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An In-Depth Exploration of Advanced Driver Drowsiness Detection Systems for Enhanced Road Safety 2

An In-Depth Exploration of Advanced Driver Drowsiness Detect...

引用

2nd International Conference on computer, Communication and Control, IC4 2024

作者： Abirami, A. Bhuvaneswari, S. Vishnuvardhan, M. Karthik, Yadam Shankar, Saisuraj Easwari Engineering College Dept. of Computer Science and Engineering Chennai India Student of Easwari Engineering College Dept. of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350387933

Drowsiness, among drivers, plays a role in causing road accidents leading to loss of life, injuries, and financial setbacks. To address the issue of driver fatigue related accidents it is crucial to develop methods for detecting driver fatigue. This survey paper presents an overview of approaches used to identify driver fatigue. These approaches can be grouped into three categories: Behavioural, Vehicular and Physiological parameters. Behavioural parameters, such as eye closure percentage, blink rate patterns, yawning, head movements and facial expressions offer intrusive techniques that examine the physical state of the driver's body and provide information about their drowsiness level. Vehicular parameter-based techniques evaluate drowsiness by observing vehicle behaviour such as lane deviations, pedal usage, and unpredictable steering movements. Physiological indicators are also employed to detect tiredness. The paper also discusses the methodology employed in the research study including image processing techniques classifiers used and measurement metrics utilized, proposed methodology of fatigue detection with adding some extra features and Lastly the paper presents conclusions based on a study that can guide future researchers in identifying potential areas for further exploration in this field. © 2024 IEEE.

关键词： Physiological models

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Using the CNN architecture based on the EfficientNetB4 model to efficiently detect Deepfake images 1

Using the CNN architecture based on the EfficientNetB4 model...

引用

IEEE AITU: 1st International student Conference: "Digital Generation", AITU 2024

作者： Zhalgasbayev, Arman Aiteni, Tolegen Khaimuldin, Nursultan Sofrware Engineering Student Astana IT University Astana Kazakhstan Computer Science Student Astana IT University Astana Kazakhstan Senior lecturer of Computer Engineering Educational Program Astana IT University Astana Kazakhstan

ISBN: (纸本)9798350364378

This study evaluates the effectiveness of using the EfficientNetB4 model architecture for deepfake image detection and identifies the main challenges in developing an accurate model for deepfake image detection. Deepfake image detection is a complex task that requires lots of data, advanced models and new approaches for handling features in face images which shows that image is real or fake. This research is based on the Deepfake Detection Challenge (DFDC), which took place in 2019-2020, where world experts showed new approaches to solving this problem. During the research and development of the model, the best practices and experience of experts were used to identify the deepfake images. ©2024 IEEE.

关键词： Deep learning

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Identifying Children's Personality Styles through Drawing Analysis using Machine Learning 14

Identifying Children's Personality Styles through Drawing An...

引用

14th International Conference on Information and Knowledge Technology, IKT 2023

作者： Mosharraf, Maedeh Banabazi, Faezeh Shahid Beheshti University Faculty of Computer Science and Engineering Tehran Iran Shahid Beheshti University Computer Engineering Student Tehran Iran

ISBN: (纸本)9798350349412

The impact of personality styles on individuals' behavior and choices is undeniable. With the advancements in datamining and machine learning techniques, various methods have been proposed to automatically identify personality styles by processing extensive data and exploring some human behavioral patterns. In this article, we examine children's personality styles by analyzing their drawings using some machine learning techniques. We employ the Murphy-Meisgeier Type Indicator for Children (MMTIC) questionnaire results to train k-nearest neighbors, random forest, and decision tree classifiers. Although we focus on a limited set of drawing features in this study, the results obtained are remarkably significant. We believe that the findings of this study will have a profound impact on early identification of children's personality styles and their decision-making throughout their lives. © 2023 IEEE.

关键词： Behavioral research

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Machine Learning for Forecasting Depression and Anxiety in University students 1

引用

8th International Conference on Information and Communication Technology for Competitive Strategies, ICTCS 2023

作者： Biswas, Tamal Bhattacharya, Diptendu Rudrapal, Dwijen Roy, Srijan National Institute of Technology Agartala Department of Computer Science and Engineering Tripura Agartala India National Institute of Technology Agartala Department of Computer Science and Engineering Tripura Agartala India National Institute of Technology Agartala Department of Computer Science and Engineering Tripura Agartala India M Tech Student National Institute of Technology Agartala Department of Computer Science and Engineering Tripura Agartala India

ISBN: (数字)9789819702107

ISBN: (纸本)9789819702091

The timely identification of mental health issues enables experts to more effectively provide treatment and enhance the well-being of patients. Mental health pertains to an individual’s emotional, mental, and interpersonal state, influencing their thoughts, emotions, and behaviors. It remains crucial across all life phases, spanning childhood, adolescence, and adulthood. Historically, categorizing mental health problems among college students demanded significant effort and time from psychologists. This research engaged five machine learning methods to classify such issues swiftly. The effectiveness of these methods was evaluated based on different standards. The five methods included logistic regression, KNN classification, decision tree classification, random forest, and stacking. A comparison and implementation of these methods revealed that stacking yielded the highest accuracy, predicting 82.20. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： students

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Enhancing Relational Database Interaction through Open AI and Stanford Core NLP-Based on Natural Language Interface 5

Enhancing Relational Database Interaction through Open AI an...

