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/-
The increasing demand for rapid and accessible healthcare information has led to the development of intelligent digital health systems. This paper presents MediDiagnose AI, a web-based healthcare platform designed to assist users in identifying potential diseases based on their symptoms. The system allows users to create accounts, log in securely, input symptoms, and receive preliminary diagnostic suggestions generated through rule-based reasoning mechanisms. In addition to symptom analysis, the platform integrates supportive healthcare features including a medical chatbot for basic health inquiries, patient information management, disease knowledge resources, and automated report generation.The application is implemented using modern web technologies, with React powering the frontend interface, Python with the Flask framework handling backend processing, and Supabase (PostgreSQL) providing secure and scalable database management. The architecture ensures efficient data processing, reliable authentication, and a responsive user experience.MediDiagnose AI aims to improve healthcare accessibility by providing users with immediate guidance regarding possible medical conditions, particularly for minor or early-stage symptoms. While the system is not intended to replace professional medical consultation, it functions as a supportive tool that encourages early awareness and informed decision-making. The proposed system demonstrates how intelligent web applications can contribute to digital health services by offering fast, user-friendly, and informative preliminary diagnosis support.
Digital Healthcare, Symptom-Based Diagnosis, Web-Based Medical System, Artificial Intelligence in Healthcare, Medical Chatbot, Disease Prediction, Healthcare Information Systems, Flask, React, Supabase.
Pagidimarri Akhila, Ryaka Deekshita, Boyapally Hrithika Reddy
Copyright (c) 2026 Sudha Laxmaiah, Pagidimarri Akhila, Ryaka Deekshita, Boyapally Hritika Reddy (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
An Hybrid Deep Learning Framework For Robust Skin Lesion Detection And Classification
Reliable early detection of skin cancer is essential for improving treatment outcomes; however, achieving consistent diagnostic accuracy remains a major challenge in dermatology. Although deep learning has advanced automated skin lesion analysis, many existing methods rely on single architectures or imaging modalities, limiting robustness across diverse clinical settings. To address this gap, this research introduces a unified hybrid diagnostic framework integrating a Vision Transformer with transfer learning, channel attention, and region-of-interest extraction to learn discriminative feature representations. The framework combines deep learning and classical machine learning to analyse macroscopic dermoscopic images while incorporating relevant patient information to enhance predictive precision. A systematic evaluation is conducted across multiple architectures, including Vision Transformer, MobileNetV2, ResNet152V2, VGG16, and Xception. Extracted features are classified using Support Vector Machine and K-Nearest Neighbours, followed by a voting-based ensemble of Random Forest and Decision Tree classifiers to improve robustness and decision reliability. Results show that the Xception-driven ensemble outperforms individual models in classification tasks. Additionally, lesion localization using advanced YOLO variants (YOLOv5x6, YOLOv8, and YOLOv9) strengthens diagnostic confidence through precise detection. A secure Flask-based web platform supports multi-dataset validation and facilitates real-world clinical deployment, establishing the framework as a scalable solution for intelligent skin lesion assessment.
Vision Transformer; Attention Mechanisms; Skin Lesion Detection and Classification; Hybrid Deep Learning Framework; Ensemble Learning.
Sateesh Yalavarthi, SK.Ayesha, D.Veeraswamy
Copyright (c) 2026 Sateesh Yalavarthi, SK.Ayesha, D.Veeraswamy, Badar SK, Sevugarajan.S (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Detection Of Cardiovascular Disease Utilizing Machine Learning And Optimal Feature Selection Techniques
One of the main causes of death worldwide, cardiovascular disease (CVD), requires early and precise prognosis. In order to identify the most pertinent medical data features, this work presents an improved machine learning-based CVD diagnosis method that uses FCBF, MRMR, LASSO, ReliefF, and Particle Swarm Optimization. Prediction performance is enhanced by the classification techniques Logistic Regression, Random Forest, ExtraTree, and Gradient Boosting. Accuracy and robustness are increased by extending the suggested system with a Voting Classifier that incorporates AdaBoost, Decision Tree, and ExtraTree. A user-friendly interface for risk visualization, real-time prediction, and health advice is offered by a Flask-based online application with secure user authentication. According to experimental results, the technology is accurate and dependable, which makes it appropriate for practical healthcare applications and early identification of cardiovascular illness.
Crop Disease Detection, Deep Learning, Convolutional Neural Networks (CNN), Image Processing, Plant Leaf Classification, Agriculture, Artificial Intelligence, Computer Vision
Kondiboyina Doondi Sri Ram Hanuman,Bathula Anil Kumar, Vuyyuru Lakshmi Priya,Daliparthi Pushpa Veera Janakiram
Copyright (c) 2026 Mrs.K.Amulya, Kondiboyina Doondi Sri Ram Hanuman,Bathula Anil Kumar, Vuyyuru Lakshmi Priya,Daliparthi Pushpa Veera Janakiram (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Codegen: An AI-Powered Visual Studio Code Extension
Artificial intelligence is increasingly transforming the way software developers write, understand, and maintain code. This paper presents CodeGen, an intelligent extension for Visual Studio Code that provides context-aware programming assistance using locally deployed AI models. The system integrates the DeepSeek Coder model to translate natural language descriptions into executable code snippets, enabling developers to generate code simply by describing the intended functionality.Beyond code generation, CodeGen incorporates several advanced development features including automated code explanation, debugging assistance, context-aware autocomplete, code refactoring support, and automatic test case generation. These capabilities not only accelerate the software development process but also support learning by helping developers better understand generated code and programming concepts.A key distinguishing aspect of CodeGen is its fully offline architecture. Unlike many existing AI-based coding assistants that rely on cloud services, CodeGen runs entirely on locally deployed models. This design ensures enhanced data privacy, removes dependence on internet connectivity, and minimizes latency, making the tool suitable for environments where security and reliability are critical. Additionally, the extension supports multiple programming languages and integrates seamlessly within the Visual Studio Code ecosystem to maintain a smooth developer workflow.By combining advanced artificial intelligence techniques with local model execution, CodeGen addresses major limitations associated with traditional AI development tools, such as privacy risks, subscription-based access, and workflow disruption caused by external services. The proposed system demonstrates how offline AI integration can improve productivity while maintaining secure and efficient development environments. This work contributes toward the development of intelligent, privacy-preserving programming assistants for modern software engineering.
