Journal of Science Engineering Technology and Management Science

Artificial Intelligence (AI) has emerged as a transformative force in contemporary recruitment and selection, reshaping how organizations identify, assess, and hire talent. The growing adoption of AI-enabled tools including automated resume screening, recruitment chatbots, predictive analytics, algorithmic matching systems, and video-based assessments has generated both opportunities and challenges for human resource management. This narrative review synthesizes scholarly literature published between 2015 and 2025 to examine the evolving role of AI in recruitment and selection and its implications for organizational decision-making. The review identifies four dominant themes: the transformation of recruitment processes through automation and data-driven decision support; the changing roles of recruiters and applicants; ethical concerns relating to algorithmic bias, transparency, and privacy; and the growing importance of human–AI collaboration in hiring decisions. The analysis indicates that AI can enhance recruitment efficiency, scalability, consistency, and candidate matching while reducing administrative workload. However, these benefits are contingent upon the quality of underlying data, the transparency of algorithmic systems, effective governance mechanisms, and continued human oversight. The review argues that AI should be viewed as a decision-support technology rather than a substitute for professional judgment. By integrating technological, organizational, ethical, and human-centered perspectives, the study advances understanding of responsible AI adoption in recruitment and selection. It further proposes that sustainable hiring outcomes depend on balancing technological capabilities with fairness, accountability, transparency, and human involvement in decision-making processes.

Keywords : Artificial Intelligence; Recruitment and Selection; Human Resource Management; Algorithmic Hiring; Human–AI Collaboration; Responsible AI.

Author : Jallipalli A. N. K. Jayanth¹, Sanka Lithish Gupta², Kella Yasaswini³

Title : Impact of Artificial Intelligence on Recruitment and Selection

Volume/Issue : 2026;03(06)

Page No : 1-23

—In today's digital financial landscape, the rise of internet banking, electronic payment systems, blockchain, and cryptocurrencies has created a significant challenge for financial fraud. Due to their inability to identify complicated relations between connected entities, conventional fraud detection techniques, such rule-based and statistical models, are inadequate for detecting more sophisticated fraud schemes. Thanks to AI's use of cutting-edge machine learning (ML) and deep learning (DL) technologies, fraud detection has never been more effective. Graph Neural Networks (GNNs) are one approach that has attracted a lot of attention; they employ GNNs to examine the relationships between commodities, customers, merchants, and devices, as well as between accounts and devices. GNNs can identify hidden fraud trends, coordinated fraud rings, money laundering, identity theft and cryptocurrency crimes among other things, using relational and structural information. This article covers all aspects related to AI fraud detection solutions, ranging from simple statistical techniques to more advanced methods like ML, DL, and graph-based solutions. Furthermore, the paper explores the primary GNN architectures, including Graph Attention Networks, Graph Convolutional Networks, and Temporal Graph Neural Networks, delving into their applications, advantages, and disadvantages in financial fraud detection. The review finds that the GNN-based methods demonstrated high detection accuracy, good real-time surveillance ability, and could be applied to develop scalable, interpretable and privacy-preserving financial security frameworks.

Keywords : Financial Fraud Detection, Artificial Intelligence (AI), Graph Neural Networks (GNNs), Machine Learning, Deep Learning, Graph Convolutional Networks (GCNs), Graph Attention Networks (GATs).

Author : Dr. Prashant Kumar Srivastava

Title : AI-Driven Financial Fraud Detection: A Review of Graph Neural Network Approaches

Volume/Issue : 2026;03(06)

Page No : 24-34

The growing integration of artificial intelligence and algorithmic systems into Human Resource Management (HRM) has transformed organisational decision-making processes while simultaneously intensifying concerns surrounding fairness, transparency, and ethical accountability. Although existing scholarship has extensively examined the operational benefits of algorithmic HRM, comparatively limited attention has been devoted to understanding how organisations can balance algorithmic efficiency with human-centred equity in consequential employment decisions. Addressing this gap, the present study develops a conceptual characterisation of Algorithmic Human Resource Management through the integration of socio technical systems theory, augmented decision-making theory, and the algorithmic fairness literature. Methodologically, the study adopts an integrative literature review approach supplemented by comparative case analysis of three widely discussed organisational cases: Amazon’s abandoned AI recruitment system, Unilever’s hybrid AI-assisted hiring model, and HireVue’s algorithmic video interview platform. These cases are further contextualised through a theoretically constructed vignette designed to examine the limits of narrow algorithmic filtering and the continuing relevance of human evaluative judgment. Through this multi-layered analysis, the paper investigates the governance structures required for effective human–AI integration in HRM and explores the broader tension between predictive efficiency and procedural equity.

