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Deep learning Based Driver Drowsiness Detection in Real Time Environment - An Enhanced Approach 4

Deep Learning Based Driver Drowsiness Detection in Real Time...

引用

4th international conference on Sentiment Analysis and Deep learning, ICSADL 2025

作者： Ajay, Ashin Ashok, Naveen Kumar Balajiram, Gokul Tamizhselvan, Abinesh Thangavel, Senthil Kumar Somasundaram, K. Amrita Vishwa Vidyapeetham M. Tech in Artificial Intelligence Coimbatore India Amrita Vishwa Vidyapeetham B. Tech in Computer Science and Engineering Coimbatore India Amrita School of Computing Coimbatore Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Coimbatore India School of Physical Sciences Department of Mathematics Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (纸本)9798331523923

Effective driver behavior monitoring is necessary due to the increase in traffic accidents caused by distracted driving. In order to identify distractions such as talking, drinking, napping, and using a phone, this research suggests a CNN-based methodology. The model uses facial characteristics such as eyes to detect and analyze these activities. Thousands of photos depicting various distracted driving postures are used to train it on a publicly accessible dataset. The strategy seeks to improve road safety by offering a reliable way to identify distractions. © 2025 IEEE.

关键词： Convolutional neural networks

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Real-Time Threat Management using Deep Q-learning and Mininet 3

Real-Time Threat Management using Deep Q-Learning and Minine...

引用

3rd international conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Sivakumar, Amrutha Bhavya Reddy, G. Bhanot, Shreya Rajagopal, Shinu M. Prashanth, B.N. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Department of Mechanical Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331527549

The research combines Deep Q-learning(DQN) with a Mininet-based network simulation and Scapy intrusions detection system (IDS) for malicious traffic prioritizing. The RL agent continuously learns to act based on real-time traffic data and builds an adaptive model that detects and prioritizes threats such as SYN Flood, UDP Flood, Slowloris, HTTP Flood etc. The RL agent then adjusts its policy via the seamless combination of network simulation and live traffic detection to make sure lossy channels representing high-severity attacks are dealt with first, lessening the damage. The system improves the identification of intrusions by stimulating the intrusion prioritization dynamically with continuous learning and feedback, increasing network security. © 2025 IEEE.

关键词： Network intrusion

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Voice AI-Intelligence Based Voice Assistant 3

Voice AI-Intelligence Based Voice Assistant

引用

3rd international conference on Intelligent and Innovative Technologies in computing, Electrical and Electronics, IITCEE 2025

作者： Garai, Pooja Nikam, Aditya Kerkar, Suyash Deshmukh, Samruddhi Mane, Atharva Bhise, Suvarna Vasantdada Patil College Of Engineering & Visual Arts Dept. of Artificial Intelligence & Data Science Mumbai India

ISBN: (纸本)9798331515911

Voice assistant applications have become integral parts of modern technology ecosystems, offering users convenient and efficient ways to interact with their devices. This paper introduces Voice Ai, a versatile voice assistant application developed using Android Studio and Google's Machine learning Kit Machine learning(MLKit) for Optical Character Recognition (OCR) functionalities. The app includes features like setting alarms, making calls, opening applications, and conducting question-answer sessions powered by G4F. A comparative analysis between G4F and Google's backend is performed, assessing their performance and user satisfaction during question-answer interactions. Voice Ai simplifies daily tasks and boosts productivity, providing insights for developers, researchers, and end-users interested in AI-based voice assistant applications. © 2025 IEEE.

关键词： Adversarial machine learning

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The Impact of Transformer Models and Regression Algorithms on Automated Short Answer Grading 5

The Impact of Transformer Models and Regression Algorithms o...

引用

5th IEEE international conference on Advances in Electrical, computing, Communication and Sustainable Technologies, ICAECT 2025

作者： Nampoothiri, Abhijith. N. Thushara, M.G. Pradeesh, N. Anand Krishna, K.R. Santhosh, Abhai Abhijith Kumar, S. Kumar, Suryan Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Applications Amritapuri India Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Amritapuri India Omnex Software Solutions Pvt. Ltd Thoraipakkam Chennai India

