, most-booked vehicles, and top-rated drivers. Admins have full CRUD (Create, Read, Update, Delete) capabilities over the car inventory and can manage all incoming booking requests, including driver assignment

Know Trade is an innovative peer-to-peer skill exchange platform that empowers individuals to both teach and learn new skills through mutual sharing. The system enables users to register, create personal profiles, and list the skills they can offer as well as those they wish to acquire. A smart skill-matching algorithm pairs users based on complementary teaching and learning preferences, promoting efficient and meaningful exchanges. Once matched, users can initiate contact, exchange messages through an in-app chat, and schedule learning sessions via text, video call, or in-person meetups. The platform supports seamless interaction by integrating scheduling tools and communication features, ensuring a flexible and user-friendly experience. Each learning session is followed by a feedback and rating system that enhances future matching quality and builds trust within the community. Users can also track their learning and teaching progress over time, earning badges and rewards that celebrate their engagement and development. Know Trade fosters a collaborative learning environment where knowledge becomes a shared currency, making skill-building more accessible, affordable, and community-driven.

FoodRescue Connect is a collaborative digital platform designed to bridge the gap between food surplus and hunger by connecting restaurants, donors, and charitable organizations. The platform allows restaurants and food providers to register and easily post surplus food donations, including key details such as food type, quantity, expiration, and pickup logistics.Donations are matched with nearby charities or individuals in need based on factors like location and food suitability. Recipients can coordinate with donors to schedule pickups or deliveries, while volunteers may also participate by assisting with food transportation.The platform enhances coordination through alerts, pickup reminders, and status updates. It incorporates optional verification and review systems to maintain food quality and build trust among users. Additionally, FoodRescue Connect offers tools for tracking the volume of food rescued, waste reduction metrics, and the number of people served—providing impact reports for participating organizations.Beyond logistics, the platform fosters community engagement by sharing educational content on food waste reduction, creative leftover recipes, and inspiring success stories. FoodRescue Connect offers a sustainable and community-driven solution to combat food waste while supporting food-insecure populations.

SmartShift AI is an intelligent workforce scheduling platform designed to streamline and optimize shift planning through advanced artificial intelligence. The system supports role-based account creation for admins, managers, and employees, enabling clear definition of permissions and responsibilities within the organization. The platform collects detailed employee information, including skills, availability, preferences, and work constraints, while maintaining historical data on attendance and performance. Managers can define shift requirements such as timing, workload needs, staffing minimums, and compliance with labor regulations. Using this data, SmartShift AI generates optimized shift schedules that align with both organizational demands and employee preferences. The AI engine leverages historical patterns and business rules to produce efficient, balanced schedules with minimal human intervention. The platform also facilitates flexible workforce management by allowing employees to request shift swaps or time off.

GreenGuardian is a civic engagement platform designed to empower residents to take an active role in improving the safety, cleanliness, and sustainability of urban spaces. By enabling citizens to report environmental hazards—such as potholes, broken streetlights, illegal dumping, graffiti, and damaged infrastructure—the platform fosters a collaborative approach to urban maintenance and community-driven improvement. Users register and set their preferred local areas to receive updates relevant to their neighborhoods. Hazard reporting is made simple and accessible: residents can submit photos, tag locations using an interactive map, categorize the type of issue, and provide optional descriptions or urgency levels. Community participation enhances the accuracy and prioritization of reports, as nearby users can confirm issues, contribute additional information or images, and upvote urgent concerns. These inputs are channeled into a centralized dashboard accessible by local authorities, utility providers, and maintenance teams, who can sort, assign, and manage repair or cleanup tasks efficiently. The platform incentivizes civic participation through gamified elements such as points, badges, and leaderboards for users who actively report, verify, or participate in community efforts. Additionally, analytics and reporting features aggregate data to identify recurring problem areas, supporting data-driven decision-making for city planners and promoting transparency. By streamlining citizen reporting and strengthening collaboration between communities and municipal bodies, GreenGuardian enhances urban livability while fostering a culture of shared responsibility, civic pride, and proactive city care.