Artificial Intelligence, Code Generation, DeepSeek Coder, Visual Studio Code Extension, Offline AI Models, Context-Aware Programming Assistance, Automated Code Explanation, Debugging Assistance, Code Refactoring, Automatic Test Case Generation, Privacy-Preserving AI, Software Development Productivity.
Copyright (c) 2026 Dr R Dinesh Kumar, Ch Advaitha,A Manasa,K Abhinaya Yadav (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Automation Framework For Sap – Design & Implementation Of An Automated Sap User Access Management And Access Governance
In modern enterprises, SAP systems play a critical role in managing business operations such as finance, logistics, human resources, and supply chain processes. As organizations grow, managing user access and ensuring proper authorization within SAP environments becomes increasingly complex. Traditional manual processes for user provisioning, role assignment, access approvals, and compliance monitoring are time-consuming, error-prone, and difficult to audit. These challenges can lead to security risks, compliance violations, and operational inefficiencies. This project focuses on the design and implementation of an automation framework for SAP User Access Management and Access Governance to streamline and secure the management of user identities and authorizations. The proposed framework integrates automation tools, predefined workflows, and governance policies to simplify processes such as user creation, role assignment, access requests, approval workflows, and periodic access reviews. By leveraging technologies such as SAP governance tools, automated scripts, and workflow-based approval mechanisms, the framework reduces manual intervention while ensuring compliance with organizational security policies. The system architecture includes modules for access request management, role-based access control (RBAC), segregation of duties (SoD) analysis, automated provisioning, and audit reporting. These components work together to provide a centralized and standardized approach to access governance. The automation framework also improves traceability and transparency by maintaining detailed logs and reports for auditing and compliance purposes. Implementation of this framework significantly enhances operational efficiency, reduces administrative workload, and minimizes the risk of unauthorized access. Additionally, it supports regulatory compliance by enforcing consistent security policies and enabling real-time monitoring of user activities. The results demonstrate that automated access governance within SAP environments can improve system security, accelerate access provisioning, and provide better control over enterprise resources. Overall, this project highlights the importance of automation in SAP security management and presents a scalable framework that organizations can adopt to strengthen access governance, improve compliance, and ensure secure management of SAP user access across enterprise systems.
SAP Security, User Access Management, Access Governance, Automation Framework, SAP GRC, Role-Based Access Control (RBAC), Segregation of Duties (SoD), Workflow Automation, Identity and Access Management (IAM), Compliance Management, Access Provisioning, Audit and Monitoring
Copyright (c) 2026 Syed Nadeem Ahmed, Dr. Syed Asadullah Hussaini (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Cybersecurity: Practices and Incident Response Management in SMEs
This research examines Ghanaian SMEs' cybersecurity and incident response. The major goals are to identify and evaluate these SMEs' cybersecurity measures, examine their problems in developing and sustaining effective cybersecurity practices, assess their cybersecurity incident response capabilities, and give practical recommendations for improvement. This study administered surveys to 200 small and medium-sized enterprise (SME) owners and IT managers in Accra and Kumasi, collecting in-depth responses from key stakeholders, and analyzing secondary data sources. SMEs in Ghana are becoming more aware of cybersecurity, but preventative and reactive measures are weak. Only 35% of SMEs employ encryption, and 25% undertake security assessments, although 85% use antivirus software. Financial constraints prevented 60% of SMEs from improving cybersecurity, whereas 40% had a basic incident response strategy and 10% had a whole staff. The study reveals that budgetary constraints, a lack of experienced people, and cybersecurity expertise prohibit SMEs from adopting full cybersecurity measures. Many SMEs lack incident response methods and infrastructure to identify, contain, and recover from cyber attacks, which is difficult. To assist SMEs in establishing and maintaining effective cybersecurity defenses, the report advises targeted marketing, government support for financial and training resources, and the adoption of cybersecurity frameworks like the NIST Cybersecurity Framework, PCI DSS, and CMMC. The survey suggests Ghanaian SMEs need stronger cybersecurity and incident response. SMEs can protect their digital assets, maintain consumer trust, and maintain business continuity in the face of growing cyber risks by bridging these gaps with established frameworks and teamwork.
Cybersecurity, Incident Response, SMEs, Ghana, Cyber Threats, Data Protection.
Copyright (c) 2026 Makafui Ofori, George K. Agordzo, Edinam Agbemava, Meshach Tettey, Klu K. Proposer (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
The Smart Campus Path system provides an intelligent navigation solution for academic institutions by integrating digital mapping with efficient pathfinding techniques. The campus environment is modeled as a graph where key locations such as classrooms, laboratories, and administrative offices are represented as nodes connected through accessible pathways. The A* search algorithm is employed to analyze multiple routes and determine the shortest and most optimal path between a selected source and destination. The application features a user-friendly interface that allows users to choose their starting point and desired location. After processing the request, the system visually displays the computed path on the campus map. This approach enhances accessibility, reduces navigation time, and assists students, staff, and visitors in moving across the campus efficiently without confusion. Additionally, the system can be extended with GPS and Augmented Reality (AR) support for real-time navigation assistance.
