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4th International Conference on Electrical Engineering and computer science, ICECOS 2024

作者： Permana Arrasyid, Fahmi Stiawan, Deris Afifah, Nurul Heryanto, Ahmad Azhar, Iman Saladin B. Bardadi, Ali Yudho Suprapto, Bhakti Pahendra Anto Saputra, Iwan Sriwijaya University COMNETS Rerearch Group Faculty of Camp. Science Palembang Indonesia Sriwijaya University Faculty of Comp. Science Computer Engineering Dept. Palembang Indonesia Sriwijaya University Faculty of Comp. Science Information System Dept. Palembang Indonesia Sriwijaya University Faculty of Engineering Electrical Engineering Dept. Palembang Indonesia

ISBN: (纸本)9798350368253

Central manager algorithm has a lower average throughput compared to the round robin algorithm. This is evidenced by a significant decrease in throughput values during measurements. Central manager algorithm provides a faster average response time than round robin algorithm, despite experiencing an increase in response time values over time. Central manager algorithm shows a higher request loss rate and many requests are not served. Under normal performance conditions, both algorithms yield the same average request loss rate, achieving stable values without failed requests. The central manager algorithm ensures relatively balanced resource availability, including CPU and memory usage, across each slave web server compared to the round robin algorithm. The results show that increasing the number of clients and client requests during system testing to better observe server resource availability and load distribution performance. For future research, it is recommended to increase the number of clients in the system testing to enhance server load and to introduce client request actions on the web server to further highlight resource availability on each web server. © 2024 IEEE.

关键词： Response time (computer systems)

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Improved Reptile Search Algorithm by Salp Swarm Algorithm for Medical Image Segmentation

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Journal of Bionic Engineering 2023年第4期20卷 1766-1790页

作者： Laith Abualigah Mahmoud Habash Essam Said Hanandeh Ahmad MohdAziz Hussein Mohammad Al Shinwan Raed Abu Zitar Heming Jia Computer Science Department Prince Hussein Bin Abdullah Faculty for Information TechnologyAl Al-Bayt UniversityMafraq25113Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman UniversityAmman19328Jordan Faculty of Information Technology Middle East UniversityAmman11831Jordan Applied Science Research Center Applied Science Private UniversityAmman11931Jordan School of Computer Sciences Universiti Sains Malaysia11800Pulau PinangMalaysia Amman Arab University AmmanJordan Department of Computer Information System Zarqa UniversityP.O.Box 13132ZarqaJordan Deanship of E-Learning and Distance Education Umm Al-Qura UniversityMakkah21955Saudi Arabia Faculty of Information Technology Applied Science Private UniversityAmman11931Jordan Sorbonne Center of Artificial Intelligence Sorbonne University-Abu DhabiAbu DhabiUnited Arab Emirates School of Information Engineering Sanming UniversitySanming365004China

This study proposes a novel nature-inspired meta-heuristic optimizer based on the Reptile Search Algorithm combed with Salp Swarm Algorithm for image segmentation using gray-scale multi-level thresholding,called *** proposed method introduces a better search space to find the optimal solution at each ***,we proposed RSA-SSA to avoid the searching problem in the same area and determine the optimal multi-level *** obtained solutions by the proposed method are represented using the image *** proposed RSA-SSA employed Otsu’s variance class function to get the best threshold values at each *** performance measure for the proposed method is valid by detecting fitness function,structural similarity index,peak signal-to-noise ratio,and Friedman ranking *** benchmark images of COVID-19 validate the performance of the proposed *** results showed that the proposed RSA-SSA outperformed other metaheuristics optimization algorithms published in the literature.

关键词： Bioinspired Reptile Search Algorithm Salp Swarm Algorithm Multi-level thresholding Image segmentation Meta-heuristic algorithm

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Improved Bat Algorithm with Deep Learning-Based Biomedical ECG Signal Classification Model

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computers, Materials & Continua 2023年第2期74卷 3151-3166页

作者： Marwa Obayya Nadhem NEMRI Lubna A.Alharbi Mohamed K.Nour Mrim M.Alnfiai Mohammed Abdullah Al-Hagery Nermin M.Salem Mesfer Al Duhayyim Department of Biomedical Engineering College of EngineeringPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems College of Science and Art at MahayilKing Khalid UniversitySaudi Arabia Department of Computer Science College of Computers and Information TechnologyTabuk UniversitySaudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Computer Science College of ComputerQassim UniversitySaudi Arabia Department of Electrical Engineering Faculty of Engineering and TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam Bin Abdulaziz UniversitySaudi Arabia