EduCycle is an innovative platform designed specifically for students to facilitate the buying, selling, and exchanging of college-related products and resources. The platform enables students to sell their usable devices and study materials—such as calculators, laptops, books, and more—at half their original price, making essential academic tools more affordable and accessible. In addition to direct sales, EduCycle offers an exchange system where students can trade items, helping them acquire needed resources without additional cost. This peer-to-peer marketplace encourages sustainability by promoting reuse and reducing waste within the student community. By providing a secure and user-friendly online environment, EduCycle empowers students to manage their academic resources efficiently, save money, and connect with peers facing similar needs. The platform supports seamless communication between buyers and sellers and includes features to verify product conditions, ensuring trust and reliability. EduCycle’s mission is to create a supportive ecosystem where students can easily share, exchange, and sell academic tools, fostering collaboration and reducing financial burdens. This initiative ultimately helps improve educational access and resource utilization, contributing to a more sustainable and connected student community.

The Campus Event Management System is a dynamic and comprehensive web application engineered to fundamentally overhaul and digitize the process of organizing and participating in college events. This project addresses the nefficiencies and communication gaps inherent in traditional, manual systems by providing a centralized, intuitive, and feature-rich digital platform. It operates on a sophisticated dual-user architecture, meticulously designed to serve the distinct roles and responsibilities of both Administrators and Students, thereby creating a seamless bridge between the college administration and its student body. The Administrator module acts as the system's command center, granting full control over the entire event lifecycle from inception to completion. Authorized administrators can effortlessly create, modify, and delete events across a wide spectrum of categories, including academic workshops, technical seminars, sports competitions, and cultural festivals, populating the platform with engaging opportunities. For each event, they gain access to a dedicated participants page, which presents a clear, tabular view of all student applicants along with their essential details. A critical function within this module is the ability to manage registrations by confirming applications with a single click, which instantly updates the student's status on their end. Furthermore, for robust record-keeping and offline analysis, administrators can export participant data into Excel formats.

This project presents a modern, full-stack web application designed to enhance the e-learning experience by creating a dynamic and interactive ecosystem for students, teachers, and administrators. Built using Python with the Flask framework and backed by a flexible MongoDB database, the platform addresses key aspects of online education, from content delivery to performance analytics and AI-driven personalization. The core of the system revolves around three distinct user roles with tailored functionalities. Students can register, browse a catalog of courses, and enroll in subjects relevant to their studies. The platform empowers them to engage directly with course material by watching uploaded video lectures, providing star ratings, and submitting detailed textual feedback. A unique "Doubt Session" feature allows students to ask specific questions, optionally with screenshots, creating a direct line of communication with instructors. To foster career growth, an integrated AI, powered by the OpenRouter API, generates personalized recommendations for advanced skills and topics based on the student's department and year of study.For teachers, the application serves as a comprehensive management and feedback hub.

Innovara is a community-driven web platform designed to foster local innovation and collaboration by connecting people within neighborhoods, streets, and colonies. The platform serves as a centralized forum where administrators can post upcoming events related to innovation, skill-building, and community development. Local residents can browse these events, learn about opportunities, and apply directly through the website. By facilitating easy access to relevant local events, Innovara empowers individuals to engage actively in their communities, share ideas, and collaborate on projects that address local needs. The platform encourages inclusivity by targeting grassroots participation, ensuring that even those in smaller localities can benefit from knowledge exchange and networking. Innovara helps bridge gaps between innovators, residents, and organizers, promoting a culture of creativity and problem-solving. Administrators manage and curate events to maintain relevance and quality, while users enjoy a simple interface to discover and participate. Through real-time updates and notifications, residents stay informed and connected. Overall, Innovara aims to strengthen community ties, support local talent, and drive collective progress by leveraging the power of local collaboration. This initiative holds potential to transform neighborhoods into hubs of innovation, enhancing social cohesion and economic opportunities at the grassroots level.

StayWise is a comprehensive digital platform that streamlines student housing management by connecting students, housing administrators, and service providers in one centralized system. It facilitates housing applications, room assignments based on preferences and eligibility, and easy reporting and tracking of maintenance requests. Administrators can efficiently manage occupancy, service requests, billing, and utilities while automating routine tasks like lease renewals and facility notices. The platform also enhances communication through an integrated messaging system and promotes community engagement with features such as announcements, event coordination, and roommate matching based on lifestyle preferences. Feedback tools allow institutions to gather insights for continuous improvement. By digitizing housing operations without using real-time monitoring or location tracking, StayWise prioritizes privacy, ease of use, and a more organized, responsive, and student-friendly living environment that reduces administrative workload and improves the overall residential experience.

