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5th International Congress on Human-computer Interaction, Optimization and Robotic Applications, HORA 2023

作者： Hasan Al Mashhadani, Asraa Ahmed Inan, Timur Ahmed, Ali Saadoon Altinbas University Department of Information Technologies Istanbul Turkey Altinbas University Department of Software Engineering Istanbul Turkey AlMaarif University College Department of Computer Science AlAnbar Iraq

ISBN: (纸本)9798350337525

Because of a phenomenon known as the 'curs e of dimensionality,' standard machine learning algorithms have difficulty dealing with high-dimensional data. There are more possible examples in the data space as the number of dimensions increases;however, as the number of dimensions increases, the amount of data that can be accessed decreases. There are a greater number of potential instances in the data space when there are more dimensions. The amount of data required by machine learning algorithms to address problems with such a high dimension increases exponentially with the number of problem-related characteristics. In this paper, we examine the suggested algorithms' methods for selecting features and their relationship to the data representation. © 2023 IEEE.

关键词： Swarm intelligence

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An Improved Bi-LSTM EEG Emotion Recognition Algorithm

Journal of Network Intelligence

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Journal of Network Intelligence 2022年第3期7卷 623-639页

作者： Ma, Shuai Cui, Jianfeng Chen, Chin-Ling Xiao, Weidong Liu, Lijuan School of Computer and Information Engineering Xiamen University of Technology Xiamen361024 China College of Software Engineering Xiamen University of Technology Xiamen361024 China Department of Computer Science and Information Engineering Chaoyang University of Technology Taichung41349 Taiwan

EEG emotion signal feature extraction is computationally intensive and the classification accuracy of the model is not high. Therefore, it can seriously affect the overall performance of classification algorithms. In order to better apply deep learning methods to EEG emotion recognition classification. To address the above problems, this paper uses the Fast Fourier Transformation algorithm to extract richer and more complete multi-channel feature information in feature extraction. At the same time, for the problem of low classification accuracy, an improved Bi-LSTM classification model is proposed. Finally, the model proposed in this paper is verified by 32-channel EEG signal data. The experimental results show that the classification method proposed improves the accuracy of Arousal to 93.35% and Valence to 93.09%. Compared with other models, the improved Bi-LSTM model has an excellent performance in EEG emotion recognition. Meanwhile, this paper explores the brain emotion mechanism by the accuracy of the improved model for different brain region channels, showing that the brain regions with the highest emotional relevance are the parietal and frontal lobes in the brain. © 2022 Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Emotion Recognition

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Streamlining Classroom and Laboratory Management in University: Enhancing Problem Reporting Processes

Streamlining Classroom and Laboratory Management in Universi...

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Computing and Information Technology (ICCIT-1441), 2020 International Conference on

作者： Qamar H. Naith Department of Software Engineering College of Computer Science and Engineering University of Jeddah Jeddah Saudi Arabia

ISBN: (数字)9798350353839

ISBN: (纸本)9798350353846

Managing the classroom and lab activities in the university and reporting process for the problems that occur during lectures is a hard process that needs more consideration. This study addresses challenges faced by technicians and academic staff in university classrooms, emphasizing the need for a streamlined automated issue reporting system. This paper proposes a comprehensive system aimed at improving communication, efficiency, and effectiveness in addressing classroom issues, benefiting technicians, students, and lecturers. Quantitative Analysis and evaluation of the system's user experience from instructors and technicians showed highly positive outcomes, indicating its high effectiveness in managing classroom issues and improving overall classroom management. Moreover, the statistical test revealed no statistically significant differences (α= 0.05) in the willingness to use the classroom issue management system between technicians and academic staff, indicating similar user perceptions and readiness for both groups to utilize the system.

关键词： Surveys Productivity Bridges Statistical analysis User experience Stakeholders Usability Information technology

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Internet of Things (IoT) in Smart Cities: A Statistical Survey 2

Internet of Things (IoT) in Smart Cities: A Statistical Surv...