Smart Campus, A* Algorithm, Campus Navigation, Digital Mapping, Augmented Reality, GPS, Intelligent Pathfinding
Kotha Akanksha, Mardhi Meghana, Tintrinipuram Navya Sree
Copyright (c) 2026 Dr P Sumalatha, Kotha Akanksha, Mardhi Meghana, Tintrinipuram Navya Sree (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
An Optimized Deep Learning Framework For Multi-Class Kidney Disease Detection Using Yolov5x6 And Yolov9
Renal dysfunction is a risk for all age groups. An AI-driven system that can independently diagnose kidney problems is required because to the global lack of nephrologists, the growing public health concern of renal failure, and technical advancements. Kidney function declines as a result of chronic kidney disease, also known as chronic renal failure. Renal failure can result from calculi, tumors, and cysts. It is possible for chronic renal disease to remain undetected until it has progressed. Spinal cells, glands, and other organs can sustain injury from compact tumors. Intestinal solid accumulation leads to kidney stones, also known as urolithiasis. In order to address the worldwide scarcity of urologists, this study employed a deep learning algorithm to detect kidney diseases. 12,446 CT scans of the entire abdomen and urograms showing kidney tumors, cysts, and stones—the three most prevalent renal pathologies—were gathered and annotated for the study. The information was separated into normal, tumor, stone, and cyst categories. Data was gathered from numerous hospitals in Dhaka. This research presents a novel and adaptable platform for clinical diagnosis of kidney cysts, calculi, and malignancies. Our YOLOv8 model's increased accuracy aids in the detection of kidney cancer, stones, and cysts. Accuracy, precision, recall, F1 score, and specificity were used to gauge its performance. The network exhibited 93.12% specificity, 85.28% recall, 85.72% F1 score, 82.52% accuracy, and 85.76% precision.
CT Imaging, YOLOv5x6, YOLOv9, Deep Learning, Renal Tumor Detection, Kidney Stone Identification, Cyst Detection, Medical Image Analysis, AI in Healthcare, Flask Web Application, Secure Authentication, Computed Diagnosis, Multi-Class Classification, Object Detection.
Copyright (c) 2026 Sri Ramya Ainagadda, Ganga Bhavani Billa (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Plant Disease Detection Using Deep Learning
This paper presents an explainable deep learning-based system for plant leaf disease detection using EfficientNetB0. The proposed approach utilizes transfer learning to effectively classify plant diseases from leaf images. To improve transparency and interpretability, Grad-CAM (Gradient-weighted Class Activation Mapping) is incorporated to highlight the regions that influence the model’s predictions. The model is trained on a balanced subset of the PlantVillage dataset consisting of 19 classes. Experimental results show high classification accuracy, demonstrating the effectiveness of the proposed approach. Additionally, the system provides disease-specific precautions and treatment recommendations, making it a practical solution for real-world agricultural applications.
Plant Disease Detection, EfficientNetB0, Deep Learning, Grad-CAM, Explainable AI Image Classification.
Vanjari Sarika, Kambalapally Srija, Banoth Kalyani
Copyright (c) 2026 Mrs.S.Nagajyothi, Vanjari Sarika, Kambalapally Srija, Banoth Kalyani (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Edumate AI-Personalized Learning Companion
The rapid growth of digital education has increased the need for intelligent systems capable of supporting personalized learning experiences. Traditional e-learning platforms often provide static content and lack adaptive mechanisms to address diverse student learning needs. This paper presents Edumate AI, an AI-driven personalized learning platform designed to transform conventional study materials into interactive and adaptive learning resources. The system employs Artificial Intelligence (AI) and Natural Language Processing (NLP) techniques to generate structured multi-level summaries from educational content such as PDF documents and video lectures. It also incorporates real-time AI chat for doubt clarification, adaptive quizzes with dynamic difficulty levels, multilingual interaction through text and voice interfaces, and gamification mechanisms to enhance student engagement. Additionally, Edumate AI provides performance analytics and a teacher dashboard that allows instructors to track individual and group progress. The platform is implemented using modern technologies including Next.js, FastAPI, Firebase Firestore, and AI-based language models to ensure scalability and efficiency. Experimental observations indicate that the platform enhances learning engagement, improves comprehension, and provides a more personalized educational experience. Edumate AI demonstrates how intelligent automation and data-driven insights can significantly improve modern digital learning environments.
Artificial Intelligence, Personalized Learning, Natural Language Processing, Adaptive Learning Systems, Educational Technology, Intelligent Tutoring Systems.
Copyright (c) 2026 N Sudha Laxmaiah, V Raja Sri,B Rani,A Reshma,D Yashaswini (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Mental health conditions are becoming increasingly common worldwide, creating a growing demand for accurate, scalable, and accessible diagnostic support systems. Conventional mental health assessment methods primarily depend on patient self-reporting and clinical observation, which may not always reflect real-time emotional fluctuations or objective behavioral indicators. This study introduces EMOSENSE AI, an intelligent framework that integrates Artificial Intelligence (AI), Computer Vision, and Natural Language Processing (NLP) to detect and interpret human emotional states from video-based interactions. The system analyzes facial expressions, vocal tone variations, and linguistic sentiment to generate a multidimensional understanding of emotional behavior. By processing these signals, EMOSENSE AI can assist clinicians in identifying early signs of psychological distress and monitoring emotional changes over time. The proposed system enables remote mental health screening, continuous emotional tracking, and data-driven insights that support personalized interventions. Additionally, the platform incorporates secure data management and clinician-support tools to ensure ethical and reliable deployment. The results demonstrate the potential of EMOSENSE AI to enhance digital mental health services by providing proactive, scalable, and technology-assisted emotional assessment.
