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IAENG International Journal of computer science 2024年第7期51卷 754-764页

作者： Teng, Yue Yang, Kai Yue Teng is a postgraduate student at the School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China

In today’s society, communication among people has become more frequent and extensive due to the rapid development of science, technology, and the Internet. This vast communication occurs in real life and virtual online worlds. However, with the overwhelming amount of information available, people often struggle with finding and accessing the information they need, leading to frustration. To address this challenge, we conducted a study to extract valuable information and insights from users’ social relationships. While existing models can help us understand and analyze users’ social relationships to some extent, there are still significant challenges in extracting valuable insights. Therefore, we conducted in-depth research to improve the GraphRec model and created a new social recommendation model called the CWYGNN model. The CWYGNN model is unique because it can identify deeper connections in users’ social interactions through in-depth learning of the social sequences between users. This is important for understanding users’ behavioral patterns, predicting their needs, and providing personalized services. To validate the effectiveness of the CWYGNN model, we conducted experiments and comparisons on several public datasets. The experimental results show that the CWYGNN model outperforms similar approaches in processing user social relationships and extracting valuable insights. This result provides a new way of thinking and methodology to help us better understand and serve users and ultimately enhance their overall experience. © (2024) International Association of Engineers. All rights reserved.

关键词： Recommender systems

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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...

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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...

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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...

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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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A Hybrid Harmony Search Algorithm based on Firefly Algorithm and Boltzmann Machine

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2022年第1期49卷 241-251页

作者： Yang, J. Zhang, Xiaoxia Fu, S. Hu, Y.Y. Master Student of School of Computer Science and Software Engineering University of Science and Technology LiaoNing Anshan114051 China Professor of School of computer Science and Software Engineering University of Science and Technology LiaoNing Anshan11041 China

Harmony Search Algorithm (HS) is a new meta-heuristic algorithm based on music creation process. The algorithm has been widely used in many fields because of its simple and flexible structure and fewer controlled parameters. However, HS is easy to be affected by the initial harmony memory and falls into local optimization. In addition, there are also problems, such as low searching accuracy and slow convergence speed of the optimal solution. Therefore, this paper proposes a hybrid harmony search algorithm based on firefly algorithm and boltzmann machine (HSFA-BM). First, in order to avoid the possibility of harmony search algorithm falling into local optimum, boltzmann machine (BM) mechanism is used to update the harmony memory in this paper. Second, in order to enhance the ability of the algorithm to exploit optimal solutions, this paper uses the movement mode of fireflies to generate new solutions. The improved algorithm enhances the ability of global optimization and search precision, and speeds up the convergence speed. In this paper, six classical test functions are used to test the effectiveness of the algorithm, and a very obvious optimization result is obtained. © 2022. All Rights Reserved.

关键词： Flexible structures

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CE-HigherHRNet: Enhancing Channel Information for Small Persons Bottom-Up Human Pose Estimation

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2022年第1期49卷 260-269页

作者： Li, M.Y. Zhao, J. Master’s Student of School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China

Accurately detecting the keypoints of small persons in an image using bottom-up multi-person pose estimation algorithms is exceptionally difficult owing to scale variation challenges. HigherHRNet initially solved the challenge of multi-player scale change pose estimation. However, because it uses repeated cross-scale fusion, owing to inherent defects in channel reduction, semantic information is lost. Furthermore, the aliasing effects produced by the miscellaneous feature maps formed after cross-scale fusion have a significant impact on the detection accuracy of small persons. In this paper, we propose a novel bottom-up human pose estimation algorithm based on HigherHRNet, called Channel-Enhanced HigherHRNet (CE-HigherHRNet). CE-HigherHRNet comprises three main components: a multiscale sub_pixel skip fusion module, a lightweight attention mechanism (with channel attention enhanced and spatial attention modules), and a high-resolution feature pyramid with an added Dupsampling module. The lightweight attention mechanism optimizes the feature map after each fusion. Deconvolution is replaced with Dupsampling, which strengthens the network’s scale awareness and makes it more sensitive to robust scale changes. The average precision (AP) of CE-HigherHRNet on the COCO test-dev dataset was 71.9% (an improvement of 1.4% compared with HigherHRNet). Furthermore, the average detection accuracy of small persons was 68.1% AP (an improvement of 1.5% AP). These results verify that the proposed CE-HigherHRNet is more robust in processing scale changes and has a stronger ability to handle crowded environments. Thus, it is more accurate in positioning small persons in images and human bodies in crowded environments. © 2022. All Rights Reserved.

关键词： Semantics

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Machine Learning for Forecasting Depression and Anxiety in University students 1

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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

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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...

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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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