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2nd International Conference on Business Analytics for Technology and Security, ICBATS 2023

作者： Iqbal, Muhammad Waseem Khaliq, Khowla Al-Dmour, Nidal A. Aqeel, Muhammad Ali, Nasir Hamid, K. Superior University Department of Software Engineering Lahore54000 Pakistan Superior University Department of Computer Sciecnce Lahore54000 Pakistan Mutah University College of Engineering Department of Computer Engineering Jordan NCBA&E School of Computer Science Lahore Pakistan Applied Science Private University Applied Science Research Center Amman11937 Jordan

ISBN: (纸本)9798350335644

Internet of things is progressing very rapidly and involving multiple domains of everyday life including environment, governance, healthcare system, transportation system, energy management system, etc. smart city is a platform for collecting and storing the information that is accessed through various sensor-based IoT devices and make their information available in required and authorized domains. This interoperability can be achieved by semantic web technology. In this paper, I have reviewed multiple papers related to IoT in Smart Cities and presented a comparison among the semantic parameters. Moreover, I've presented my future domain of research which is about delivering the COVID-19 patients report to the concerned domains by the healthcare system domain. © 2023 IEEE.

关键词： Internet of things

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Model for Recognition of Sign Language and Hand Gesture by Hard Hearing People

Model for Recognition of Sign Language and Hand Gesture by H...

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2023 International Conference on Sustainable Communication Networks and Application, ICSCNA 2023

作者： Manjula, R. Jagadeesan, S. Ramalingam, Venkadeshan Vetriselvi, T. Johnpeter, T. Srm Institute of Science and Technology College of Engineering and Technology Department of Computing Technologies Chennai India Vellore Institute of Technology School of Computer Science Engineering and Information Systems Department of Software and Systems Engineering Tamilnadu Vellore India Information Technology Department University of Technology and Applied Sciences-Shinas Branch Sultanate of Oman Oman Vellore Institute of Technology School of Computer Science and Engineering Department of Iot Tamilnadu Vellore India K.Ramakrishnan College of Engineering Department of Computer Science and Engineering Trichy India

ISBN: (纸本)9798350313987

Hard hearing and deaf people are communicating with other people using the sign language. This is the only way of communication of such people to convey Since this is the only means by which these individuals can communicate, it is imperative that others be able to understand their language. Sign language recognition by computer begins with the acquisition of sign gestures and ends with recognition and conversion to text or voice. Static and dynamic are the two ways of sign gestures used. This study aims at focusing on the dynamic gesture recognition and the steps for recognizing sign language are discussed. The collected data are preprocessed and feature are extracted for classification. Finally the results are all investigated and developed for single user. © 2023 IEEE.

关键词： Gesture analysis Hand gesture recognition Hand tracking Sign language

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Enhancing Multi-Class Cyberbullying Classification with Hybrid Feature Extraction and Transformer-Based Models

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computer Modeling in engineering & sciences 2025年第5期143卷 2109-2131页

作者： Suliman Mohamed Fati Mohammed A.Mahdi Mohamed A.G.Hazber Shahanawaj Ahamad Sawsan A.Saad Mohammed Gamal Ragab Mohammed Al-Shalabi Information Systems Department College of Computer and Information SciencesPrince Sultan UniversityRiyadh11586 Saudi Arabia Information and Computer Science Department College of Computer Science and EngineeringUniversity of Hail Hail55476Saudi Arabia Software Engineering Department College of Computer Science and EngineeringUniversity of HailHail55476Saudi Arabia Computer Engineering Department College of Computer Science and EngineeringUniversity of Ha'ilHail55476Saudi Arabia Department of Computer and Information Sciences Universiti Teknologi PetronasSeri Iskandar32610Malaysia

