International Journal of Information Technology and Computer Engineering
JOURNAL SUMMARY: Editor- In-Chief: Dr. Abdul Wasay Title proper: International Journal of Information Technology and Computer Engineering ISSN : 2347-3657 Subject: Computer Sciences & Information Technology, AI & ML, Data Science Publisher: International Journal of Information Technology and Computer Engineering Dates of publication: 2013 Frequency: Quarterly Language: English Country: India Medium: Online Indexed by: CROSSREF Indexed by: ROAD Indexed by: THE KEEPERS Title DOI of the Journal: http://doi.org/10.62647/issn.2347-3657 APC: $100/-
AI-Powered Real Time Navigation Aid For Visually Impaired Using Mobile Live Video
This project presents a real-time navigation aid that utilizes a mobile camera integrated with on-device Artificial Intelligence (AI), specifically leveraging YOLO and TensorFlow Lite models, to provide continuous guidance and environmental awareness. Visually impaired individuals often face significant challenges in detecting obstacles and recognizing objects in real time, particularly in unfamiliar or crowded environments. The proposed system captures live video streams, which are processed using neural network models to detect objects and estimate their positions. The detected information is transformed into simple navigational cues and delivered to the user through real-time audio feedback using Text-to-Speech (TTS) technology for immediate guidance. Haptic feedback (vibration) is incorporated as a key feature, where the vibration intensity and duration are adjusted based on the object's proximity (area ratio), enabling users to perceive obstacle distance and direction effectively. The complete detection and feedback mechanism operates entirely offline, ensuring reliable functionality without requiring internet connectivity. However, for enhanced accessibility during startup, the application can be launched and closed using voice assistants such as Google Assistant, Gemini, and Bixby, which require an active internet connection. Once activated, all core functionalities operate fully offline. The application is designed for continuous operation through an Android Foreground Service, allowing efficient performance even when the device screen is turned off. In addition, an auto-torch feature is integrated to support accurate obstacle detection under varying lighting conditions. The proposed system aims to provide an efficient, accessible, and dependable navigation solution for visually impaired users through real-time mobile AI-based assistance.
Assistive Technology, Visual Impairment, Real-Time Object Detection, YOLO, TensorFlow Lite, Offline Navigation System, Audio Feedback, Haptic Feedback, Android Foreground Service, Mobile Artificial Intelligence.
Copyright (c) 2026 S Revathi, B. Samhitha, E. Sarika, K. Usharani (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Spectral Efficiency of MU-Massive MIMO System under Perfect and Imperfect CSI Conditions
The increasing performance requirements of fifth-generation (5G) wireless networks have intensified the need for communication techniques capable of delivering high data throughput, improved spectral utilization, and reliable multi-user connectivity. Multi-User Massive Multiple-Input Multiple-Output (MU-Massive MIMO) has emerged as a promising solution by employing large-scale antenna arrays at the base station to simultaneously support multiple users within the same spectral resources. This study investigates the spectral efficiency performance of a MU-Massive MIMO system operating in the Sub-6 GHz frequency band under both perfect and imperfect Channel State Information (CSI) conditions. A MATLAB-based simulation framework is developed to model channel behavior and evaluate system performance using key parameters including spectral efficiency, signal-to-noise ratio (SNR), and channel estimation uncertainty. To enhance transmission performance and suppress inter-user interference, linear precoding methods such as Zero Forcing (ZF) and Maximum Ratio Transmission (MRT) are implemented and analyzed. Simulation observations indicate that systems operating with accurate CSI achieve superior spectral efficiency, whereas imperfect channel knowledge leads to measurable performance degradation due to estimation inaccuracies and interference effects. The proposed model demonstrates the suitability of MU-Massive MIMO for modern 5G communication environments requiring efficient spectrum management and high-capacity user support. Furthermore, the developed framework provides a flexible foundation for future extensions involving advanced channel estimation schemes, hybrid beamforming approaches, and multi-cell deployment scenarios. The findings of this study contribute to a deeper understanding of the relationship between CSI accuracy and spectral efficiency, offering practical guidance for the design and optimization of next-generation wireless communication systems.
Multi-User Massive MIMO, Spectral Efficiency, Channel State Information, 5G Wireless Communication, Sub-6 GHz, Zero Forcing, Maximum Ratio Transmission, MATLAB Simulation, Precoding Techniques, Wireless Networks.
Copyright (c) 2026 Ms K Srinidhi Reddy, V Rithvika, M Varshitha Reddy, G Vanaja (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Smart Driver Drowsiness and Distraction Detection
In recent years, road accidents have increased significantly due to human errors, among which driver drowsiness and distraction are the major causes. Long driving hours, lack of sleep, and the use of mobile phones while driving reduce the alertness of drivers, leading to dangerous situations. To overcome this problem, a smart driver drowsiness and distraction detection system is proposed to improve road safety and reduce accident rates. The main objective of this project is to develop a real-time monitoring system that continuously observes the driver’s behavior and detects signs of fatigue and distraction. The system uses a camera to capture live video of the driver’s face and applies image processing techniques to analyze facial features such as eyes, mouth, and head movements. Drowsiness detection is performed by measuring parameters like Eye Aspect Ratio (EAR), blink rate, and yawning frequency. When the driver’s eyes remain closed for a longer duration or frequent yawning is detected, the system identifies it as a sign of fatigue. Similarly, distraction detection is carried out by monitoring head pose, gaze direction, and activities such as looking away from the road or using a mobile phone. The system is implemented using computer vision and machine learning algorithms, typically with tools like Python and OpenCV. These technologies enable accurate and fast processing of real-time video data. The system is designed to work under different lighting conditions and can adapt to various driver behaviors. Once drowsiness or distraction is detected, the system immediately generates an alert in the form of a buzzer sound or voice notification. This helps the driver to regain attention and take corrective action, thereby preventing potential accidents. The proposed system is cost-effective, efficient, and easy to integrate into existing vehicles. It does not require complex hardware and can be implemented using a simple camera and processing unit. The system provides high accuracy and reliability in detecting unsafe driving conditions.