Artificial Intelligence, Emotion Recognition, Mental Health Monitoring, Computer Vision, Natural Language Processing, Digital Healthcare, Sentiment Analysis, Behavioral Analytics
Chindala Pavani,Burgu Sathwika,Shaik Nasreen
Copyright (c) 2026 Amtul Shanaz, Chindala Pavani,Burgu Sathwika,Shaik Nasreen (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Ensuring the safety of travelers in unfamiliar environments has become an important challenge in modern tourism. This paper presents a Tourist Security Application, a mobile-based system designed to enhance traveler safety through integrated trip management, real-time monitoring, emergency response, and location-aware recommendations. The proposed system allows users to plan structured day-wise itineraries where destinations are automatically mapped using open-source geolocation services. During travel, the application continuously monitors user location and visually represents movement through an interactive mapping interface.An administrative dashboard is implemented to support centralized supervision, including user account management, trip activity monitoring, and emergency alert handling. The system architecture utilizes Flutter for cross-platform mobile development, while the backend is developed using Flask integrated with MongoDB for scalable data management. Open-source geospatial technologies such as OpenStreetMap and Nominatim are employed for location mapping and geocoding services.
Tourist safety, mobile travel application, emergency response system, real-time location tracking, trip management, location-based recommendations, geospatial mapping, OpenStreetMap, Nominatim geocoding, Flutter mobile development, Flask backend framework, MongoDB database, secure authentication, travel assistance technology, scalable tourism security system.
Copyright (c) 2026 G Dayakar Reddy, P Rishitha Reddy,Ch Roshini,K Shresta (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Student Certificate Verification System Using Blockchain And Ai
In the digital era, ensuring the authenticity of academic certificates has become essential to prevent fraud, maintain institutional credibility, and streamline verification processes. Conventional verification methods rely on manual procedures and centralized databases, which are time-consuming, error-prone, and vulnerable to tampering. These challenges often lead to certificate forgery, administrative inefficiencies, and reduced trust among stakeholders.This paper proposes a secure and automated academic certificate verification system that integrates Blockchain technology with Artificial Intelligence (AI). Blockchain provides a decentralized and immutable ledger for storing certificate hashes, ensuring transparency, data integrity, and tamper-proof record management. AI-based algorithms are incorporated to analyze certificate data, identify anomalies, and detect potential fraudulent entries before certificates are committed to the blockchain network.A user-friendly web interface developed using HTML, CSS, and Bootstrap enables institutions to issue digital certificates and allows employers or verification authorities to authenticate them instantly using hash-based validation. The system also incorporates decentralized storage through IPFS, smart contract-driven automation, and certificate revocation mechanisms to enhance security and control. The integration of blockchain immutability, AI-driven validation, and a lightweight web interface ensures scalability, efficiency, and practical deployment in academic environments. This approach provides a reliable solution for trusted digital credential verification.
Blockchain, Academic Certificate Verification, Artificial Intelligence, Smart Contracts, IPFS, SHA-256, Digital Credentials, Decentralized Ledger, Fraud Detection, Proof-of-Authority.
Gundlapally Akshaya, Thota Deekshitha, Dandeboina Mamatha
Copyright (c) 2026 T Santosh, Gundlapally Akshaya, Thota Deekshitha, Dandeboina Mamatha (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Selecting appropriate academic courses is a significant challenge for many students due to limited guidance, excessive online resources, and unclear career pathways. To address this issue, this paper proposes a Smart Course Recommendation System that assists learners in identifying suitable courses based on their interests, academic history, and career aspirations. The system analyzes student profiles, educational background, and learning preferences to generate personalized course suggestions.The architecture of the system includes several key modules such as student management, course repository, recommendation engine, career guidance module, and feedback analysis unit. The recommendation engine acts as the central component, employing machine learning techniques including content-based filtering, collaborative filtering, and data-driven analytics to provide accurate and adaptive course suggestions. Furthermore, the system incorporates real-time feedback mechanisms that enable the model to improve its recommendation accuracy over time. The scalable and user-friendly design allows the platform to support a large number of students simultaneously while functioning as an intelligent academic advisor.
Smart Course Recommendation, Personalized Learning, Recommendation Systems, Machine Learning, Content-Based Filtering, Collaborative Filtering, Educational Data Mining, Student Profiling, Career Guidance Systems, Intelligent Learning Platforms.
Bheem Reddy Akshaya, Konjarla Anvitha,Samudrala Kavya
Copyright (c) 2026 N Sudha Laxmaiah, Bheem Reddy Akshaya, Konjarla Anvitha,Samudrala Kavya (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Violence Detection Network For Video Surveillance Using Deep Learning
The rapid expansion of video content generated through surveillance systems and social media platforms has intensified concerns regarding public safety and timely identification of violent activities. Manual monitoring of large-scale video streams is time-consuming, prone to human error, and often leads to delayed responses. To address these limitations, this work proposes an automated violence detection framework based on deep learning techniques for efficient identification of aggressive behavior in video sequences.The proposed approach integrates Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks to capture both spatial and temporal information. A pre-trained VGG16 model is utilized for extracting discriminative spatial features from individual frames, while the LSTM network analyzes sequential dependencies to understand motion patterns across time. This hybrid architecture enables robust classification of video segments into violent and non-violent categories.The system is implemented using Python with the Flask framework and provides a user-friendly web interface for uploading video inputs. A preprocessing pipeline performs frame extraction, resizing, normalization, noise filtering, and sequence generation before feeding the data into the model. The output includes classification results, confidence scores, and inference time. To mitigate class imbalance, data augmentation and synthetic sampling techniques are applied.The proposed solution supports scalability, real-time monitoring, and seamless integration with existing surveillance infrastructure. Deployment in public spaces, educational institutions, and transportation hubs can facilitate early detection of violent incidents, thereby enhancing security and enabling faster emergency response.