Cyberbullying on social media poses significant psychological risks,yet most detection systems over-simplify the task by focusing on binary classification,ignoring nuanced categories like passive-aggressive remarks or indirect *** address this gap,we propose a hybrid framework combining Term Frequency-Inverse Document Frequency(TF-IDF),word-to-vector(Word2Vec),and Bidirectional Encoder Representations from Transformers(BERT)based models for multi-class cyberbullying *** approach integrates TF-IDF for lexical specificity and Word2Vec for semantic relationships,fused with BERT’s contextual embeddings to capture syntactic and semantic *** evaluate the framework on a publicly available dataset of 47,000 annotated social media posts across five cyberbullying categories:age,ethnicity,gender,religion,and indirect *** BERT variants tested,BERT Base Un-Cased achieved the highest performance with 93%accuracy(standard deviation across±1%5-fold cross-validation)and an average AUC of 0.96,outperforming standalone TF-IDF(78%)and Word2Vec(82%)***,it achieved near-perfect AUC scores(0.99)for age and ethnicity-based bullying.A comparative analysis with state-of-the-art benchmarks,including Generative Pre-trained Transformer 2(GPT-2)and Text-to-Text Transfer Transformer(T5)models highlights BERT’s superiority in handling ambiguous *** work advances cyberbullying detection by demonstrating how hybrid feature extraction and transformer models improve multi-class classification,offering a scalable solution for moderating nuanced harmful content.

关键词： Cyberbullying classification multi-class classification BERT models machine learning TF-IDF Word2Vec social media analysis transformer models

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Systematic analysis of artificial intelligence in the era of industry 4.0

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Journal of Management Analytics 2023年第1期10卷 89-108页

作者： Weiru Chen Wu He Jiayue Shen Xin Tian Xianping Wang Department of Information Technology&Decision Science Old Dominion UniversityNorfolkVAUSA Department of Computer&Information Science Arkansas Tech UniversityRussellvilleARUSA Department of Engineering Technology SUNY Polytechnic InstituteUticaNYUSA College of Computing and Software Engineering Kennesaw State UniversityMariettaGAUSA Computer Information Technology&Graphics Department Purdue University NorthwestHammondINUSA

Artificial Intelligence has been playing a profound role in the global economy,social progress,and people’s daily *** the increasing capabilities and accuracy of AI,the application of AI will have more impacts on manufacturing and service areas in the era of industry *** study conducts a systematic literature review to study the state-of-the-art on AI in industry *** paper describes the development of industries and the evolution of *** paper also identifies that the development and application of AI will bring not only opportunities but also challenges to industry *** findings provide a valuable reference for researchers and practitioners through a multi-angle systematic analysis of *** the era of industry 4.0,AI system will become an innovative and revolutionary assistance to the whole industry.

关键词： Industry 4.0 AI deep learning neural network supervised learning unsupervised learning reinforced learning

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AI-Enhanced Multi-Cloud Security Management: Ensuring Robust Cybersecurity in Hybrid Cloud Environments

AI-Enhanced Multi-Cloud Security Management: Ensuring Robust...

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2023 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2023

作者： Srimathi, J. Kanagasabapathi, K. Mahajan, Kirti Ahamad, Shahanawaj Soumya, E. Barthwal, Shivangi KPR College of Arts Science and Research Department of Information Technology Covai Arasur641407 India Karpagam College of Engineering Computer Science and Technology Coimbatore India Bharati Vidyapeeth Deemed University Institute of Management & Entrepreneurship Development Department of Information Technology Pune411030 India University of Hail College of Computer Science and Engineering Department of Software Engineering Hail City Saudi Arabia St. Martins Engineering College Department of Computer Science and Engineering Telangana Secunderabad India Graphic Era Deemed to Be University Department of Electronics & Communication Engineering Uttarakhand Dehradun248002 India

ISBN: (纸本)9798350319200

The complex landscape of multi-cloud settings is the result of the fast growth of cloud computing and the ever-changing needs of contemporary organizations. Strong cyber defenses are of fundamental importance in this setting. In this study, we investigate the use of AI in hybrid cloud settings for the purpose of multi-cloud security management. To help businesses improve their productivity and resilience, we provide a mathematical model for optimal resource allocation. Our methodology streamlines dynamic threat assessments, making it easier for security teams to efficiently priorities vulnerabilities. The advent of a new age of real-time threat response is heralded by the incorporation of AI-driven security tactics. The technique we use has real-world implications that may help businesses stay ahead of constantly changing threats. In the future, scientists will focus on autonomous security systems, interoperability, ethics, interoperability, and cutting-edge AI models that have been validated in the real world. This study provides a detailed road map for businesses to follow as they navigate the complex cybersecurity landscape of multi-cloud settings, therefore promoting resilience and agility in this era of digital transformation. © 2023 IEEE.