Recommender systems (RSs) are essential tools across various domains, including smart tourism, for delivering personalized content by analyzing user preferences and behavior. Despite existing frameworks like Apache Mahout and NReco, developing RSs remains complex, requiring deep expertise in software engineering and machine learning. This paper introduces a Domain-Specific Language (DSL) framework that leverages Model-Driven Engineering (MDE) to simplify and accelerate RS development for smart tourism applications. The framework automates code generation utilizing Apache Mahout’s recommendation algorithms, abstracting technical complexity and reducing the need for extensive programming or ML knowledge. It includes a modeling tool and code generator, enabling developers to focus on high-level design while ensuring system quality. Unlike generic or low-code platforms, this domain-specific approach addresses the unique challenges of smart tourism, enhancing adaptability, productivity, and the quality of recommender systems.

EcoTrack is a privacy-conscious, Python-based application that enables individuals to monitor and reduce their carbon footprint by analyzing their consumption, travel habits, and energy usage—without relying on GPS or real-time location tracking. The platform leverages machine learning, Optical Character Recognition (OCR), and Natural Language Processing (NLP) to process user-provided data and deliver actionable environmental insights. Users can upload images of receipts for groceries, fuel, and travel. OCR tools like Tesseract extract text, while NLP libraries such as spaCy and transformers interpret itemized purchases and assign carbon emission values based on verified environmental impact databases. Travel behavior is entered manually, allowing users to specify transportation modes and distances, which the system uses to estimate emissions without tracking physical movement. Additionally, users can input home energy data such as electricity and gas usage. The application processes this data using pandas and visualizes trends with tools like matplotlib or plotly, offering a clear view of monthly and long-term environmental impact. EcoTrack features a personalized dashboard, tips for sustainable living, gamification elements (such as green points and challenges), and forecasting tools built with scikit-learn to predict future carbon output based on user habits. By combining data analysis with a strong focus on user privacy, EcoTrack provides a smart, secure way for individuals to make environmentally conscious decisions and work toward reducing their ecological footprint.

Ecart is an advanced e-commerce platform that integrates artificial intelligence, ethical retail principles, and user-centric features to promote sustainable and responsible shopping. The platform is built to help consumers make conscious choices by offering transparency, personalization, and incentives that align with eco-friendly values. At the core of Ecart is the Sustainability Score & Impact Badge system, which evaluates products based on carbon footprint, material sustainability, and ethical sourcing. Each product is clearly marked with an eco-rating, allowing users to filter and shop according to their environmental preferences. Ecart enhances the shopping experience with features such as Dynamic Pricing, which adjusts based on inventory and demand, and Social Shopping, allowing users to unlock shared discounts through group purchases. The Personalized Ethical Shopping Assistant recommends items tailored to user-defined values—such as vegan, cruelty-free, or locally produced—across all product categories. To encourage low-waste behavior, the platform offers Waste Reduction Incentives, such as rewards for selecting eco-packaging or recycling returned materials. The AI-Driven Return Prediction engine reduces unnecessary returns by analyzing product attributes and customer behavior to offer more accurate recommendations.

BookBite is a user-friendly platform that provides instant summaries of book content, making reading more efficient and purposeful for readers. It distills the essential ideas and key points from books into concise, easy-to-read summaries that highlight the most useful information. This helps readers quickly grasp the core concepts without having to go through the entire book. BookBite is designed to save time while ensuring readers get meaningful insights, aiding in better understanding and retention. Whether for study, work, or personal growth, BookBite makes reading accessible and practical by focusing on what matters most in each book. The platform is ideal for readers who want to maximize their learning with minimal effort, transforming the way people approach reading

The AI-Powered Resume Analyzer & Career Advisor is an advanced platform designed to help job seekers navigate the competitive job market with personalized AI-driven support. It offers three core features: Resume Analysis, Interview Assistance, and Cover Letter Generation. The Resume Analyzer uses Natural Language Processing (NLP) to evaluate the structure, content, and relevance of resumes, identifying skill gaps, keyword omissions, and formatting issues to optimize them for better screening results. The Interview Assistant simulates realistic interviews tailored to the user’s industry and role, providing common and specific questions, evaluating responses, and giving feedback to improve communication and confidence. The Cover Letter Generator creates customized, professional cover letters by integrating user details, job descriptions, and company data, enhancing candidates’ chances of catching employers’ attention. By combining AI automation with expert insights, this platform streamlines job applications and career preparation, making the process more efficient, personalized, and accessible. Ultimately, it empowers users to create compelling application materials, master interview skills, and boost their employability in a data-driven job market.

