International Journal of AI EBioMedicine Innovations
An Explainable Federated Vision Transformer Framework for Privacy-Preserving Multi-Modal Alzheimer's Disease Diagnosis Using Distributed Healthcare Data
Alzheimer's Disease (AD) is one of the most prevalent neurodegenerative disorders affecting millions of elderly individuals worldwide, causing progressive cognitive decline, memory impairment, behavioral abnormalities, and irreversible neurological damage. Early diagnosis of Alzheimer's Disease is critical for initiating timely therapeutic interventions, slowing disease progression, improving patient quality of life, and reducing healthcare costs. Recent advances in Artificial Intelligence (AI), particularly Deep Learning, have significantly improved the automated diagnosis of Alzheimer's Disease using neuroimaging modalities such as Magnetic Resonance Imaging (MRI), Positron Emission Tomography (PET), Computed Tomography (CT), and clinical biomarkers. However, developing highly accurate diagnostic models requires access to large-scale, diverse, and representative healthcare datasets distributed across multiple hospitals. Due to stringent healthcare privacy regulations, including HIPAA and GDPR, direct sharing of sensitive patient data among healthcare institutions is often prohibited, limiting the development of generalized diagnostic models. This paper proposes an Explainable Federated Vision Transformer Framework for privacy-preserving multi-modal Alzheimer's Disease diagnosis using distributed healthcare data. The proposed framework integrates Federated Learning, Vision Transformers, multi-modal feature fusion, explainable artificial intelligence using attention visualization and Grad-CAM, differential privacy, and secure model aggregation to establish a collaborative diagnostic architecture that preserves patient privacy while maintaining high diagnostic accuracy. Experimental evaluation conducted on benchmark Alzheimer's Disease datasets demonstrates that the proposed framework significantly improves classification accuracy, model interpretability, federated learning efficiency, privacy preservation, and generalization capability compared with conventional centralized deep learning approaches. The proposed architecture provides a trustworthy and scalable solution for next-generation intelligent healthcare systems supporting collaborative neurological disease diagnosis across distributed medical institutions.
Copyright (c) 2026 https://creativecommons.org/licenses/by-nc-nd/4.0
An Intelligent Federated Learning Framework for PrivacyAware Threat Detection in Secure Cloud Computing Environments
The widespread adoption of cloud computing has enabled organizations to deliver scalable digital services and manage distributed computing resources efficiently. However, the continuous exchange of sensitive information across cloud infrastructures has intensified concerns regarding data confidentiality, user privacy, and cyber resilience. Conventional machine learningbased security solutions often rely on centralized data collection, which increases the risk of unauthorized data exposure, regulatory non-compliance, and privacy breaches. These limitations highlight the need for collaborative security mechanisms that can learn from distributed data while preserving its confidentiality.This study proposes an intelligent federated learning framework for privacy-aware cloud security that enables multiple cloud participants to jointly develop an effective threat detection model without sharing their raw datasets. The proposed framework performs local model training at each participating node and securely exchanges only encrypted model parameters for global model aggregation. This decentralized learning strategy minimizes data transmission, protects sensitive information from external disclosure, and strengthens compliance with modern privacy requirements. Machine learning techniques are employed to recognize malicious behaviors, abnormal access patterns, and emerging cyber threats by learning from diverse security events generated across distributed cloud environments. The framework further incorporates secure aggregation and iterative model optimization to improve learning efficiency while maintaining data integrity and model robustness.The effectiveness of the proposed approach is assessed using standard performance metrics, including accuracy, precision, recall, F1-score, and communication efficiency, to evaluate both security performance and collaborative learning capability. Experimental analysis demonstrates that the federated learning framework achieves reliable threat detection while significantly reducing privacy risks associated with centralized data processing. By combining decentralized artificial intelligence, privacy-preserving model training, and collaborative threat intelligence, the proposed framework provides a scalable and secure solution for protecting modern cloud infrastructures against evolving cyber threats without compromising the confidentiality of organizational data.
Online Classroom System with Course Management Assignment Grading and Attendance Tracking
The research presents a full-stack web-based Online Classroom Management System (OCMS) developed to automate and digitalize academic classroom activities through a centralized, secure, and efficient platform. The proposed system addresses challenges associated with traditional classroom management, including manual record keeping, delayed communication, inefficient assignment handling, and limited transparency in attendance and academic evaluation. The application is developed using React JavaScript (React JS) to provide a responsive, interactive, and component-based user interface with RoleBased Access Control (RBAC) for administrators and students. Communication between the frontend and backend is achieved using Axios and Representational State Transfer Application Programming Interfaces (REST APIs). The backend is implemented using the Django Representational State Transfer (REST) Framework, which manages user authentication, authorization, business logic, and data processing. My Structured Query Language (MySQL) serves as the relational database for secure and reliable storage of user information, course details, attendance records, assignments, and grades. The platform incorporates an administrator-controlled user approval mechanism to ensure secure access and authenticated participation. Administrators can create courses, upload assignment Portable Document Format (PDF) files, record attendance, evaluate submissions, and assign grades with feedback, while students can enroll in courses, download assignments, upload PDF solutions, and monitor their academic progress through personalized dashboards. The proposed system enhances teaching and learning by automating routine academic processes, reducing administrative workload, improving data accuracy, and ensuring transparency, scalability, and efficient academic management for modern educational institutions.