Keywords : Algorithmic Human Resource Management; Artificial Intelligence in HRM; Algorithmic Governance; Human-in-the-Loop Systems; Algorithmic Fairness; Ethical AI; Socio Technical Systems Theory; HR Analytics.

Author : Ruwayda Hussain¹*, Dalli Vineetha Reddy²

Title : Towards a Characterisation of Algorithmic Human Resource Management: Exploring the Duality of Efficiency and Equity

Volume/Issue : 2026;03(06)

Page No : 35-58

Detecting pedestrians in low-light and nighttime environments is a major challenge for autonomous systems, mainly due to poor visibility and the limitations of relying on a single sensor. To address this issue, this project introduces a multimodal pedestrian detection framework that combines infrared (IR) imaging with millimeter-wave (MMW) radar data to improve detection accuracy, reliability, and real-time performance during night conditions. An enhanced YOLOv5-based deep learning model is utilized to extract meaningful spatial and semantic features from infrared images, while radar signals are processed to obtain important information such as distance, speed, and position of moving objects. To further enhance tracking accuracy, an Extended Kalman Filter (EKF) is applied to reduce noise in radar data and to predict the motion of pedestrians over time. The system performs spatiotemporal fusion by aligning radar target points with corresponding regions in infrared images, followed by a correlation gating mechanism to ensure proper association between the two data sources. Finally, a decision-level fusion strategy integrates the outputs to produce precise and consistent pedestrian detection results. Experimental results show that the proposed approach outperforms single-sensor methods, offering improved precision and robustness under challenging nighttime conditions. This work contributes to enhancing the safety and reliability of autonomous vehicles and intelligent monitoring systems operating in low-visibility environments.

Keywords : autonomous vehicles, pedestrian detection, sensor fusion, YOLOv5, infrared camera, millimeter-wave radar, target tracking, Extended Kalman Filter, nighttime detection, real-time object detection

Author : G.Swathi Reddy1, Boga Venkat Sai2, Bommadeni Sricharan3, Bookya Madhu Nayak4, Chininga Praveen5, Jula Shiva Rama Krishna6

Title : Infrared and Millimeter-Wave Radar Fusion for Robust Night Pedestrian Recognition

Volume/Issue : 2026;03(06)

Page No : 59-64

The electrification of transportation, especially city buses, is becoming increasingly important for achieving sustainable mobility and reducing environmental impact. However, designing and operating battery electric buses (BEBs) efficiently requires a deep understanding of real-world driving conditions, including variations in speed, passenger load, traffic congestion, and route characteristics. One of the major challenges in this domain is accurately predicting energy demand, as uncertainties often lead to overly conservative system designs, resulting in higher costs, oversized batteries, and reduced operational efficiency. This study addresses these challenges by introducing a set of novel explanatory variables that effectively capture speed patterns and driving behavior. These variables provide a more detailed representation of real-world conditions and are integrated into advanced machine learning models to enhance prediction accuracy. A total of five different algorithms were developed and evaluated, considering factors such as prediction accuracy, robustness, computational efficiency, and practical applicability. The experimental results demonstrate that the proposed models achieve prediction accuracy above 94%, indicating their strong capability in modeling complex energy consumption patterns. Additionally, the approach reduces reliance on complex physical simulations, making it more scalable and suitable for real-time applications. This enables fleet operators to make better decisions regarding route planning, battery sizing, and charging infrastructure placement. Overall, the proposed methodology contributes to cost reduction, improved operational efficiency, and supports the wider adoption of clean and sustainable public transportation systems. Furthermore, the proposed framework can be easily adapted to different cities and operational scenarios, making it highly flexible for future deployments. It also opens opportunities for integrating real-time data analytics and smart transportation systems, enabling continuous monitoring and optimization of energy usage. This adaptability ensures long-term benefits in evolving urban mobility environments.