Violence Detection, Video Surveillance, Deep Learning, CNN-LSTM, VGG16, Temporal Analysis, Video Classification, Real-time Monitoring, Public Safety
Vaddireddy Lahari, Anugu Meghana, Nasera Begum
Copyright (c) 2026 P Ganesh Kumar, Vaddireddy Lahari, Anugu Meghana, Nasera Begum (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
With the rapid advancement of artificial intelligence, facial emotion recognition has emerged as a key component in human–computer interaction. Studies suggest that non-verbal communication, particularly facial expressions, contributes significantly more to interpersonal communication than verbal interaction. Facial Emotion Recognition (FER) systems are designed to automatically identify emotional states by analyzing facial features, thereby providing insights into human behavior and psychological conditions. This paper presents MoodMap, a real-time facial emotion recognition system capable of detecting seven fundamental emotions, namely happy, sad, angry, fear, surprise, neutral, and disgust. The proposed approach integrates YOLOv2 for efficient face detection and SqueezeNet for emotion, age, and gender classification. YOLOv2 enables accurate localization of faces in real time using anchor-based detection, while SqueezeNet extracts meaningful high-level features with reduced computational complexity. Cross-validation techniques are incorporated to enhance model robustness and reliability. The proposed system achieves faster processing speed and improved accuracy, making it suitable for real-time applications such as smart classrooms, healthcare monitoring, customer behavior analysis, and intelligent interactive systems.
Facial Emotion Recognition, YOLOv2, SqueezeNet, Deep Learning, Real-Time Detection, MoodMap, Human-Computer Interaction
Copyright (c) 2026 AVS Radhika, S Ashrutha, M Jyothi, G Mani Kumari (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
The increase in crimes against women has highlighted the urgent need for reliable personal safety solutions. This paper presents the design and development of a Women Protection Application that provides immediate assistance during emergency situations using modern mobile technology. The proposed system is a smartphone-based application that enables users to send instant alerts to trusted contacts and emergency authorities. The application integrates features such as one-touch SOS activation, real-time GPS location tracking, emergency notifications, and audio or video recording. Once the alert is triggered, the system transmits the user’s live location through SMS and internet-based services to pre-registered contacts, enabling quick response. Additional functionalities such as voice-triggered alerts, fake call simulation, and nearby police station identification improve usability and enhance safety. The application is implemented using Android technology, GPS services, and cloud-based storage to ensure reliability and scalability. The main objective of this project is to reduce emergency response time and provide a sense of security for women. This work demonstrates how mobile computing and real-time communication technologies can effectively address social safety challenges and contribute to empowering women in modern society.
Women Safety, Emergency Application, GPS Tracking, SOS Alert, Mobile Computing, Android, Real-Time Monitoring, Personal Security
Copyright (c) 2026 Mohammed Arshad Hussaain, L Prasanna, M Priyanka, K Sneha (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Cogniscan-Deep Learning Based Brain Tumor Classification
CogniScan is a web-based application that employs deep learning techniques to classify brain tumors from magnetic resonance imaging (MRI) scans in an efficient and structured manner. Conventional tumor identification depends heavily on manual interpretation by medical professionals, which is time-consuming and may introduce inconsistencies, particularly when processing large datasets. The proposed system automates this process by allowing users to upload MRI images and receive accurate classification results.The application supports multiple categories, including glioma, meningioma, pituitary tumor, and no tumor, enabling comprehensive diagnostic assistance. A custom Convolutional Neural Network (CNN) model is used for feature extraction and classification. To improve performance, preprocessing steps such as resizing and normalization are applied to ensure consistency across input images.The system incorporates secure user authentication, allowing users to register and log in before accessing prediction features. A user-friendly interface built with Streamlit provides smooth interaction, enabling image uploads and visualization of results along with confidence scores. SQLite is used for secure storage of user credentials and authentication management. Additional features include efficient data handling, rapid prediction generation, and reliable output delivery.Overall, the proposed system offers a scalable and accessible solution for automated medical image analysis, reducing manual effort and supporting healthcare professionals in diagnostic decision-making.
V Saranya, A Slycee Leyona, Tokala Sreeja
Copyright (c) 2026 N Sony, V Saranya, A Slycee Leyona, Tokala Sreeja (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Posey: An AI-Powered Virtual Fitness Trainer With Real-Time Posture Correction
Posey is an intelligent, AI-driven fitness training system designed to deliver real-time posture evaluation and exercise guidance using computer vision and machine learning techniques. The platform assists users in performing workouts correctly without requiring a physical trainer, thereby minimizing injury risks and improving exercise effectiveness. The system captures live video input through a webcam and applies pose estimation algorithms to identify key body joints and compute joint angles.Based on posture analysis, Posey provides immediate feedback through on-screen notifications and voice instructions, enabling users to correct their form during exercises such as squats, push-ups, lunges, and yoga postures. The system also incorporates repetition counting, accuracy assessment, performance tracking, and progress monitoring to increase user engagement and motivation.The application is developed using HTML, CSS, and JavaScript for the frontend, while Python handles backend processing. Libraries such as MediaPipe, OpenCV, and NumPy are utilized for pose detection and movement analysis. By combining real-time posture evaluation with intelligent feedback and gamification elements such as streak tracking and leaderboards, Posey encourages safe, consistent, and effective workouts. The system bridges the gap between professional fitness coaching and home-based training, making personalized fitness guidance more accessible and interactive.