Hierarchical Probabilistic Learning for Tri-Class Hair Health Analytics and Hormonal Status Identification
Hair-related disorders have become increasingly prevalent due to factors such as unhealthy lifestyles, environmental pollution, genetic predisposition, nutritional deficiencies, and hormonal imbalances. Conventional diagnostic approaches primarily depend on clinical examination and expert interpretation, making early diagnosis challenging and limiting the ability to accurately evaluate the combined influence of multiple health-related factors. To overcome these limitations, this study proposes an Explainable Artificial Intelligence (XAI)-driven framework for intelligent hair health prediction using machine learning and deep learning techniques. The proposed system is implemented using the Flask web framework and incorporates comprehensive data preprocessing, exploratory data analysis, and comparative evaluation of several machine learning models, including Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), KNearest Neighbors (KNN), Gradient Boosting (GB), and AdaBoost (AB). In addition, a novel hybrid model combining a Probabilistic Neural Network (PNN) and a Sparse Representation Classifier (SRC) is developed to improve predictive performance. Experimental evaluation demonstrates that the proposed hybrid model achieves an accuracy of 0.9950, outperforming the baseline classifiers. To enhance model transparency and clinical interpretability, XAI techniques are integrated to identify the most influential prediction factors, estimate individual risk levels, and generate personalized treatment recommendations. Furthermore, the proposed framework supports multiple prediction tasks, including hair loss assessment, treatment recommendation, and hormonal impact analysis, providing an interpretable and reliable decision-support system for intelligent hair healthcare.
An Explainable Multi-Output Learning Framework for Biosensor-Based User Interaction Analysis
The growing emphasis on user-centered product design has increased the demand for objective and automated approaches to evaluate user satisfaction. Conventional assessment methods, including manual surveys and subjective feedback, are often time-consuming, prone to bias, and incapable of providing realtime insights into user experience. To address these limitations, this study presents a Machine Learning (ML)-based framework for predicting user satisfaction using biosensor data. Baseline models, including Decision Tree (DT), Extra Trees (ET), Linear, and Gradient Boosting (GB), are evaluated within the Classification and Regression Trees (CART) framework. To improve predictive performance, the proposed approach integrates Adaptive Boosting (AB) with CART, enhancing both classification and regression capabilities. The framework simultaneously performs interaction duration prediction as a regression task and user satisfaction classification into High and Medium categories. Experimental evaluation demonstrates that the proposed AB-enhanced CART framework achieves superior predictive performance by improving classification accuracy while reducing regression error compared with baseline models. The developed framework provides a scalable and interpretable solution for real-time user satisfaction assessment, enabling designers, manufacturers, and product developers to analyse user interactions more effectively and support the development of user-centric products and interactive systems.
Intelligent Cellular Performance Monitoring and Fault Diagnosis Using Deep Graph Analytics
The continuous evolution of wireless communication technologies, including WiFi, 4G, and 5G, has generated vast amounts of network data that require intelligent analysis to ensure reliable connectivity and efficient communication. Accurate prediction of network characteristics, such as connection type and signal strength, has become increasingly important for maintaining service quality, optimizing resource utilization, and improving user experience. Conventional network monitoring techniques mainly rely on visualization tools that display performance indicators such as latency, throughput, and received signal strength. Although these approaches assist in monitoring network conditions, they provide limited predictive capabilities and require significant manual interpretation. In addition, traditional methods often fail to perform effective data preprocessing, including missing value handling, categorical feature encoding, and data normalization, which can negatively affect prediction accuracy. To overcome these challenges, this work presents a Flask-based web application that integrates machine learning techniques for intelligent network signal analysis and prediction. The proposed framework employs Ridge Classifier (RC), Ridge Regressor (RR), Decision Tree Classifier (DTC), Decision Tree Regressor (DTR), and a Hybrid MLP with Catboost model to perform both classification and regression tasks. Network type identification is formulated as a classification problem, whereas signal strength estimation is treated as a regression problem. The developed system evaluates model performance using accuracy, precision, recall, F1-score, MAE, MSE, RMSE, and R² metrics, enabling comprehensive comparison of predictive models. The proposed framework offers an efficient, scalable, and user-friendly solution for intelligent network performance analysis and future wireless communication applications.
Local Service Finder Platform with Provider Verification Booking and Review Systems
The rapid growth of urbanization has significantly increased the demand for reliable, secure, and easily accessible local services such as electricians, plumbers, mechanics, and cleaning professionals. However, the absence of a centralized platform for identifying verified service providers often leads to trust issues, booking difficulties, and inconsistent service quality. To address these challenges, the Local Service Finder Platform with Provider Verification, Booking, and Review System has been developed as a web-based application that connects customers with authenticated local service providers through a secure and efficient digital environment. The application is developed using React.js for the frontend, Django Representational State Transfer (REST) Framework for the backend, and My Structured Query Language (MySQL) as the database management system. The platform supports three primary user roles: administrator, service provider, and customer. The administrator verifies and approves user and service provider registrations to ensure platform authenticity. Approved service providers can publish service details, pricing information, manage bookings, and review customer feedback. Customers can search available services, book verified providers, and submit ratings and reviews only after successful service completion, thereby preventing fraudulent feedback. A key feature of the platform is its automated quality assurance mechanism, where service providers receiving consistently poor ratings are permanently removed from the platform and restricted from re-registering, ensuring high service standards and user confidence. The system enhances transparency, reliability, and convenience while demonstrating the effective integration of modern frontend technologies, backend Application Programming Interfaces (APIs), and database management techniques to develop a scalable, secure, and user-centric full-stack web application.
