American Journal of AI Digital Transformation and Regenerative Pharmacist
Development of Metal–Organic Framework-Based Nanocomposites for Selective Gas Separation and Carbon Capture Applications
The rapid increase in greenhouse gas emissions resulting from industrialization, fossil fuel combustion, and energy-intensive manufacturing processes has intensified global concerns regarding climate change and environmental sustainability. Carbon dioxide (CO₂), methane (CH₄), nitrogen oxides (NOₓ), and sulfur dioxide (SO₂) constitute the primary greenhouse gases responsible for global warming and atmospheric pollution. Conventional gas separation technologies, including cryogenic distillation, pressure swing adsorption, membrane separation, and chemical absorption using amine solutions, often suffer from high energy consumption, limited selectivity, solvent degradation, corrosion, and substantial operational costs. Consequently, there is an urgent need for advanced porous materials capable of achieving highly efficient, selective, and energy-efficient gas separation and carbon capture. Metal–Organic Frameworks (MOFs), characterized by exceptionally high surface area, tunable pore structures, adjustable chemical functionality, and excellent adsorption properties, have emerged as one of the most promising classes of porous materials for next-generation gas separation technologies. Nevertheless, the practical implementation of pristine MOFs is often limited by inadequate mechanical stability, moisture sensitivity, and processing challenges
Copyright (c) 2026 https://creativecommons.org/licenses/by-nc-nd/4.0
Design, Synthesis, and Electrochemical Evaluation of Eco-Friendly Schiff Base Corrosion Inhibitors for Mild Steel in Acidic Environments
Corrosion of mild steel in acidic environments represents one of the most significant challenges encountered in industrial processes such as acid pickling, oil and gas production, petrochemical refining, acid descaling, and chemical manufacturing. Excessive corrosion not only shortens the service life of metallic structures but also increases maintenance costs, environmental risks, and operational downtime. Although numerous synthetic corrosion inhibitors have been developed, many conventional inhibitors exhibit high toxicity, poor biodegradability, and adverse environmental impacts, limiting their practical application under increasingly stringent environmental regulations. Schiff base compounds have recently emerged as promising eco-friendly corrosion inhibitors because of their facile synthesis, excellent adsorption characteristics, abundant electron-donating functional groups, and strong coordination ability with metal surfaces. This research presents the design, synthesis, characterization, and electrochemical evaluation of environmentally benign Schiff base corrosion inhibitors for protecting mild steel in acidic media
Copyright (c) 2025 https://creativecommons.org/licenses/by-nc-nd/4.0
Ultra-Low Latency Emergency Event Detection Using Cloud Integrated Teen Sensor Architecture
WSNs (WSNs) play a critical role in environmental monitoring, industrial automation, and real-time event detection by enabling large-scale data collection through energy-constrained sensor nodes. However, efficient data transmission remains a major challenge due to limited battery power, dynamic network topology, and increasing communication overhead. Traditional routing approaches in WSNs primarily rely on hopbased shortest path routing, where data packets are forwarded through the minimum-hop route without considering node energy levels or sensing conditions. While simple to implement, such methods often lead to uneven energy consumption, increased latency, higher error rates, and premature node failures, thereby reducing overall network lifetime and reliability. To address these challenges, this research focuses on the design and analysis of an energy-aware routing mechanism inspired by the Threshold Sensitive Energy Efficient Network (TEEN) concept. The proposed system models an optimized routing strategy that emphasizes efficient path selection using weighted links and adaptive routing behavior, aiming to reduce unnecessary transmissions and improve communication performance. A simulation-based framework is developed using graph-based network modeling to compare the proposed TEEN routing approach and traditional hop-based routing. Performance evaluation is carried out using key metrics such as latency, mean square error (MSE), bit error rate (BER), hop count, and path length. Experimental results demonstrate that the proposed approach significantly outperforms traditional hop-based routing in terms of reduced latency, lower error rates, and improved routing efficiency. The proposed system highlights the importance of energyaware routing strategies in enhancing the performance, scalability, and sustainability of WSNs.