Artificial Intelligence, Computer Vision, Pose Estimation, Real-Time Feedback, Fitness Training, Human Pose Detection, MediaPipe, Virtual Trainer.
Copyright (c) 2026 A Hima Bindu, M Samyuktha, D Ujwala, G Yashaswini (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Intellipath AI: Generative Platform For Career And Mental Readiness
IntelliPath AI is an intelligent career guidance platform designed to improve employability, confidence, and mental preparedness among students, particularly those from Tier-3 institutions. The system integrates Generative AI, semantic similarity models, resume parsing, job-fit evaluation, and mental wellness support into a unified ecosystem. The platform analyzes user resumes and compares them with job descriptions using transformer-based models such as BERT and Sentence-BERT to determine semantic alignment. Based on this analysis, the system generates ATS-optimized improvement suggestions, personalized cover letters, and structured career roadmaps tailored to individual skill gaps.In addition to technical guidance, IntelliPath AI incorporates a mental health support chatbot that provides motivational feedback, stress management suggestions, and confidence-building interactions during the job preparation process. Gamification features such as quizzes, badges, and leaderboards further encourage user engagement and continuous learning. By combining career analytics, personalized recommendations, and emotional support, IntelliPath AI offers a scalable web-based solution that bridges the gap between academic learning and industry expectations, ultimately improving student readiness and employability outcomes.
Generative AI, Semantic Similarity, Resume Parsing, Job-Fit Scoring, Natural Language Processing, BERT, SBERT, Career Recommendation System, Skill Gap Analysis, ATS Optimization, AI Chatbot, Sentiment Analysis, Mock Interview, Career Roadmap, Gamification, Web Platform, Employability Enhancement.
Amala Kura, Bhavya Sri L, Pranathi Poloju
Copyright (c) 2026 G Jyothi, Amala Kura, Bhavya Sril, Pranathi Poloju (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Monkeypox Disease Detection From Skin Images Using Deep Learning
Monkeypox is a viral zoonotic disease that presents with skin lesions similar to other dermatological conditions such as chickenpox, measles, and smallpox, making early and accurate diagnosis challenging. Delayed diagnosis can lead to disease spread and complications, highlighting the need for automated and reliable detection systems. This study proposes a deep learning–based approach for the detection of monkeypox from skin lesion images using convolutional neural networks (CNNs). Skin image datasets are collected and preprocessed through resizing, normalization, and augmentation to improve model generalization and reduce overfitting. Pre-trained deep learning models such as VGG16, VGG19, MobileNetV2, and ResNet50 are utilized through transfer learning to improve classification performance with limited medical image data. The models are trained to classify images into monkeypox and non-monkeypox categories. Performance evaluation is conducted using metrics such as accuracy, precision, recall, F1-score, and confusion matrix. Experimental results demonstrate that deep learning models, particularly transfer learning architectures, achieve high classification accuracy and can effectively assist in early monkeypox detection. The proposed system can support healthcare professionals by providing a fast, cost-effective, and automated diagnostic tool for preliminary screening and outbreak control..
Deep learning, monkeypox, disease diagnosis, transfer learning, image processing.
Battula.Sudharsana Rao, Pedalanka. Jyoshnasree Lakshmi,Shaik.Shameera, Siripurapu. Nagabrahmachari,Veeranki.Sravya
Copyright (c) 2026 Mrs.Shaik.Shahina, Battula.Sudharsana Rao, Pedalanka. Jyoshnasree Lakshmi,Shaik.Shameera, Siripurapu. Nagabrahmachari,Veeranki.Sravya (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Maternal healthcare demands continuous observation, timely medical intervention, and reliable access to supportive resources throughout pregnancy. Conventional care models largely depend on periodic hospital visits and manual documentation, which may lead to delayed responses and limited personalization. To overcome these limitations, this paper proposes CareNine, an intelligent maternal health monitoring and support platform that integrates artificial intelligence with digital healthcare services to enhance pregnancy management. The system incorporates modules such as user registration, real-time health tracking, automated notifications, AI-based chatbot assistance, virtual consultations, and hospital-managed educational content. Expectant mothers can record vital health parameters including blood pressure, glucose levels, and body weight, while the system analyzes inputs and generates alerts for abnormal values. A natural language processing-based chatbot provides instant responses to maternal queries, delivers personalized recommendations, and promotes health awareness. CareNine also enables home-based healthcare services, allowing users to request laboratory sample collection and access diagnostic results through the platform. Healthcare providers can manage patient records, assign medical staff, and update educational materials dynamically. By leveraging AI, NLP, and secure web technologies, the system ensures scalability, reliability, and data protection. The proposed solution improves healthcare accessibility, minimizes unnecessary hospital visits, and empowers pregnant women with timely information and continuous monitoring, thereby establishing a comprehensive digital ecosystem for maternal wellbeing from early pregnancy to delivery.
Maternal Healthcare, Artificial Intelligence, Pregnancy Monitoring, AI Chatbot, Natural Language Processing, Remote Healthcare, Risk Prediction, Digital Health, Virtual Consultation.