Salva Srinivas Phanindhra Chary, Anil Kokku
Geo Tagged Road Damage Reporting System with Citizen Evidence Attached Progress Alerts
Urban road infrastructure plays a vital role in ensuring safe, efficient, and reliable transportation while supporting economic growth and sustainable urban development. However, the increasing number of vehicles, aging road networks, changing weather conditions, and heavy traffic loads have significantly contributed to the deterioration of road surfaces, resulting in potholes, cracks, and various forms of infrastructure damage. Conventional road maintenance systems primarily rely on periodic field inspections, manual surveys, and informal citizen complaints, which often lead to delayed identification of road defects, inconsistent reporting procedures, poor coordination among maintenance departments, and inefficient allocation of repair resources. These shortcomings increase the risk of road accidents, vehicle damage, traffic congestion, higher maintenance costs, and reduced public satisfaction with municipal services. To overcome these challenges, this research proposes a web-based Pothole and Infrastructure Damage Reporter that provides a centralized digital platform for reporting, monitoring, and managing road defects. The system enables citizens to submit infrastructure damage reports with relevant information, while municipal authorities can review complaints, assign maintenance tasks, update repair progress, and monitor service performance through an integrated management interface. Developed using a responsive web architecture with a scalable backend and centralized database, the platform supports efficient data management, real-time status tracking, and structured maintenance workflows. Furthermore, the collected infrastructure data facilitates performance analysis, resource optimization, and evidencebased urban planning. By digitizing the complete reporting and maintenance lifecycle, the proposed system enhances transparency, improves operational efficiency, strengthens citizen participation, accelerates road repair activities, and contributes to safer, smarter, and more sustainable urban infrastructure management.
Cloud Database Maintenance Fee Collection with Secure Complaint Handling
The widespread adoption of cloud computing has significantly increased the demand for secure storage, controlled resource access, and efficient administrative management of organisational information. Conventional cloud-based database platforms frequently depend on centralized security models that provide limited authorisation controls, insufficient activity monitoring, and inadequate protection of sensitive information. These limitations increase the possibility of unauthorised access, data misuse, and inefficient handling of administrative tasks such as maintenance fee collection, payment verification, and grievance management. To address these challenges, this paper presents a Secure Role-Based Cloud Database Maintenance Fee Collection System (CBFCS) developed using the Flask framework, Tiny Database (TinyDB), and a hybrid cryptographic architecture. The proposed solution enforces role-oriented authorization by defining separate privileges for administrators, database managers, and end users, ensuring that each user accesses only permitted resources. Sensitive documents and stored information are protected through a hybrid encryption mechanism that combines Rivest–Shamir–Adleman (RSA) for secure key exchange and Advanced Encryption Standard–Galois/Counter Mode (AES-GCM) for high-performance data encryption and integrity verification. TinyDB serves as a lightweight NoSQL repository for storing user records, encrypted file metadata, payment information, maintenance details, complaint records, and system audit logs. Furthermore, the inclusion of secure complaint processing, transaction tracking, and comprehensive auditing enhances system transparency and accountability. Experimental implementation demonstrates that the proposed framework provides stronger data protection, improved operational reliability, and scalable management for modern cloud database environments.
Biometric Polling Verification Framework with Audit Monitoring Based Offline Validation Support
Elections are a fundamental component of democratic governance, and maintaining their integrity is essential for ensuring transparency, accountability, and public confidence in the electoral process. As voter populations continue to increase, conventional polling systems encounter numerous challenges, including voter impersonation, duplicate voting, manual identity verification errors, lengthy authentication procedures, and inadequate monitoring of polling activities. Traditional election processes rely heavily on physical identity documents, printed electoral rolls, and manual record verification, making them vulnerable to human error, procedural inefficiencies, and fraudulent practices. Furthermore, the absence of real-time identity verification and centralized digital record management limits the ability of election authorities to detect irregularities and maintain accurate audit trails during polling. To address these limitations, the proposed Biometric Polling Assurance System provides a secure digital platform that integrates biometric-based voter authentication with centralized monitoring and structured relational data management. Developed using React for the frontend, Node.js for backend processing, and MySQL for relational database management, the system securely verifies voter identities before vote casting, thereby enforcing the oneperson-one-vote principle. The MySQL database employs normalized tables with primary and foreign key relationships to ensure data consistency, integrity, and efficient storage of election records. The platform also maintains detailed authentication logs and administrative records to support transparency, accountability, and post-election auditing. Role-Based Access Control (RBAC) restricts system access to authorized users, while transaction management ensures accurate processing of concurrent authentication requests. The proposed system enhances election security, operational efficiency, scalability, voter privacy, and the overall credibility of modern electoral processes.
Hybrid Transformer-Based Semantic Feature Learning for Real-Time Political Event Detection from Twitter Streams
The extensive use of Twitter as a real-time social media platform has led to the continuous generation of massive volumes of short-text messages related to political, social, and emergency events. Extracting meaningful insights from these rapidly evolving tweets is a challenging task because of their unstructured format, contextual ambiguity, linguistic diversity, and high velocity. Conventional machine learning techniques often fail to capture the rich semantic and contextual information embedded in short-text data, resulting in reduced event classification performance. To address these limitations, this study proposes an intelligent political event detection framework that integrates SBERTenhanced Lightweight RoBERTa with advanced machine learning algorithms. Initially, the collected tweets undergo comprehensive Natural Language Processing (NLP) preprocessing, including normalization, tokenization, stop-word removal, and lemmatization, to improve textual quality and consistency. Exploratory Data Analysis (EDA) is then performed to analyze data characteristics and identify meaningful linguistic patterns. A lightweight SBERTbased RoBERTa model subsequently generates semantic-rich contextual embeddings that serve as effective feature representations. These embeddings are utilized by multiple classifiers, including Stochastic Gradient Descent (SGD), Histogram Gradient Boosting (HGB), Random Forest Classifier (RFC), and Greedy Tree Classifier (GTC). Furthermore, a Deep Neural Network (DNN) performs intermediate feature learning, followed by an optimized SGD classifier to enhance predictive capability. The proposed framework classifies tweets into six event categories: disaster, political, positive, protest, riot, and terror. Experimental results demonstrate high classification accuracy, robustness, scalability, and strong generalization ability, highlighting the effectiveness of the proposed framework for real-time political event detection and intelligent decision-support applications.