The Mind Map Study Scheduler is an interactive, web-based application designed to empower students and learners with a structured, visual approach to planning and managing their study routines. Centered around mind map principles, the system enables users to input subjects along with estimated effort (in hours), priority levels, and available free time. It then intelligently generates a personalized, session-by-session schedule that visually maps out the study plan through dynamic radial, bubble, and timeline charts. Evolving beyond a one-time schedule generator, the enhanced platform now supports persistent, user-specific study management. With secure user authentication and robust backend integration via Flask and SQLAlchemy, users can create, edit, and manage multiple study schedules. Each session can be tracked in real time, with statuses such as completed, skipped, or partially completed. This data feeds into an integrated analytics dashboard, offering insights into planned versus actual study time, completion rates, and subject-wise performance. The system supports PDF export of schedules and emphasizes continuous improvement by helping users visualize their study patterns and adjust accordingly. By blending intelligent scheduling with engaging visualizations and progress analytics, the MindMap Study Scheduler transforms how users plan, execute, and refine their learning strategies.

ProjectNest is an interactive online platform designed to facilitate a structured, stage-by-stage project development process between students and mentors. The collaboration begins at Level 1, where students propose and confirm their project titles with their mentors, setting a clear direction. At Level 2, students upload their project abstracts for review, allowing mentors to provide detailed feedback and request corrections through the platform. Subsequent levels continue this iterative process, guiding students to develop their projects progressively while receiving timely mentor support and suggestions. ProjectNest promotes clear communication, accountability, and transparency by documenting each stage’s submissions and mentor comments. The platform’s workflow ensures that projects advance methodically, reducing confusion and improving the quality of work. Notifications and deadlines help keep both students and mentors on track throughout the project lifecycle. With a user-friendly interface, ProjectNest simplifies the exchange of ideas and revisions, making collaboration efficient and organized. Ultimately, the system empowers students to refine their projects step-by-step under expert mentorship, leading to successful and well-managed project outcomes.

This study explores how automation and machine learning technologies can streamline medical claims management, reducing errors and processing times. By integrating intelligent algorithms, the system automates data extraction, validation, and fraud detection, improving accuracy and operational efficiency. The approach minimizes manual intervention and accelerates claim settlements. Machine learning models adapt over time to evolving patterns in claims data, ensuring continuous improvement. This leads to cost savings for healthcare providers and insurers while enhancing patient satisfaction. Overall, the paper demonstrates the transformative impact of AI-driven automation in healthcare administration.

This work presents an AI-powered system designed to analyze resumes and match candidates with suitable job openings effectively. Natural language processing (NLP) techniques extract key skills, experiences, and qualifications from resumes. Machine learning models then assess compatibility between candidate profiles and job requirements. The system enhances recruitment speed, reduces human bias, and improves hiring accuracy by prioritizing the best-fit candidates. Additionally, the platform supports dynamic learning to refine matches based on recruiter feedback. This approach revolutionizes talent acquisition by leveraging AI to optimize both employer and applicant experiences.

This research introduces an automated ensemble multimodal machine learning framework designed to integrate diverse healthcare data sources, such as medical images, clinical records, and genomics. The ensemble approach combines multiple model predictions to improve diagnostic accuracy and patient outcome predictions. Automation simplifies model selection, feature extraction, and training processes, enabling scalable application in clinical settings. By harnessing heterogeneous data, the framework supports comprehensive analysis and personalized treatment strategies. The study underscores the potential of multimodal ML to advance precision medicine and healthcare delivery.

This paper describes a machine learning-powered chatbot designed to automate responses to college-related enquiries. The chatbot uses NLP to understand and respond accurately to student questions regarding admissions, courses, fees, and campus facilities. Its learning capabilities enable continuous improvement by analyzing interaction patterns and feedback. The system reduces administrative workload and enhances student engagement by providing instant, 24/7 support. It also facilitates data collection on common enquiries to inform institutional decision-making. Overall, the chatbot improves communication efficiency within educational institutions.

This study proposes an AI-driven personalized learning application that tailors educational content based on individual student profiles and learning behaviors. Machine learning algorithms analyze student performance, preferences, and engagement metrics to recommend customized study plans and resources. The app adapts in real-time to optimize learning outcomes and maintain motivation. It supports diverse learning styles and identifies knowledge gaps for targeted interventions. By leveraging AI, the application enhances student autonomy and academic success. This approach exemplifies the integration of technology in modern education.

This project develops a machine learning-based chat application aimed at providing mental health support through conversational agents. The app uses NLP to recognize emotional states and distress signals in user messages, offering empathetic responses and coping strategies. It can direct users to professional resources or emergency contacts if necessary. The system continuously learns from interactions to improve accuracy and personalize support. This tool addresses accessibility barriers in mental healthcare by offering discreet, immediate assistance. The study highlights the role of AI in augmenting mental health services.