Copyright (c) 2026 P Sumalatha, K Pavani, N Sakshitha, N Sharanya (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Sight GPT is a web-based application designed to assist visually impaired users in obtaining detailed information about images captured with their smartphones. The platform enables interactive exploration of photographs and the surrounding visual environment through a multimodal interface that combines speech input, haptic feedback, and visual cues. Primarily, Sight GPT aims to help visually impaired users mentally visualize content they cannot perceive, while also accommodating users with partial vision. The system integrates advanced Natural Language Processing (NLP) and Computer Vision techniques to provide precise answers to user queries about on-screen images. Image analysis is further enhanced by background removal and segmentation models, which isolate and emphasize salient elements of the scene. Preliminary evaluations demonstrate the effectiveness of the application in improving accessibility and user experience, marking an initial step toward leveraging AI-powered multimodal systems to support visually impaired individuals.
Sight GPT, visually impaired users, multimodal interface, speech interaction, haptic feedback, computer vision, natural language processing, image segmentation, background removal, accessibility, AI-assisted visualization, smartphone applications.
Anpa Chandana, Kadam Geethanjali, Challa Manumitha
Copyright (c) 2026 A Hima Bindu, Anpa Chandana, Kadam Geethanjali, Challa Manumitha (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
SQRBLOCK: QR-Driven Food Product Traceability Platform For Consumer Safety
Food safety is a critical global concern, and traditional food supply chains often lack complete traceability, making it difficult to track products and quickly identify the source of contamination or food-borne outbreaks. These limitations delay response times and compromise consumer health. Although blockchain technology has shown potential in improving traceability, many existing solutions lack user-friendly interfaces, reducing accessibility for both producers and consumers. To address these challenges, the proposed SQRBlock framework introduces a blockchain-based food product traceability system that digitizes production and supply chain information, making it accessible, verifiable, and tamper-evident through QR codes. By recording key product details at every stage of the supply chain from production to retail, SQRBlock generates an immutable record for each food item. QR codes are generated for each batch, enabling consumers to instantly view product information and verify authenticity by scanning the QR code. The system uses SHA-256 hashing to secure each block of data, ensuring that any attempt to alter stored information breaks the chain and immediately reveals tampering. Integrated through a lightweight backend and scalable storage, SQRBlock supports efficient data retrieval, real-time validation, and reliable performance for both small-scale and large-scale operations. Experimental evaluation demonstrates that SQRBlock maintains data integrity, provides seamless traceability, and offers a practical verification mechanism for consumers, retailers, and regulatory authorities. With its transparent, secure, and user-friendly design, SQRBlock enhances trust in the food supply chain and contributes to improved global food safety standards.
Blockchain, Food Supply Chain Security, QR Code Authentication, SHA-256 Hashing, Tamper Detection, Traceability System.
Shalmali Kadem,Sharvaani Kamlekar,Gayathri Mahadevoju
Copyright (c) 2026 Mr K Vikram Reddy, Shalmali Kadem,Sharvaani Kamlekar,Gayathri Mahadevoju (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Vibeshield – Multimodal Misinformation Detection In Social Media
The rapid growth of social media has led to an unprecedented spread of misinformation in the form of text, images, and videos, creating significant challenges for public awareness and online content moderation. Detecting such deceptive content is particularly difficult when it involves multiple types of data simultaneously. This study presents a multimodal misinformation detection system that analyzes both textual and visual information to improve accuracy. The framework utilizes advanced models for understanding text and extracting image features, which are then combined and processed by a neural network to classify content as real or fake. In addition to providing predictions, the system offers interpretability by using techniques that visually highlight influential regions in images and focus areas in text, helping users understand the reasoning behind the model’s decisions. The system is implemented using a user-friendly interface and a backend processing framework, supported by modern deep learning and computer vision libraries. It can handle text-only, image-only, and combined inputs, offering flexibility and robustness. By integrating multimodal analysis with explainable outputs, the proposed approach provides an effective and transparent solution for tasks such as social media monitoring, fact-checking, and content moderation.
This study focuses on multimodal misinformation detection by combining text and image analysis to classify digital content as real or fake. The framework employs deep learning techniques, including neural networks and feature fusion, and incorporates explainable artificial intelligence (XAI) methods to provide interpretability for users. Applications of the system are particularly relevant for social media monitoring, content moderation, and fact-checking, offering a flexible and transparent approach to detecting deceptive online information.
Copyright (c) 2026 Ms Sameera Begum, S.Pranavi,V.Sahithi,L.Srihitha (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Addict Aware is a web-based application designed to detect and analyze smartphone addiction using Artificial Intelligence (AI), Machine Learning (ML), and Natural Language Processing (NLP) techniques. The platform collects user data, including screen time, phone usage patterns, and self-reported behavioral inputs, to predict addiction levels using machine learning algorithms such as Support Vector Machines (SVM), Random Forest, and Neural Networks. The system categorizes users into different risk levels (low, moderate, and high) and provides personalized intervention strategies, including awareness suggestions and digital detox recommendations. By analyzing textual feedback and social media interactions through NLP, the system identifies emotional and psychological patterns associated with excessive smartphone usage. The proposed solution promotes mental health awareness and encourages healthier digital habits through intelligent monitoring and prediction.