Keywords : Electric buses, energy demand prediction, machine learning, speed profile analysis, sustainable transportation, battery electric vehicles, fleet optimization

Author : Dr.Amita Johar1, Bobbala Bhavana2, Annarapu Meghana3, Chavva Vishwateja4, G Arpan Varma5

Title : Data-Driven Analysis of Energy Efficiency in Battery Electric City Buses

Volume/Issue : 2026;03(06)

Page No : 65-70

This study introduces an AI-driven system for detecting and localizing road surface damage using deep learning methods. The approach employs three variants of the YOLO (You Only Look Once) architecture—YOLOv5, YOLOv7, and YOLOv8—to analyze and classify road images. A custom dataset containing multiple categories of road defects, including potholes, cracks, and surface irregularities, was used for training and evaluation. Comparative results indicate that YOLOv8 delivers the best performance, achieving an accuracy of 85%, followed by YOLOv7 at 82% and YOLOv5 at 65%. Additional evaluation metrics such as precision, recall, and confusion matrices further confirm the superior detection capability of YOLOv8 with reduced misclassification rates. The system also supports real-time detection, visually marking damaged areas with bounding boxes. Overall, the proposed framework demonstrates the effectiveness of deep learning in automating road condition assessment, with practical applications in smart infrastructure management and autonomous transportation systems.

Keywords : road damage detection, YOLOv5, YOLOv7, YOLOv8, deep learning, object detection, computer vision, image processing, automated road maintenance, smart infrastructure

Author : G.Swathi Reddy1, Maidamshetti Ajay2, Palagani Anil3, Chowdari Eshwar4, Bhukya Somesh5, Alugulla Vamshi Kiran6

Title : Automated Road Damage Detection via UAV Imaging and Deep Neural Networks

Volume/Issue : 2026;03(06)

Page No : 71-77

Cloud storage has become a popular solution for handling large volumes of data, but its centralized nature raises serious concerns related to security and data privacy. To address these issues, this work presents a decentralized approach using blockchain technology, which eliminates dependency on a single storage authority. In this system, any node connected to the internet can participate in the network, forming a peer-to-peer structure that improves resource utilization and reliability. The proposed model ensures data security by encrypting user files before storage and distributing them across multiple nodes using the IPFS protocol. IPFS generates a unique hash value for each file, which acts as an identifier and is securely recorded on the blockchain. This approach ensures data immutability, transparency, and easy retrieval. Overall, the system provides a secure, efficient, and highly available method for storing data in a decentralized cloud environment.

Keywords : Blockchain, Decentralized Cloud Storage, Data Security, IPFS, Peer-to-Peer Network, Data Encryption, Distributed Systems, Data Integrity, Secure Storage, Cloud Computing

Author : Dr.A.Anil Kumar Reddy1, Kankatala Kiran2, Kurakula Kushlu3, Oribindi Ramakrishna4, Samala Badrinath5

Title : Blockchain-Enabled Secure Data Storage in Decentralized Cloud Environments

Volume/Issue : 2026;03(06)

Page No : 78-83

Floods are one of the most destructive natural disasters, causing serious damage to lives, agriculture, and infrastructure. Predicting floods in advance is a difficult task due to changing environmental conditions and complex water systems. This study presents a flood forecasting model based on federated learning, which allows multiple locations to train models without sharing raw data. This approach helps reduce network delays while maintaining data privacy and security. Instead of sending large datasets to a central server, local models are trained and only the learned parameters are shared. The proposed system analyzes data from 18 different stations to identify areas at risk and provides flood alerts up to five days in advance. A feedforward neural network (FFNN) is used to predict water levels using factors such as rainfall, snowmelt, flow routing, and hydrodynamics. The model was tested on data from 2010 to 2015 and achieved an accuracy of 84%.

Keywords : Flood Forecasting, Federated Learning, CNN2D, FFNN, Water Level Prediction, Hydrological Data, Rainfall-Runoff, Disaster Management

Author : P.Arun Reddy1, Pemmanaboina Rohith Karthikeya2, Ettaboina Shiva Krishna3, Dadige Shiva Kumar4, Navapet Sharan Kumar Yadav5

Title : Advanced Flood Prediction Using Federated Learning with 2D Convolutional Networks

Volume/Issue : 2026;03(06)

Page No : 84-89

Lung cancer is one of the most life-threatening diseases, mainly because it is often detected at a late stage. Identifying the disease early and determining its stage accurately can greatly improve a patient’s chances of survival. Although various techniques such as image processing, biomarker analysis, and automated systems are available, achieving high accuracy in early diagnosis still remains a major challenge for medical professionals. In this study, CT scan images obtained from the LIDC-IDRI dataset are used to develop an improved detection system. Unlike traditional approaches that depend on manual observation, this research introduces a Hybrid Neural Network-based method called Cancer Cell Detection using Hybrid Neural Network (CCDC-HNN). The model uses deep learning techniques to extract important features from CT images, which helps in identifying cancer at an early stage. Additionally, a 3D Convolutional Neural Network is applied to enhance the accuracy of detection and to clearly distinguish between benign and malignant tumors. The performance of the proposed model is evaluated using standard statistical measures, and the results show that it provides reliable and accurate predictions, making it a promising approach for early lung cancer diagnosis.

