IJETE VOLUME 1, ISSUE 1 MAY – 2026
IJETEV1I1A001 - E-MEDICARE REVOLUTIONIZING HEALTHCARE DELIVERY WITH AI-DRIVEN SOLUTIONS
AUTHORS : Mrs. R. Arthi M.E, Manoj V, Dinesh D, Tejesh Reddy M
ABSTRACT – Artificial intelligence is revolutionizing healthcare, and EmAI is leading the way with smart, AI-driven solutions. Our platform, E-Medicare, enhances healthcare accessibility and efficiency by offering features like an Emergency Alert System, Quick Medicine Finder, AI-powered appointment scheduling, real-time monitoring, and personalized treatment plans. With a focus on secure medical records, user friendly interfaces, and seamless provider collaboration, EmAI creates a more connected and patient-centric healthcare experience. By bridging gaps in medical services, the platform ensures timely interventions, better resource management, and improved patient outcomes. In this presentation, we’ll explore how EmAI is redefining digital healthcare, its cutting-edge technology, and its real-world impact in making medical services more efficient, accessible, and holistic.
IJETEV1I1A002 - BLOSTERING WEB SECURITY BY INTEGRATING FRAMEWORKS TO DETECT SQL INJECTION AND XSS VULNERABILITIES
AUTHORS : G. Sivakumar, Arthi M.E, Sahasra S, Gowtham Siddarth N
ABSTRACT – With the increasing number of web applications, cyber threats such as SQL Injection (SQLi) and Cross-Site Scripting (XSS) have become more prevalent. This paper presents a framework for bolstering web security by integrating multiple detection mechanisms for SQLi and XSS vulnerabilities. The system combines automated scanning techniques with penetration testing tools such as SQLMap for SQL Injection and script-based analysis for XSS detection. The proposed framework enhances web security by assisting security professionals in identifying vulnerabilities and mitigating potential threats before exploitation occurs. The findings emphasize the importance of proactive security assessment in modern web applications. Additionally, this research explores novel techniques in heuristic analysis, machine learning-based anomaly detection, an
IJETEV1I1A003 - ANDROID HACKING AND EXPLOITATION USING KALI LINUX : A CYBER FORENSIC ANALYSIS OF VULNERABIILTIES AND MITIGATION
AUTHORS : G.Vishvanath Sundharam, Sudhakaran S, Vijayasarathi S, James Godwin G.
ABSTRACT – IT security is a paramount concern in today’s internet landscape due to the prevalence of communication activities. Testing for personal data theft using social engineering methods aims to identify potential security vulnerabilities in the system and network of a user’s Android device. These vulnerabilities could be exploited by hackers if users are unaware of social engineering tactics, allowing for data theft through the use of a Remote Administration Tool (RAT) inadvertently downloaded onto the Android device. The deployment of a RAT via social engineering represents a viable and effective method for compromising the privacy of Android users. This study provides an overview of fundamental concepts related to data theft, ranging from recent call and personal data breaches to the remote manipulation of Android users’ cameras and microphones. users no longer need to install additional tools manually. Apart from that, Kali Linux also has an intuitive user interface so that users can efficiently operate the system and the tools provided. Another advantage is modifying and customizing the tools according to user needs through manual configuration or built-in features such as meta packages. Thus, Kali Linux is one of the right choices for android hacking practitioners in conducting security testing on the Android system.
IJETEV1I1A004 - ART INTEGRATION- A CROSS-CULTURAL APPROACH TO TEACHING AND LEARNING IN THE TWENTY FIRST CENTURY
AUTHORS : Dr. Biju.G
ABSTRACT – Integration of art is an innovative teaching learning approach through which the concepts can be taught and evaluated equitably in and through art. Due to the variety of implementation methods, arts integration is complicated. There is no one right way to include arts into the classroom, and the planning and implementation of art integration can be creative work in and of itself. The degree to which the arts can be incorporated into the curriculum varies; it may be done on a daily to monthly basis, with the discussion of a historical piece of art, or it may be done more complexly, with the inclusion of a hands-on art project to support the students’ active learning. Additionally, because instructors traditionally have more opportunity in how much time they spend with their kids, arts integration is more likely to start in the elementary grades. To meet the demands of pupils in the 21st century, art integration is marketed as a potent educational strategy. Teaching through arts can assist students to conceive, create, and express their understanding about themselves, their communities, and about the wider world. Teachers must balance the arts and non-arts learning and curriculum areas in a co-equal, cognitive manner of integration, and they must focus on investigating common concepts in the classroom. The present chapter is dealing with art integration and its elements and importance, art integrated pedagogy and its integration in the classroom, implementation of art integration at different levels of education, strategies for imparting art education and tools and practices for the assessment of learning through the integration of art into the curriculum.
IJETEV1I1A005 - REAL TIME DRIVE FATIGUE AND DETECTION RECOGNITION USING COMPUTER VISION
AUTHORS : Vishvanath Sundharam G, Muthusamy P, Sam Sundar N, Hari Haran N, Selvanesan M
ABSTRACT – Driver drowsiness and distraction are among the leading causes of road accidents worldwide, necessitating the development of intelligent, real-time monitoring systems to enhance driving safety. This paper presents a non-intrusive Driver Monitoring System (DMS) based on computer vision and deep learning techniques for the detection of driver fatigue and inattentive behavior. The proposed approach leverages a YOLO-powered object recognition framework to identify critical facial regions such as the eyes, mouth, and head orientation from live in-vehicle camera feeds. Key behavioral metrics, including the Percentage of Eye Closure (PERCLOS) and Mouth Aspect Ratio (MAR), are computed to assess alertness levels. The system is designed to operate under varying lighting conditions and diverse driver profiles, providing timely audio or visual alerts upon detecting drowsiness or distraction.