Driver Drowsiness Detection, Driver Distraction Detection, Computer Vision, Eye Aspect Ratio.
Copyright (c) 2026 Ms S Surekha, S Rishitha, S Sahithya, N Usha Sri (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Spatial Distribution Of Crop Rotation Using GIS And RemoteSensing Of Mansa District, Punjab (2024–25)
Agriculture in Punjab, India, plays an essential role in national food security, with the region contributing an important role in the country's wheat and rice production. Unsustainable crop rotation practices, groundwater depletion, and soil fertility loss have become major issues in recent years. The research focuses on mapping and assessing the spatial distribution of major crops such as cotton, paddy, wheat, and mustard, which shows the region’s double-cropping system. Unsupervised classification was carried out in ERDAS Imagine, and the classified outputs were further analyzed and mapped in ArcGIS 10.8 software. The study identifies that cotton and paddy are the major Kharif crops, while wheat and mustard dominate during the Rabi season. The crop rotation analysis shows a strong dependence on irrigation infrastructure, particularly canal and tube well systems, influencing the intensity and type of crop rotation. This study demonstrates the potential of remote sensing and GIS for monitoring crop dynamics, supporting sustainable land use planning, and aiding policymakers in improving agricultural resource management and soil health in Punjab’s semi-arid zones.
Crop rotation, Kharif and Rabi, Mansa district, Remote sensing, GIS, Sentinel-2, agricultural monitoring
Copyright (c) 2026 Renu Soni, Ishant (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Deep Learning And Vision Transformer-Based Approaches For Automated Fundus Image Analysis In Diabetic Retinopathy And AMD Detection
The Global prevalence of Diabetic Retinopathy(DR) and age-related macular degeneration(AMD) is anticipated to rise knowingly in the coming decades, posing significant social and economic challenges. Early detection and diagnosis of these retinal diseases are crucial, and recent advances in artificial intelligence (AI) and digital image processing have opened new avenues for automated and accurate screening. This study explores the feasibility and perception of using the state-of-theart AI approaches in analyzing retinal fundus images for DR and AMD detection. The optometrists partaking in the study tested an AI-assisted diagnostic system integrating the latest deep learning methods, such as convolutional neural networks (CNNs) and vision transformer (VT) architectures. Data were collected through the semistructured consultations and analyzed using inductive content analysis, following the Consolidated Criteria for Reporting Qualitative Research (COREQ) guidelines. The findings exposed that constantly evolving the AI system significantly augments the image interpretation accuracy, expedites workflow automation, and optimizes patient referral management. Participants acknowledge that AI-driven digital image processing can effectively support clinical decisionmaking; however, broader implementation requires increased education, reliable data governance, and financial sustainability. The result indicates that hybrid AI models combining deep learning and traditional image processing techniques represent a promising direction for future integration of intelligent diagnostic tools in ophthalmology.
Artificial intelligence · Deep learning · Convolutional neural networks · Vision transformers · Digital image processing · Diabetic retinopathy · Age-related macular degeneration · Fundus image analysis · Ophthalmic diagnosis
Copyright (c) 2026 Dr. Anastraj K, Mr. Rajaprabhu. A (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
A Robust VTAC-Assisted Machine Learning Framework for MoneyLaundering Detection in Block chain Systems
Money laundering through bitcoin transactions has grown to be a significant issue due to the decentralized and pseudonymous nature of blockchain networks. Traditional machine learning methods for identifying illegal transactions often have limited accuracy and poor generalization when working with large and complex transaction datasets. In order to get over these limitations, this study proposes an enhanced money laundering detection system that uses advanced boosting and hybrid machine learning methods. The system uses a hybrid model that combines powerful boosting algorithms like LIGHTGBM and CATBOOST with XGBOOST and Random Forest to improve detecting capabilities. Value-driven Transactional Analytics for Crypto Compliance (VTAC) is utilized to identify high-frequency, multilayer transaction patterns that are often associated with money laundering. Experimental evaluation on the Elliptic Bitcoin Dataset demonstrates that the proposed approach significantly improves prediction performance, with LIGHTGBM achieving the highest accuracy of 99.85%. Since the results confirm that advanced boosting and hybrid models provide better accuracy, scalability, and resilience, the recommended technique is very effective for real-time blockchain compliance and anti-money laundering applications.
Associate Professor; Department of CSE BVC Engineering College, Odalarevu Andhra Pradesh, India.
Copyright (c) 2026 Ranapratap Pola, B. Ganga Bhavani (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Ad Click Fraud Detection Using Machine Learning and DeepLearning Algorithms
Online advertising systems face a serious threat from ad click fraud, which produces fraudulent clicks that skew campaign results and raise losses. This paper provides an attention-based Convolutional Neural Network (CNN) framework for real-time ad click fraud detection in order to overcome the shortcomings of traditional machine learning and deep learning models in collecting complicated contextual patterns. In order to improve prediction reliability, the suggested improvement incorporates an attention mechanism into the CNN design to highlight the most interesting characteristics and sequential relationships in clickstream data. Furthermore, realtime fraud prediction via an interactive interface is made possible by a web-based deployment utilizing the Flask framework. The attention-enhanced CNN outperforms baseline models in terms of accuracy and resilience, according to experimental assessment, underscoring its usefulness for scalable and realistic ad click fraud detection in dynamic online advertising contexts.
