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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Subbulakshmi, T. Meghana, G. Sunkara, S. Ravi Sailendra Shanmukha Sai, V. School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798331522667

Phishing attacks have become a widespread and intricate threat, targeting individuals and organizations through deceptive URLs to steal sensitive information such as login credentials and financial data. These attacks exploit human vulnerabilities and frequently employ advanced techniques like URL obfuscation and social engineering to evade traditional security measures. Despite ongoing advancements in cybersecurity, phishing remains a persistent issue due to its adaptability and the continual evolution of attack strategies. This paper introduces a comprehensive phishing detection system that implements both rule-based analysis with machine learning models to address these challenges effectively. The system utilizes YARA rules to detect known phishing patterns and integrates real-time threat intelligence feeds for up-to-date phishing data. Additionally, it employs machine learning algorithms to enhance detection accuracy and adapt to emerging threats. The system is further strengthened by its integration with a password manager, providing real-time URL validation and alerts to users during their online activities. This multi-faceted approach aims to improve the accuracy of phishing detection and offer robust protection against the increasingly advanced methods employed by attackers. © 2025 IEEE.

关键词： Phishing

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Steel Defect Detection Using a ResNet-Inspired Model and Fourier Transforms 4

Steel Defect Detection Using a ResNet-Inspired Model and Fou...

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4th International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Anand, Philip Sudharsan, Rohit Madhavan Ramachandran, Vishwath Prusty, Manas Ranjan Vellore Institute of Technology School of Computer Science and Engineering Chennai India Vellore Institute of Technology Centre for Cyber Physical Systems Chennai India

ISBN: (纸本)9798350343670

Defect detection in steel materials is a critical task to ensure product quality and reliability in various industrial applications. Traditional methods for detecting defects in steel surfaces are often labor-intensive, time-consuming, and prone to subjectivity and errors. However, the advent of deep learning-based models and techniques has provided a promising avenue for automating this process, significantly reducing error rates. In this research, the authors propose a Steel Defect Detection system with the help of a custom designed deep neural network inspired by the ResNet architecture. Notably, the model incorporates innovative attention layers not previously integrated into similar architectures, enhancing its predictive capabilities. The authors make use of data augmentation techniques to improve the model performance to generalize, and detect defects in imperceptible data. The novel multiclass semantic segmentation model has achieved over 91% accuracy and can be used to automate the Steel defect detection process, which can remarkably decrease the costs and time associated with manually inspecting the Steel sheets. © 2024 IEEE.

关键词： Attention layers augmentation Deep learning Residual Network Steel Defect

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Personalized Mental Health Assistance: Integrating Emotion Prediction with GPT-Based Chatbot

Personalized Mental Health Assistance: Integrating Emotion P...

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2025 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2025

作者： Kifayathullah, Muhammad Sekar, Rajeev Abinaya, R. Vijayaprabakaran, K. Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331529833

Mental health is a fundamental aspect of overall wellbeing, despite its importance, it remains one of the most neglected issues. By integrating Artificial Intelligence with Natural Language Processing into this domain helps to bridge the gaps and challenges. The proposed methodology provides easier access to mental health support by integrating emotion prediction, using the Bi-LSTM Model, with GPT-based chatbots. This system is designed to analyse user inputs, estimating the likelihood of various emotional states such as Anxiety, Depression, Stress, and Suicidal thoughts. These emotional probabilities are then used to inform and guide the responses generated by the GPT model, ensuring that the interactions are more relevant and supportive. Additionally, our approach focuses on providing personalized mental health care using sentiment analysis and prompt engineering. By understanding and interpreting users' emotional states, the chatbot can dynamically adjust its interactions to better meet the needs of everyone, thus improving the quality of mental health support. © 2025 IEEE.

关键词： Electronic health record

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Spatial attention-based hybrid VGG-SVM and VGG-RF frameworks for improved cotton leaf disease detection

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Neural Computing and Applications 2025年第14期37卷 8309-8329页

作者： Pandiyaraju, V. Anusha, B. Senthil Kumar, A.M. Jaspin, K. Venkatraman, Shravan Kannan, A. School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Chennai India Department of Computer Science and Engineering Marian Engineering College Trivandram Kerala Trivandrum India Department of Computer Science and Engineering St. Joseph’s Institute of Technology Tamil Nadu Chennai India Department of Information Science and Technology College of Engineering Guindy Anna University Tamil Nadu Chennai India