Encrypted Digital Evidence Storage with Controlled Access Verification
The rapid growth of digital technologies, cloud computing, and online communication platforms has significantly increased the volume of digital evidence generated in domains such as cybercrime investigation, law enforcement, forensic analysis, and judicial proceedings. Traditional evidence management relied on physical media and manual verification processes, making evidence handling inefficient, time-consuming, and prone to human error. Although digital evidence management systems have improved storage and retrieval capabilities, many existing solutions still lack robust security mechanisms, fine-grained access control, reliable integrity verification, and comprehensive auditability. These limitations expose sensitive evidence to unauthorized access, data tampering, insider threats, and reduced legal admissibility. This work addresses the challenge of securely storing, managing, and preserving digital evidence within cloud-based environments, where distributed access increases security risks. The proposed framework is implemented as a Flask-based web application that integrates the Advanced Encryption Standard (AES) to ensure evidence confidentiality and the Secure Hash Algorithm (SHA-256) to verify data integrity and detect tampering. Role-Based Access Control (RBAC), request-driven authorization workflows, and detailed audit logging provide secure access management, accountability, and complete traceability throughout the evidence lifecycle. Additionally, a Lightning Memory-Mapped Database (LMDB) is employed to enable highperformance, transaction-consistent storage with efficient read and write operations. The framework delivers a secure, scalable, and efficient solution that enhances evidence confidentiality, integrity, forensic reliability, and overall operational performance in modern digital evidence management systems.
Adaptive Semantic Intelligence for Workforce Experience Attribute Inference Using Transformer Representations
The widespread adoption of digital workplace platforms has enabled employees to share detailed feedback regarding their professional experiences, creating valuable textual resources for understanding organizational performance and workforce satisfaction. Employee reviews provide important insights into workplace culture, career opportunities, compensation, job security, and work-life balance. However, manually analyzing these large volumes of unstructured feedback is inefficient, subjective, and incapable of supporting timely organizational decision-making. Furthermore, conventional text mining and machine learning approaches often struggle to capture contextual semantics, handle imbalanced datasets, and simultaneously predict multiple workforcerelated factors with high accuracy. To address these challenges, this research proposes an intelligent fine-grained opinion mining framework for employee review analysis. The proposed system initially performs comprehensive Natural Language Processing (NLP) preprocessing to improve textual quality, followed by contextual feature extraction using Google Pathways Language Model (PaLM). To mitigate class imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) is employed to generate representative samples for minority classes, thereby improving model generalization. The extracted semantic representations are subsequently processed using the proposed Transformer-Guided Adaptive Model (TGAM) for simultaneous prediction of multiple organizational factors, including work-life balance, skill development, salary and benefits, job security, career growth, and overall work satisfaction. Comparative performance analysis is conducted using Quadratic Discriminant Analysis (QDA), Linear Discriminant Analysis (LDA), and Histogram-Based Gradient Boosting (HGB) as baseline models. Experimental results demonstrate that the proposed TGAM consistently outperforms the existing approaches by achieving superior predictive accuracy across all target variables. The developed framework provides an effective decision-support system for workforce analytics, enabling organizations to understand employee perceptions, improve workplace policies, and support data-driven human resource management.
Cloud Controlled LoRaWAN Communication Smart Agriculture Gateway Load Regulation
Agriculture remains the primary source of livelihood for a significant proportion of the population in developing countries, making the adoption of advanced technologies essential for achieving sustainable and productive farming practices. The integration of engineering innovations with Sustainable Development Goals (SDGs) supports precision agriculture through intelligent soil preparation, automated irrigation, crop nutrient monitoring, smart fertilizer application, and efficient multi-cropping strategies. This study proposes a low-power communication framework for smart agriculture by modelling distributed sensor nodes as a weighted network topology. Two routing approaches are evaluated: the conventional Flooding protocol and a LoRaWAN-based shortest-path routing mechanism implemented using Dijkstra’s algorithm. The comparative analysis focuses on key performance metrics, including communication latency, data accuracy, false alarm rate, and gateway load regulation. Experimental results demonstrate that the Flooding protocol suffers from excessive packet duplication, network congestion, increased hop count, and reduced communication efficiency. In contrast, the proposed LoRaWAN-based routing strategy identifies optimal transmission paths, minimizes communication distance, reduces latency, improves data delivery accuracy, and lowers the false alarm rate while maintaining balanced gateway utilization. The framework is implemented using Python, Flask for webbased interaction, NetworkX for network topology modelling, TinyDB for lightweight data storage, and Matplotlib for performance visualization. The findings confirm that intelligent shortest-path routing enhances communication reliability, scalability, and energy efficiency, providing a robust foundation for sustainable precision agriculture and next-generation smart farming applications.