Ad Click Fraud Detection, Attention Mechanism, Convolutional Neural Network (CNN), Deep Learning, Clickstream Analysis, Real-Time Prediction, Flask Deployment, Online Advertising Security, Fraud Analytics, Feature Learning
Copyright (c) 2026 Dondapati Veera Swamy, Motha Prameela, Sk. Badar, Sevugarajan, Sateesh Yalavarthi (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
The rapid growth of Artificial Intelligence (AI) and Machine Learning (ML) technologies has significantly transformed the healthcare sector by enabling intelligent and automated medical solutions. The proposed AI-Based Disease Detection System is developed to predict diseases based on symptoms entered by users through a web-based application. The main objective of this system is to provide fast, accurate, and efficient preliminary disease prediction that can assist both users and healthcare professionals in early diagnosis. The system uses machine learning algorithms such as Decision Tree, Naïve Bayes, and Support Vector Machine (SVM) to analyze symptom data and identify the most probable disease. Medical datasets containing symptoms and disease information are collected, preprocessed, and used for training and testing the prediction models. The application is implemented using Python and Flask for backend processing, while HTML and CSS are used for developing the frontend interface. The developed system provides a user-friendly environment where users can enter symptoms and receive prediction results within a short time. The proposed model helps reduce manual effort, improves accessibility to healthcare support, and assists in early disease identification. Experimental analysis shows that the system achieves reliable prediction accuracy and efficient performance. This project demonstrates the effective application of Artificial Intelligence in healthcare and highlights the potential of machine learning techniques in improving disease prediction systems and supporting modern medical services.
Copyright (c) 2026 Ms. Reshma Begum, Syed Mohammed Badruddin Junaid (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
The increasing complexity of modern System-on-Chip (SoC) architectures demands fast, scalable, and reliable communication mechanisms between integrated modules. Conventional shared-bus communication methods face significant challenges such as bandwidth limitations, congestion, latency, and poor scalability when the number of connected components increases. To address these issues, packet-switched routing architectures have emerged as an effective solution for high-speed digital communication systems.This paper presents the design and implementation of a Verilog HDL-based 1×3 packet router capable of transmitting data packets from a single input channel to one of three output ports according to destination address information embedded in the packet header. The proposed router architecture consists of a Finite State Machine (FSM) controller, synchronizer, register unit, FIFO buffers, and parity checker modules. The FSM supervises packet reception, routing, buffering, synchronization, and transmission processes to ensure smooth data flow. FIFO memories are incorporated to prevent packet loss and manage temporary storage during varying transmission rates.To enhance communication reliability, parity-based error detection is implemented for identifying corrupted packets during transmission. The complete router design is modeled using Verilog HDL and functionally verified through simulation using standard FPGA/ASIC design tools. Simulation outcomes confirm accurate packet routing, synchronization, and error handling operations.The proposed architecture offers improved throughput, reduced transmission delay, and efficient packet management compared with conventional bus-oriented communication systems. Owing to its modular and scalable structure, the router can be extended for Network-on-Chip (NoC) architectures and advanced multi-core communication applications.
1×3 Router, Verilog HDL, System-on-Chip (SoC), Packet Routing, Finite State Machine (FSM), FIFO Buffer, Network-on-Chip (NoC), Digital Communication, Packet Switching, Error Detection.
Vanam Pravalika, Chittepu Shreya, Molagasi Saraswati
Copyright (c) 2026 Ms B Eleena, Vanam Pravalika, Chittepu Shreya, Molagasi Saraswati (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Computational Methods for Solving Nonlinear Equations in Engineering and Science
Nonlinear equations are widely encountered in various fields of engineering and science, including mechanical systems, electrical circuits, fluid dynamics, thermodynamics, and chemical engineering processes. These equations often describe complex real-world phenomena where variables interact in a non-proportional manner, making analytical solutions difficult or, in many cases, impossible to obtain. As a result, computational methods have become essential tools for approximating solutions to such equations with acceptable accuracy and efficiency. This paper focuses on the study and application of numerical techniques for solving nonlinear equations, emphasizing methods such as the Bisection Method, Newton–Raphson Method, Secant Method, and Fixed Point Iteration. Each method is discussed in terms of its mathematical formulation, algorithmic procedure, and convergence characteristics. The Bisection Method, a bracketing technique, is known for its reliability and guaranteed convergence, although it may exhibit slower performance. In contrast, open methods like Newton–Raphson and Secant methods offer faster convergence rates but depend heavily on initial guesses and may fail under certain conditions. Graphical interpretations of these methods are considered to provide intuitive understanding of how successive approximations approach the root of a nonlinear function. Additionally, error analysis and convergence criteria are examined to evaluate the accuracy and stability of each method. A comparative study is presented to highlight the advantages and limitations of these techniques in practical scenarios.
Nonlinear Equations, Numerical Methods, Bisection Method, Newton–Raphson Method, Secant Method, Convergence, Engineering Applications, Scientific Computing
Copyright (c) 2026 Dr. K. Karuppiah, DrT.Thirumalaikumari, Mrs R.Aruna, Dr.R.Praveena, DR.R.Venugopal (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
The Smarter Banking – ChatFin is an intelligent conversational banking assistant designed to simplify and enhance user interaction with financial services. The system leverages Artificial Intelligence and Natural Language Processing (NLP) to provide real-time responses to user queries related to account information, transactions, and financial guidance. ChatFin enables users to perform tasks such as checking balances, transferring funds, viewing transaction history, and receiving personalized financial insights through a user-friendly chat interface.The application is designed to improve accessibility and efficiency by reducing the need for physical bank visits and long waiting times. It ensures secure communication using authentication mechanisms and data encryption techniques to protect user information. ChatFin can operate on standard devices without requiring specialized hardware, making it highly scalable and cost-effective.Overall, the system aims to deliver a seamless, fast, and reliable digital banking experience, bridging the gap between customers and banking services while promoting smarter financial management.