The agriculture industry's production and food quality have been impacted by plant leaf diseases in recent years. Hence, it is vital to have a system that can automatically identify and diagnose diseases at an initial stage for enhancing the quality of agricultural output and also for stopping the overall plant extinction. Most state-of-the-art methods do not provide appropriate accuracy of identification when the similarity among different diseases is higher and the input leaf images have complex background. In this work, a transfer learning-based automated crop disease recognition system is proposed to handle this problem. The cotton leaf disease detection framework being proposed here includes two key stages: (1) the feature extraction stage, where the convolutional neural network (CNN) model from the visual geometry group (VGG-16) is employed to obtain the deep features from cotton leaf images with complex background. (2) Cotton leaf disease detection stage: Here, the extracted optimal features are input to the machine learning classifiers, namely random forest (RF) and nonlinear support vector machine (SVM), for the initial classification of the diseases. For providing an optimal cotton crop disease detection performance, Kaggle’s four-class cotton disease dataset with complex background is chosen in this work. For the final decision making and analysis, two hybrid models named spatial attention-based hybrid VGG-RF and hybrid VGG-SVM have been proposed in this work for enhancing the decision accuracy. These hybrid models offered an average accuracy of approximately 98.29% and 99.31%, respectively, which is higher than the accuracy provided by the related work. This proved that the proposed hybrid model, namely spatial attention-based VGG-SVM and VGG-RF models, with spatial attention outperformed the state-of-the-art classifiers. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Convolutional neural networks

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InsightNav - Empowering Desktop Navigation with computer Vision

InsightNav - Empowering Desktop Navigation with Computer Vis...

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2025 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2025

作者： Sharma, Ayush Kumar Wakchaure, Supriya Bharat Twinkle, Tanya Vignesh, U. Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331529833

InsightNav redefines desktop interaction by harnessing cutting-edge computer vision and AI technologies to deliver a transformative user experience. Beyond its intuitive gesture-based navigation, InsightNav pioneers two groundbreaking features that elevate human-computer interaction. The first, Personalized Desktop Themes Based on Emotion Detection, leverages real-time facial expression analysis to dynamically alter the desktop's visual aesthetics based on the user's mood. This innovation empowers the system to foster emotional well-being, transitioning to serene themes during stress or energizing designs to amplify motivation. The second feature, Voice-Activated Multitasking Snap Layouts, revolutionizes task management by enabling users to orchestrate optimized application layouts through simple voice commands. By uttering phrases like "Focus Mode,"users can seamlessly arrange tools such as browsers, editors, and readers, thereby accelerating productivity and streamlining workflows. Powered by MediaPipe, OpenCV, and Google Speech-to-Text, InsightNav transcends traditional interaction paradigms, offering unparalleled accessibility, efficiency, and personalization. Together, these innovations position InsightNav as a revolutionary tool, setting new benchmarks in adaptive and immersive human-computer interaction. © 2025 IEEE.

关键词： Gesture recognition

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Exploring the Microcosm: A Comprehensive Survey of Micro Sensor Applications Across Multidisciplinary Research

Exploring the Microcosm: A Comprehensive Survey of Micro ...

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International Conference on Power engineering and Intelligent systems, PEIS 2024

作者： Nandhini, R. Radha, R. School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9789819767090

The main aim of this survey is to provide wider insight about the use of MEMS (Micro-Electro-Mechanical-System) technology in various interdisciplinary fields. The areas include IoT (Internet-Of-Things) for smart automations, agricultural field for performing precision farming. Automotive sector to predict failures, environmental aspects to detect factors affecting ecosystem, food control to measure the quality. Healthcare for identifying the disease in prior stages, robotics to carry out multiple operations. Machine Learning algorithms play vital role in classifying and extracting the necessary feature from the raw data. Deep Learning techniques like ANN (Artificial Neural Network) is employed for estimation of optimal input and output parameters. CNN (Convolutional Neural Network) for representing the designed structure in binary pixelated image form. The various types of sensors, energy harvesters, transducers and actuators which are designed using MEMS technology and their predominant role in recent advancements has been discussed in brief. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Automotive industry

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Research on facial expression recognition based on multimodal data fusion and neural network

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International Journal of Wireless and Mobile Computing 2024年第1期27卷 47-55页

作者： Han, Yi Wang, Xubin Lu, Zhengyu School of Mechanical Science and Engineering Huazhong University of Science and Technology Hubei Wuhan China Anyang Institute of Technology Department of Computer Science and Information Engineering Henan Province Anyang China School of Computer Science and Information Engineering Shanghai Institute of Technology FengXian District Shanghai China

Facial expression recognition is a challenging task when neural network is applied to pattern recognition. Most of the current recognition research is based on single source facial data, which generally has the disadvantages of low accuracy and low robustness. In this paper, a neural network algorithm of facial expression recognition based on multimodal data fusion is proposed. The algorithm is based on the multimodal data, and it takes the facial image, the histogram of oriented gradient of the image and the facial landmarks as the input, and establishes Convolutional Neural Network (CNN) designed to extract features from facial image, neural network designed to extract features from facial Landmarks and Neural Network (LNN) designed to extract features from Histogram of gradient (HNN), three sub-neural networks to extract data features, using multimodal data feature fusion mechanism to improve the accuracy of facial expression recognition. Experimental results show that, the algorithm has a great improvement in accuracy, robustness and detection speed. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Data fusion

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Deep Learning-Based Helmet Detection for In-Vehicle Compliance Monitoring on Low-Cost Edge Devices

Deep Learning-Based Helmet Detection for In-Vehicle Complian...

