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IEEE Education engineering (EDUCON)

作者： Zhangying He Thomas Nguyen Tahereh Miari Mehrdad Aliasgari Setareh Rafatirad Hossein Sayadi Departmentof Computer Engineering and Computer Science California State University Long Beach CA USA Center for Information Systems and Technology Claremont Graduate University CA USA Department of Computer Science University of California Davis CA USA

ISBN: (数字)9798350394023

ISBN: (纸本)9798350394030

Artificial Intelligence (AI), with ChatGPT as a prominent example, has recently taken center stage in various domains including higher education, particularly in computer science and engineering (CSE). The AI revolution brings both convenience and controversy, offering substantial benefits while lacking formal guidance on their application. The primary objective of this work is to comprehensively analyze the pedagogical potential of ChatGPT in CSE education, understanding its strengths and limitations from the perspectives of educators and learners. We employ a systematic approach, creating a diverse range of educational practice problems within CSE field, focusing on various subjects such as data science, programming, AI, machine learning, networks, and more. According to our examinations, certain question types, like conceptual knowledge queries, typically do not pose significant challenges to ChatGPT, and thus, are excluded from our analysis. Alternatively, we focus our efforts on developing more in-depth and personalized questions and project-based tasks. These questions are presented to ChatGPT, followed by interactions to assess its effectiveness in delivering complete and meaningful responses. To this end, we propose a comprehensive five-factor reliability analysis framework to evaluate the responses. This assessment aims to identify when ChatGPT excels and when it faces challenges. Our study concludes with a correlation analysis, delving into the relationships among subjects, task types, and limiting factors. This analysis offers valuable insights to enhance ChatGPT's utility in CSE education, providing guidance to educators and students regarding its reliability and efficacy.

关键词： Systematics Limiting Focusing Machine learning Chatbots Reliability Task analysis

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Speech Emotion Recognition (SER) on Live Calls while Creating Events 15

Speech Emotion Recognition (SER) on Live Calls while Creatin...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Lavanya, C. Mishra, Abhinav Amruth, R.B. Departmentof Computer Science and Engineering New Horizon College of Engineering Bangalore India

ISBN: (纸本)9798331300579

This paper proposes a system that integrates real-time Speech Emotion Recognition (SER) into live calls, allowing event organizers to dynamically personalize content based on participants' emotional *** machine learning models, the system analyzes speech patterns, adapting communication strategies for enhanced engagement.[8] The approach prioritizes user privacy and scalability, offering a novel way to create more impactful and tailored virtual *** system utilizes state-of-the-art machine learning models to analyze the acoustic features of speech and accurately classify the speaker's emotional *** embedding SER capabilities into live calls, event organizers can gain valuable insights into participants' emotions, enabling them to tailor event content, adapt communication strategies, and create a more personalized experience. © Grenze Scientific Society, 2024.

关键词： Emotion Recognition

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SocialChain: A Decentralized Social Media Platform on the Blockchain 1

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2nd EAI International Conference on Pervasive knowledge and collective intelligence on Web and Social Media, PerSOM 2023

作者： Prashanth, Mallellu Sai Maheswari, V. Uma Aluvalu, Rajinikanth Kantipudi, M. V. V. Prasad Departmentof Computer Science and Engineering Vardhaman College of Engineering Hyderabad India Departmentof Computer Science and Engineering Chaitanya Bharathi Institute of Technology Hyderabad India Departmentof Information Technology Chaitanya Bharathi Institute of Technology Hyderabad India Pune India