Keywords : Lung Cancer Detection, Hybrid Neural Network, Deep Learning, 3D Convolutional Neural Network (3D-CNN), CT Scan Imaging, Early Diagnosis, Feature Extraction, Tumor Classification, Benign and Malignant Detection, Medical Image Analysis, LIDC-IDRI Dataset, Computer-Aided Diagnosis (CAD).

Author : Aseena Shaik Babu1, Oruganti Aravindu2, Komatireddy Sai Vardhan3, Dupati Abhilash4, Empati Harshit5

Title : An Integrated Hybrid Neural Network Framework for Early Prediction of Lung Cancer

Volume/Issue : 2026;03(06)

Page No : 90-95

The rapid progress in natural language generation has made it easier to create realistic text, which can be misused to influence opinions on social media platforms. Modern deep learning models are capable of generating content that closely resembles human-written text, making it difficult to distinguish between genuine and machine-generated messages. This creates serious concerns regarding misinformation and the spread of misleading content through automated accounts. To address this issue, this study focuses on detecting machine-generated tweets using a deep learning approach. A Convolutional Neural Network (CNN) model is designed and combined with FastText word embeddings to effectively capture semantic patterns in text data. The model is trained and evaluated using the Tweepfake dataset, which contains both human-written and bot-generated tweets. For comparison, several baseline machine learning models and alternative deep learning architectures such as LSTM and CNN-LSTM are also tested. The results show that the proposed CNN with FastText embeddings achieves an accuracy of around 93%, demonstrating its effectiveness in identifying synthetic tweets. This approach offers a reliable solution for improving the detection of fake content on social media

Keywords : Deep Learning, Deepfake Detection, Machine-Generated Text, Social Media Analysis, FastText Embeddings, Convolutional Neural Network (CNN), Tweet Classification, Natural Language Processing, Fake Content Detection

Author : P.Arun Reddy1, Md Rabbani2, Chowla Manikya Kalyan3, Masampally Venkat Ramana4, Sriperumbuduru Shyam Sundar5, Gudeti Vivek Vardhan6

Title : Detection of Machine-Generated Social Media Content Using Hybrid Deep Learning and Fast Text Representation

Volume/Issue : 2026;03(06)

Page No : 96-101

Quality Assurance and Food Authenticity using Blockchain is an advanced system designed to improve transparency, traceability, and trust in the food supply chain. The increasing incidents of food adulteration, counterfeit products, and lack of proper monitoring have raised serious concerns regarding food safety and consumer health. Traditional food tracking systems often suffer from poor transparency, data manipulation, and inefficient record management. To overcome these challenges, blockchain technology provides a decentralized and tamper-resistant platform for securely storing and sharing food supply chain information. In the proposed system, every stage of the food lifecycle, including production, processing, packaging, transportation, and distribution, is recorded as immutable transactions on the blockchain network. Smart contracts are used to automate verification processes and ensure compliance with food quality standards. Consumers, suppliers, farmers, and regulatory authorities can access real-time product information such as origin, manufacturing details, storage conditions, and authenticity verification through QR codes or digital tracking systems. The integration of blockchain enhances food safety, reduces fraud, improves supply chain efficiency, and increases consumer confidence. This system provides a reliable solution for maintaining food quality assurance and ensuring authenticity in modern agricultural and food distribution industries.