With new developments experienced in Internet of Things(IoT),wearable,and sensing technology,the value of healthcare services has *** evolution has brought significant changes from conventional medicine-based healthcare to real-time observation-based *** Electrocardiogram(ECG)signals are generally utilized in examination and diagnosis of Cardiovascular Diseases(CVDs)since it is quick and non-invasive in *** to increasing number of patients in recent years,the classifier efficiency gets reduced due to high variances observed in ECG signal patterns obtained from *** such scenario computer-assisted automated diagnostic tools are important for classification of ECG *** current study devises an Improved Bat Algorithm with Deep Learning Based Biomedical ECGSignal Classification(IBADL-BECGC)*** accomplish this,the proposed IBADL-BECGC model initially pre-processes the input ***,IBADL-BECGC model applies NasNet model to derive the features from test ECG *** addition,Improved Bat Algorithm(IBA)is employed to optimally fine-tune the hyperparameters related to NasNet ***,Extreme Learning Machine(ELM)classification algorithm is executed to perform ECG classification *** presented IBADL-BECGC model was experimentally validated utilizing benchmark *** comparison study outcomes established the improved performance of IBADL-BECGC model over other existing methodologies since the former achieved a maximum accuracy of 97.49%.

关键词： Data science ECG signals improved bat algorithm deep learning biomedical data data classification machine learning

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Effects of Social e-Commerce on Consumer Behavior

HighTech and Innovation Journal

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HighTech and Innovation Journal 2022年第4期3卷 376-384页

作者： Gaol, Ford Lumban Denavi, Mulia Danny, Jonathan Anggaragita, Bagas Ditya Hartanto, Andry Matsuo, Tokuro Department of Computer Science Bina Nusantara University Jakarta Indonesia Faculty of Information System Management Bina Nusantara University Jakarta Indonesia Advanced Institute of Industrial Technology Tokyo Japan

Objectives: The purpose of this research is to conduct an examination of intention factors for using social commerce in Indonesia. Methods/Analysis: This research is a quantitative study that applies the customer analysis model to four big social commerce sites in Indonesia. This study uses the multivariate regression method and IBM SPSS 25 software to prove the relationship between research variables. Findings: Variables will include performance expectations, effort expectations, societal effects, supportive circumstances, and cost savings. Data from 210 online respondents in Indonesia were collected. Novelty and Improvements: Positive outcomes are provided by the model as a result of changes in the use of social commerce. © Authors retain all copyrights.

关键词： Consumer behavior

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Exploring Neutrosophic Numeral system Algorithms for Handling Uncertainty and Ambiguity in Numerical Data: An Overview and Future Directions

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Neutrosophic Sets and systems 2024年 65卷 253-295页

作者： Salama, A.A. Shams, Mahmoud Y. Elseuofi, Sherif Khalid, Huda E. Dept. of Math and Computer Sci. Faculty of Science Port Said Univ. Egypt Dept. of Machine Learning Faculty of Artificial Intelligence Kafrelsheikh University Egypt Information System Department Higher Institute for Computers & Specific Studies Ras-Elbar Damietta Egypt Telafer University The Administration Assistant for the President of the Telafer University Telafer Iraq

The Neutrosophic Numeral system Algorithms are a set of techniques designed to handle uncertainty and ambiguity in numerical data. These algorithms use Neutrosophic Set Theory, a mathematical framework that deals with incomplete, indeterminate, and inconsistent information. In this paper, we provide an overview of different approaches used in Neutrosophic Numeral system Algorithms, including Neutrosophic Binary system, Neutrosophic Decimal system, and Neutrosophic Octal system. These systems use different bases and representations to account for degrees of truth, indeterminacy, and falsity in numerical data. We also explore the relationship between Neutrosophic Numeral system Algorithms and Number Neutrosophic systems, which are another type of Neutrosophic system used for representing numerical data. Number Neutrosophic systems use Neutrosophic Numbers to represent degrees of truth, indeterminacy, and falsity in numerical data, and they can be used in conjunction with Neutrosophic Numeral system Algorithms to handle uncertainty and ambiguity in decision-making and artificial intelligence applications. Moreover. We discuss the advantages and disadvantages of each algorithm and their potential applications in various fields. Finally, we highlight the importance of Neutrosophic cryptography in addressing uncertainty and ambiguity in decision making and artificial intelligence and discuss future research directions. Understanding Neutrosophic Numeral system Algorithms and their relationship with Number Neutrosophic systems is crucial for developing effective techniques for handling uncertainty and ambiguity in numerical data in decision-making, pattern recognition, and artificial intelligence applications. © (2024) All Rights Reserved.