Trusted Alumni Communication and Career Networking Portal with Secure Access
Alumni networks play a vital role in strengthening institutional relationships, professional collaboration, and career development. However, maintaining accurate communication with graduates remains a significant challenge, as alumni frequently change their contact details after employment without updating institutional records. Traditional alumni management methods, including printed directories, social media groups, and manual communication, often suffer from fragmented data, fake profiles, and the absence of reliable graduate verification, reducing trust among alumni and employers. This research presents a secure Flask web-based platform designed to provide verified networking and career opportunities for graduates. The proposed system employs the Lightning Memory-Mapped Database (LMDB) for efficient data storage and rapid information retrieval. It incorporates two dedicated modules: Alumni Network Support and Administration (ANSA) for institutional verification and management of alumni records, and Verified Alumni Networking Member (VANM) for authenticated graduates to connect, search the alumni directory, and apply for career opportunities. To ensure data confidentiality and system integrity, the portal integrates secure password hashing and CrossSite Request Forgery (CSRF) protection against unauthorized access and malicious attacks. By combining institutional verification with secure communication and career networking, TACCNP establishes a trusted digital ecosystem that enhances alumni engagement, strengthens professional connections, and promotes reliable employment opportunities
Deep Decision Analytics for Customer Review Understanding and Predictive Opinion Intelligence
Customer reviews play a crucial role in today's e-commerce ecosystem by providing valuable information about customer satisfaction, product performance, and the overall shopping experience. As online retail continues to expand, an enormous amount of textual feedback is generated every day. Conventional evaluation approaches, such as average product ratings and the total number of reviews, offer only a broad assessment and are unable to fully capture the detailed opinions and emotions expressed within review texts. Consequently, a significant amount of useful customer feedback remains unexplored. In addition, manually examining large volumes of review data is both labor-intensive and impractical, creating a strong demand for intelligent automated analysis techniques. To overcome these limitations, this study presents a machine learning-driven framework for customer review analysis that predicts both product recommendation decisions and product ratings. The proposed framework includes data preprocessing, exploratory data analysis, and textual feature extraction using the Term Frequency–Inverse Document Frequency (TFIDF) technique. Several machine learning algorithms are employed, including Restricted Boltzmann Machine (RBM) integrated with Logistic Regression (LR) for classification, RBM combined with Ridge Regressor (RR) for regression, Gradient Boosting (GB), Extreme Gradient Boosting (XGB), and a Multi-Task Neural Network with Extra Trees (MTNN-ET), where these models operate using the Classification and Regression Tree (CART) methodology. The experimental findings indicate that the proposed MTNN-ET-CART model outperforms the existing approaches, achieving a classification accuracy of 0.9640 and a regression R² score of 1.0000. The framework efficiently handles large-scale customer review datasets while delivering accurate prediction results, thereby supporting informed decision-making and enhancing the overall customer experience across ecommerce platforms.
Animal Rescue Coordination Platform with Approval of Citizen Reporting and Adoption Listings Management
The increasing population of stray animals in urban and semi-urban areas has emerged as a significant social and public health challenge due to rapid urbanization, animal abandonment, and the absence of coordinated animal welfare management. Traditional rescue operations rely heavily on fragmented communication, manual record maintenance, and limited coordination among animal shelters, rescue organizations, volunteers, and adopters, leading to delayed rescue responses and inefficient adoption processes. This paper presents a Stray Animal Rescue Coordination Platform with Adoption and Volunteer Management (SARCP-AVM), a centralized digital solution designed to streamline rescue, rehabilitation, and adoption activities. The proposed platform enables realtime reporting of stray animal sightings, intelligent rescue task allocation based on volunteer location, availability, and urgency, and structured volunteer participation. It also maintains comprehensive animal profiles containing health records, vaccination history, and behavioral information to facilitate responsible adoption decisions. The system integrates Geographic Information System (GIS)-based location tracking, Global Positioning System (GPS)-enabled rescue coordination, and automated notification services to provide timely updates regarding rescue status, volunteer assignments, and adoption progress. Furthermore, a centralized database ensures secure information management and enhances collaboration among all stakeholders. By integrating rescue coordination, adoption management, volunteer tracking, and automated communication into a unified platform, SARCP-AVM significantly improves operational efficiency, reduces rescue response time, increases adoption success rates, and promotes sustainable and ethical animal welfare practices. The proposed system ultimately contributes to effective stray animal population management while strengthening community engagement in animal rescue initiatives.
Encrypted Forest Trekking Ticket Booking And Real-Time Validation Platform
With the rapid growth of digital tourism and forest-based adventure activities, managing trekking permissions, ticket reservations, and visitor information has become increasingly complex and security-sensitive. Traditionally, trekking bookings and validations were maintained using manual registers or semidigital systems that relied on paper records or unsecured databases. These approaches often resulted in delayed booking confirmation, inaccurate slot availability, unauthorized record modifications, lack of transparency, poor audit capabilities, and difficulty in preventing duplicate bookings and overbooking. Furthermore, the absence of secure data management mechanisms exposed sensitive user information to potential security threats, creating the need for a reliable and automated trekking management solution. The proposed system introduces a secure web-based trekking ticket booking and real-time validation platform that integrates role-based authentication, automated slot verification, encrypted document management, and controlled information sharing within a unified framework. Advanced Encryption Standard (AES) encryption and RACE Integrity Primitives Evaluation Message Digest-160 (RIPEMD-160) hashing are employed to ensure the confidentiality, integrity, and protection of sensitive booking records and user data. The platform is developed using the Flask framework with TinyDB as the database and supports secure user registration, online route exploration, automated booking confirmation, schedule management, administrative monitoring, and encrypted file handling. Realtime validation prevents overbooking and scheduling conflicts, while role-based access control restricts unauthorized activities. The research enhances operational transparency, strengthens data security, improves booking efficiency, and provides a scalable foundation for secure smart tourism and digital forest management.