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Author : Sonti Swathi, Venkata Ratnam Ganji

Title : QUALITY ASSURANCE & FOOD AUTHENTICITY USING BLOCKCHAIN

Volume/Issue : 2026;03(06)

Page No : 102-110

“Smart Irrigation System Using AI Powered Sensors” presents an intelligent water management solution designed to optimize irrigation practices in agriculture using artificial intelligence and sensorbased technology. Traditional irrigation methods often lead to water wastage due to over-irrigation or inefficient scheduling, which negatively impacts crop growth and resource sustainability. The proposed system integrates AI-powered sensors to continuously monitor soil moisture, temperature, humidity, and environmental conditions in real time. These sensor readings are processed using machine learning algorithms to predict the precise water requirements of crops and automatically control irrigation systems. The framework enables smart decision-making by adjusting water supply based on soil conditions and weather forecasts, ensuring optimal irrigation levels. This approach helps in conserving water resources, reducing manual intervention, and improving crop yield efficiency. Experimental analysis shows that AI-based irrigation systems significantly enhance water usage efficiency compared to conventional irrigation methods. The proposed system contributes to sustainable agriculture by promoting precision farming techniques and intelligent resource management.

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Author : Maddala Ramya Sri, Ketineni Lakshmi Prasuna

Title : SMART IRRIGATION SYSTEM USING AI POWERED SENSORS

Volume/Issue : 2026;03(06)

Page No : 111-118

“Time Series Forecasting and Modelling of Food Demand Supply Chain Based on Regressors Analysis” presents an intelligent predictive framework for analyzing and forecasting food demand within supply chain management systems using time series modelling and regression-based analytical techniques. The study focuses on identifying the influence of various regressors such as seasonal variations, population growth, consumer purchasing behavior, climate conditions, transportation costs, and market trends on food demand and supply fluctuations. By applying machine learning algorithms, statistical forecasting models, and regression analysis to historical supply chain data, the proposed system aims to improve demand prediction accuracy, reduce food wastage, optimize inventory management, and enhance distribution efficiency. The framework supports real-time decision-making for suppliers, retailers, and policymakers by providing reliable forecasts that help maintain supply chain stability and ensure food availability. Experimental results demonstrate that integrating regressor analysis with time series forecasting significantly improves prediction performance and contributes to the development of sustainable and efficient food supply chain management systems.

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Author : Polagani Naga Sateesh, Lnv Rao

Title : TIME SERIES FORECASTING AND MODELLING OF FOOD DEMAND SUPPLY CHAIN BASED ON REGRESSORS ANALYSIS

Volume/Issue : 2026;03(06)

Page No : 119-130

Introduction: Artificial Intelligence (AI) is revolutionizing the nature of governance, economic systems, and knowledge infrastructures around the globe. The emergence of generative AI and algorithmic governance is escalating the existing difficulties associated with fragmentation of regulation, economic consolidation, institutional lagging, and disparities in technological capabilities between advanced and developing countries. Purpose: To explore how the AI transformation process influences the evolution of regulatory, economic, and knowledge systems while discussing the limitations of traditional models of governance. Methodology: Mixed methods comparative institutional research design which applies governance mapping, policy analysis, quantitative data, and conceptual systems modeling techniques. Conclusion: The paper argues that AI is creating new opportunities but also poses significant risks, especially for the Global South. In particular, the results show important disparities in AI governance maturity, with the EU exhibiting higher levels of institutional adaptability whereas Global South countries experience governance and infrastructural deficiencies. Moreover, less than ten multinational companies account for almost 78% of AI infrastructures' capacity on the planet, increasing economic concentration and algorithmic dependencies. Contribution: The paper provides a new model of institutional analysis based on a combination of institutional theory, political economy, governance, and AI ethics.

Keywords : Artificial Intelligence; Institutional Transformation; AI Governance; Algorithmic Governance; Regulatory Systems; Political Economy; Digital Governance

Author : Madhu Goyal, S. Peter Emil Jebakumar, Deepti Singh, Tanya Chopra, Divesh Sinha, Shantanu Kumar Sahu

Title : Artificial Intelligence and Institutional Transformation across Regulatory Economic and Knowledge Systems

Volume/Issue : 2026;03(06)

Page No : 131-146

The AI Complaint Management System is an advanced digital platform designed to streamline and automate the process of registering, managing, and resolving user complaints using Artificial Intelligence technologies. Traditional complaint systems often suffer from delays, manual errors, lack of prioritization, and inefficient handling of large volumes of requests. This system addresses these challenges by integrating intelligent algorithms that enhance efficiency, accuracy, and user satisfaction. The primary purpose of this project is to develop a smart and automated complaint handling system that minimizes human intervention and improves response time. By leveraging Artificial Intelligence techniques such as Natural Language Processing (NLP) and Machine Learning (ML), the system can understand user complaints, categorize them into relevant domains, assign priority levels, and route them to the appropriate departments automatically. The system provides a user-friendly interface where users can easily submit complaints and track their status in real time. On the administrative side, it offers a powerful dashboard that enables authorities to monitor complaints, analyse trends, and make data-driven decisions. The AI module plays a crucial role in analysing complaint text, detecting urgency, and even suggesting possible solutions or automated responses. Key features of the system include intelligent complaint classification, priority detection, automated routing, real-time tracking, and data analytics. These features not only reduce the workload of administrators but also ensure faster and more effective complaint resolution. Additionally, the system enhances transparency and accountability by maintaining proper records and providing timely updates to users.