关键词： Decision making

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Medical Image Analysis of Lung Tumor Diagnosis Based on Generalized Fuzzy C-Means Clustering Algorithm

Medical Image Analysis of Lung Tumor Diagnosis Based on Gene...

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International Conference on Emerging Electronic and Automation Technology 2022, ICEEAT 2022

作者： Zwaid, Jassim Mohammed Ahmed Murad, Masrah Azrifah Azmi Khalid, Fatimah Binti Manshor, Noridayu Al-Jumaily, Abdulmajeed Faculty of Computer Science and Information Technology University Putra Malaysia UPM Serdang 43400 Malaysia Affiliated to University of Anbar Ramadi Iraq Department of Software Engineering and Information System Faculty of Computer Science and Information Technology University Putra Malaysia UPM Serdang 43400 Malaysia Multimedia Department Faculty of Computer Science and Information Technology University Putra Malaysia UPM Serdang 43400 Malaysia Department of Signal Theory and Communications Universidad Carlos III de Madrid Leganés Madrid28911 Spain

In recent years, the rapid development of medical imaging technology has brought medical image analysis into the era of big data. CT imaging technology is one of the most common imaging methods for disease screening. This paper introduces medical image analysis of lung tumor diagnosis based on Generalized fuzzy c - means clustering algorithm was proposed. Brief introduce lung CT imaging to diagnose lung tumors requires a huge workload, which is often accompanied by long-term image reading and subjective evaluation. The experimental data comes from 15 patient samples which include two groups of samples are selected as test objects. The result shows the positive rate is high, resulting in misdiagnosis. In addition, great success of deep learning technology in the field of computer vision has made it possible to realize computer-aided diagnosis and screening of lung cancer. © Published under licence by IOP Publishing Ltd.

关键词： Deep learning

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Recent Progress in Reinforcement Learning and Adaptive Dynamic Programming for Advanced Control Applications

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IEEE/CAA Journal of Automatica Sinica 2024年第1期11卷 18-36页

作者： Ding Wang Ning Gao Derong Liu Jinna Li Frank L.Lewis IEEE the Faculty of Information Technology Beijing Key Laboratory of Computational Intelligence and Intelligent SystemBeijing Laboratory of Smart Environmental Protectionand Beijing Institute of Artificial IntelligenceBeijing University of TechnologyBeijing 100124China the School of System Design and Intelligent Manufacturing Southern University of Science and TechnologyShenzhen 518055China the Department of Electrical and Computer Engineering University of Illinois at ChicagoChicago IL 60607 USA the School of Information and Control Engineering Liaoning Petrochemical UniversityFushun 113001China the UTA Research Institute the University of Texas at ArlingtonArlington TX 76118 USA

Reinforcement learning(RL) has roots in dynamic programming and it is called adaptive/approximate dynamic programming(ADP) within the control community. This paper reviews recent developments in ADP along with RL and its applications to various advanced control fields. First, the background of the development of ADP is described, emphasizing the significance of regulation and tracking control problems. Some effective offline and online algorithms for ADP/adaptive critic control are displayed, where the main results towards discrete-time systems and continuous-time systems are surveyed, ***, the research progress on adaptive critic control based on the event-triggered framework and under uncertain environment is discussed, respectively, where event-based design, robust stabilization, and game design are reviewed. Moreover, the extensions of ADP for addressing control problems under complex environment attract enormous attention. The ADP architecture is revisited under the perspective of data-driven and RL frameworks,showing how they promote ADP formulation ***, several typical control applications with respect to RL and ADP are summarized, particularly in the fields of wastewater treatment processes and power systems, followed by some general prospects for future research. Overall, the comprehensive survey on ADP and RL for advanced control applications has d emonstrated its remarkable potential within the artificial intelligence era. In addition, it also plays a vital role in promoting environmental protection and industrial intelligence.