Manubothula Suprathika, Karramareddy Sharmila
Adaptive Preference Intelligence for Personalized Tourism Recommendation through Hybrid Computational Reasoning
The increasing availability of digital tourism information has transformed the way travelers plan their journeys, creating a demand for intelligent systems capable of delivering recommendations tailored to individual preferences. Conventional trip planning methods and rule-based recommendation platforms generally provide the same suggestions to a broad range of users, often overlooking personal interests, financial limitations, and available travel time. This research introduces an intelligent tourism recommendation framework designed to generate customised travel options by analysing destination-related information collected from Indonesian tourism resources. To evaluate predictive capability, several machine learning algorithms with Classification and Regression Trees (CART), including Logistic Regression (LR), Support Vector Machine (SVM), and Random Forest (RF), are implemented as benchmark models. The proposed framework incorporates a hybrid architecture named Neuro-Tree-Net (NTN), which combines the feature-learning ability of an Artificial Neural Network with the ensemble decision-making strength of the Extra Trees Classifier (ETC) algorithm. This integration enhances the system's capacity to identify intricate patterns and hidden relationships within tourism datasets, leading to more precise recommendation outcomes. Travelers can specify criteria such as budget, trip duration, and desired destination ratings, allowing the system to generate recommendations that closely match their requirements. Data preprocessing, feature engineering, model development, and performance evaluation are carried out using Python-based libraries such as pandas, scikit-learn, TensorFlow/Keras, and Matplotlib, while an interactive Django-based web application provides seamless access to the recommendation engine. Experimental findings indicate that the proposed hybrid framework consistently achieves superior predictive performance compared with conventional machine learning techniques, offering an efficient and practical solution for personalised travel planning and tourism decision support.
Anonymous Whistleblower Protection Platform with Secure Evidence Submission for E-Commerce
Business organizations have huge potential to increase their customer base by offering ecommerce services, especially in the postpandemic era. Ensuring secure e-commerce applications plays an important role in increasing the customer base. To develop appropriate policies and secure technological infrastructures, business organizations first need to establish an understanding of the reservations of their customers toward ecommerce, as well as their perception of security and privacy of e-commerce applications. This research presents a Flask web application designed to ensure confidentiality, integrity, and accountability in digital marketplaces. The system enables secure user authentication with role-based access control for Admin, Seller, and Customer roles, ensuring controlled access to platform functionalities. Sensitive files and confidential messages are encrypted using a 16-round Feistel cipher (FC) before storage, protecting data from unauthorized access or tampering. Encrypted files are securely stored in the server directory, while metadata, user information, encryption keys, and audit logs are maintained in Tiny Database (TinyDB). The platform also includes encryption key rotation, secure file decryption upon authorization, and detailed audit logging for traceability of system activities. By integrating encryption mechanisms, session security, and structured access control within a unified web framework, the proposed system enhances digital trust, supports ethical reporting, and strengthens governance in modern e-commerce environments.
CENTRALIZED STUDENT ORGANIZATION AND EVENT MANAGER- CSOEM
Educational institutions require efficient systems to manage student organizations, club memberships, and event-related activities in a structured and transparent manner. Conventional management approaches that rely on paper records, spreadsheets, or isolated digital tools often result in data redundancy, communication delays, inefficient membership handling, and increased administrative workload. To overcome these challenges, this project presents the Centralized Student Organization and Event Manager (CSOEM), a web-based platform that integrates modern frontend, backend, and database technologies to streamline the management of student clubs and organizational activities. The proposed system enables administrators to manage user registrations, approve memberships, create and maintain club information, and publish notifications through a centralized interface. Students can register securely, explore available clubs, submit membership requests, monitor approval status, and receive real-time announcements regarding organizational activities. The application is developed using React.js for the user interface, Node.js with Express.js for backend services, and MySQL for reliable relational data storage. Role-based access control ensures secure authorization by providing separate functionalities for administrators and members while maintaining data integrity and operational transparency. The centralized architecture minimizes manual intervention, improves communication efficiency, and supports consistent data management across all modules. Experimental implementation demonstrates that the proposed system enhances administrative productivity, simplifies club coordination, reduces processing time for membership workflows, and provides a scalable and user-friendly solution for managing student organizations and events in educational institutions. The platform offers a reliable foundation for future enhancements such as event scheduling, attendance tracking, mobile accessibility, and advanced analytics.
Institute Performance Ranking Platform With Transparent Multi-Metric Evaluation
The Institute Performance Ranking Platform with Transparent Multi-Metric Evaluation is a webbased application developed to provide students and stakeholders with a reliable and transparent method for evaluating educational institutions. Traditional institute selection often depends on advertisements, unofficial websites, and incomplete information, which may lead to biased or inaccurate decisions. The proposed system addresses this problem by presenting verified institutional data through a centralized platform using multiple performance metrics. The application is developed using React.js for the frontend, Django Representational State Transfer (REST) Framework for backend services, and My Structured Query Language (MySQL) for database management. It follows a three-tier architecture that ensures secure communication, efficient data processing, and scalable performance. The platform provides role-based access with two types of users: administrator and regular user. Administrators verify user registrations, manage institute information, and ensure the authenticity of performance data, while approved users can view, filter, compare, and analyse institutes based on verified metrics such as placement percentage, student strength, location, and contact information. The system computes institute rankings automatically using a weighted multimetric evaluation approach, ensuring fair, transparent, and reproducible assessment. Application Programming Interfaces (APIs) facilitate seamless communication between the frontend and backend, while Hypertext Transfer Protocol (HTTP) enables secure data transmission. By integrating structured data management, administrative validation, and automated ranking mechanisms, the proposed platform improves transparency, reduces misinformation, and supports informed academic decision-making. The modular architecture also enables future integration of Artificial Intelligence (AI) and predictive analytics for advanced institutional performance evaluation.