ISBN: (数字)9783031660443

ISBN: (纸本)9783031660436

The emergence of blockchain technology has paved the way for innovative solutions in various domains, including social media. This research paper presents the design and implementation of "SocialChain," a decentralized social media platform built on the Ethereum blockchain using the Solidity programming language. SocialChain aims to address issues related to data privacy, content ownership, and censorship prevalent in traditional social media platforms. The proposed system leverages blockchain’s inherent properties such as immutability, transparency, and decentralization to create a trustless and secure environment for social interactions. The platform allows users to create posts, share content, and engage in discussions while maintaining ownership and control over their data. The underlying blockchain ensures that the history of posts and interactions remains tamper-proof and verifiable. The core components of the SocialChain smart contract include data structures for posts and comments, functions for creating posts and adding comments, as well as mechanisms for retrieving post-related information. Each user’s identity is associated with their Ethereum address, eliminating the need for traditional authentication methods and enhancing user pseudonymity. To ensure efficiency, the contract optimizes data storage by using mappings and arrays, minimizing gas costs and improving scalability. The contract also incorporates modifiers to validate post and comment existence, enhancing security and preventing unauthorized access. The results of this research showcase the feasibility of integrating blockchain technology into social media platforms. However, it is important to note that while the implementation provides a foundational framework, practical deployment requires addressing challenges such as user experience, scalability, and incentivization mechanisms. In conclusion, SocialChain exemplifies the potential of blockchain technology to revolutionize the landscape of s

关键词： Decentralized finance

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Accelerate Implementation of Timsort Algorithm Using CUDA 26

Accelerate Implementation of Timsort Algorithm Using CUDA

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26th International Conference on computer and Information Technology, ICCIT 2023

作者： Disha, Dilara Yasmin Mondal, Md. Nazrul Islam Rajshahi University of Engineering & Technology Departmentof Computer Science and Engineering Rajshahi6240 Bangladesh

ISBN: (纸本)9798350359015

Sorting algorithms are fundamental in various computing *** this paper, we introduce a CUDA-based acceleration of the Timsort algorithm, leveraging the parallel processing of GPUs. Our implementation strategically decomposes the sorting process into parallel tasks, effectively surmounting inherent challenges related to synchronization and load balancing. Our experimental findings reveal a substantial speedup compared to CPU-based Timsort implementations. This acceleration renders our approach highly promising for real-world applications. Notably, our results demonstrate that the Timsort algorithm achieves a remarkable speed enhancement ranging from 22x to 48x when employing CUDA, relative to CPU-based executions, across diverse dataset sizes. These findings underscore the efficacy and applicability of our CUDA-accelerated Timsort in addressing the growing demand for faster sorting algorithms in contemporary computing scenarios. © 2023 IEEE.

关键词： CUDA GPU Parallel Processing Sorting Algorithm Timsort

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Risk Factors Categorizations of Ischemic Heart Disease in South-Western Bangladesh

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Data Intelligence 2024年第3期6卷 834-868页

作者： M.Raihan Sami Azam Laboni Akter Md.Mehedi Hassan Ryana Quadir Asif Karim Saikat Mondal Arun More Department of Computer Science and Engineering North Western UniversityKhulna 9100Bangladesh Faculty of Science and Technology Charles Darwin UniversityNT 0909Australia Departmentof Biomedical Engineering Khulna University of Engineering&TechnologyKhulnaBangladeh Computer Science and Engineering Discipline Khulna UniversityKhulna 9208Bangladesh Department of Computer Science and Engineering Daffodil International University(DIU)Bangladesh Department of Cardiology Ter Institute of Rural Health and ResearchMurud-413510India

Ischemic heart disease(IHD)is one of the leading causes of death ***,different geographic regions show different variations of the risk factors of this disease based on the different lifestyles of *** study examines the current IHD condition in southern Bangladesh,a Southeast Asian middle-income *** main approach to this research is an Al-based proposal of a reduced set of the greatest impact clinical traits that may cause *** approach attempts to reduce IHD morbidity and mortality by early detection of risk factors using the reduced set of clinical ***,diagnostic,and symptomatic features were considered for analysing this clinical *** pre-processing utilizes several machine learning techniques to select significant features and make meaningful interpretations.A proposed voting mechanism ranked the selected 138 features by their impact *** this regard,diverse patterns in correlations with variables,including age,sex,career,family history,obesity,etc.,were calculated and explained in terms of voting *** the 138 risk factors,three labels were categorized:high-risk,medium-risk,and low-risk features;19 features were regarded as high,25 were medium,and 94 were considered low impactful *** research's technological methodology and practical goals provide an innovative and resilient framework for addressing IHD,especially in less developed cities and townships of Bangladesh,where the general population's socioeconomic conditions are often *** data collection,pre-processing,and use of this study's complete and comprehensive IHD patient dataset is another innovative *** believe that other relevant research initiatives will benefit from this work.