This paper introduces an AI-driven virtual try-on application leveraging image recognition to enhance online shopping experiences. Users can virtually try clothing or accessories by overlaying items on their images or live camera feeds. Machine learning models ensure accurate fit, size, and style recommendations. The app reduces return rates and increases customer satisfaction by providing realistic previews. It also supports personalized suggestions based on user preferences and body metrics. This technology bridges the gap between physical and digital retail, driving ecommerce innovation.

This research presents a machine learning-based application for predicting real estate prices and recommending properties tailored to user preferences. The system analyzes historical price trends, location data, property features, and market conditions. Predictive models offer accurate price estimates, while recommendation engines suggest optimal properties based on buyer requirements. The app supports informed decision-making for buyers, sellers, and investors. It continuously updates predictions with new market data, ensuring relevance. This solution leverages AI to simplify complex real estate transactions.

This study develops a personalized fitness and diet recommendation application powered by machine learning algorithms. The app collects user data such as physical activity, dietary habits, health goals, and biometric information. ML models analyze this data to generate customized workout plans and nutritional guidance. The recommendations adapt dynamically based on user progress and feedback to optimize results. The app promotes healthy lifestyles by integrating behavioral insights and scientific knowledge. It provides an accessible tool for users seeking tailored wellness solutions.

This project describes a machine learning-based document scanner capable of extracting text, classifying document types, and generating summaries. Using optical character recognition (OCR) and NLP techniques, the system digitizes and processes documents efficiently. Text classification models categorize content for easy retrieval, while summarization algorithms produce concise overviews. This technology enhances document management by reducing manual effort and improving information accessibility. It is applicable across industries such as legal, healthcare, and education, streamlining workflows.

This paper presents a smart meal identification system that uses computer vision and machine learning to recognize food items and estimate nutritional content. Users capture images of their meals, which the system analyzes to identify ingredients and portion sizes. Nutritional information such as calories, macros, and micronutrients is then calculated. The app supports diet tracking and health management by providing accurate, real-time meal assessments. Continuous learning improves recognition accuracy across diverse cuisines. This innovation empowers users to make informed dietary choices through AI technology.

The online platform is designed to ensure the safety and well-being of its users, offering tailored features for children, women, and counselors. Users securely sign up by providing personal information, which is encrypted and stored in a protected database. A verification email or SMS is sent to confirm user identity, and access is granted based on their role, ensuring personalized experiences. The platform’s dashboard caters to different user groups children can engage with games, mental health resources, and set up emergency contacts women have access to legal rights education, emergency alerts, and support services and counselors can manage appointments, track ongoing sessions, and review session notes. The platform also facilitates real-time text chat between users and counselors, with the option to book sessions and track progress through stored records (with consent). Gamified activities are included to educate users on safety, mental health, and legal rights, with badges and assessments to encourage participation and progress. Emergency alerts and a panic button allow immediate notifications to trusted contacts and local authorities. Data privacy and security are prioritized with strong encryption, and users can control their data, ensuring complete autonomy over their information and privacy settings.

This system connects farmers with local markets by allowing them to register and manage their profiles, crops, pricing, and demand/supply information. The platform provides real-time price updates using machine learning algorithms to forecast price trends, ensuring transparency and fairness in pricing. Farmers can manage their stock, while markets can update demand, facilitating efficient matching of supply and demand. Using historical data, ML models such as Random Forest and Passive Classifier are employed to forecast price trends and help farmers make informed decisions. A web interface enables users to register, upload data, view price trends, track inventory, and access information about government schemes.

This study aims to predict the likelihood of diabetes in individuals using machine learning techniques applied to genomic datasets. By leveraging the power of XGBoost and Gradient Boosting (GB) algorithms, we analyzed genomic data to identify key genetic factors that influence diabetes susceptibility. The dataset used comprises various genetic markers and their corresponding health outcomes, which are utilized to train predictive models. XGBoost, known for its efficiency and high accuracy, and Gradient Boosting, a robust ensemble technique, were employed to build and evaluate the prediction models. The models' performance was assessed using standard metrics such as accuracy, precision, recall, and F1 score. The results demonstrated that both algorithms could effectively predict diabetes risk, with XGBoost outperforming Gradient Boosting in terms of predictive accuracy. This research highlights the potential of integrating genomic data with machine learning algorithms to enhance early diabetes detection and personalized healthcare strategies