Artificial Intelligence, Natural Language Processing, Chatbot, Digital Banking, Financial Assistant, Secure Payment Integration, User Authentication, Data Security, Real-time Processing, Smart Banking System.
Copyright (c) 2026 B Anitha, T.V.Sai Mahathi, V.Sheethal, M.Sriteja (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Enhanced Hotel Booking Cancellation Forecasting Using PCA-Based Feature Selection And Stacking Ensemble Learning
Due to revenue loss and erroneous demand projections, hotel booking cancellations provide a significant issue to the hospitality sector. Although stacking-based ensemble learning models have demonstrated good prediction performance, duplicated and irrelevant information may have an impact on their effectiveness. This study offers an improved stacking meta-model using Principal Component Analysis (PCA) for feature selection and dimensionality reduction in order to overcome this drawback. The most pertinent characteristics from the hotel booking dataset are found using PCA, and the stacking model is retrained with these features to increase prediction accuracy and efficiency. The improved model outperforms the original stacking model with an accuracy of 97.3% by integrating many machine learning methods and using logistic regression as the final estimator. Additionally, SHapley Additive exPlanations (SHAP) are used to offer comprehensible insights into important variables affecting reservation cancellations. The suggested expansion shows how PCA-driven feature optimization greatly improves model robustness, computational efficiency, and interpretability, providing a useful and efficient solution for operational decision-making and hotel revenue management.
Hotel booking cancellation prediction, stacking meta-model, principal component analysis (PCA), feature selection, ensemble learning, explainable artificial intelligence (XAI), SHAP, machine learning, revenue management, hospitality analytics.
Copyright (c) 2026 Sasi Preetham Gundu, Dr. Meerasharif Sheik (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Student Wellbeing Dropout Prediction System
The Student Wellbeing Dropout Prediction System is an integrated platform that combines academic tracking, attendance monitoring, and health management within a unified application. Developed using a Flask-based API and enhanced with Machine Learning techniques, the system enables institutions to monitor, analyze, and support student wellbeing more effectively.The platform aggregates data on academic performance, attendance, lifestyle habits, and physical and emotional health to provide a holistic view of each student. Machine Learning models process this data to generate risk scores and classify students into low- and high-risk categories. High-risk cases trigger automated alerts for educators, enabling timely intervention.An interactive dashboard visualizes trends, risk levels, and key insights, while report generation features support documentation and follow-up. By integrating predictive analytics with real-time monitoring, the system facilitates early identification of at-risk students and supports proactive decision-making.Additionally, the platform helps uncover patterns and correlations between academic outcomes and wellbeing factors, enabling institutions to design targeted preventive strategies. Overall, the system aims to reduce dropout rates and foster a more supportive and data-driven educational environment.
Student Dropout Prediction, Academic Performance Monitoring, Wellbeing Analytics, Machine Learning, Attendance Tracking, Risk Assessment, Flask API, Dashboard Visualization, Predictive Analytics
Janapala Ruchitha, Neela Sathvika, Perala Srija
Copyright (c) 2026 A Vasavi Sujatha, Janapala Ruchitha, Neela Sathvika, Perala Srija (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Covariance-Supervised And Scalable Dimensionality Reduction (Cspca): A Comparative Study And Implementation-Based Analysis For High-Dimensional Data
Principal component analysis (PCA) is a widely used unsupervised dimensionality reduction technique. How- ever, because it ignores target variables, PCA does not guar- antee that derived principal components are informative for predictive tasks. This paper presents an implementation-based analysis of Covariance-Supervised Principal Component Anal- ysis (CSPCA). Our custom implementation incorporates label information directly into the feature extraction process by bal- ancing the covariance between projections and responses against intrinsic explained variance, controlled via a tunable regulariza- tion parameter. Furthermore, to address ultra-high-dimensional genomic datasets, we implement a hybrid pipeline utilizing ANOVA feature pre-selection to isolate high-confidence mark- ers. Experimental evaluations demonstrate that the CSPCA algorithm efficiently captures task-relevant features. It achieves competitive classification accuracies—matching strictly super- vised methods like Linear Discriminant Analysis—while sub- stantially reducing computational execution time compared to non-linear Kernel PCA techniques and deep neural Autoen- coders. By bridging the gap between variance retention and label separability, CSPCA provides a highly robust, generalized framework for both classification and continuous regression tasks.
Covariance-Supervised PCA, Dimensionality Reduction, High-Dimensional Data, Autoencoders, Supervised PCA
Copyright (c) 2026 Dr. T. Prem Chander, Naresh T, Sri Varshith (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Cyber-Sociology: Understanding Risk And Vulnerability In The Digital Era.
This research investigates the sociological dimensions of cybersecurity and digital vulnerability, examining how the ongoing digital transformation interacts with the social structures that define cyberspace. The central premise suggests that digital risks are not merely technical anomalies but are deeply embedded social phenomena emerging from the confluence of technological disparities, collective digital awareness, and human behavioral patterns. As society becomes increasingly reliant on digital media, the resulting expansion of the digital divide has given rise to sophisticated cyberthreats that challenge the security of individuals, institutional integrity, and state sovereignty. To analyze these dynamics, the study synthesizes three primary theoretical frameworks: Ulrich Beck’s Risk Society, which provides a lens for the production of digital hazards in late modernity; Erving Goffman’s Symbolic Interactionism, which elucidates the management of digital identity within networked spaces; and Manuel Castells’s Network Society, which highlights the restructuring of power and the intensification of digital vulnerability. The analysis reveals that vulnerability is primarily driven by deficient security awareness, the digital divide, and a lack of robust institutional governance. Ultimately, the research positions cybersecurity as an essential pillar of broader social security, asserting that a sociological understanding is a prerequisite for mitigating digital risk and ensuring societal stability in a hyper-connected era. DOI: https://doi-ds.org/doilink/05.2026-54869193
cybersecurity, digital age, social vulnerability, digital society, cyber risk.