Real-Time Threat Detection and Alerting in Social Streams Using a DualTarget Transformer Architecture
The rapid growth of digital technologies and online communication platforms has significantly increased cybersecurity-related discussions across social media, online forums, and cyber incident reporting platforms. These platforms generate large volumes of unstructured textual data containing valuable information about cyber threats, vulnerabilities, and attack activities. Traditional cybersecurity monitoring methods mainly rely on manual analysis and rule-based filtering techniques, where predefined keywords and patternmatching approaches are used to identify potential threats. Although these methods are effective for basic monitoring, they become inefficient when handling large-scale datasets and often fail to capture the contextual meaning of textual information. One of the major challenges in cybersecurity intelligence is extracting meaningful insights from complex, noisy, and unstructured text. Conventional approaches struggle with inconsistent language, irrelevant content, and high data volume, reducing the accuracy of cyberattack classification. To address these challenges, this research proposes a machine learning-based framework for automated cyberattack detection using textual data. The framework begins with natural language preprocessing to clean and normalize the text, followed by semantic feature extraction using Sentence-Bidirectional Encoder Representations from Transformers (SBERT) embeddings trained with an Open Question Answering objective. To address class imbalance, the Synthetic Minority Oversampling Technique (SMOTE) is applied to generate balanced training data. The framework evaluates multiple classification models, including Stochastic Gradient Descent (SGD), Complement Naïve Bayes (CNB), and a hybrid model combining Dense Neural Networks (DNN) with Linear Discriminant Analysis (LDA). Experimental results demonstrate that the hybrid approach improves classification performance, enhancing the accuracy and efficiency of automated cybersecurity text analysis for large-scale cyberattack detection.
Medication Donation Center Locator with Category-Based Guidelines and Timings
Medication wastage is a growing concern in the healthcare sector, while many individuals and communities continue to face limited access to essential medicines. The system is a web-based application developed to address this imbalance by providing a secure and structured platform for managing medicine donations. The system enables individuals to donate unused or surplus medicines to authorized donation centers, ensuring proper utilization while maintaining safety and compliance standards. The application is built using a React.js frontend for an interactive and user-friendly interface, combined with a Django Representational State Transfer Application (REST) backend that handles secure data processing and business logic. A My Structured Query Language (MySQL) database is used for reliable storage of user information, donation center details, categories, and operational status. The system follows a rolebased architecture with controlled access for administrators and users. Users must complete a registration process and receive admin approval before gaining access, ensuring authenticity and preventing misuse. Administrators are provided with a centralized dashboard to approve users, add and manage donation centers, define accepted medication categories, control donation timings, and enable or disable centers as required. Users can view only active and approved donation centers along with location details, contact information, donation categories, and operational timings. The category-based organization and timing control help ensure that medicines are donated safely and at appropriate locations. The system promotes responsible medicine donation, reduces medical waste, improves accessibility to essential drugs, and supports community healthcare efforts through a controlled, transparent, and technology-driven approach.
Contextual Preference Cognition through XLNet Semantic Encoding and Trivariate Decision Intelligence
Customer support platforms generate an enormous number of service requests every day, making it difficult for organisations to analyse customer feedback and deliver timely responses. Extracting useful information from these text-based interactions is essential for improving service quality, customer retention, and operational efficiency. This research presents a multi-target prediction model that simultaneously determines the priority of a support ticket, classifies the expected customer satisfaction level, and predicts the possibility of successful issue resolution. Unlike conventional approaches that address each prediction task independently, the proposed framework exploits the relationship among these outputs to produce more reliable and consistent results. Existing customer support systems often depend on manual inspection, keyword-based techniques, or simple text analysis methods, which struggle to process large volumes of unstructured textual data and frequently produce inconsistent outcomes. To overcome these limitations, this work utilises the XLNet transformer model to generate rich contextual representations of customer messages. These features are evaluated using several classification algorithms, including Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), Stochastic Gradient Descent (SGD), and Nearest Centroid (NC). Their performance is compared with the Histogram-Based Gradient Boosting (HGB) classifier, which demonstrates the highest prediction accuracy and overall effectiveness. The proposed framework provides a scalable and intelligent solution for customer support analytics by enabling faster ticket assessment, more accurate satisfaction estimation, and improved resolution forecasting, thereby assisting organisations in making better service decisions and enhancing the overall customer experience.