Event Ticketing Platform Supporting Secure Payments Tickets Categories Booking Management
The rapid expansion of online event services has transformed the way users discover, book, and manage event participation. However, conventional ticket booking systems often rely on manual processes, resulting in inefficient ticket management, booking errors, delayed updates, and limited flexibility for cancellations or schedule modifications. This research presents a Web-Based Event Ticket Booking and Rescheduling System (ETBRS) that streamlines event management through an integrated and automated digital platform. The system enables organizers to create events, define ticket pricing, monitor ticket availability, and efficiently manage event schedules. Users can browse event details, including venue, date, time, and ticket price, and securely reserve tickets through an interactive interface. The application automatically calculates booking costs based on the selected number of tickets and updates ticket availability in real time, thereby preventing overbooking and improving operational efficiency. A key feature of the platform is its support for booking cancellations and event rescheduling. When users cancel reservations, ticket availability is automatically restored, while rescheduling requests are processed through organizer approval workflows based on predefined policies and seat availability. The system can also apply additional rescheduling charges whenever applicable. The application follows a layered architecture comprising a Frontend (UI), Backend (Server-Side Logic), and Database Management System (DBMS) to ensure scalability, maintainability, secure data handling, and reliable performance. The proposed solution enhances user convenience, optimizes organizer operations, improves booking transparency, and provides an efficient, secure, and scalable platform for modern event ticket management.
Student Placement Preparation Hub with Mock Test Tracking and Skill Analytics
The Student Placement Preparation Hub (SPPH) is a comprehensive web-based platform developed to enhance students' readiness for campus recruitment through structured learning, continuous assessment, and performance monitoring. Increasing competition in placement drives demands a centralized system that provides systematic evaluation, timely feedback, and effective collaboration among teachers, students, and parents. Conventional placement preparation methods often rely on fragmented resources, manual record keeping, and limited performance tracking, resulting in inadequate assessment and reduced transparency. The proposed system addresses these limitations by integrating placement-oriented academic activities into a unified digital platform with secure role-based access. Teachers function as administrators to approve registrations, manage attendance, create Multiple Choice Question (MCQ)-based mock tests, and monitor student performance. Students can access attendance records, attempt mock examinations, receive automated evaluations, and analyze their performance through grading and progress reports. Parents are provided with controlled access to monitor their child's attendance, assessment results, and overall placement preparedness, thereby promoting transparency and active participation. The frontend is developed using React JavaScript (React.js) to deliver a responsive and interactive user interface, while the backend is implemented using the Django Representational State Transfer (REST) Framework for secure Application Programming Interface (API) communication and business logic management. My Structured Query Language (MySQL) is employed for reliable storage of user profiles, attendance data, examination records, and evaluation results. Automated assessment and analytical performance summaries enable students to identify strengths and improvement areas, ultimately enhancing placement readiness, academic performance, confidence, and overall employability.
Encrypted Life Insurance Verification Portal with Public Access and Audit Trails
The rapid growth of digital insurance services has increased the need for secure information management, controlled user access, and protected communication among administrators, agents, and clients. Insurance platforms manage highly sensitive data, including customer details, policy information, financial transactions, and confidential documents, making strong security and efficient system management essential. However, many existing insurance management systems rely on conventional web applications with limited security features, manual processing, and inadequate monitoring. These limitations often result in unauthorised access, poor workflow management, insecure document handling, and reduced operational transparency. Furthermore, the lack of encrypted communication, structured audit mechanisms, and role-based authorisation increases security risks and affects overall system reliability. To overcome these challenges, this work presents a secure webbased insurance management system developed using the Flask framework for application development and request handling. Tiny Database (TinyDB) is used to maintain customer information, policy records, encrypted documents, payment details, notifications, communication logs, and audit records. The system implements Role-Based Access Control (RBAC) to provide separate privileges for administrators, insurance agents, and clients. ElGamal encryption is employed to secure file storage and message exchange, while password hashing protects user authentication credentials. Additional features include automated premium calculation, policy comparison, secure file upload and download, notification management, and activity logging. Experimental implementation demonstrates that the proposed framework enhances data confidentiality, operational efficiency, transparency, and secure workflow management, providing a reliable and scalable solution for modern digital insurance platforms.
MindTrack Journal: A Privacy-First Intelligent Journaling System with Emotion Detection
MindTrack Journal is a secure, privacy-first, and intelligent journaling application designed to empower users to express their thoughts while maintaining complete control over their personal data. In a world where data privacy is increasingly important, MindTrack Journal stands out by ensuring that all user data remains private, encrypted, and never shared with third-party services. The application provides a seamless and user-friendly experience for managing personal journals, including core features such as user registration, login, and profile management, along with a rich block-based text editor supporting multiple content types for a more expressive writing experience. What differentiates MindTrack Journal from traditional journaling platforms is its intelligent analytics layer powered by a selfhosted AI service. Each journal entry is automatically analysed for sentiment using state-of-the-art transformerbased Natural Language Processing (NLP) models— specifically, finiteautomata/bertweet-base-sentimentanalysis for sentiment classification and bhadreshsavani/bert-base-uncased-emotion for emotion detection— ensuring that sensitive user data is never transmitted to external services. The results are visualised through an analytics dashboard, offering users meaningful insights into their emotional patterns over time. Security and privacy form the core pillars of the system, implemented via JWT-based stateless sessions validated by Keycloak, BCrypt password hashing, HTTPS/TLS encryption, database encryption at rest, and application-level journal content encryption. The architecture follows the Citadel Architecture pattern, combining Spring Boot 4.x Spring Modulith for core domain workflows with supporting microservices for analytics, payments, notifications, and ML processing. The system is implemented as a full-stack monorepo comprising a React 19/TypeScript frontend, a Java 21/Spring Boot 4.x backend, and a Python/FastAPI ML service, all containerised using Docker and orchestrated via Docker Compose, enabling repeatable deployment with a single command.