Smartphone Addiction Detection, Machine Learning, Natural Language Processing, Behavioral Analysis, Addiction Prediction, Mental Health, User Behavior, Digital Detox
B. Madhumitha, Ch. Naga Puja, K. Navya Sree
Copyright (c) 2026 N. Sony, B. Madhumitha, Ch. Naga Puja, K. Navya Sree (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Cyber Bullying Prevention Through Intelligent Text Monitoring
The rapid growth of social media has amplified the prevalence of cyberbullying, posing significant challenges for online safety. This study presents a deep learning-based framework for the detection and mitigation of cyberbullying across multilingual social platforms. Leveraging a Semantic-enhanced Marginalized Stacked Denoising Autoencoder (smSDA), the system preprocesses user-generated content, extracts intent- and context-aware features through natural language processing and word embeddings, and accurately classifies messages as bullying or non-bullying, even in the absence of explicit profane language. Unlike traditional Bag-of-Words and topic-based models, the proposed approach incorporates intent detection, multilingual support, and robust feature reconstruction, significantly improving detection of subtle, context-dependent harassment.The framework processes all user interactions—including messaging, posting, and profile engagement—through a Data Preprocessing Module, followed by Bullying Feature Construction and smSDA-driven classification. Upon identifying harmful content, an Admin Module issues real-time alerts to moderators, enabling timely review, intervention, or account suspension. Designed with privacy-by-design principles and deployed on scalable cloud infrastructure, the system achieves high accuracy with minimal false positives, efficiently scales with limited labeled data, and reduces reliance on hand-crafted features. This research addresses key limitations of conventional models, including feature sparsity, language bias, and dependence on manual annotation, contributing to AI-powered social safety and fostering inclusive, secure digital communities in India and globally.
Cyberbullying Detection, smSDA, Multilingual NLP, Intent and Sentiment Analysis, Deep Learning for Social Safety, Code-Mixed Language Processing, Real-Time Alert System, Admin Intervention Dashboard, Subtle Harassment Detection, Privacy-Preserving AI
Aashritha Konjarla, Akhila Koukuntla, Manvitha Katika
Copyright (c) 2026 Tasneem Rahath, Aashritha Konjarla, Akhila Koukuntla, Manvitha Katika (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Automated Brain Stroke Detection Using Image Processing
Stroke remains one of the leading causes of mortality and long-term disability across the globe. It occurs when the blood supply to a region of the brain is interrupted, resulting in neuronal damage and functional impairment. Rapid identification of stroke is critical for minimizing neurological damage and improving patient prognosis. Traditional diagnostic procedures primarily rely on expert radiologists to manually interpret brain Magnetic Resonance Imaging (MRI) scans. This manual evaluation is often time-consuming, expensive, and susceptible to inter-observer variability. Furthermore, in rural and resource-limited healthcare facilities, the shortage of experienced specialists frequently leads to delayed diagnosis and treatment.To address these challenges, this work proposes an Artificial Intelligence (AI)-based framework for automated detection of stroke-affected regions in brain MRI images. The system integrates image processing techniques with machine learning algorithms to analyze medical images and detect abnormal tissue patterns associated with stroke. By automating the identification process, the proposed approach aims to enhance diagnostic speed and accuracy while reducing dependence on manual interpretation.The developed model assists healthcare professionals in early decision-making by providing reliable and consistent detection results. Consequently, the system supports timely medical intervention, reduces diagnostic workload, and improves clinical outcomes. Overall, this AI-driven solution offers a promising tool for efficient stroke screening, particularly in underserved regions, thereby contributing to improved patient recovery and healthcare delivery.
Stroke Detection, Brain MRI, Machine Learning, Image Processing, Artificial Intelligence, Medical Image Analysis, Early Diagnosis, Healthcare Technology, Automated Detection, Deep Learning
Pinjara Akshita, Poreddy Harthika, Godavari Manyasree
Copyright (c) 2026 A Vasavi Sujatha, Pinjara Akshita, Poreddy Harthika, Godavari Manyasree (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
The Sign‑Language Media Hub is a video streaming platform designed to improve digital accessibility for individuals who are deaf or hard of hearing. The system enables content creators to upload videos with captions, which are then processed using external application programming interfaces (APIs) to generate synchronized sign‑language interpretation. The platform emphasizes inclusivity, usability, and scalability by incorporating features such as side‑by‑side video playback, adjustable playback speed, and customizable delay controls. These capabilities aim to enhance comprehension and provide a flexible viewing experience for users with hearing impairments. Additionally, the platform simplifies the creation of accessible multimedia content for content creators while maintaining efficient processing and delivery. With a scalable architecture and user‑centered design, the Sign‑Language Media Hub contributes to advancing inclusive digital media and improving accessibility in online video platforms.
Accessibility, Sign Language, Video Streaming, Deaf Community, Inclusive Technology, Media Platform, Assistive Technology
Copyright (c) 2026 K Shireesha, R Bhanupriya,T Likitha,P Manogna (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Live Multi-Modal Language Translation System
Live Multimodal Language Translation is an advanced software solution designed to bridge communication gaps across diverse linguistic and sensory formats. By integrating Computer Vision, Natural Language Processing (NLP), and Speech Recognition, the system provides a unified platform for real-time translation of text, speech, and images. The application leverages high-performance models like BERT for contextual understanding and Tesseract OCR for visual text extraction, ensuring high accuracy across various input types .Designed with a focus on seamless user experience, the system allows for instantaneous conversion between multiple global languages, catering to international travelers, students, and professionals. Key features include a Live Translator interface, automated speech normalization using MFCC and LSTMs, and a robust backend built on Python and Next.js15. This project represents a significant advancement in multimodal AI, offering a scalable, intuitive, and highly accessible tool that transforms how individuals interact across different languages and media formats.
Live Multimodal Translation, Computer Vision, Natural Language Processing, Speech Recognition, BERT, Tesseract OCR, Optical Character Recognition, MFCC, LSTM, Real-Time Language Translation, Multimodal AI.
K Reena Smith, T Sri Nikitha, S Vaishnavi
Copyright (c) 2026 Ms Sameera Begum, K Reena Smith, T Sri Nikitha, S Vaishnavi (Author) https://creativecommons.org/licenses/by-nc-nd/4.0