Farmer Logistics Connector For Truck Sharing And Route Matched Load Optimization
Agricultural transportation plays a vital role in connecting farmers with markets, but conventional logistics management often suffers from poor coordination, inefficient vehicle utilization, and increased transportation costs. Manual communication methods such as phone calls and local agents frequently result in delayed deliveries, empty return trips, and limited visibility of available transport resources. To address these challenges, this research proposes the Farmer Logistics Connector for Truck Sharing and Route Matched Load Optimization (FLCTS), a web-based platform that digitally coordinates truck sharing among farmers and truck owners. The system is developed using React.js for the frontend, Django Representational State Transfer (REST) Framework for backend services, and My Structured Query Language (MySQL) for centralized data storage. It provides secure user registration, administrator approval, role-based access control, truck posting, route-based transport matching, and transport request management through Application Programming Interfaces (APIs). The centralized database enables efficient storage and retrieval of user, truck, and request information while ensuring transparency and data consistency. The proposed system minimizes empty vehicle trips, improves truck capacity utilization, reduces fuel consumption, and decreases transportation delays through structured digital coordination. Additionally, it enhances communication between stakeholders, supports secure information sharing, and provides real-time visibility of transport availability. Experimental implementation demonstrates that the platform improves operational efficiency, strengthens agricultural supply chain coordination, and offers a scalable, reliable, and cost-effective solution for rural transportation management. The proposed framework contributes to sustainable agricultural logistics by replacing traditional manual processes with an intelligent, transparent, and technology-driven
An Interpretable Semantic Intelligence Framework for Concurrent Tourist Behaviour Modeling and Tourism Demand Forecasting
The continuous expansion of digital tourism platforms has produced an extensive collection of online customer feedback, creating new opportunities to understand traveller preferences and anticipate tourism demand. Despite this abundance of information, accurately interpreting unstructured textual reviews remains a significant challenge. Traditional tourism forecasting methods mainly rely on historical trends and numerical data, making them less effective in capturing the underlying opinions, emotions, and behavioural patterns expressed by travellers. Furthermore, tourism datasets often suffer from imbalanced class distributions, where certain visitor categories and rating levels have considerably fewer samples. Conventional classification techniques, such as Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), and Histogram Gradient Boosting (HGB), are limited in their ability to extract contextual semantic information and perform consistently on skewed datasets. To address these challenges, this research presents GPS-Tourism, an intelligent prediction framework that integrates Google Pathways Language Model (PaLM) embeddings, the Synthetic Minority Oversampling Technique (SMOTE), and a Sparse Linear Integer Model (SLIM) Classifier. Initially, PaLM converts textual reviews into rich 768-dimensional semantic representations that preserve contextual meaning. SMOTE subsequently balances the training data by generating synthetic minority samples, enabling improved learning across underrepresented classes. Finally, the SLIM-based ensemble of oblique decision trees performs the classification of tourist ratings and demand categories. Experimental findings demonstrate that the proposed framework achieves higher predictive accuracy than LDA, QDA, and HGB, providing tourism stakeholders with a reliable decisionsupport system for demand forecasting, strategic resource planning, and personalized marketing initiatives.
CENTRALIZED CITY COMPLAINT PORTAL WITH CITIZEN REPORTING AND STATUS TRACKING DEPENDENT FEEDBACK ANALYTICS
Rapid urbanization has significantly increased the demand for efficient civic services and responsive urban governance. City administrations are responsible for resolving a wide range of public issues, including road damage, waste accumulation, water supply interruptions, streetlight failures, and sanitation-related problems. However, in many municipalities, citizen complaints are still handled through traditional methods such as phone calls, written applications, or in-person visits to government offices. These manual processes often lack proper documentation, transparency, and real-time tracking, making complaint management slow and inefficient. Delayed responses, poor coordination among departments, loss of complaint records, limited accountability, and the absence of a centralized monitoring mechanism reduce the overall effectiveness of civic service delivery. As urban populations continue to grow, these challenges emphasize the need for a scalable and technology-driven complaint management solution. The research introduces a centralized web-based platform that enables citizens to report civic issues, upload relevant information, and monitor complaint status throughout the resolution process. The system allows administrators to categorize complaints, assign them to appropriate departments, update progress, and monitor service performance through a unified dashboard. Developed using React and CSS for the frontend, Node.js for backend processing, and MySQL for database management, the platform ensures secure access, reliable data handling, and efficient workflow management. The proposed system enhances transparency, improves response efficiency, strengthens citizen participation, and supports informed decision-making, ultimately contributing to accountable governance and smarter, more responsive urban civic service management.
COMMUNITY REUSE AND EXCHANGE HUB FOR NEIGHBOURHOOD ITEM SHARING
The System is a web-based application designed to promote sustainable living and strengthen community collaboration by enabling local residents to share, exchange, and reuse unused household items. In today’s consumer-driven society, many usable items are discarded prematurely, leading to increased waste and environmental impact. This system addresses the problem by providing a digital platform where individuals can connect within their neighborhood to efficiently redistribute resources. The application is developed using React.js for the frontend to deliver a responsive, user-friendly interface and Django Representational State Transfer (REST) Framework for the backend to handle business logic, authentication, and secure data processing. A My Structured Query Language (MySQL) database is used for reliable storage and management of user information, items, and request records. The system follows a role-based access model consisting of Admin and User roles. New users must register and wait for admin approval to ensure platform authenticity and safety. Once approved, users can add items they wish to share, browse items posted by others, and send item requests. Item owners have full control to approve or reject incoming requests. Upon approval, the requester is granted access to the owner’s contact details, ensuring privacy and controlled communication. Administrators are responsible for approving users, monitoring listed items, and removing inappropriate or inactive entries. The platform emphasizes transparency, data integrity, and ease of use while preventing duplicate requests and unauthorized access. By encouraging reuse over disposal, the System contribute to waste reduction, resource optimization, and stronger neighborhood engagement. The system demonstrates a practical implementation of full-stack web development principles while addressing real-world environmental and social challenges.