A FULL-STACK MERN HOSPITAL MANAGEMENT SYSTEM
The rapid advancement of web technologies and digital health platforms has significantly transformed the medical ecosystem, particularly in the areas of appointment scheduling, automated patient communication, and clinic administration. Patients seeking medical care often face difficulties tracking doctor availability, booking reliable appointments, and securing diagnostic services due to fragmented traditional management systems. Conventional hospital consultancy relies on physical queues, manual front-desk operations, and disjointed payment tracking, which become highly inefficient during peak operational hours. This research presents the design, development, and implemen-tation of a Full-Stack Hospital Management System leveraging the modern MERN stack (MongoDB, Express.js, React.js, and Node.js). The proposed platform provides centralized healthcare administration, distinct rolebased access for Major Admins, Doctors, and Patients, dynamic appointment scheduling, auto-mated welcome email onboarding, and secure online payment workflows. A critical advancement in this architecture is the implementation of robust Stripe payment synchronization, engi-neered to ensure database consistency even if a user immediately closes their browser post-transaction. Additionally, the platform enhances medical documentation by integrating a custom React RichTextEditor utilizing AIChatSession to generate automated, intelligent medical work summaries. Developed utilizing Vite, Tailwind CSS, Clerk for authentica-tion, Cloudinary for asset management, and secure JWT proto-cols, the system guarantees a highly responsive user interface and a scalable backend. Experimental analysis demonstrates that the platform eliminates scheduling overlaps, reduces administrative overhead, ensures seamless financial transactions, and provides an accessible digital healthcare environment.
Market Matrix: A Scalable Full-Stack Stock Trading Simulation Platform Using the MERN Stack and Cloud Architecture
The rapid expansion of digital financial architec-tures has structurally modified global equity environments, replacing brick-and-mortar brokerage structures with high-availability web platforms[cite: 1089, 1106, 1125]. While modern trading ecosystems maximize investor accessibility, their deep interface complexity and associated capital liabil-ities present sharp barriers to entry for novice practitioners trying to understand transactional mechanisms safely[cite: 1091, 1108, 1226]. This paper proposes the engineering details, systemic workflows, and structural evaluations of Market Matrix, a scalable web application that accurately replicates commercial brokerage platforms like Zerodha Kite within a risk-free environment[cite: 1092, 1093, 1111]. Built upon the MERN stack paradigm, the application co-ordinates an interactive React.js presentation layer with a high-throughput Node.js/Express.js service core and a document-oriented MongoDB storage schema[cite: 1092, 1112, 1320]. To isolate transactional risks completely, the system utilizes predefined, deterministic mathematical data structures to simulate market pricing adjustments across distinct asset categories[cite: 1097, 1116, 1383]. Core com-putational processes handle user-specific portfolio indices, account liquidity constraints, and live transactional teleme-try blocks dynamically, executing automated balance rec-onciliation updates instantly upon order completion[cite: 1095, 1113, 1361]. Cloud deployment profiles optimized over Amazon Web Services (AWS) hardware allocations indicate high responsiveness profiles alongside sub-50 ms data processing bounds[cite: 1100, 1120, 1332]. Empirical testing matrices confirm robust structural reliability under concurrency stresses, establishing the open architecture as an entry-grade simulation blueprint for academic software engineering research, financial tech onboarding, and system analysis operations[cite: 1102, 1123, 1987].
TalentSync: A Real-Time Multi-Interview Platform for Technical Hiring
TalentSync is a web-based multi-interview platform designed to simplify and improve the process of conducting technical and behavioural interviews in a remote environment. The platform enables candidates and interviewers to communicate and collaborate in real time through integrated video calling, live chat, and a collaborative code editor. The main objective of the system is to provide an interactive and structured interview experience that closely replicates a real-time face-to-face technical interview process. In many existing online interview systems, organizations often depend on multiple applications for communication, coding assessments, and interview management. This creates difficulties such as communication gaps, inefficient coordination, and lack of proper collaboration during interviews. TalentSync addresses these issues by providing all essential interview features within a single centralized platform. The system allows users to create interview sessions, join meetings securely, communicate effectively, and solve coding problems together in real time. The project is developed using the MERN stack, which includes MongoDB, Express.js, React.js, and Node.js. MongoDB is used for storing user and session data, Express.js and Node.js manage backend operations and APIs, while React.js provides a responsive and userfriendly interface. The platform is scalable, efficient, and suitable for organizations conducting multiple interviews simultaneously.
Supply Chain Forecasting: A Data-Driven Analytical Framework Using Python and Power BI
Supply chain management constitutes one of the most critical and strategically consequential functions of modern retail business operations, encompassing the complete lifecycle of a product from procurement and production through warehousing, distribution, and final delivery to the end customer. In an era of growing market complexity, rapidly shifting consumer expectations, and increasing competitive pressure on profit margins, the ability to extract timely and accurate intelligence from supply chain transactional data has emerged as a defining competitive advantage for retail organizations. This paper presents a comprehensive supply chain analytics study applied to the widely used Sample Superstore dataset, a multi-dimensional transactional dataset representing four years of retail operations across the United States. The study implements an end-to-end data analytics pipeline encompassing raw data ingestion, systematic preprocessing and feature engineering using Python and its scientific ecosystem — including Pandas, NumPy, Matplotlib, and Seaborn — followed by in-depth exploratory data analysis (EDA) and the development of an interactive, multi-page business intelligence dashboard built using Microsoft Power BI. The preprocessing phase engineers five derived analytical features: Delivery Duration, Profit Margin Percentage, Discount Band, Order Year, and Order Month, significantly enriching the dataset’s analytical depth. The EDA reveals several critical findings: a strong and consistent negative correlation (r ≈ −0.22) between discount rate and profit margin that intensifies at discount levels above 40%; stark category-level profitability divergence with Technology generating approximately 17.4% profit margin versus Furniture at only 2.5%; significant regional profitability disparities with the West and East regions outperforming the Central and South; an on-time delivery rate of 81.75% indicating logistics execution gaps; and a 99.37% customer repeat rate indicating strong customer loyalty. The Power BI dashboard translates these findings into sixteen Key Performance Indicators (KPIs) across four pages covering sales performance, profitability analysis, customer behaviour, and logistics operations. Six actionable business recommendations are derived, including the implementation of a 20% discount cap policy, restructuring the Furniture category strategy, adopting a regionally differentiated discount framework, and targeting a 95% on-time delivery rate. The project demonstrates the transformative potential of applied supply chain analytics and establishes a reproducible, scalable framework that organizations can adapt to their own operational datasets.
