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检索条件"机构=Shool of Computer Science and Engineering Vellore Institute of Technology"

共 3948 条记录，以下是51-60 订阅

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

Personalized Mental Health Assistance: Integrating Emotion P...

引用

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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Enhancing Web Security: A Phishing Detection System Integrated with Password Managers 6

Enhancing Web Security: A Phishing Detection System Integrat...

引用

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

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

引用

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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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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SmokerBeacon: Hybrid Real-Time Smoking Detection Using Object Detection and Gas Sensor

SmokerBeacon: Hybrid Real-Time Smoking Detection Using Objec...

引用

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

作者： Harshavardhan, S. Upender, Patri Ganesh, N. Giftsy, Sharon Vellore Institute of Technology School of Computer Science and Engineering Chennai India Vellore Institute of Technology School of Electronics Engineering Chennai India

ISBN: (纸本)9798331529833

Enforcing smoking bans in public areas remains a challenge due to limitations in conventional monitoring methods. This paper introduces SmokerBeacon, an innovative system for real-time detection of smoking activities in restricted zones. SmokerBeacon uses gas sensors and advanced image classification - YOLO to identify smoking activities. The system catego-rizes detection scenarios into three types: (i) instances where smoking actions are detected by YOLO without corresponding signals from gas sensors, (ii) cases where YOLO fails to detect smoking actions despite gas sensors detecting smoking-related substances, and (iii) situations where both smoking activities and corroborating signals from gas sensors confirm smoking. Each scenario triggers specific actions to improve enforcement and foster healthier environments. By generating timely alerts and enabling proactive intervention, SmokerBeacon mitigates risks associated with smoking in prohibited areas, leveraging IoT technology. © 2025 IEEE.

关键词： Chemical sensors

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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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Advancing Cancer Diagnosis: Federated Learning for Tumor Classification

Advancing Cancer Diagnosis: Federated Learning for Tumor Cla...

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Congress on Smart Computing Technologies, CSCT 2023

作者： Upadhyay, Siddharth Singh, Khushi Suganeshwari, G. School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9789819780952

Ensuring data privacy and security is a critical challenge in cancer diagnosis, driving the adoption of Federated Learning as a collaborative and privacy-preserving solution for tumour classification. Tumour classification plays a significant role in enabling precise diagnoses and treatments. However, centrally aggregating medical data raises concerns regarding the privacy and security of an individual. To tackle these challenges, we use Federated Learning, which offers a privacy-preserving approach that involves collaborative model training on decentralized data sources. This paper showcases the research contributions and gaps in the field of healthcare data, focusing on the significance of ethics. This survey introduces the concepts of federated learning, emphasizing healthcare studies highlighting the importance of tumor classification in healthcare decision-making. Moreover, this paper discusses the recent advancements in Federated Learning concerning tumor classification, including a wide variety of data such as medical imaging, genetics, and clinical data. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Federated learning

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Graph Based Retrieval-Augmented Generation for Personalized Dietary Guidance with LLMs

Graph Based Retrieval-Augmented Generation for Personalized ...

引用

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

作者： Akilesh, S. Sekar, Rajeev Tulasi Raman, R. Suganya, R. Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331529833

This paper presents a personalized diet recommendation system that leverages a graph database and retrieval-augmented generation (RAG) with large language models (LLMs) to provide accurate and tailored nutritional plans. The system focuses on vegetarian Indian dishes and analyzes individual health metrics such as physical activity levels, weight age and height to generate customized diet plans. A Neo4j graph database, built from scraped recipe data, stores dish names, ingredients, and nutritional values, enabling precise information retrieval via LLMs. Additionally, the system provides nutritional details and overall health assessments of the dishes. To enhance overall health, the system also suggests basic workout plans based on users' physical metrics and dietary recommendations. This approach aims to provide a low-cost, accessible alternative to traditional diet consultations while mitigating hallucinations in AI outputs through RAG and structured data. © 2025 IEEE.

关键词： Recommender systems

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Optimized-Memory and Enhanced-Security Approaches to RSA and ElGamal

Optimized-Memory and Enhanced-Security Approaches to RSA and...

引用

2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Kumar, Maheswer Sunil Balaji, Vinithra Sakkarapani, Sandosh School of computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798331509828

This paper demonstrates 2 methods, a reduced memory technique, and a reduced memory along with more security techniques in RSA (Rivest-Shamir-Adleman) and ElGamal which are both asymmetric cryptographic algorithms. Regular RSA and ElGamal are known to be performance efficient, but memory wise, they were not efficient. Also, to enhance security of any algorithm, the key size is increased leading to more memory and computation. To solve these challenges, hashing method is used through which, the memory can be reduced without affecting the security or the performance of the algorithms. Along with these hashmaps, we create an array manipulation method, which enhances the security of data in the algorithm by making the cipher text more complex. The above is performed using pipe-lining and use of python. The objective of this research work is to optimize RSA and ElGamal algorithm with respect to memory load and increasing security, without affecting the overall computational overhead of the system. The results aim to contribute to the advancement of these cryptographic techniques and especially resource constrained environments, where memory is an important factor and for more security required procedures. The hashing method optimizes the performance of RSA and ElGamal and their use in transferring large confidential files and the array method enhances the security using a modulus acting as another key. Furthermore, this optimized technique can be applied to other cryptographic solutions © 2025 IEEE.

关键词： Pipe linings

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Enhancing Cardiac Health: Machine Learning in Coronary Artery Disease Prediction 6th

Enhancing Cardiac Health: Machine Learning in Coronary Arter...

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6th International Conference on Computational Intelligence in Pattern Recognition, CIPR 2024

作者： Vijayaraj, Anu Ragavi Pasupathi, Subbulakshmi School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9789819780891

The alarming surge in cardiovascular diseases, with a particular focus on Coronary Artery Disease (CAD), is causing premature fatalities. This escalation is exacerbating the inefficiency of the diagnostic process, burdened by both time constraints and the strain on human resources, as more patients endure extended wait times for diagnosis. Addressing this pressing issue calls for the fusion of analytical decision-making with digital patient data, aiming to develop a robust and efficient predictive model for early-stage cardiac illness. Machine Learning (ML) emerges as a promising tool to bolster CAD detection and prognosis within the healthcare sector. This chapter introduces an ML-based approach for CAD prediction, leveraging the Z-Alizadeh Sani dataset sourced from the UCI repository. The classification task is tackled using three distinct ML models—Artificial Neural Network (ANN), Decision Tree (DT), and Random Forest (RF). The findings underscore the superior performance of ANN, achieving an impressive 89.01 percent accuracy in CAD diagnosis. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cardiology

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