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Author : Mir Abusalam, Md Sartaz, Dr. Sourav Ghosh

Title : AI-COMPLAINT: AN INTELLIGENT WEB-BASED COMPLAINT MANAGEMENT SYSTEM UTILIZING NATURAL LANGUAGE PROCESSING FOR AUTOMATED COMPLAINT CLASSICATION AND SMART DEPARTMENT ROUTING

Volume/Issue : 2026;03(06)

Page No : 147-154

The digital transformation of recruitment processes has emerged as a strategic imperative for modern organizations. This paper presents NextHire, an intelligent web-based recruitment and professional networking platform that addresses the fragmentation and coordination inefficiencies inherent in contemporary hiring ecosystems. The platform integrates centralized job management, real-time bidirectional communication, cloud-based media handling, multi-role recruiter evaluation workflows, and professional social networking into a single scalable system. NextHire is built on the MERN stack (MongoDB, Express.js, React.js, Node.js), augmented with Socket.IO for real-time WebSocket communication, ImageKit for cloud-based media management, and a comprehensive multi-layered security model comprising JSON Web Tokens (JWT), bcrypt.js password hashing, and OTP-based email verification. A four-role RoleBased Access Control (RBAC) system governs access for Candidate, Employer, Recruiter, and Coordinator users, each with dedicated dashboards and permission-controlled functionalities. Comprehensive testing across functional, security, performance, and integration dimensions validates the platform's reliability, scalability, and readiness for production digital hiring environments. The project demonstrates how modern full-stack web technologies, cloud computing services, and real-time communication architectures can be synthesized to build an intelligent, unified recruitment ecosystem.

Keywords : Web-Based Recruitment, MERN Stack, Socket.IO, Real-Time Communication, JWT, Role-Based Access Control, Cloud Storage, ImageKit, Job Portal, Professional Networking, Full-Stack Development, MongoDB, React.js, Node.js.

Author : Subhendu Prasad Hembram, Sourav Dalei, Asst. Prof. Swarnananda Muduli

Title : SmartHire: An Intelligent Web-Based Job Recruitment and Management System

Volume/Issue : 2026;03(06)

Page No : 155-162

The Online Mock Portal is a web-based examination platform developed to provide students with a secure, efficient, and userfriendly environment for practicing online tests. The system is designed to help students improve their knowledge, time management, and examination skills through automated mock tests. It allows users to register, log in, attend exams, view scores, and track their performance in real time.

Keywords : Mock Test Portal , Online Examination System, Automated Result Generation, Web Based Learning.

Author : Ms. Lopamudra Prusty, Asst. Prof. Pranab Kumar Mahanta

Title : Online Mock Portal

Volume/Issue : 2026;03(06)

Page No : 163-169

The rapid growth of online shopping platforms and digital marketplaces has significantly increased the need for intelligent recommendation systems capable of improving user experience and customer satisfaction. Traditional e-commerce platforms often struggle to provide personalized product recommendations based on user interests, browsing behavior, and purchase history. This paper presents the design and implementation of ONEKART, an AI-based E-Commerce Recommendation Platform developed to provide personalized product suggestions using user preferences and hybrid recommendation techniques. The proposed system integrates content-based filtering, collaborative filtering, and intelligent recommendation mechanisms to improve product discovery and customer engagement. The platform is designed using modern web technologies including React.js, Tailwind CSS, Node.js, Express.js, MongoDB, and RESTful APIs to ensure scalability, responsiveness, and secure communication. ONEKART provides features such as user authentication, product management, shopping cart functionality, order tracking, AI-based recommendations, secure payment integration, and admin management. The implementation demonstrates improved recommendation accuracy, better user engagement, and enhanced shopping experiences compared to traditional static e-commerce systems. The proposed platform provides a scalable foundation for future intelligent e-commerce applications.