Malay Maity, Ranjan Banerjee, Misha Samanta
Copyright (c) 2026 Malay Maity, Ranjan Banerjee, Misha Samanta (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
AI Powered ATS Resume And Skill Gap Analyzer
The exponential rise in digital job applications has made manual resume evaluation increasingly impractical, introducing inefficiencies, high operational costs, and susceptibility to subjective bias. Conventional Applicant Tracking Systems (ATS) attempt to address scalability through rule-based keyword filtering; however, such systems depend heavily on exact lexical matches, often discarding competent candidates due to variations in terminology or document structure. This study proposes an intelligent recruitment framework developed using the Django web framework and enhanced with advanced Natural Language Processing (NLP) techniques. The system leverages Sentence-BERT (SBERT) embeddings to represent resumes and job descriptions in a semantic vector space, enabling contextual comparison beyond surface-level keyword matching. A cosine similarity mechanism is applied to quantify alignment between candidate profiles and dynamically generated job requirements. To address the limitations of static job descriptions, the framework integrates a real-time data acquisition module that constructs role-specific descriptions using DuckDuckGo-based metadata extraction. Additionally, a skill gap analysis component identifies discrepancies between candidate competencies and job expectations, providing actionable insights for both recruiters and applicants. An administrative dashboard further supports decision-making by offering interpretable matching scores and career guidance suggestions. The proposed system enhances recruitment efficiency, reduces bias, and improves candidate-job alignment by combining semantic understanding with automated data generation, thereby presenting a scalable alternative to traditional ATS models.
Applicant Tracking System, Natural Language Processing, Sentence-BERT, Semantic Similarity, Recruitment Automation, Resume Screening, Skill Gap Analysis, Django, Cosine Similarity
Copyright (c) 2026 G Sudhakar Raju, B.Sounidhi, K. Varshitha, B.Navyanjali (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
The Gym Management System is a web-based application developed to simplify and automate the daily operations of a gym. The system focuses on managing member information, membership plans, payments, and attendance efficiently without the need for manual record-keeping. The primary objective of this project is to provide an easy-to-use platform for both administrators and members. Members can register, log in, view available membership plans, select suitable plans, make payments, Gym Assistant and mark their attendance. The administrator can manage member details, update membership plans, monitor payments. This system is developed using web technologies such as HTML, CSS, PHP, and MySQL, ensuring a user-friendly interface and reliable data storage. By eliminating manual processes, the system reduces errors, saves time, and improves overall efficiency in gym management. The project is designed without a trainer module, focusing only on core functionalities such as membership management and payment handling. It is suitable for small to medium-sized gyms and serves as a basic model for further enhancements.
Gym Management System, Membership registration, Workout plans, Member Management, Payment System, Gym Assistant, Admin Dashboard.
Balabathuni Srividya, Syed Muskan, Kanchanapally Sandhya
Copyright (c) 2026 G Geetha Devi, Balabathuni Srividya, Syed Muskan, Kanchanapally Sandhya (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
The AI-based Study Planner is an intelligent system developed to assist students in efficiently organizing and managing their academic schedules through advanced computational techniques. Students often encounter difficulties related to ineffective time management, improper prioritization of subjects, and the pressure of handling multiple deadlines simultaneously. This system aims to overcome such challenges by leveraging artificial intelligence to evaluate key parameters, including subject complexity, examination timelines, assignment deadlines, available study hours, and individual learning speed. Based on this analysis, the application generates a customized and adaptive study schedule tailored to each student’s needs. The system is designed to dynamically modify the timetable in response to changes, such as newly assigned tasks or missed study sessions, ensuring continuous optimization. Additionally, it incorporates features like automated reminders, performance monitoring, progress visualization, and intelligent study suggestions to enhance consistency and engagement. By integrating smart scheduling algorithms with personalized learning strategies, the AI Study Planner contributes to improved academic productivity, reduced stress levels, and the development of effective study habits. Ultimately, the system supports students in achieving their educational objectives in a more structured and efficient manner.
Artificial Intelligence, Study Scheduling, Time Optimization, Personalized Education, Academic Planning, Task Prioritization, Productivity Enhancement, Progress Monitoring, Reminder Systems.
Rithika Bandigari, N Rithwika, M Santahalle
Copyright (c) 2026 Tasneem Rahath, Rithika Bandigari, N Rithwika, M Santahalle (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Leaf Disease Detection With Severity Analysis
Agriculture remains a fundamental pillar for global food security and economic development, making early and accurate plant disease detection essential. Leaf diseases significantly impact crop yield and quality, often leading to substantial economic losses. Conventional disease identification methods primarily depend on manual inspection, which is labor-intensive, time-consuming, and prone to human error, particularly in large-scale farming environments. While existing automated approaches employ machine learning for disease classification, they generally lack the capability to assess infection severity and provide actionable insights.This study proposes an integrated system for leaf disease detection coupled with severity analysis using deep learning and image processing techniques. The system processes leaf images through preprocessing steps and employs a trained deep learning model to classify the type of disease. Subsequently, image segmentation techniques are applied to identify and isolate infected regions. The proportion of the affected area is computed to estimate disease severity, which is categorized into levels such as low, medium, and high.In addition to detection, the system visually highlights diseased regions and generates appropriate treatment recommendations through an intuitive web-based interface. The implementation utilizes Python as the core programming language, with TensorFlow/Keras for model development, OpenCV for image analysis, and MySQL for data storage and management.The proposed approach offers a reliable, efficient, and scalable solution for real-time plant disease monitoring. By combining classification, severity estimation, and advisory support, the system enables farmers and agricultural stakeholders to make informed decisions, thereby enhancing crop productivity and minimizing losses.