This project presents the development of a fully functional Online Chatbot-Based Ticketing System for Museums, aimed at simplifying and automating the museum ticket booking process through a user-friendly conversational interface. The system allows users to interact with a smart chatbot to perform a variety of tasks, including browsing museum information, checking exhibition schedules, viewing ticket prices, and booking tickets—all directly within the chat interface.Developed as a complete web-based application, the chatbot integrates with backend databases to manage user data, booking records, and ticket availability. Users can also book tickets for multiple people (family or friends) in a single session, with secure payment gateway support. The chatbot employs Natural Language Processing (NLP) to understand and respond to user queries effectively, ensuring an intuitive and interactive experience.Additional features include user authentication, booking history management, automated email confirmations, and administrative control for museum staff to manage schedules and ticket inventory. The system is scalable and adaptable, making it suitable for integration with different museums or cultural institutions.This chatbot-based system enhances accessibility, reduces manual workload, and offers a modern digital solution for museum visitor engagement and ticket management.

The Cafe Monitoring System is an innovative web-based application designed to enhance the operational efficiency of cafes. Developed using the Flask framework and controlled by a WAMP server, the system integrates essential features such as crowd detection, inventory management, and customer support, all within a user-friendly dashboard. The application includes a registration and login system for user access, followed by a comprehensive dashboard divided into three key categories: Monitoring, Inventory, and Contact. The Monitoring section provides real-time crowd detection and manages student details, helping cafe owners keep track of the number of customers and student visitors. The Inventory section enables staff to monitor food-related data, track stock levels, and make suggestions for ordering based on popular items. The Contact section offers a streamlined way for customers to reach out to cafe management for support or inquiries.

The Personalized Learning Platform aims to create an adaptive and engaging educational experience by tailoring content to individual learner needs. Through detailed user profiles, it tracks learning goals, preferred pace, and styles, ensuring a customized journey. Immediate feedback and assessments empower learners to stay on track, while dynamic progress tracking and analytics provide insights into strengths and areas for improvement. Gamification elements, such as badges and leaderboards, enhance motivation and engagement. With mobile accessibility and cross-platform compatibility, the platform offers flexibility, allowing students to learn anytime, anywhere. Built with Python, MySQL, and Flask, the platform provides a seamless and interactive experience for learners.

Efficient grocery management is essential for reducing waste, saving time, and optimizing household or commercial inventory control. This paper presents an automated grocery management system designed to streamline inventory tracking, shopping list generation, and expense monitoring. By utilizing barcode scanning and smart algorithms, the system provides real-time updates on stock levels, expiry dates, and usage patterns. Users receive alerts for low-stock items, can automate reordering from preferred suppliers, and access detailed reports on consumption and spending trends. Integration with mobile applications and cloud-based platforms ensures seamless user interaction and data synchronization across multiple devices. The automated grocery management system aims to enhance accuracy, minimize food waste, and optimize budget management, contributing to a more organized, efficient, and convenient grocery shopping experience.

The Blood Bank Management System is designed to streamline and automate the processes involved in blood donation, storage, and distribution. The system aims to facilitate the management of blood donors, recipients, inventory tracking, and ensure efficient handling of blood donations. By providing a user-friendly interface for both administrators and donors, it helps to maintain accurate records, track blood availability, and ensure timely delivery to hospitals and clinics. The system encompasses key features such as donor registration, blood group classification, inventory management, and real-time blood stock monitoring. Additionally, it can send alerts for low stock levels or upcoming blood expiration dates. The system improves the overall efficiency of blood banks, enhances transparency in operations, and supports quick decision-making in critical situations. Through the use of technology, the Blood Bank Management System aims to contribute to saving lives by ensuring an organized and reliable blood supply.

The Teacher’s Diary Website is designed to enhance the professional management and development of educators by providing a comprehensive digital platform for logging and tracking teaching-related activities. The system facilitates user registration and secure login for teachers, ensuring personalized access to their dashboard, which offers an overview of key activities, upcoming seminars, workshops, and recent logs.Teachers can efficiently record various activities, including teaching methodologies, participation in academic seminars, administrative duties, and professional development tasks. Activities can be categorized, tagged, and detailed with relevant information, enabling easy organization and retrieval. The platform supports editing, deleting, and searching for specific activities, enhancing user flexibility and control.An integral feature of the system is the automated generation of activity summaries and reports over customizable time frames. Teachers can export these reports in PDF format for record-keeping or professional presentations, detailing activity types, durations, and key achievements.Additionally, the Teacher’s Diary Website offers comprehensive profile management, allowing teachers to update their personal and professional details, configure notifications and reminders, and control activity visibility for privacy and administrative sharing purposes.This platform aims to streamline teachers’ administrative tasks, support professional growth, and ensure efficient documentation of educational activities, ultimately fostering an organized and reflective teaching environment.

