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Deep Learning Innovations for Underwater Waste Detection: An In-Depth Analysis

IEEE Access 2025年 13卷 88917-88929页

作者： Walia, Jaskaran Singh Haridass, Kavietha Pavithra, L.K. Vellore Institute of Technology School of Computer Science and Engineering Chennai India Vellore Institute of Technology Centre for Human Movement Analytics Chennai India

Underwater waste detection is a critical challenge for preserving aquatic ecosystems, particularly due to inherent underwater distortions such as light refraction, occlusion, and scattering. In this study, we present a novel deep learning framework for real-time underwater waste detection by evaluating state-of-the-art object detection algorithms on a manually annotated custom dataset comprising 10,000 curated augmented images across various water bodies to simulate real-world turbidity, illumination, and occlusion, consisting of three classes: Underwater Trash, Rover, and Biological Life. Our approach incorporates robust feature extraction and specialized data augmentation techniques that effectively mitigate the adverse effects of underwater distortions. We investigate multiple architectures, including YOLOv8n, YOLOv7, YOLOv6s, YOLOv5s, Faster R-CNN, and Mask R-CNN, analyzing key parameters such as mean average precision (mAP), inference speed, and computational efficiency. Our results demonstrate that YOLO-based models achieve faster & accurate performance, with YOLOv8n and YOLOv7 reaching a detection accuracy of 96%, compared to 81% and 83% for Faster R-CNN and Mask R-CNN, respectively. Computationally, YOLOv8n achieves 76 ms per frame, while YOLOv5s runs at 14 ms per frame, confirming real-time viability for AUV deployment. The proposed approach offers significant advantages over existing methods by enabling rapid, accurate detection with low computational overhead, thereby paving the way for integration with autonomous underwater vehicles (AUVs) for environmental monitoring and waste management. © 2013 IEEE.

关键词： Image segmentation

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Sentiment Analysis of Financial News: Implications for Corporate Strategy in Risk Management, Mergers and Marketing

Sentiment Analysis of Financial News: Implications for Corpo...

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

作者： Sendhil, R. Lijo, Joshua Abinaya, E. School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798331509828

This research study examines the ways to use sentiment analysis on financial news for corporate strategy making. We examine the impact of sentiment in financial news on corporate decisions (beyond technology) regarding risk management, mergers and marketing strategy using recent developments from research into sentiment analysis as input and contrasts it with our findings. This study presents unsupervised and supervised sentiment analysis methods, statistical methods and AI tools for sentiment analysis, and their contributions to identifying business needs. In addition, the paper underscores existing difficulties and received a number of limitations on current techniques in sentiment analysis that have to be tended for better supporting corporate strategy. © 2025 IEEE.

关键词： Decentralized finance

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An Energy Efficient Hybrid Communication Protocol for Large Area Wireless Sensor Networks 4

An Energy Efficient Hybrid Communication Protocol for Large ...

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4th International Conference on Evolutionary Computing and Mobile Sustainable Networks, ICECMSN 2024

作者： Karthigeyan, Shreyas Bhol, Sampark Jain, Akshat Yadav, Karthik Sinha, Akash Padma Priya, R. School of Computer Science and Information Systems Vellore Institute of Technology Vellore Campus Tamil Nadu Vellore632014 India School of Computer Science and Engineering Vellore Institute of Technology Vellore Campus Tamil Nadu Vellore632014 India Department of Computer Science and Engineering Maulana Azad National Institute of Technology Madhya Pradesh Bhopal462003 India

Energy conservation is an indispensable aspect of the protocols designed for Wireless Sensor Networks (WSNs). The communication protocols for WSN fall mainly under two categories: centralized and distributed. Centralized protocols are not feasible for real-world WSNs as the network can span across large areas where most of the nodes cannot directly communicate with the base station (BS). The routing issues in such networks can be addressed using multi-hop protocols. However, the process of BS guided clustering in such methods cannot be performed. Distributed clustering methods that minimize the intervention of BS in clustering can provide feasible solutions in such cases, but the energy expenditure is considerably high. This paper, hence, proposes a hybrid clustering based routing protocol for large WSN that employs certain aspects of centralized as well as that of distributed protocols. Experimental evaluation of the proposed protocol in terms of network lifetime gives promising results that clearly indicate the energy efficiency and consistency of the proposed work in different scenarios. The proposed work can prove beneficial in real-life scenarios where the deployed nodes are far away from the BS. © 2025 The Authors. Published by Elsevier B.V.

关键词： Hybrid power

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

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

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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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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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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...

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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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