Keywords : E-Commerce, Recommendation System, Artificial Intelligence, Collaborative Filtering, React.js, Node.js, MongoDB, Personalized Shopping

Author : Mr. Chinmay Kumar Pradhan, Mr. Muktiranjan Patra, Prof. Smruti Smaraki Sarangi

Title : ONEKART: AN E-COMMERCE PLATFORM

Volume/Issue : 2026;03(06)

Page No : 170-179

A Billing Management System (BMS) is an integrated comprehensive Billing Management System that provides a software solution that automates and manages the complete billing centralized, automated, and user-friendly platform to manage the lifecycle between businesses and customers. In today’s digital economy, manual billing processes are inefficient, error-prone, and can lead to revenue loss, customer dissatisfaction, and compliance issues. This project proposes a scalable Billing Management System that simplifies invoice generation, payment processing, and financial reporting using modern technologies such as database automation, real-time analytics, and secure payment integration. The system supports complex pricing models, including subscriptions, usage-based billing, discounts, and recurring payment cycles. It enables administrators to complete billing process efficiently.

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Author : Sawan Kerai, Ayush Raj

Title : Billing Management System

Volume/Issue : 2026;03(06)

Page No : 180-183

Blood donation plays a crucial role in saving lives during medical emergencies, yet traditional blood bank systems suffer from inefficiencies such as manual donor discovery, lack of real-time communication, and absence of donor safety enforcement. In emergency scenarios, delays in locating compatible donors during the critical “Golden Hour” significantly impact patient survival. This paper presents Pulse, a modern web-based Blood Donation and Emergency Finder System designed to address these limitations. The system integrates a context-aware smart matching algorithm, secure authentication mechanisms, and automated donor safety protocols. Pulse uses a decoupled architecture with React.js frontend and FastAPI backend, ensuring scalability and high performance. A key innovation of the system is the implementation of a 56- day automated cooldown protocol, which prevents unsafe repeated donations. Additionally, the platform provides real-time inventory tracking and centralized administrative control. The proposed system improves emergency response time, ensures donor safety, and enhances healthcare logistics efficiency.

Keywords : Blood Donation, Smart Matching, MERN Stack, FastAPI, Healthcare System, Emergency Response

Author : Somya Ranjan Barik, Smruti Chandan Rath, Dr. Pratyush Ranjan Mohapatra

Title : Pulse: A Context-Aware Blood Donation and Emergency Response System

Volume/Issue : 2026;03(06)

Page No : 184-185

The ShelfSense: Expiry Management System is an intelligent web-based application developed to help users efficiently manage inventory items and monitor product expiry dates. Many households, shops, pharmacies, and small businesses face problems such as product wastage, financial loss, and health risks due to improper tracking of expiry dates. Traditional inventory management methods are mostly manual and do not provide timely notifications or proper waste management support. The proposed system collects and manages product details such as product name, category, quantity, manufacturing date, and expiry date using a centralized digital platform. The application processes this data using backend logic and generates alerts for products that are nearing expiry or already expired. By continuously monitoring product shelf life, the system helps users reduce wastage and improve inventory organization. The project also integrates a smart dashboard that provides visual analysis of inventory status, expiry trends, and waste statistics through charts and graphical reports. In addition, the system includes an AI-based assistant that provides suggestions related to food preservation, product handling, waste reduction, and eco-friendly disposal methods.

Keywords :

Author : Rajat Kumar Behera, Soumyaranjan Sahoo, Allupati Chakradhar Patro

Title : SHELFSENSE: EXPIRY MANAGEMENT SYSTEM

Volume/Issue : 2026;03(06)

Page No : 186-194

In modern campus environments, efficient electricity monitoring and billing management are essential for reducing energy wastage and improving operational efficiency. Traditional electricity management systems mainly depend on manual meter reading and paper-based billing methods, which often lead to calculation errors, delayed bill generation, poor data management, and increased administrative workload. Due to the growing demand for digital campus management systems, it becomes difficult to manage electricity consumption and billing operations efficiently using conventional methods. Recent advancements in web technologies, database systems, and automation techniques have enabled the development of intelligent electricity monitoring and billing management systems. Modern web-based systems allow users and administrators to enter electricity readings, monitor consumption data, generate bills dynamically, and access reports through responsive interfaces. These systems improve billing accuracy, reduce manual effort, and enhance monitoring efficiency. However, many existing systems lack centralized management, secure authentication, scalable architecture, and efficient data handling capabilities. The proposed Electricity Consumption Monitoring and Bill Management System for Campus is designed as a centralized web-based platform that integrates electricity monitoring, bill generation, user management, report generation, and secure authentication into a single system. The application is developed using modern full-stack technologies such as HTML, CSS, JavaScript, Node.js/Python, and MySQL/MongoDB. The system provides responsive user interfaces, automated bill calculation, secure database management, and centralized administrative control for efficient campus electricity management