关键词： Ischemic heart disease Machine learning CVD Data categorization Medical data

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Satellite-Based Estimation of Air Quality in South Asian Countries Using Neural Networks: A Review 8

Satellite-Based Estimation of Air Quality in South Asian Cou...

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8th International Conference on Advanced Computing and Communication Systems, ICACCS 2022

作者： Chaki, Souptik Kumar Kumar, Rakesh Gupta, Meenu Chandigarh University Departmentof Computer Science and Engineering Punjab India

ISBN: (纸本)9781665408165

A rapid change in climate has major consequences like impurity occurs in Air Quality. Global warming is one of the major causes of pollution where all domain of science and many researchers are trying to address it in their ways. It is not only creating challenges to air quality but it is also leading to serious health hazard scenarios. Improving acquiescence with ecological regulations is dangerous for generating clean environment areas and healthy populaces. In South Asia, there are various small scale and large-scale industries which is a major source of pollution and it is majorly subjugated by small-scale industries, there are various unceremonious creators which are difficult to monitor and regulate and is a common encounter in low-income settings. The focus is on how a low-cost and an easy approach can be used in determining the air quality present in that particular cities with its consequent health diseases attached and also taking consideration of various imprecisions and attributes like PM10, PM2.5, NO, etc. with regard to local protocols in the governmentdata. © 2022 IEEE.

关键词： Global warming

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Theoretical Framework for Blockchain Secured Predictive Maintenance Learning Model Using Digital Twin 1

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International Workshop on Advanced in Information Security Management and Applications, AISMA 2024

作者： Vinodha, D. Jenefa, J. Anita, E. A. Mary Babenko, Mikhail Departmentof Computer Science and Engineering Christ University Bangalore India North-CaucasusCenter for Mathematical Research North-Caucasus Federal University Stavropol Russia

ISBN: (数字)9783031721717

ISBN: (纸本)9783031721700

The automotive sector benefits from Digital Twins (DTs), software replicas of physical assets or processes. DTs enable engineers and data scientists to obtain deeper insights into the system and solve the most difficult problems faster and more affordably. Blockchain technology is a developing and exciting technology that has the potential to offer DTs monitoring capabilities, strengthening security and enhancing DTs’ transparency, dependability, and immutability. Intelligent behavior can be integrated into blockchain-based DTs to foresee important maintenance tasks and successfully manage machine functions. Our research involves creating a theoretical framework that leverages emerging technologies such as blockchain, artificial intelligence and DTs to facilitate resolution in the predictive maintenance of industry machines with minimised governing cost. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Predictive maintenance

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Deep Regression hybridized Neural Network in human stress detection 1

Deep Regression hybridized Neural Network in human stress de...

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1st IEEE International Conference on Smart Technologies and Systems for Next Generation Computing, ICSTSN 2022

作者： Prasanalakshmi, B. Kumar, T Ananth King Khalid University Department of Computer Science Abha Saudi Arabia Ifet College of Engineering Departmentof Computer Science and Engineering Villupuram India

ISBN: (纸本)9781665421119

In today's environment, psychological problems such as stress, sadness, and anxiety become significant. Millions of individuals suffer from depression every year, but only a least percentage of them undergo timely treatment. As a result, it becomes critical to identify individual tension and means to relax instantly. It is necessary to recognize and handle stress before it becomes a severe problem. The primary goal is to use Machine Learning techniques to detect anxiety in people to improve their quality of life. The vast majority of stress datasets have been collected while consumers were subjected to high-stress situations, such as music, video files, significant equipment failure, visual information, and gameplay situations. However, whether certain databases are used to construct models that can accurately identify anxiety in various situations is still an open question. This paper offers a hybrid strategy DR-CNN for multimodal stress detection that uses a convolutional neural network (CNN) and a regression classifier. Our findings show that this technique has promise, but they also highlight the need for more research to understand better the relevance of deep-learning approaches in designing generally applicable designs for multidimensional emotion detection. © 2022 IEEE.