关键词： Adaptive Security Systems Artificial Intelligence Autonomous Security Cybersecurity Hybrid Cloud Multi-Cloud Security Quantum Computing Real-Time Threat Response Threat Assessment

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EEiF: Efficient Isolated Forest with e Branches for Anomaly Detection 24

EEiF: Efficient Isolated Forest with e Branches for Anomaly ...

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24th IEEE International Conference on Data Mining, ICDM 2024

作者： Zhang, Yifan Xiang, Haolong Zhang, Xuyun Xu, Xiaolong Fan, Wei Zhang, Qin Qi, Lianyong School of Software Nanjing University of Information Science and Technology China Jiangsu Province Engineering Research Center of Advanced Computing and Intelligent Services China School of Computing Macquarie University Australia University of Oxford Uffield Department of Women's & Reproductive Health United Kingdom College of Computer Science and Software Engineering Shenzhen University China Faculty of Science and Engineering China

ISBN: (纸本)9798331506681

Anomaly detection is a popular research topic in Artificial Intelligence and has been widely applied in network security, financial fraud detection, and industrial equipment failure detection. Isolation forest based methods are the base algorithms to detect anomalies in these scenarios for their simplicity and efficiency, which has been further exploited with multi-folk trees and learning mechanisms to realize the optimal isolation forest for high detection accuracy. However, the optimal isolation forest is time-consuming with the learning mechanisms, resulting in the task failing of time-constrained applications. Moreover, the original optimal isolation forest fails to construct the optimal tree structure restricted by the time complexity. To address the above challenges, we propose an efficient anomaly detection method called EEIF, which realizes the real e-folk structure of the optimal isolation forest in our practical algorithm design. Specifically, we design a distribution that perfectly matches the e-branch theory to construct the optimal isolation forest. Then, we design an FR clustering scheme to achieve fast training of the isolation forest with learning to hash and provide related proofs of accuracy and efficiency. Besides, a parallel algorithm is integrated into our method to reduce prediction time. Finally, extensive experiments are conducted on a large amount of real-world datasets and the results demonstrate that our method significantly improves efficiency while ensuring effectiveness, compared with the state-of-the-art methods. © 2024 IEEE.

关键词： Anomaly Detection Efficient FR clustering Parallel Algorithm

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Deep Reinforcement Learning for Joint Service Placement and Request Scheduling in Mobile Edge Computing Networks 28

Deep Reinforcement Learning for Joint Service Placement and ...

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28th IEEE Symposium on computers and Communications, ISCC 2023

作者： Deng, Yuxuan Li, Xiuhua Sun, Chuan Hao, Jinlong Wang, Xiaofei Leung, Victor C. M. School of Big Data and Software Engineering Chongqing University Chongqing China Chongqing Changan Automobile Software Technology Co. Ltd Chongqing China TKLAN College of Intelligence and Computing Tianjin University Tianjin China College of Computer Science and Software Engineering Shenzhen University Shenzhen China The University of British Columbia Department of Electrical and Computer Engineering Vancouver Canada

ISBN: (纸本)9798350300482

Mobile edge computing aims to provide cloud-like services on edge servers located near Mobile Devices (MDs) with higher Quality of Service (QoS). However, the mobility of MDs makes it difficult to find a global optimal solution for the coupled service placement and request scheduling problem. To address these issues, we consider a three-Tier MEC network with vertical and horizontal cooperation. Then we formulate the joint service placement and request scheduling problem in a mobile scenario with heterogeneous services and resource limits, and convert it into two Markov decision processes to decouple decisions across successive time slots. We propose a Cyclic Deep Q-network-based Service placement and Request scheduling (CDSR) framework to find a long-Term optimal solution despite future information unavailability. Specifically, to solve the issue of enormous action space, we decompose the system agent and train them cyclically. Evaluation results demonstrates the effectiveness of our proposed CDSR on user-perceived QoS. © 2023 IEEE.

关键词： Reinforcement learning

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