ENCRYPTED ONLINE EXAMINATION QUESTION PAPER DISTRIBUTION WITH ROLE-BASED ACCESS CONTROL
The rapid digitalization of academic and examination processes has increased the demand for secure mechanisms to handle sensitive examination question papers. The question paper distribution relies on manual methods such as physical transport, sealed envelopes, or basic electronic sharing, which offered limited security and were prone to leakage, unauthorized access, and human error. Even in semi-digital systems, question papers are often stored or transmitted in plaintext with weak access control, lacking proper authentication, authorization, and audit mechanisms. These limitations pose serious risks to examination integrity and confidentiality, especially in large-scale or remote examination environments. To address these challenges, this project presents a secure online examination question paper distribution framework that integrates strong cryptographic techniques with role-based access control. Examination content is protected using Advanced Encryption Standard Galois/Counter Mode (AES-GCM) symmetric encryption, while Rivest–Shamir–Adleman (RSA) publickey cryptography is employed for secure key generation and key exchange, and Secure Hash Algorithm-256 (SHA-256) hashing is used for secure password protection. Access to encrypted papers is strictly controlled through authentication and authorization checks, and decryption is performed dynamically only for authorized users, minimizing plaintext exposure and maintaining Role Based Access Control (RBAC). The system also maintains detailed audit logs to ensure accountability and traceability. By automating encryption, authorization, and access workflows using a lightweight web-based architecture, the proposed system enhances performance, reliability, and security. The significance of this work lies in providing a scalable, transparent, and robust solution that preserves examination integrity and strengthens trust in modern digital academic environments.
SECURE FREELANCER CONTRACT CREATION PLATFORM WITH DIGITAL SIGNING AND PAYMENT TRACKING
The rapid growth of freelance and contractbased digital work has increased the need for secure platforms that can manage contracts, payments, and confidential document exchange. Traditional freelance management methods rely heavily on email communication, manual contract handling, and unprotected file sharing, which expose sensitive contractual and financial information to unauthorized access, data leakage, and manipulation. Most existing systems focus on workflow automation but provide limited encryption and insufficient control over document-level security. The traditional system lacks centralized access control, encrypted storage, and reliable audit mechanisms. Contracts and deliverables are often exchanged as plain files without cryptographic protection, making them vulnerable to interception and misuse. Password handling and role validation are frequently weak, and data storage mechanisms do not ensure confidentiality or traceability of user actions. To address these limitations, this research proposes a secure freelance contract management and encrypted file-sharing system developed using Python and the Flask web framework. The system implements role-based authentication for administrators, employers, and freelancers, supported by secure login sessions. Sensitive data such as files, confidential messages, and shared secrets are protected using ShangMi 4 (SM4) symmetric encryption with Secure Hash Algorithm 256 (SHA-256) derived keys before storage or transmission. User credentials are stored in hashed form to prevent direct exposure. The application uses TinyDB, a lightweight NoSQL database, to store users, contracts, payments, encrypted file metadata, messages, secrets, and audit logs in structured JSON format. Encrypted files are stored in protected server directories, and access is strictly controlled based on user roles and ownership. Modular separation between the frontend, backend, encryption utilities, and database layer ensures secure and efficient workflow execution. This research enhances confidentiality, access control, and traceability while maintaining low computational overhead. It is suitable for academic environments and small-scale secure collaboration platforms, demonstrating the effective integration of encryption with webbased contract management systems.
DEEP LEARNING-DRIVEN ADAPTIVE CLUSTERING FRAMEWORK FOR ENERGY-EFFICIENT AND HIGH-PERFORMANCE INTERNET OF THINGS NETWORKS
The rapid proliferation of Internet of Things (IoT) devices has intensified the need for intelligent network management strategies capable of addressing challenges related to limited energy resources, constrained processing capabilities, and restricted communication bandwidth. Clustering has emerged as an effective approach for enhancing network scalability, reducing communication overhead, and improving resource utilization in IoT environments. This study presents a comprehensive evaluation of conventional and deep learning-based clustering techniques for optimizing IoT network performance. Traditional clustering algorithms, including K-Means, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), and Fuzzy C-Means, are compared against advanced deep learning approaches such as Variational Autoencoder (VAE)-Based Clustering and Recurrent Neural Network (RNN)-Based Clustering. Simulations are conducted using the Contiki operating system to emulate realistic IoT network conditions characterized by heterogeneous and resource-constrained devices. Performance evaluation is carried out using key metrics including latency, throughput, energy consumption, cluster stability, and communication efficiency. Experimental results reveal that deep learningdriven clustering techniques significantly enhance throughput and energy efficiency by effectively capturing complex data patterns and dynamic network behavior. However, these improvements are accompanied by increased computational complexity and processing overhead compared with conventional clustering methods. The findings highlight the trade-offs between performance gains and resource requirements, providing valuable insights for selecting suitable clustering strategies across diverse IoT deployment scenarios. The proposed framework contributes to the development of intelligent, scalable, and energy-aware IoT networks capable of supporting next-generation smart environments and connected systems.