ISBN: (纸本)9798350356236

Grading short answer questions in higher education often poses significant challenges due to its subjective nature and time-consuming process. As educational institutions expand, the need for efficient and objective grading solutions becomes critical. This paper investigates the use of machine learning and natural language processing to automate the evaluation of short answer responses. Our study employs a two-phase methodology: first, several transformer models - including BERT, Sentence-BERT, T5, Genism, and XLNet - are used to transform text into numerical representations. In the second phase, various regression techniques, such as Linear Regression, Support Vector Machine (SVM), Random Forest, and AdaBoost, are applied to these representations to estimate scores. Our findings indicate that the combination of the BERT model with Random Forest performs the best, demonstrating superior accuracy with the lowest error metrics. This optimal model pairing is proposed for integration into the Ample learning Management System (LMS) to enhance grading efficiency and provide quicker, more reliable feedback. The significant implications of this research provides teachers with a robust tool for automated grading, which can enhance the educational experience by delivering timely and accurate evaluations. © 2025 IEEE.

关键词： teaching

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IoT based Line of Control Monitoring System 2

IoT based Line of Control Monitoring System

引用

2nd international conference on Machine learning and Autonomous Systems, ICMLAS 2025

作者： Petkar, Taniya Faculty of Engineering and Technology Department of Computer Science And Medical Engineering Maharashtra Wardha442001 India

ISBN: (纸本)9798331505745

This paper presents a novel line-of-control (LoC) monitoring system that leverages the Internet of Things (IoT) to improve border security. The system creates a strong infrastructure for real-time monitoring throughout critical border regions, especially the Line of Control, by utilizing IoT devices, sensors, and communication technologies. The coverage, timeliness, and data quality of conventional border surveillance systems are frequently compromised. As opposed to this, the suggested system makes use of a wirelessly networked network of intelligent sensors, such as infrared, acoustic, and video cameras. By utilizing sophisticated image processing algorithms and machine learning to analyze the gathered data, the system is able to differentiate between regular activities and any security risks. To help security staff make decisions quickly, real-time updates are transmitted to a centralized control center. Integration with cloud computing also makes data analysis, retrieval, and storage simpler and guarantees data accessibility for forensic investigations and trend research. © 2025 IEEE.

关键词： Network security

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Real-Time Object Detection from Surveillance using Deep learning 3

Real-Time Object Detection from Surveillance using Deep Lear...

引用

3rd international conference on Intelligent and Innovative Technologies in computing, Electrical and Electronics, IITCEE 2025

作者： Tanisha, A. Tanisha, N. Chaitra, M. Tejasri, P.R. Bnm Institute of Technology Vtu Dept. of Computer Science and Engineering Bangalore India

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515911

Object detection in surveillance systems leverages advanced deep learning techniques to enhance security measures through real-time analysis of dynamic video feeds. This project integrates the YOLOv5 model for detecting weapons in both pre-recorded videos and live camera feeds. The model, trained on a dataset with 4000 images labeled for handguns and knives, utilizes image preprocessing steps such as resizing, normalization, and augmentation to improve detection accuracy. Implementing the system with OpenCV and Tkinter, the application processes video streams, identifying potential threats with high precision rate of 0.85 and recall rate of 0.90. The integration of a graphical user interface ensures user-friendly operation for security personnel. Key outcomes demonstrate the efficacy of deep learning models in real-time object detection, significantly contributing to proactive surveillance and enhanced situational awareness. © 2025 IEEE.

关键词： Graphical user interfaces

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Machine and Deep learning Classifications for IoT-Enabled Healthcare Devices

Machine and Deep Learning Classifications for IoT-Enabled He...

引用

2025 international conference on Pervasive Computational Technologies, ICPCT 2025

作者： Tyagi, Lalit Kumar Gupta, Anish Sisodia, Vibhash Singh Chandigarh Group of Colleges Chandigarh Engineering College Department of Computer Science & Engineering Mohali India Sri Bhawani Niketan Institute of Technology and Management Jaipur India

ISBN: (纸本)9798331508685

The Internet of Things (IoT) is transforming academic research and will impact various industries, including healthcare. IoT has transformed healthcare from blast-shaped outdated centralized methods to decentralized smart devices. This led to tailored health systems. Recent advances in wearables, sensor networks, and computing cloud have made large-scale health care IoT usage easier. Despite its serious limitations, IoT can change medicine. Maintenance of various appliances, pricing increases, and the need for greater storage space all issues. IoT is a diverse and durable technology, thus this study will analyze its health sector use. How machine learning and deep learning interact is being studied. The Internet of Things (IoT) health system architecture is also covered in this article, along with its benefits and use cases. This helps highlight IoHT difficulties and encourages academics to create innovative solutions. This paper examines the current work of numerous researchers in machine learning and deep learning, which are heavily linked with IoT-based healthcare approaches. © 2025 IEEE.