Leaf Disease Detection, Deep Learning, Image Segmentation, Severity Analysis, OpenCV, TensorFlow, Precision Agriculture, Crop Health Monitoring
B. Niharika, K. Sravya Reddy, K. Seshikalpana
Copyright (c) 2026 G Sudhakar Raju, B. Niharika, K. Sravya Reddy, K. Seshikalpana (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Real-Time Network Intrusion Detection System
The increasing reliance on network systems and cloud-based infrastructure has considerably increased both the complexity and frequency of cyber threats, making traditional instrusion detection methods less efficient.
Copyright (c) 2026 D Navaneetha, N. Sandhya, B. Sowmya, Ch. Srigowri (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Image Based Animal Type Classification - (Cattle & Buffalo)
Cattle and buffalo breed identification plays a crucial role in agricultural management, livestock breeding programs, and veterinary practices, enabling farmers and researchers to make informed decisions about animal health, productivity, and genetic diversity. However, traditional breed classification methods often rely on manual inspection, which can be subjective, time consuming, and prone to errors, especially with the increasing diversity of breeds across different regions. The advent of computer vision and deep learning has opened new avenues for automated breed identification, but challenges remain in handling variations in lighting, pose, and image quality. This project presents a multi-model ensemble deep learning framework for cattle and buffalo breed classification that integrates spatial features, preprocessing techniques, and confidence scoring mechanisms from image data. The proposed system processes input images by applying data augmentation and analyzing them through an ensemble of convolutional neural networks, including ResNet50, MobileNetV3, and EfficientNet-B3.
Cattle & Buffalo Classification, Ensemble Deep Learning, Convolutional Neural Networks, Resnet50, MobileNetV3, EfficientNetB3, Feature Extraction.
Copyright (c) 2026 Mr P Bikshapathy, M Rakesh, S Manish Chandra, K Siddu (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
The AI-Based Interview Practice System is an advanced, automated platform designed to support students and job seekers in improving their interview skills in a structured and effective manner. In a highly competitive job market, many candidates face challenges such as limited interview exposure, low confidence, and inadequate communication abilities. This system addresses these issues by offering an interactive and realistic interview simulation powered by Artificial Intelligence (AI) and Machine Learning (ML).The platform allows users to create accounts, log in securely, and choose specific job roles, domains, and interview categories, including technical, human resources (HR), and aptitude-based assessments. Based on these selections, the system dynamically generates relevant and context-aware interview questions. Users can respond using either text or voice input, enabling a more natural and flexible interaction. Voice responses are processed through speech recognition technology and converted into text for further evaluation.A key component of the system is its intelligent evaluation engine. By leveraging Natural Language Processing (NLP), the system analyzes user responses for accuracy, coherence, grammatical correctness, and contextual relevance. Additionally, voice-based inputs may be evaluated for delivery aspects such as speaking pace, tone, and confidence. Based on these analyses, the system generates a performance score along with detailed feedback highlighting strengths, areas for improvement, and actionable suggestions.The system also includes a performance monitoring dashboard that records user attempts, tracks progress over time, and visualizes improvement trends. A recommendation module further enhances learning by identifying weak areas and suggesting targeted topics and practice questions, enabling a personalized preparation experience.Designed for accessibility and scalability, the platform offers a cost-effective alternative to traditional interview coaching by minimizing reliance on human evaluators. Continuous interaction with the system helps users strengthen their communication skills, build confidence, and improve their readiness for real-world interviews.
Artificial Intelligence, Machine Learning, Natural Language Processing, Interview Training, Speech Recognition, Automated Assessment, Mock Interviews, Feedback Mechanism, Performance Tracking, User Analytics, Communication Enhancement, Confidence Evaluation.
Copyright (c) 2026 K Madhuravani, B Spoorthi, K Tanusri, V Vaishnavi (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Trusta -Threat Recognition And User Safety Model
Phishing attacks have become one of the most considerable and dangerous threat amongst internet. Users are often unable to identify whether the received mail is phishing or legitimate. These phishing attacks may lead to financial loss , credentials theft , misuse of personal information. Traditional blacklist and rule-based approaches are often fail to detect phishing as attackers generate variety of phishing attempts. There is a need of system which is trained with data and detect suspicious emails and URLs efficiently. TRUSTA- Threat Recognition and User Safety Model, an AI-powered, web-based phishing detection platform developed using Python and Flask framework that identifies malicious URLs and emails by analyzing their features. Suspicious features such as abnormal URL patterns ,misleading domains and suspicious keywords are considered during analysis. TRUSTA is trained on machine learning techniques to detect phishing threats across three major regions: URLs, email content, and SMS messages . The system achieves ensures a URL detection accuracy of 95% and an email detection accuracy of 98%, producing an explainable result . This system allow users to submit a URL or e-mail and receive a predicted result stating it is phishing or safe. TRUSTA is proposed to improve phishing awareness among users and provide a fast and easy to use detection platform.