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5th IEEE International Conference on Recent Trends in computer science and Technology, ICRTCST 2024

作者： Kumar, Surjeet Alam, Md Shamsher Khursheed, Zeeshan Bashar, Shadan Kalam, Neha Government Engineering College Computer Science & Engineering Bihar Sheikhpura India R.V.S College of Engineering & Technology Computer Science and Engineering Jamshedpur India Jio Platform Limited DGM Mumbai India R.V.S College of Engineering & Technology Master of Computer Application Jamshedpur India R.V.S College of Engineering & Technology Computer Science and Engineering Student Jamshedpur India

ISBN: (纸本)9798350351378

In the contemporary digital era, data has evolved into the cornerstone of myriad sectors, encompassing academia, commerce, and medical research. Yet, the acquisition of insights from this expansive reservoir of information has historically demanded a level of proficiency in formal languages such as SQL, thereby imposing a substantial barrier for individuals lacking technical expertise. In response to this pervasive challenge, the advent of Natural Language Interface to Databases (NLIDB) has emerged as a promising and transformative solution. NLIDB offers users the capability to engage with databases through the utilization of their natural language, whether expressed in textual or verbal form. This paradigm shift empowers both seasoned experts and neophytes to effortlessly extract valuable information from databases without the prerequisite of specialized technical skills. In this research paper, we propose an innovative system that capitalizes on an intermediary SQL generation process, facilitating the seamless bridge between natural language input and subsequent database retrieval. This novel approach eliminates the need for users to possess awareness of the intricate underlying complexities of the database management process. By simplifying the interaction between users and databases, our system endeavours to democratize data access and promote the universal utilization of this invaluable resource across diverse domains. © 2024 IEEE.

关键词： Natural language processing systems

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Designing an AI driven intelligent Tutorial System 5

Designing an AI driven intelligent Tutorial System

引用

5th IEEE International Conference on Recent Trends in computer science and Technology, ICRTCST 2024

作者： Alam, Shamsher Kumar, Surjeet Khursheed, Zeeshan Mahato, Hemant Kumar Bashar, Shadan Suman, Anjali R.V.S College of Engineering & Technology Computer Science and Engineering Jamshedpur India Government Engineering College Sheikhpura Computer Science and Engineering Bihar India Jio Platform Limited DGM Mumbai India NSHM Master of Computer Application Durgapur India R.V.S College of Engineering & Technology Master of Computer Application Jamshedpur India R.V.S College of Engineering & Technology Computer Science & Engineering Student Jamshedpur India

ISBN: (纸本)9798350351378

With the fast-evolving field of education, there is an increase in the demand for intelligent tutorial systems that can adapt to the diverse learning needs of individual students. This research paper explores the design and implementation of an AI-driven Intelligent Tutorial System (ITS) aimed at enhancing the personalized learning experience. The system leverages advanced machine learning algorithms to dynamically adjust instructional content, pace, and assessments based on real-time analysis of individual student performance and preferences. The paper delves into the key components of the proposed ITS, including adaptive learning models, natural language processing capabilities for interactive communication, and data-driven decision-making mechanisms. Emphasis is placed on the seamless integration of AI technologies to create an intuitive and responsive educational *** considerations in the development and deployment of the intelligent tutorial system are discussed, addressing issues related to data privacy, algorithmic bias, and the role of human oversight. The paper also highlights the collaborative potential between AI and human educators, emphasizing the system's capacity to support rather than replace teaching professionals. Through a combination of theoretical frameworks and practical examples, the research examines the potential impact of the AI-driven ITS on student engagement, learning outcomes, and the overall educational experience. Case studies and pilot implementations are presented to illustrate the system's efficacy in diverse educational *** research contributes to the ongoing discourse on AI in education by providing insights into the design principles, implementation challenges, and ethical considerations associated with developing an intelligent tutorial system. The findings aim to inform educators, policymakers, and technologists about the potential of AI to revolutionize personalized learning and contribute to the ong

关键词： students

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Harnessing Linguistic Markers for Early Mental Health Detection via Social Media

Harnessing Linguistic Markers for Early Mental Health Detect...