The Online Exam Cheating Detection System is designed to maintain the integrity of remote assessments by using both screen activity monitoring and facial recognition technology to monitor the student's actions during the exam.It tracks the student’s screen activity, specifically looking for attempts to minimize the exam window or switch to different tabs, which are often indicators of potential cheating. If such actions are detected, the system triggers a warning pop-up that notifies the student about the breach of rules. If the student ignores this warning, the system automatically logs them out of the exam, preventing any further dishonest behavior. Additionally, the system uses the camera to continuously track the student’s face throughout the exam. Facial recognition technology ensures that the student remains focused on th screen.If the system detects the student looking away or turning their head, it triggers another warning pop-up to prompt the student to refocus. This dual-monitoring approach—covering both screen activity and behavior—creates a secure and controlled testing environment, making it harder for students to cheat or engage in dishonest practices during online assessments.

Expensify is a comprehensive personal finance tracker designed to help users manage their income, expenses, and financial goals. The application allows users to securely register, log in, and track their daily financial activities, categorizing expenses and income. It features budget management, where users can set monthly limits for various categories and receive alerts when nearing their budget. The platform also leverages machine learning to provide predictive analysis, offering personalized forecasts based on historical spending data. Data visualization tools, such as charts and graphs, help users understand their financial habits and generate detailed reports. Cloud-based notifications keep users informed about important financial events, like bill payments or budget thresholds. Additionally, the platform supports goal setting and progress tracking, helping users achieve their financial objectives. Admin functionalities allow platform monitoring and user management. The system ensures data security with encrypted storage, role-based access controls, and periodic backups, offering a safe and efficient way for users to manage their financial health. The backend is built using Python and Flask, while the web application is optimized for modern browsers like Chrome or Edge.

This career guidance system project aims to provide personalized career recommendations to students based on their skill sets, educational backgrounds, and interests. The system classifies careers into two main categories: technical and non-technical, with further subdivisions such as Artificial Intelligence, Data Science, Computer Science, and other technical fields, as well as non-technical roles. By leveraging an SQLite database, the system stores detailed information about various careers, skills, and associated educational courses. It allows users to input their high school completion status and skill sets, then matches these inputs with appropriate career paths and course suggestions. The system offers guidance on further studies, recommending specific college courses and certifications, ensuring that each user receives targeted career advice based on their individual profile. This structured, data-driven approach aims to empower students in making informed decisions about their academic and professional futures.

The Smart Notice Board System for Educational Institutions is a comprehensive web-based platform designed to streamline communication between the administration, students, parents, and external guests. The system features distinct modules for each user role: Admin, Student, Parent, and Guest. Admins can securely log in to create, update, delete, and manage notices, send targeted email notifications, and handle feedback or queries. Students and parents can log in to view personalized, categorized notices, search for relevant updates, and submit feedback. The system also supports event-specific notices for guests, with RSVP functionalities. The platform allows for a seamless user experience with features like offline mode and notice categorization by year. Built with Python, Flask, and designed for use in Chrome or Edge browsers, the system ensures efficient communication within educational institutions.

This project, developed under the name EduSpark, is an interactive and student-centric web platform designed to enhance learning through engaging tools and features. It includes multiple modules such as a Skill Quiz System with career guidance, a Certification Generator, a Student Project Showcase, and a Hosting Services Page for student portfolios. Each page is designed with a professional aesthetic, featuring a responsive layout, a soft pale pink to light blue gradient background, and a consistent navigation bar highlighting the EduSpark brand with its logo. The platform also features a 3x3 Sliding Puzzle Game as an educational and recreational element. Overall, EduSpark integrates functionality with polished UI design to provide students with a seamless and supportive learning experience.

The Art Gallery Management System is a robust and efficient platform designed to streamline the entire operation of an art gallery, catering to the needs of both administrators and users (visitors and artists). Admins are equipped with tools to securely log in and manage various tasks, such as adding, updating, or removing artist profiles, cataloging artworks, tracking inventory, and scheduling exhibitions. They can also handle sales transactions, generate reports, manage customer inquiries, and plan events. . The system also supports virtual exhibition tours and detailed exhibition views. Built using Python as the backend language and Flask for the frontend, the platform offers an intuitive experience across popular web browsers like Chrome and Edge. The entire system is designed to enhance gallery operations, improve user engagement, and facilitate seamless interactions between artists, visitors, and administrators.