关键词： Adaptive dynamic programming(ADP) advanced control complex environment data-driven control event-triggered design intelligent control neural networks nonlinear systems optimal control reinforcement learning(RL)

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Malicious URL Classification Using Artificial Fish Swarm Optimization and Deep Learning

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computers, Materials & Continua 2023年第1期74卷 607-621页

作者： Anwer Mustafa Hilal Aisha Hassan Abdalla Hashim Heba G.Mohamed Mohamed K.Nour Mashael M.Asiri Ali M.Al-Sharafi Mahmoud Othman Abdelwahed Motwakel Department of Electrical and Computer Engineering International Islamic University Malaysia 53100 Kuala LumpurMalaysia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Electrical Engineering College of EngineeringPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Computer Science College of Computers and Information TechnologyUniversity of BishaSaudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt

Cybersecurity-related solutions have become familiar since it ensures security and privacy against cyberattacks in this digital *** Uniform Resource Locators(URLs)can be embedded in email or Twitter and used to lure vulnerable internet users to implement malicious data in their *** may result in compromised security of the systems,scams,and other such *** attacks hijack huge quantities of the available data,incurring heavy financial *** the same time,Machine Learning(ML)and Deep Learning(DL)models paved the way for designing models that can detect malicious URLs accurately and classify *** this motivation,the current article develops an Artificial Fish Swarm Algorithm(AFSA)with Deep Learning Enabled Malicious URL Detection and Classification(AFSADL-MURLC)*** presented AFSADL-MURLC model intends to differentiate the malicious URLs from genuine *** attain this,AFSADL-MURLC model initially carries out data preprocessing and makes use of glove-based word embedding *** addition,the created vector model is then passed onto Gated Recurrent Unit(GRU)classification to recognize the malicious ***,AFSA is applied to the proposed model to enhance the efficiency of GRU *** proposed AFSADL-MURLC technique was experimentally validated using benchmark dataset sourced from Kaggle *** simulation results confirmed the supremacy of the proposed AFSADL-MURLC model over recent approaches under distinct measures.

关键词： Malicious URL cybersecurity deep learning machine learning metaheuristics gated recurrent unit

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TNUE-Fight Detection: A New Challenge Benchmark for Fighting Recognition 1

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2nd International Conference on Advances in information and Communication Technology, ICTA 2023

作者： Vu, Duc-Quang Nguyen, Thu Hien Nguyen, Mai Nguyen, Bao Yen Phung, Trung-Nghia Thu, Trang Phung T. Department of Computer Science and Information System Thai Nguyen University of Education Thai Nguyen Viet Nam Thai Nguyen University of Information and Communication Technology Thai Nguyen Viet Nam Faculty of Basic Science School of Foreign Language Thai Nguyen University Thai Nguyen Viet Nam

ISBN: (数字)9783031508189

ISBN: (纸本)9783031508172

Today, violence is one of the most common abnormal actions that need to be monitored and detected early. Therefore, a violence recognition system is very necessary and has great practical significance. In recent years, deep learning has achieved remarkable achievements in various different problems, however, deep learning models for violent action recognition have not been properly studied. One of the main reasons is the lack of rich labeled datasets. Specifically, the current datasets are usually only collected from movies and sports, which makes the videos in the dataset far different from actual fight actions. To overcome this drawback, in this paper, we propose a new challenge dataset named TNUE-Fight Detection. In which, our proposed dataset is collected from many real-life fights from videos uploaded to social networks. Furthermore, the TNUE-Fight Detection dataset not only provides labels for each video but also provides bounding boxes for fighting and non-fighting objects, which help to solve the problem in both cases of classification and detection. The TNUE-Fight Detection dataset is available at https://***/vdquang1991/TNUE_FightDetection. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Object detection

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A Lightweight Attention Model for Face Recognition 1

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2nd International Conference on Advances in information and Communication Technology, ICTA 2023

作者： Vu, Duc-Quang Nguyen, Thu Hien Nguyen, Danh Vu Nguyen, Yen Quynh Phung, Trung-Nghia Thu, Trang Phung T. Department of Computer Science and Information System Thai Nguyen University of Education Thai Nguyen Viet Nam Thai Nguyen University of Information and Communication Technology Thai Nguyen Viet Nam Faculty of Basic Science School of Foreign Language Thai Nguyen University Thai Nguyen Viet Nam

ISBN: (数字)9783031508189

ISBN: (纸本)9783031508172

Facial recognition is one of the most popular biometric methods in the real world. To address this task, various methods have been proposed especially deep learning-based approaches. However, most of them usually focus on improving performance by building deeper and more complex networks. This makes an important limitation for the ability to deploy them on embedded or mobile devices without GPU. In this paper, we introduce an efficient network called SeesawAttentionFaceNet which is a hybrid of SeesawFaceNet and CBAM attention to improve the performance of face recognition while keeping the simple to suitable for edge devices. The experiments conducted on various datasets have shown that our proposed framework outperforms the other state-of-the-art lightweight models for facial recognition. Moreover, we also provide an ablation study to demonstrate that when the face area of the removed position increases, the recognition results typically are reduced. This has important implications in feature extraction and processing of facial images. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Face recognition

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