Phishing Detection,URL Analysis, Explainable AI, Real-Time Threat Detection
K. Saipallavi, Sana Tabassum, D. Sindhu, T. Satvika
Copyright (c) 2026 K. Saipallavi, Sana Tabassum, D. Sindhu, T. Satvika (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Prompt-Driven AI Web Automation Using Large LanguageModels
This paper presents a Prompt-Driven AI Web Automation system that bridges the gap between natural language intent and browser-level execution by integrating Anthropic's Claude AI with the Playwright browser automation framework. The platform enables non-technical users to automate complex web tasks — data extraction and form filling — by describing their goals in plain English. Unlike traditional rule-based automation tools such as Selenium scripts that break on DOM changes and require programming expertise, this system uses Claude AI's tool_use capability to reason about page state and generate adaptive action sequences. The architecture follows a ReAct-inspired interleaved reasoning-action paradigm: at each step, the AI agent receives the current page state (URL, visible text, live JPEG screenshot), reasons about the next action, and issues a structured tool call from a seven-tool vocabulary (click, fill, select_option, navigate, scroll, extract_data, done). Real-time transparency is delivered via Flask-SocketIO 5.4.1, streaming live browser screenshots and color-coded action logs to the user's browser. The system is deployed as a Flask 3.1 web application with Bootstrap 5.3.3 frontend, Flask-Login authentication, SQLite user management, and pandas/openpyxl Excel export. Performance testing on localhost demonstrates: page load time of 120ms, SocketIO handshake at 45ms, screenshot streaming at 150ms per frame, simple scraping tasks completing in ~35 seconds, 10-field form fill in ~45 seconds, and PDF parsing in 1.2 seconds. All 10 unit tests and 8 integration tests pass. The system demonstrates that LLM-powered agentic automation can be deployed practically for both technical and non-technical users with full real-time visibility into AI decision-making.
Web Automation · LLM · Claude AI · Playwright · Flask-SocketIO · ReAct · Tool Use · NLP · Form Filling · Data Extraction · PDF Parsing
Mohd Ishaq · Abdur Rahman · Mohd Aman · Syed Shafeeq Ahmed
Copyright (c) 2026 Mohd Ishaq · Abdur Rahman · Mohd Aman · Syed Shafeeq Ahmed, Ms Molugu Bhavana (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Automated Web Application Vulnerability Scanner: An IntelligentSystem for Detecting OWASP Top 10 Security Risks with IntegratedRemediation Engine
Web application security vulnerabilities represent a major threat vector in modern cybersecurity, with the Verizon 2023 Data Breach Report attributing 43% of security breaches to web application exploits. The OWASP Top 10 highlights critical risks such as SQL Injection, Cross-Site Scripting (XSS), and Cross-Site Request Forgery (CSRF); however, manual penetration testing is often prohibitively expensive ($150–300 per hour) and time-consuming for resource-constrained organizations. This paper presents an Automated Web Application Vulnerability Scanner, a comprehensive security assessment platform capable of detecting 11 distinct classes of vulnerabilities while providing integrated remediation intelligence. The system utilizes pattern-based detection algorithms supported by curated payload databases, HTTP request fuzzing with 847 SQL injection variants and 236 XSS payloads, security header analysis across 12 critical headers, and response pattern matching through regular expressions. The architecture consists of a Flask 3.0 backend with an SQLite database, BeautifulSoup4 for DOM parsing, and a Bootstrap 5 dark-themed responsive frontend. Key innovations include an intelligent fix suggestion engine that leverages a CSV-based knowledge base mapping more than 500 vulnerable code patterns to secure implementations, a severity classification algorithm using weighted scoring (Critical: CVSS ≥ 9.0, High: 7.0–8.9, Medium: 4.0–6.9, Low: < 4.0), and a comprehensive scan history with detailed vulnerability reporting. System evaluation demonstrates a 94.2% true positive rate and a 5.8% false positive rate across 50 deliberately vulnerable test applications, with an average scan completion time of 42 seconds for typical web applications containing 15–20 pages. The platform successfully detects 89% of OWASP Top 10 vulnerabilities and achieves 100% accuracy in security header validation. Comparative analysis with commercial tools such as Burp Suite, Nessus, and Acunetix indicates comparable detection accuracy (94.2% versus 96–98%) at zero cost, thereby making professional-grade security testing accessible to small businesses, educational institutions, and independent developers. The proposed platform effectively democratizes web security assessment while maintaining high-quality vulnerability detection capabilities.
Web Security, Vulnerability Scanner, SQL Injection, Cross-Site Scripting, OWASP Top 10, Automated Testing, Security Headers, Penetration Testing, Remediation Engine, Flask Framework.
Abdul Aziz Khan, Amin, Syed Abdur Razzaq
Copyright (c) 2026 Abdul Aziz Khan, Amin, Syed Abdur Razzaq, Ms. Zeba Masroor (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Exploring Deep Learning and Machine Learning Approaches forAutomated Brain Hemorrhage Detection: A Comparative CNN-BasedWeb Application Framework
Brain hemorrhage is a severe and life-threatening medical emergency that demands rapid and accurate diagnosis through Computed Tomography (CT) scan interpretation.