Effective waste management is crucial for sustainable urban development, especially in complex environments like Delhi-NCR. This study introduces WasteIQNet, an intelligent deep hybrid model designed for fine-grained waste classification across 18 categories under Wet and Dry types. The model combines MobileNetV3 for semantic feature extraction with GraphSAGE to capture structural relationships among images. To tackle class imbalance and feature sparsity, WasteIQNet incorporates Feature-wise Attention, Top-K Mixture of Experts, and advanced regularization methods such as Dynamic Sparse Training and Model-Agnostic Meta-Learning. A novel Hierarchical Tree Loss function further improves classification by penalizing semantically distant errors based on waste hierarchy. Trained on the large-scale WEDR dataset containing 360,000 images, WasteIQNet achieves a classification accuracy of 97.87%, showing enhanced interpretability and generalization. This framework offers a scalable, robust solution ready for real-time deployment in smart city waste segregation programs.

The Online Question Paper Generation System is an advanced software solution designed to automate and optimize the creation of exam question papers. Targeted at educational institutions, this system simplifies the process by providing an organized question bank, allowing teachers to select and customize questions based on subject, topic, and difficulty level. With features like randomization of questions to ensure unique papers for each student, the system promotes fairness and reduces cheating. The platform also allows for secure teacher login and provides tools to manage the question bank, including adding, updating, or deleting questions. The system incorporates three key user modules: Staff, HOD (Head of Department), and Students. Staff members are responsible for generating the question papers and submitting them for HOD approval. The HOD reviews the generated papers and grants or denies access for students to view the finalized question paper. Only after the HOD’s approval are the question papers made available to the students. Both staff and students are required to register and log in to access the system securely.

The "Movie Review Application" is a web-based platform developed using Flask that allows both admins and users to interact with movie data in a dynamic way. The admin has the ability to add movies to the platform, including uploading posters, adding movie details, and categorizing movies by genre. Users can browse the list of movies, view their posters and descriptions, and post reviews or comments about their movie experience. The application also includes a special feature called "Collections," where users can search for a specific movie by name. Upon searching, the system displays the requested movie along with related movies from the same genre, helping users discover similar films. This system provides an engaging experience by combining movie exploration, user feedback, and personalized recommendations based on genres, all within a user-friendly interface built with Flask.

The "Job Application Assistant" project is a web-based application developed using Flask that assists job seekers in managing and streamlining their job application process. The system automates key tasks such as tracking job applications, generating customized resumes and cover letters, and sending applications to multiple employers through integrated platforms. By leveraging user input, such as job preferences, qualifications, and experience, the assistant generates tailored application documents and provides recommendations for suitable job openings from various sources. The Flask application acts as a lightweight backend for handling user data, sending notifications, and managing application statuses. Additionally, the system can offer insights and tips on improving job applications based on the user’s profile, increasing the chances of success. This assistant simplifies the job search process, helping users stay organized and focused on their career goals.

The "AI-powered Class Timetable Generator with Chatbot" project aims to develop an intelligent system for automatically generating class timetables using Python, Flask, and an integrated chatbot. The system is designed to create optimized timetables that include the time, subject, and teacher name for each class. In cases where a teacher is unavailable, the system will automatically assign an alternative staff member to ensure continuity. The chatbot interface allows users—such as administrators or students—to interact with the system, request timetable information, or even make adjustments in real-time. Leveraging machine learning algorithms, the system learns from historical data to optimize timetables based on teacher availability, subject requirements, and student preferences. Flask is used to create a seamless web interface, allowing users to view, update, or modify the timetable effortlessly. This AI-powered solution aims to streamline the scheduling process, reduce administrative workload, and ensure an efficient educational experience for both students and staff.

This project implements a distributed learning management system using Python and Flask, featuring core functionalities such as course browsing, user registration and login, course enrollment, quizzes, and a static About Us page. The backend initializes with dummy course data stored in SQLite and supports user authentication with secure password hashing and session management. Users can view both free and paid courses, enroll after logging in, and take associated quizzes, with their results recorded and displayed. The system includes a user profile page showing enrolled courses and quiz performance, while the frontend presents a clean, navigable interface for course selection, quiz taking, and accessing platform information, all developed for ease of use within a non-real-time, dummy payment environment.