关键词： Stresses

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Multi-objective Feature Attribution Explanation for Explainable Machine Learning

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ACM Transactions on Evolutionary Learning and Optimization 2024年第1期4卷 1-32页

作者： Wang, Ziming Huang, Changwu Li, Yun Yao, Xin Research Institute of Trustworthy Autonomous Systems Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Departmentof Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China The Advanced Cognitive Technology Lab Huawei Technologies Co. Ltd Shanghai201206 China School of Computer Science University of Birmingham BirminghamB15 2TT United Kingdom

The feature attribution-based explanation (FAE) methods, which indicate how much each input feature contributes to the model's output for a given data point, are one of the most popular categories of explainable machine learning techniques. Although various metrics have been proposed to evaluate the explanation quality, no single metric could capture different aspects of the explanations. Different conclusions might be drawn using different metrics. Moreover, during the processes of generating explanations, existing FAE methods either do not consider any evaluation metric or only consider the faithfulness of the explanation, failing to consider multiple metrics simultaneously. To address this issue, we formulate the problem of creating FAE explainable models as a multi-objective learning problem that considers multiple explanation quality metrics simultaneously. We first reveal conflicts between various explanation quality metrics, including faithfulness, sensitivity, and complexity. Then, we define the considered multi-objective explanation problem and propose a multi-objective feature attribution explanation (MOFAE) framework to address this newly defined problem. Subsequently, we instantiate the framework by simultaneously considering the explanation's faithfulness, sensitivity, and complexity. Experimental results comparing with six state-of-The-Art FAE methods on eight datasets demonstrate that our method can optimize multiple conflicting metrics simultaneously and can provide explanations with higher faithfulness, lower sensitivity, and lower complexity than the compared methods. Moreover, the results have shown that our method has better diversity, i.e., it provides various explanations that achieve different tradeoffs between multiple conflicting explanation quality metrics. Therefore, it can provide tailored explanations to different stakeholders based on their specific requirements. © 2024 Copyright held by the owner/author(s). Publication rights licensed

关键词： Machine learning

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Multiclass Cricket Shot Classification using CNN 26

Multiclass Cricket Shot Classification using CNN

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26th International Conference on computer and Information Technology, ICCIT 2023

作者： Ahmed, Mirza Sabbir Hasan, Md Nahid Zinnah Ayon, Walid Ibn North South University Department of Electrical & Computer Engineering Dhaka Bangladesh Deggendorf Institute of Technology Departmentof Building Products and Processes Deggendorf Germany Chittagong University of Engineering & Technology Department of Computer Science & Engineering Chattogram Bangladesh

ISBN: (纸本)9798350359015

An acknowledgement of context-based advertising for the audience watching cricket can involve identifying the game of cricket based on the various batting shots, creating sensor-based commentary systems, and creating coaching assistants. This research identifies different cricket shot problems from a vast-scale cricket shot video dataset. The study uses a large dataset of annotated cricket shots that spans a variety of game formats and captures different shot types and player styles. Some cricket shots are the action we find most attractive in cricket videos. Those shots are often captured by cameras installed at the cricket ground's stands at both ends of the cricket pitch. To identify 4 different cricket batting shots from offline footage, this research paper presents a novel approach to classify cricket shots based on deep learning techniques. In this research, we propose a multi-stage classification pipeline for cricket shots, which combines dimensionality reduction, feature extraction, and classification techniques of cricket shots. Before classification, we have preprocessed the dataset that we prepared. The image data was extracted from the video containing around 3000 image frames. The dataset has four classes: i.e. pull shot, square drive, straight drive, and uppercut. The preprocessing steps are normalization, resize, and histogram equalization. After that a scratch model is designed and the model is trained based on the architecture of the scratch model. Finally, we have found the model's accuracy for our prepared dataset is 98.18 percent. © 2023 IEEE.

关键词： batting shots classification convolutional neural network deep learning

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