IoT Device Clustering, Energy Efficiency, Deep Learning Algorithms, Throughput Optimization, Latency Reduction, Contiki Operating System
HireShield: Deep Learning Based Detection of Fraudulent Online Job Postings
The rapid proliferation of online job portals has been accompanied by a surge in fraudulent job postings, causing significant financial and psychological harm to job seekers globally. This paper presents HireShield, a deep learning-based binary classification system for real-time detection of fraudulent online job advertisements. HireShield integrates a fine-tuned BERT transformer backbone with a hybrid feature engineering pipeline encompassing textual TF-IDF features, semantic sentence embeddings, and structured metadata flags extracted from the Employment Scam Aegean Dataset (EMSCAD) augmented with 4,680 scraped real-world postings (17,880 records total). A SMOTE-balanced training strategy and progressive BERT layer unfreezing address severe class imbalance (approximately 5% fraudulent postings). HireShield achieves a classification accuracy of 96.8%, precision of 95.4%, recall of 94.9%, and F1-score of 95.1%, outperforming all evaluated baselines including Seq2Seq (70.2% F1), Logistic Regression (75.8%), Random Forest (79.1%), LSTM (83.6%), and fine-tuned BERT without hybrid features (87.3%). Deployed as a Flask REST API with sub-second inference latency, HireShield provides a practical and scalable solution for protecting employment platforms and job seekers from recruitment fraud.
Illuri Usha Komali¹, N Akhila2 , P Rajitha3 , Naspuri Akshaya4 , Sai Harshitha5 , Jakkula Sneha6
This paper presents the design and development of an Urban Waste Tracker — an intelligent, IoT-integrated and AI-powered system for real-time monitoring, collection routing, and citizen reporting of municipal solid waste (MSW). Rapid urbanisation has created enormous pressure on waste management infrastructure in Indian cities, resulting in overflowing bins, delayed collection cycles, and environmental degradation. The proposed system addresses these challenges by combining Internet-ofThings (IoT) sensors embedded in public dustbins with a cloud-connected mobile and web application that enables citizens to report waste accumulation, track collection vehicle routes, and view live bin-fill status. An Artificial Intelligence engine processes historical fill-rate data and predicts optimal collection schedules, thereby reducing fuel consumption and operational cost. The back-end is built on Node.js with Express, the mobile front-end on React Native, and the data layer on Firebase Realtime Database. Pilot testing across a simulated urban ward demonstrated a 35% reduction in missed-collection events and a 28% improvement in route efficiency. The results confirm that the platform can serve as a scalable, cost-effective backbone for smart-city waste governance.
The Grievance Portal is a web-based application designed to provide an efficient, transparent, and user-friendly platform for registering, tracking, and resolving complaints raised by users. Traditional grievance handling systems depend heavily on manual paperwork, emails, and physical visits, resulting in delays, lack of transparency, poor accountability, and inefficient record management. This survey paper analyzes the need for digital grievance management systems and explores the technologies, architecture, methodologies, challenges, and future opportunities associated with modern grievance portals. The study focuses on the use of technologies such as HTML, CSS, JavaScript, JSP, MySQL, JDBC, and Apache Tomcat Server in developing a scalable and secure grievance management system. Furthermore, the paper discusses the limitations of existing systems, advantages of automation, centralized database management, complaint tracking mechanisms, and system security features.
Crime Management System (CMS) is a centralized, digital platform designed to modernize law enforcement operations by automating the handling of criminal records, investigations, and citizen complaints. It replaces error-prone, paperbased workflows with secure, searchable databases, thereby improving case resolution speeds and facilitating inter-departmental data sharing Core Objectives Digitization: Convert physical First Information Reports (FIRs) and investigation files into digital formats. Efficiency: Streamline daily administrative tasks for police personnel. Public Accessibility: Provide secure portals for citizens to file complaints and track case statuses without physically visiting a station. Key Features and Module Citizen Portal: Allows public users to register, file online complaints, and upload evidence securely .Crime & Case Tracking: Enables officers to update case logs, manage evidence, and track investigation progress in realtime. Criminal Database: Stores centralized records of suspects, most wanted individuals, missing persons, and unidentified bodies. Administrative Dashboard: Gives high-level officials and administrators the ability to assign cases, monitor station performance, and generate statistical reports.