引用

2023 International Conference on Advanced Computing and Communication Technologies, ICACCTech 2023

作者： Aggarwal, Vishal Kaur, Jaismine Walia, Tapan Kaur, Daljeet Chandigarh University Student of Computer Science & Engineering Gharuan Mohali India Chandigarh University Gharuan Mohali India

ISBN: (纸本)9798350380880

The advent of social media platforms in recent years has presented researchers with massive textual data that can provide significant insights into individuals' mental health. This research investigates linguistic indicators found in social media posts and shows how these markers can be used to construct a deep learning model. This model can provide individuals with early insights into their mental health status, outperforming traditional approaches. We demonstrate the feasibility and efficacy of this strategy in improving mental health monitoring and assistance by utilizing natural language processing (NLP) techniques and deep learning algorithms. © 2023 IEEE.

关键词： Sentiment analysis

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Implement Deep Neuron Networks on VPipe Parallel System: A ResNet Variant Implementation 3

Implement Deep Neuron Networks on VPipe Parallel System: A R...

引用

3rd International Conference on Artificial Intelligence and computer engineering, ICAICE 2022

作者： Zhu, Xinrong Student of Computer Science College of Science and Engineering Wenzhou-Kean University 88 Daxue Road Wenzhou Province Ouhai325006 China

ISBN: (纸本)9781510663473

Deep Neuron Networks (DNNs) rapidly develop across numerous industries and fields. The Residual Network (ResNet) is prevalent for its high performance and novel residual network for image classification. One new architecture derived from ResNet, the ResNet-RS, is proving to have higher accuracy while having lower training difficulty. Furthermore, with the growing network depth and expanding training set size, it is vital to apply different parallel methods to the ResNet to improve the training speed. To evaluate the parallel efficiency of the new architecture, this paper made the following efforts: (1) Implemented the ResNet-RS network on VPipe with a 2 GPU environment, (2) proposed a formal procedure for DNN implementation on VPipe, and (3) Compared the top1 and top5 accuracy, loss, and epoch time of training between an orthodox ResNet and the derived ResNet-RS on VPipe parallel system. © 2023 SPIE.

关键词： Image classification

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Marine Robot for Underwater Surveillance

Marine Robot for Underwater Surveillance

引用

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

作者： Vishnu Gowda, H.P. Reddy, Deepa N Gowda, Tejas B Shrikala, R. Yogitha, H.N. Bms Institute of Technology and Management Student of Department of Electronics and Communication Engineering Bengaluru560064 India Bms Institute of Technology and Management Department of Electronics and Communication Engineering Bengaluru560064 India Bms Institute of Technology and Management Student of Department of Computer Science Engineering Bengaluru560064 India

ISBN: (纸本)9798331529833

Underwater surveillance has become a crucial area of research, particularly for marine exploration and environmental monitoring, with marine robots playing a vital role. These robots, equipped with sensors and cameras, capture real-time data and images from underwater environments, reducing the need for human intervention in hazardous areas. However, a major challenge in this field is developing reliable communication networks, as underwater environments face limitations in bandwidth, range, and signal attenuation, hindering real-time data transmission. This paper presents a simple marine robot prototype based on the Raspberry Pi Model 4B, utilizing Electronic Speed Controllers (ESCs) and brushless motors for efficient movement. The robot's body is manually designed using 3D printing technology, with PETG (polyethylene terephthalate glycol) material chosen for its water resistance, durability, and buoyancy. The design ensures the robot's stability and functionality underwater, offering a cost-effective solution for marine robotics while underscoring the need for improved underwater communication technologies. © 2025 IEEE.

关键词： Marine communication

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Fakenews AI: Identifying and Flagging Misinformation through Artificial Intelligence 4

Fakenews AI: Identifying and Flagging Misinformation through...

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4th International Conference on Advancement in Electronics and Communication engineering, AECE 2024

作者： Kumar, Aman Raj, Rohan Sharma, Ajay Kumar, Abhishek Sharma, Shamneesh Lovely Professional University School of Computer Science and Engineering Phagwara India UpGrad Education Private Limited Delivery and Student Success Karnataka Banglore India

ISBN: (纸本)9798350364729

Misinformation has become a pervasive problem in today's society. With the rise of social media and the ease of sharing information, false or misleading information can spread rapidly and have a significant impact on public opinion and decision-making. The challenge of identifying and flagging misinformation has become increasingly difficult, as conventional methods of fact-checking and verification struggle to keep up with the scale and speed of information dissemination. In this study, the author will explore the function of artificial intelligence (AI) in identifying and flagging misinformation, and the potential implications and future directions of the technology, looking at different types of models like Naive Bayes, LR, DT, RF, SVM, and LSTM. The author has tested how well they worked using measures like accuracy, precision, recall, and F1-score. The author has observed that the Decision Tree model is the best one overall. It had the highest accuracy and did the best job at classifying things compared to the other models. © 2024 IEEE.

关键词： Contrastive Learning

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