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2025 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2025

作者： Abinaya, R. Sekar, Rajeev Pai, Tarun Vinod Thippa, Subhash Praveen Joe, I.R. School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798331529833

This study presents a highly efficient helmet detection system designed for real-time operation on low-cost edge devices, targeting helmet compliance monitoring in vehicular environments. A novel dataset was created by capturing images from an innovative perspective by positioning the camera above the vehicle's read-view mirror. This provided a unique top-down view that enhances the performance of real-time helmet detection in dynamic traffic conditions. The dataset comprises three defined classes: (2) no face, (1) face without a helmet, and (0) face with a helmet. This reflected the diverse scenarios encountered in real-world situations. A Deep Learning (DL) model was trained and compiled into TensorFlow Lite (TFLite) format which optimizes the model for low-power edge devices with minimal computational resources. The proposed methodology achieved a high classification accuracy of 99.56%. This work not only concentrates on maximizing the computational efficiency of helmet detection on edge devices but also offers a scalable and cost-effective solution for traffic safety monitoring. By integrating advanced deep learning capabilities with affordable hardware, this study provides a valuable tool for automated helmet detection, contributing to the improvement of road safety rules compliance. © 2025 IEEE.

关键词： Magnetic levitation vehicles

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ADVANCED CHAT APPLICATION INTEGRATING MACHINE LEARNING FOR SENTIMENTAL ANALYSIS

ADVANCED CHAT APPLICATION INTEGRATING MACHINE LEARNING FOR S...

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2024 International Conference on Computing and Intelligent Reality Technologies, ICCIRT 2024

作者： Layek, Santanu Sharma, Gauransh Agarwal, Vihan Chandru Vignesh, C. School of Computer Science and Engineering Vellore Institute of Technology Tami Nadu Vellore India School of Computer Science and Engineering Tami Nadu Vellore India

ISBN: (纸本)9798331510299

The "Advanced Chat Application Integrating Machine Learning for Sentimental Analysis" is a progressive interface that utilizes machine learning (ML) algorithm to analyze user's interaction and offer compatibility services with emotional intelligence in real-time. This platform offers the chat user an intelligent map of a comprehensive communication interface with the ability to also complete the human ability in the sense of the differentiation between emotions where it will show empathy based on the context of the user. By the implementation of sentiment analysis feature, the chat application is able to identify the emotions as happy, sad, angry or neutral that people portray when in a conversational mode of text. This is made possible by natural language processing (NLP) encoding using the artificial intelligence models that enjoy the capacity to determine the emotional content of the text messages by virtue of deep learning algorithms which were trained on large data. Therefore, the sentiment detection is able to increase the level of participation, improve on the services offered to patrons, and most importantly, the identification of symptoms of mental disorders in their early stages. © 2024 IEEE.

关键词： Adversarial machine learning

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An efficient cloud based face recognition system for e-health secured login using steerable pyramid transform and local directional pattern

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Journal of Ambient Intelligence and Humanized Computing 2024年第1期15卷 1017-1027页

作者： Dosaj, Ayush Satapathy, Suresh Chandra Soundrapandiyan, Rajkumar Kaur, Mannat Hannoon, Naeem School of Computer Science and Engineering Vellore Institute of Technology Vellore India School of Computer Science and Engineering Kalinga Institute of Industrial Technology Deemed to be University Odisha Bhubaneswar India Faculty of Electrical Engineering Universiti Teknologi MARA Shah Alam Malaysia

This paper proposes a face recognition system based on steerable pyramid transform (SPT) and local directional pattern (LDP) for e-health secured login in cloud domain. In an e-health login, patients periodically forget their self-made login username and password. This face recognition system can replace the traditional login system. In the proposed system, SPT can decompose a face image into several sub-bands of different scales and orientations, and LDP can encode the sub-bands in binary texture pattern. The LDP binary pattern was obtained by computing the edge response values in all eight directions at each pixel position and generating a code from the relative strength magnitude. Therefore, SPT–LDP design represents a face image in a robust way which includes multiple information sources from different scales and orientations. The proposed system is tested on standard benchmark facial recognition technology (FERET) and extended Yale-B databases. Further, the efficiency of the proposed system is proved by comparing recognition rates with other existing descriptor based methods. From the experimental results it is observed that the proposed system achieves 99.79% recognition in fb set, 90.72% in fc set, 84.97% in dup I set, and 87.69% in dup II set for FERET database and 100% in sub2 set, 100% in sub3 set, 96% in sub4 set and 95% in sub5 set for extended Yale-B database which is better than the existing methods. © Springer-Verlag GmbH Germany, part of Springer Nature 2018.

关键词： Face recognition

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