Smart Travel Planner: Intelligent Web-Based Travel Management System
The Smart Travel Planner is a modern web-based application developed to simplify and improve travel planning and management for users. Traditional travel planning methods require users to visit multiple websites and applications for transportation booking, hotel reservations, route planning, budgeting, and destination management, which increases complexity and consumes valuable time. The proposed Smart Travel Planner system provides a centralized platform where users can organize their complete travel activities efficiently. The system integrates destination search, itinerary management, budget estimation, hotel and transportation details, route planning, and personalized travel recommendations into one platform. Modern web technologies such as HTML, CSS, JavaScript, Node.js, and database systems are used to develop the application. APIs for maps, weather updates, and travel information are integrated to provide real-time travel assistance. The Smart Travel Planner improves travel convenience, reduces manual effort, enhances scheduling accuracy, and provides better user experience through responsive interfaces and intelligent planning features.
A Full-Stack MERN Smart Tour Booking System
The tourism industry has grown rapidly with the advancement of internet technologies, shifting travel services from traditional offline booking systems to online management systems. Conventional tour management relies heavily on manual processes through travel agencies, paper records, and phone calls, resulting in timeconsuming, inefficient, and error- prone operations. This research presents the comprehensive design, development, and implementation of a Smart Tour Booking System leveraging the modern MERN stack (MongoDB, Express.js, React.js, and Node.js). The proposed platform provides an efficient, centralized web-based solution for users to explore destinations, customize their bookings, and manage their travel itineraries seamlessly. Furthermore, the system includes a comprehensive administrative panel that allows operators to efficiently manage users, dynamically update tour packages, and track bookings and payments. Developed utilizing React.js for a highly responsive, mobile- first frontend and Node.js with Express.js for robust API integration, the system ensures real-time updates and secure data handling. JSON Web Tokens (JWT) are implemented for strict role-based authentication, while MongoDB serves as the NoSQL database ensuring flexible and scalable data management. Experimental analysis demonstrates that the platform eliminates manual booking delays, improves transactional accuracy through advanced state management, and provides a highly accessible, centralized digital platform for modern travel management.
Smart Inventory and Product Management System
Smart Inventory and Product Management System is a real-time inventory monitoring solution designed to improve the efficiency and management of laboratory and institutional assets. The system provides QR-based product tracking and centralized inventory management, helping students, faculty, and administrators monitor equipment usage in real time. It also includes request management, approval handling, maintenance tracking, and product status monitoring features. Students can request and scan products using QR technology, while administrators and faculty can manage inventory and user activities through a centralized dashboard. The system is developed using React.js, Node.js, Express.js, and MongoDB. Overall, the system improves management, reduces manual work, and provides a reliable solution for modern inventory and product management.
Real-Time Sign Language Chat Application with Speech Output
The proliferation of digital communication technologies has dramatically transformed human interaction, yet individuals with hearing and speech impairments continue to face significant barriers in mainstream communication systems. Sign language remains the primary mode of communication for the deaf and hard-of-hearing community; however, the vast majority of people outside this community are unable to comprehend it. This communication gap necessitates the development of intelligent, technology-driven bridge systems that can translate sign language into readable and audible formats in real time. Traditional solutions such as human interpreters are expensive, logistically complex, and unavailable in many situations. Recent advances in artificial intelligence, computer vision, and real-time web technologies have opened new possibilities for automated sign language translation. Technologies such as Google’s MediaPipe hand landmark detection framework, WebSocketbased real-time communication, and neural text-to-speech synthesis have matured to the point where accurate, low-latency sign language recognition and translation is practically achievable in browser-based applications. However, many existing automated translation systems suffer from critical limitations: they require specialized, expensive hardware such as data gloves or motion capture suits, are designed as standalone offline applications rather than integrated communication platforms, support only a limited vocabulary, and lack real-time performance necessary for fluid conversation. This project presents the design and implementation of a RealTime Sign Language Chat Application with Speech Output—a full-stack AI-powered system that enables seamless communication using hand gesture recognition. The gesture recognition engine is built using MediaPipe’s hand landmark detection framework, which tracks 21 key points on the human hand in real time through a webcam feed. A rule-based classifier processes the geometric relationships between these landmarks to accurately identify 55 distinct gesture categories: 26 American Sign Language (ASL) alphabetic letters, 10 numeric gestures, and 19 special phrase gestures including greetings, expressions, and commonly used functional words.
SOCIAL MEDIA AND GOOGLE REVIEW SENTIMENT ANALYSIS USING VADER AND FLASK FRAMEWORK
Social and Google Review Sentiment Analyzer is a web-based application developed using Python and Flask to analyze customer sentiments from online reviews. The system focuses on extracting review data from web sources and Googlerelated platforms, followed by preprocessing and sentiment classification. It utilizes the VADER sentiment analysis model to categorize reviews into positive, negative, and neutral classes based on polarity scores and compound thresholds. The backend architecture is designed using Flask REST APIs, enabling functionalities such as sentiment analysis, historical data tracking, and daily analytics generation. Data persistence is handled through Flask-SQLAlchemy with SQLite as the default database, along with optional support for MySQL integration. The application incorporates automated data collection techniques using Selenium, BeautifulSoup, and Requests to extract relevant textual content while filtering out noise. The frontend is built using HTML, CSS, and JavaScript, with Chart.js providing interactive visualizations such as sentiment distribution and trend analysis. Overall, the project presents a complete end-to-end sentiment analysis pipeline, combining data acquisition, natural language processing, data storage, and visualization to support decision-making and business insights.