Brain Hemorrhage Detection, Convolutional Neural Network, CT Scan Classification, Deep Learning, Random Forest, SVM, Logistic Regression, Flask, PyTorch, Binary Cross-Entropy, Medical Image Analysis
Muhammad Aasim Uz Zaman Syed Altamash Uddin Siddiqui Nawaz Khan Faiz Ur Rehman
Copyright (c) 2026 Muhammad Aasim Uz Zaman Syed Altamash Uddin Siddiqui Nawaz Khan Faiz Ur Rehman, Mohammed Maseehuddin (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Machine Learning Based Detection of DDoS Attacks
Distributed Denial of Service (DDoS) attacks represent one of the most disruptive threats to online services, overwhelming networks and servers with illegitimate traffic to cause service degradation or complete outages. Traditional rule-based detection systems fail to recognize novel or evolving attack patterns, leaving critical infrastructure exposed. This paper presents a machine learning-based DDoS detection system that achieves 99.39% accuracy in classifying network traffic within Software-Defined Networking (SDN) environments. The system employs a Random Forest classifier trained on the CICDDoS2019-SDN dataset comprising 104,345 network flow records with 13 features including packet count, byte count, flow duration, and transmission rates. Five machine learning algorithms — Random Forest, Decision Tree, Support Vector Machine, K-Nearest Neighbors, and Gradient Boosting — were evaluated, with Random Forest delivering the highest accuracy at 99.39%, a false positive rate of only 72 in 20,869 test samples, and inference time under 5 milliseconds. The system is deployed as a Flask web application providing user authentication, real-time traffic classification via a 13-feature input form, an attack simulation engine, an interactive Chart.js analytics dashboard, context-aware security recommendations, and comprehensive model performance metrics including confusion matrices and feature importance rankings. Experimental results confirm that the MLbased approach significantly outperforms traditional signature-based detection in both accuracy and adaptability to unseen attack patterns.
DDoS Detection · Machine Learning · Random Forest · Software-Defined Networking · Flask · Anomaly Detection · Intrusion Detection · Network Security
Zeba Fatima, Sania Khan, Shaik Fathima, Fouzia Fatima
Copyright (c) 2026 Zeba Fatima, Sania Khan, Shaik Fathima, Fouzia Fatima, Dr. Mohammed Abdul Khaleel (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Big Data ML-Based Fake News Detection Using Distributed Learning
Syed Fardeen, Mohd Irfan Khan, Mohammed Omer Ali
Copyright (c) 2026 Syed Fardeen, Mohd Irfan Khan, Mohammed Omer Ali, Dr. Khaja Mizbahuddin Quadry (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
RAG Knowledge Assistant:A Multi-Product Document Intelligence Platform
The exponential growth of organizational documentation — product manuals, HR policies, clinical protocols, legal contracts, and website content — has created an urgent need for intelligent systems capable of retrieving accurate answers from domain-specific knowledge bases without hallucination. Traditional keyword-search engines return documents rather than direct answers, while general-purpose AI chatbots generate responses from parametric training data that may be outdated, institution-agnostic, or factually incorrect. This paper presents the RAG Knowledge Assistant, a multiproduct document intelligence platform powered by Retrieval- Augmented Generation (RAG). The system enables organizations to upload PDF, DOCX, and TXT documents or scrape website content per product, providing a naturallanguage Q&A interface that retrieves semantically relevant document chunks and generates grounded answers using Anthropic Claude. The RAG pipeline uses the all-MiniLM-L6- v2 sentence-transformer model to encode queries into 384- dimensional dense embeddings, FAISS IndexFlatL2 for perproduct vector similarity search, and claude-sonnet-4- 20250514 with a strict five-rule no-hallucination prompt for answer generation. The platform features a multi-product architecture with isolated FAISS vector indexes per knowledge base, role-based access control via JWT Bearer tokens and bcrypt password hashing, product-level user permission assignment, a BFS web scraping module, and a configurable admin panel. The backend is built on FastAPI with SQLite storage. Experimental results demonstrate end-to-end query response times of 2.1–3.1 seconds with 100% hallucination-prevention accuracy across all tested queries.
Retrieval-Augmented Generation, FAISS, Sentence-BERT, Large Language Models, Claude, FastAPI, Vector Search, Knowledge Management, JWT, NLP
Abdul Matin Moiz Ahmed · Habeeb Mujtaba Wasif · Abubakr Khushtar, Mrs. Ashwini Gulhane
Copyright (c) 2026 Abdul Matin Moiz Ahmed · Habeeb Mujtaba Wasif · Abubakr Khushtar, Mrs. Ashwini Gulhane, Mk Rahmat Ullah (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
AI Driven Smart Dental Management System
This paper presents an AI-Driven Smart Dental Management System developed to improve operational efficiency, patient care, and decision-making processes in modern dental clinics. Conventional dental management systems largely depend on manual appointment scheduling, fragmented patient records, and minimal automation, which often result in inefficiencies, increased workload for dental practitioners, and limited patient engagement. The proposed system integrates Natural Language Processing (NLP) for chatbot and voice-assisted communication, Computer Vision techniques for preliminary dental image analysis, and intelligent scheduling mechanisms based on rule-based and machine learning approaches to dynamically assign appointment durations according to treatment complexity. A centralized database architecture enables structured storage and efficient retrieval of patient histories, supporting data-driven clinical decision-making. In addition, a predictive supply management module utilizes historical appointment trends to forecast inventory requirements and improve resource utilization. By combining intelligent automation with real-time interaction capabilities, the system minimizes administrative burden, improves patient experience, and facilitates proactive clinical management. Experimental evaluation indicates enhanced scheduling efficiency, reduced manual intervention, and improved patient follow-up compliance. The proposed platform provides a scalable and extensible framework for next-generation AI-assisted dental healthcare ecosystems.
Mir Asghar Ali Khan, Mustafa, Muntazir Mehdi Ali, Syed Mohammed Abbas Kazmi
Copyright (c) 2026 Mohammed Mazheruddin, Mir Asghar Ali Khan, Mustafa, Muntazir Mehdi Ali, Syed Mohammed Abbas Kazmi (Author) https://creativecommons.org/licenses/by-nc-nd/4.0