关键词： Deep learning

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Soft computing Paradigms for Load Balancing in Cloud computing 3rd

Soft Computing Paradigms for Load Balancing in Cloud Computi...

引用

3rd international conference on Computational Electronics for Wireless Communications, ICCWC 2023

作者： Ghafir, Shabina Alam, M. Afshar Alankar, Bhavya Department of Computer Science and Engineering School of Engineering Sciences and Technology Jamia Hamdard New Delhi India

ISBN: (纸本)9789819719457

This paper provides a comprehensive comparison of load-balancing algorithms employed in cloud computing, spanning from the earliest to the most recent developments. Emphasizing the significance of load balancing for system performance, resource optimization, and equitable allocation among users, the paper offers insights into various techniques, their effectiveness across workloads, and associated limitations. With an overview of each algorithm, this study caters to practitioners and researchers in the dynamic field of cloud computing, aiming to contribute valuable perspectives for enhanced system efficiency. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Utilizing the Lean Forward learning Model for Enhancing Student Online English Listening Ability 10

Utilizing the Lean Forward Learning Model for Enhancing Stud...

引用

10th international conference on Digital Arts, Media and Technology and 8th ECTI Northern Section conference on Electrical, Electronics, Computer and Telecommunications engineering, ECTI DAMT and NCON 2025

作者： Gui, Yingying Kongjit, Chalermpon Chiang Mai University College of Arts Media and Technology Chiang Mai Thailand

ISBN: (纸本)9798331543273

With the development of technology, China's online language education industry is booming, and college students are increasingly choosing to improve their language competency through online courses and platforms. However, the online learning environment is very complex, and students' listening learning will be affected by many factors, resulting in students' English levels not being satisfactorily improved, which indicates that students urgently need a new learning process model to help improve their listening ability. Therefore, the main objective of this study is to identify the key influencing factors of students' listening learning and propose a new learning process model to help students improve their listening ability. This study takes Y online English listening course as an example, based on lean forward learning, adopts the design thinking framework to find problems, and explores a new learning process model to help students improve their listening ability. In addition, this study provides useful information for educators to strengthen online listening teaching and promote more effective teaching practices. © 2025 IEEE.

关键词： Students

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Lightweight Adaptive learning Algorithm for Energy-Latency Tradeoff in IoMT-Enabled Edge computing 17

Lightweight Adaptive Learning Algorithm for Energy-Latency T...

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17th international conference on Machine Vision, ICMV 2024

作者： Yadav, Rahul Shafiq, Muhammad Kumar, Mohit Wei, Li Elaziz, Mohamed Abd College of Computer Science and Technology Harbin Engineering University Harbin China Cyberspace Institute of Advance Technology Guangzhou University Guangzhou China Department of Information Technology National Institute of Technology Jalandhar India Department of Mathematics Faculty of Science Zagazig University Zagazig Egypt

ISBN: (纸本)9781510688278

Smart Healthcare is witnessing widespread implementation of Internet-of-Medical-Things (IoMT) devices. These devices play a crucial role in collecting vast amounts of data from various smart healthcare applications, which are then processed to facilitate informed decision-making. Edge computing has emerged as a valuable platform offering computational resources to handle this data collection efficiently. However, a critical challenge arises from the inappropriate and inefficient classical approaches to fair resource allocation in these energy-intensive, short battery life and delay-intolerant portable devices. To address this issue, this paper presents a Lightweight Adaptive learning Offloading Algorithm designed to optimize energy and latency in an edge computing environment. The proposed algorithm formulates the problem as a combined minimization of latency and energy costs while satisfying constraints related to limited battery capacity and service latency deadlines. To achieve this, the algorithm employs the adaptation parameters (α, β, γ), and the tradeoff target (C′) is configured based on the specific task requirements. Experimental results demonstrate the advantages of the proposed scheme, including energy savings and minimized latency in an edge environment. To validate the effectiveness of the proposed scheme under a realistic scenario using the iFogSim2 simulator. © 2025 SPIE.

关键词： Electronic health record

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