IJETEV1I1A006 - SMART HIGHWAY SAFETY SYSTEM USING DEEP LEARNING FOR ANIMAL DETECTION
AUTHORS : Arthi R, Mohanakumaresan B, BhoobashR V, Vigneshwaran M,Vignesh S
ABSTRACT – Road accidents caused by unexpected animal crossings have become a significant concern, particularly during nighttime when visibility is severely limited. These incidents not only lead to property damage but also pose serious risks to human life and wildlife. The proposed system addresses this challenge by introducing an intelligent animal detection and alert framework designed to enhance road safety through continuous real-time monitoring. High-resolution night-vision cameras capture live footage of roadways, which is then processed using advanced deep learning techniques, including Convolutional Neural Networks (CNN) and YOLO-based detection models. These algorithms enable accurate identification of animals even under low-light, foggy, or adverse weather conditions. By automatically distinguishing animals from other objects such as vehicles or pedestrians, the system reduces the reliance on human intervention and ensures precise detection in dynamic environments.
IJETEV1I1A007 - DEEPINSIGHT: ENDOSCOPIC IMAGE-BASED COLORECTAL CANCER CLASSIFICATION USING SEQUENTIAL CNN ARCHITECTURE
AUTHORS : Muthusamy P, Arthi R, Mowleshwaran K, Arunkumar S, Yasar S
ABSTRACT – Colorectal cancer (CRC) is one of the most common and life-threatening cancers worldwide, where early detection significantly improves survival rates. Conventional diagnostic approaches such as colonoscopy and histopathological examination rely heavily on manual interpretation by clinicians, making the process time-consuming. This paper presents an artificial intelligence–based computeraided diagnosis (CAD) system for automated colorectal cancer classification using endoscopic images. The proposed system employs a Sequential Convolutional Neural Network (CNN) architecture to classify colorectal images into normal, polyp, and cancerous categories. Image preprocessing techniques such as resizing, noise removal, normalization, and data augmentation are applied to enhance feature learning and improve model performance. Experimental results demonstrate improved classification accuracy, reduced false detection rates, and real-time diagnostic support
IJETEV1I1A008 - CHAT CT – A SECURE WEB CHAT APPLICATION
AUTHORS : Sathiyapriya P, Kohila R, Kavinkumar G, Praveenkumar B, Abinesh R
ABSTRACT – This paper describes the design and implementation of a secure webbased chat application that combines four protection layers: encryption, steganography, identity verification, and optional passwordbased access control. The system enables realtime messaging via WebSockets and implements end-to-end encryption with clientside key management so that the server stores only ciphertext and metadata. An optional steganography mode embeds encrypted payloads into lossless image carriers (PNG) for covert transmission, with client-side embedding and extraction. Identity verification is provided through hashed credentials and optional TOTP-based twofactor authentication, while an additional permessage password-derived key can be applied for extra access control. The implementation targets a modern web stack supports media sharing, private and group chats, and secure key exchange. The paper documents architecture, data flows, database schema, API and real-time endpoints, and a phased development plan for integrating encryption, steganography, and operational hardening.
IJETEV1I1A009 - ADVANCED TECHNIQUES FOR STRENGTHENING CROSS-MEDIA BIOMETRIC AUTHENTICATION
AUTHORS : Kohila R, Brightlin B C, Muralidharan P, Livanthan S, Nitheesh Kumar B
ABSTRACT – The rapid growth of digital banking has intensified security threats such as phishing, identity theft, shoulder surfing, and unauthorized access. Traditional authentication methods using passwords, PINs, and OTPs are increasingly vulnerable due to their static nature. This paper proposes a secure multilayer authentication framework integrating behavioral biometrics, Illusion PIN mechanisms, facial biometric recognition, and blockchain-based data security. Keystroke dynamics are used for behavioral authentication, while Illusion PIN protects against observation attacks. Facial recognition using the Grassmann algorithm ensures accurate user verification, and blockchain technology provides tamper-proof transaction storage, enhancing trust and security in digital banking systems. The proposed approach improves resistance to both cyber and physical attacks while maintaining user convenience. This framework offers a reliable solution for next-generation secure digital financial platforms
IJETEV1I1A010 - EARLY DETECTION OF ALZHEIMER’S DISEASE FROM BRAIN MRI USING VGG16 - BASED DEEP LEARNING
AUTHORS : Vishvanath Sundharam G, Muthusamy P, Harihaaran S, Arjun S, Lokamanya Reddy C H
ABSTRACT – Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that leads to memory loss and cognitive decline. Early diagnosis is essential for effective disease management and improved patient outcomes. This paper presents an automated deep learning-based approach for early detection of Alzheimer’s disease using brain MRI images and a VGG16-based convolutional neural network. Transfer learning is employed to finetune the pre-trained VGG16 model for identifying disease-specific patterns in MRI scans. Image normalization and data augmentation are applied to enhance model performance and generalization. Experimental results show that the proposed model achieves high accuracy while reducing reliance on manual diagnosis, making it a reliable and scalable tool for clinical decision support.