In modern digital business environments, ecommerce platforms play an important role in providing efficient and accessible online shopping services for customers. Traditional shopping systems mainly depend on physical stores and manual purchasing methods, which often create limitations in accessibility, inventory management, customer convenience, and transaction efficiency. Due to increasing internet usage and growing demand for digital services, it becomes difficult for businesses to manage products, orders, and customer interactions efficiently using conventional methods [1]. The lack of proper online management systems also affects customer satisfaction and operational productivity. Recent advancements in web technologies, database systems, and secure online payment methods have enabled the development of intelligent e-commerce management systems [2]. Modern online shopping platforms allow users to browse products, search and filter items, place orders, and perform secure transactions through responsive web interfaces. These systems improve customer convenience, reduce manual effort, and enhance business management efficiency [3]. However, many existing e-commerce systems mainly focus on product selling and do not provide complete operational functionalities such as centralized admin management, advanced product handling, secure authentication, payment integration, and efficient order tracking. The proposed E-Commerce Web Application is designed as a centralized online shopping platform that integrates product management, secure authentication, shopping cart operations, order processing, and payment gateway integration into one system. The platform provides responsive product browsing and secure transaction facilities using modern MERN stack technologies. It also includes centralized admin dashboard functionalities for managing products, users, orders, and overall business operations.
BusBee: Real-Time School Bus Monitoring System
BusBee is a real-time school bus monitoring system designed to improve the safety and efficiency of school transportation. The system provides live bus tracking using GPS and map integration technologies, helping students, parents, and school authorities monitor bus locations in real time. It also includes complaint management, emergency alerts, expense recording, and maintenance handling features. Drivers can update trip details and live locations, while administrators can manage buses and routes through a centralized dashboard. The system is developed using React.js, Node.js, Express.js, and MongoDB. Overall, BusBee improves communication, reduces manual work, and provides a reliable solution for modern school transportation management.
Oretes Insight System: A Multi-Agent AI Platform for Automated IEEE Research Paper Generation
In the modern academic and professional landscape, producing high-quality, technically rigorous research papers remains a complex and time-intensive process. Researchers and students often face challenges in organizing ideas, retrieving relevant information, and expanding content into well-structured documents that meet formal standards such as the IEEE format. To address these limitations, the Oretes Insight System, implemented as the Auto-Researcher-AI platform, presents an automated solution for end-to-end research and technical report generation. The system adopts a multi-agent architecture that decomposes the research workflow into four specialized components: a Planner for generating structured outlines, a Search Agent for retrieving relevant information, an Analyst for synthesizing and expanding technical content, and a Reviewer for refining and validating the final output. This modular pipeline enables a more coherent, scalable, and accurate generation process compared to traditional single-prompt AI systems. The platform is implemented as a full-stack web application using the Django framework, where the backend manages agent orchestration, request handling, and data processing. The frontend leverages standard web technologies, including HTML, CSS, and JavaScript, to provide a clean and interactive user interface for initiating research tasks and visualizing results. The system integrates AI-driven modules within a structured backend architecture, enabling seamless communication between agents and efficient execution of the research pipeline. By automating planning, information retrieval, analysis, and review, the Oretes Insight System significantly reduces manual effort while improving the depth, structure, and quality of generated reports. The platform serves as a powerful tool for accelerating academic writing and producing professional- grade technical documents efficiently.
AI Resume Analyzer: An NLP-Driven Full-Stack Platform for Intelligent Resume Screening and Candidate-Job Matching
In today’s highly competitive job market, recruiters face an overwhelming volume of resumes that must be screened efficiently, while job seekers struggle to determine whether their resumes are tailored to specific job descriptions. This mismatch between candidate profiles and job requirements leads to inefficiencies on both sides of the hiring process. To address this challenge, the AI Resume Analyzer is proposed as a complete, web-based full-stack solution that intelligently analyses resumes against job descriptions using Natural Language Processing (NLP) techniques. The system provides candidates with actionable feedback, skill gap identification, keyword optimisation suggestions, and an overall match score, while also offering a dedicated recruiter panel for managing job postings and viewing ranked candidates, along with a super admin dashboard for overseeing the entire platform. The frontend is built using ReactJS with Vite for a fast and responsive interface, styled with Tailwind CSS, and uses React Router DOM for navigation among candidate, recruiter, and administrator screens, with Axios handling backend communication. The system implements JWT-based authentication with BCrypt password hashing to secure access across all user roles. Resume files are uploaded and stored using Multer and Cloudinary, ensuring reliable file delivery. The AI analysis engine uses pdf-parse for text extraction, TF-IDF vectorisation, and cosine similarity algorithms to compute a resume-to-job-description match score, providing candidates with precise, data-driven feedback. The platform adopts a three-tier MERN-stack architecture (MongoDB, Express.js, React, Node.js) and demonstrates how full-stack web technologies and NLP can be combined to produce an efficient, fair, and transparent recruitment tool.