Keywords : Electricity Monitoring, Bill Management, Web Application, Node.js, Python, MySQL, MongoDB, Campus Management, Full-Stack Development

Author : Ratiranjan Pati, Arup Mohanty, Prof. Allupati Chakradhar Patro

Title : Electricity Consumption Monitoring and Bill Management System for Campus

Volume/Issue : 2026;03(06)

Page No : 195-202

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.

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Author : Mr. Debadatta Tripathy, Asst. Prof. Mohapatra Girashree Sahu

Title : INTERNSHIP MANAGEMENT SYSTEM

Volume/Issue : 2026;03(06)

Page No : 203-206

The rapid expansion of digital media has created an urgent need for purpose-built, enterprise-grade content management platforms tailored specifically to newsroom operations. This paper presents Prime Pulse, a fully functional web-based news publishing portal that addresses the fragmentation and workflow inefficiencies prevalent in contemporary digital journalism ecosystems. The platform integrates a complete editorial lifecycle management system encompassing article creation, multi-level approval workflows, breaking news designation, scheduled publishing, and reader engagement management into a single unified system. Prime Pulse is built on a classic LAMP stack (PHP, Apache, MySQL) augmented with a granular Role-Based Access Control (RBAC) system governing four user roles — Administrator, Editor, Reporter, and Moderator — each with dedicated dashboards and middleware-enforced permission controls. A comprehensive analytics module delivers newsroom-specific performance metrics including category performance, perreporter productivity rankings, peak traffic hour analysis, and breaking news versus regular news comparison. Security mechanisms include CSRF token protection, bcrypt-compatible password hashing, SQL injection prevention via prepared statements, MIME-type file validation, and rate limiting. A newsletter broadcasting system automatically notifies subscribers upon article publication without dependency on third-party services. Comprehensive testing across fifteen functional, security, and compatibility dimensions validates the platform's correctness, robustness, and readiness for production newsroom deployment. The project demonstrates that a feature-rich, enterprise-quality news content management system can be constructed entirely using open-source technologies at minimal operational cost.

Keywords : Web-Based CMS, PHP, MySQL, Role-Based Access Control, News Publishing Portal, Editorial Workflow, Analytics Dashboard, CSRF Protection, Newsletter Broadcasting, LAMP Stack, Content Approval, Newsroom Management, RBAC, Agile Development, Scheduled Publishing.

Author : Chinmaya Mohanta, Debendra Prasad Samal, Prof. Smruti Smaraki Sarangi

Title : Prime Pulse: An Enterprise Role-Based Web-Based News Content Management System

Volume/Issue : 2026;03(06)

Page No : 207-215

The rapid growth of digital technology has significantly transformed various sectors, including commerce, transportation, and food delivery. However, street vendors—especially in Tier-2 and Tier-3 cities—still rely on traditional methods of selling, limiting their reach and efficiency. This paper presents the development of a Street Vendor Digital Platform, a hyperlocal web application designed to connect nearby users with street vendors such as chai sellers, pani puri vendors, and tiffin service providers. The platform enables users to discover nearby vendors using location-based services, browse menus, and place orders efficiently. A key challenge in cash-on-delivery systems is the prevalence of fake orders, which negatively impacts vendors. To address this, the system introduces an OTP-based pickup verification mechanism and a trust scoring system that tracks user behavior. The system is developed using React.js for the frontend, Tailwind CSS for styling, and FastAPI (Python) for backend development. SQLite serves as the database for storing user, vendor, and order data. OCR-based AI integration is used for extracting menu items from images. The proposed solution enhances vendor visibility, improves customer convenience, and ensures secure transactions. The system is scalable, user-friendly, and designed for real-world impact in local communities.

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Author : Mr. Maheswar Malik, Mr. Karanjeet Debasish Beura, Lecturer Subhalaxmi Nayak

Title : Street Vendor Digital Platform: A Hyperlocal Ordering System

Volume/Issue : 2026;03(06)

Page No : 216-222