Resume screening; Natural Language Processing; TF-IDF; Cosine similarity; MERN stack; Applicant Tracking System; ReactJS; Node.js; MongoDB; Cloudinary; JWT authentication; Recruitment automation
AI Career Coach: A Full-Stack Intelligent Web Application for Personalized Career Development in the AI Domain
The modern AI job market presents significant challenges for both aspiring and experienced professionals — from identifying relevant skills and understanding emerging industry trends to preparing for technical interviews and crafting impactful application materials. Traditional career coaching methods and existing online platforms, while helpful in certain respects, lack the integration, personalization, and AI-driven intelligence required to effectively guide individuals through the complex AI career landscape. This paper presents the AI Career Coach, a comprehensive, full-stack intelligent web application designed to empower individuals at every stage of their AI career journey — from beginners entering the field to seasoned professionals seeking advancement. The platform is built upon a modern technology stack comprising Next.js for a performant server-rendered frontend and backend, Neon DB as a serverless PostgreSQL database, Prisma as a type-safe ORM for streamlined data management, Tailwind CSS and Shadcn UI for a consistent and accessible user interface, and Inngest for reliable scheduled background task processing. At its intelligence core, the platform integrates Google's Gemini AI API to deliver dynamic and personalized career guidance. Key functionalities include a personalized onboarding and profile management module, an AIgenerated industry insights engine, an AI-powered mock interview generator with performance analytics, an AIassisted resume builder with Markdown generation and PDF export, and a contextual cover letter generator. The system architecture follows a modular and iterative Agile development approach, emphasizing scalability, developer productivity, and seamless AI integration. Testing encompassed functional validation, performance evaluation of API endpoints, and security hardening through input validation, CSRF protection, and HTTPS enforcement. The platform demonstrates how modern fullstack development paradigms can be effectively combined with large language model APIs to create practical, highimpact AI-driven applications.
Sopiko: E-commerce website for buying and selling books
Sopiko is an e-commerce web application developed for buying and selling books through an online platform. The main purpose of this project is to provide an easy, fast, and reliable system where users can purchase new or old books and also sell their used books to other users. In today’s digital world, many students and readers face difficulties in finding affordable books in nearby stores. Traditional book purchasing methods consume more time and effort. Sopiko solves this problem by creating a centralized online marketplace where books are available at different price ranges and categories. The website provides separate functionalities for buyers, sellers, and administrators. Buyers can search books, view details, compare prices, add products to the cart, and place orders online. Sellers can upload book details, set prices, manage stock, and track orders. The admin manages users, products, categories, and overall website activities. The system also includes features like user registration, secure login, payment integration, order management, and feedback systems to improve user experience. The project is developed using modern web technologies to ensure better performance, responsiveness, and security. Sopiko aims to encourage book reading by making books more accessible and affordable for everyone. It also promotes the reuse of old books, reducing wastage and supporting environmental sustainability. The platform can be useful for students, teachers, professionals, and book lovers who want a convenient way to buy or sell books from anywhere at any time.
The Apartment Rent Management System is a web-based application developed to automate apartment management operations such as rent collection, tenant management, visitor security verification, complaint handling, and user authentication. Traditional apartment management methods mainly depend on manual paperwork and physical record maintenance, which often lead to operational delays, human errors, poor communication, and security issues. The proposed system provides a centralized digital platform that simplifies apartment administration and improves operational efficiency. The application is developed using MERN Stack technologies including MongoDB, Express.js, React.js, and Node.js with MySQL database integration for secure data management. The system provides separate dashboards for administrators, tenants, and security staff with role-based access control mechanisms. The rent collection module maintains payment records and generates rent reports, while the visitor management module improves apartment security through digital visitor entry and exit monitoring. The complaint management module allows tenants to submit maintenance requests online for faster issue resolution. The system reduces paperwork, improves communication, enhances security, and provides real-time access to apartment-related information. The developed application demonstrates how modern web technologies can improve apartment management operations through automation and digital transformation. Apartment Management, MERN Stack, Rent Collection, Visitor Security, Complaint Management, Web Application, MySQL, React.js, Node.js.
The Hospital Management System (HMS) is a comprehensive web-based application developed using PHP and MySQL. The system is designed to automate the various administrative, clinical, and financial operations of a hospital. It aims to replace manual, paper-based processes with a fast, reliable, and efficient digital platform. In the modern healthcare environment, managing large volumes of patient data, doctor records, laboratory results, pharmaceutical inventory, and financial transactions manually is not only timeconsuming but also error-prone. This system addresses all these challenges by providing an integrated software solution that streamlines hospital operations. The Hospital Management System supports multiple user roles including the System Administrator and Doctors. The Administrator has complete control over all modules such as patient registration, employee management, payroll processing, equipment inventory, pharmaceutical records, laboratory management, surgery records, accounting, and report generation. Doctors can log in to manage their patients, view prescriptions, access lab results, and update patient records. The system is built as a multi-page PHP web application with a responsive front end using Bootstrap 4, HTML5, and CSS3. The back end uses PHP 5.6/7.4 with a MySQL database (hmisphp) that contains 17 well-structured tables. The database stores all data related to patients, doctors, vitals, medical records, laboratory tests, prescriptions, surgery, payrolls, pharmaceuticals, equipment, vendors, and accounting. Key features of the system include patient registration and discharge, inpatient and outpatient management, patient vital signs recording, prescription management, laboratory test and result management, medical records, surgery/theatre management, employee payroll processing, pharmaceutical inventory, equipment tracking, vendor management, and financial accounting.