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A Modified Oppositional Chaotic Local Search Strategy Based Aquila Optimizer to Design an Effective Controller for Vehicle Cruise Control System

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Journal of Bionic Engineering 2023年第4期20卷 1828-1851页

作者： Serdar Ekinci Davut Izci Laith Abualigah Raed Abu Zitar Department of Computer Engineering Batman University72100BatmanTurkey Center for Engineering Application and Technology Solutions Ho Chi Minh City Open UniversityHo Chi MinhVietnam Hourani Center for Applied Scientific Research Al-Ahliyya Amman UniversityAmman19328Jordan Computer Science Department Prince Hussein Bin Abdullah Faculty for Information TechnologyAl Al-Bayt UniversityMafraq25113Jordan Faculty of Information Technology Middle East UniversityAmman11831Jordan Applied Science Research Center Applied Science Private UniversityAmman11931Jordan School of Computer Sciences Universiti Sains Malaysia11800PenangPulau PinangMalaysia Sorbonne Center of Artificial Intelligence Sorbonne University-Abu DhabiAbu DhabiUnited Arab Emirates

In this work,we propose a real proportional-integral-derivative plus second-order derivative(PIDD2)controller as an efficient controller for vehicle cruise control systems to address the challenging issues related to efficient *** this regard,this paper is the first report in the literature demonstrating the implementation of a real PIDD2 controller for controlling the respective *** construct a novel and efficient metaheuristic algorithm by improving the performance of the Aquila Optimizer via chaotic local search and modified opposition-based learning strategies and use it as an excellently performing tuning *** also propose a simple yet effective objective function to increase the performance of the proposed algorithm(CmOBL-AO)to adjust the real PIDD2 controller's parameters *** show the CmOBL-AO algorithm to perform better than the differential evolution algorithm,gravitational search algorithm,African vultures optimization,and the Aquila Optimizer using well-known unimodal,multimodal benchmark ***2019 test suite is also used to perform ablation experiments to reveal the separate contributions of chaotic local search and modified opposition-based learning strategies to the CmOBL-AO *** the vehicle cruise control system,we confirm the more excellent performance of the proposed method against particle swarm,gray wolf,salp swarm,and original Aquila optimizers using statistical,Wilcoxon signed-rank,time response,robustness,and disturbance rejection *** also use fourteen reported methods in the literature for the vehicle cruise control system to further verify the more promising performance of the CmOBL-AO-based real PIDD2 controller from a wider *** excellent performance of the proposed method is also illustrated through different quality indicators and different operating ***,we also demonstrate the good performing capability of the CmOBL-AO algorithm for real traffic *** show

关键词： Aquila optimizer Chaotic local search Modified opposition-based learning Real PIDD^(2)controller Vehicle cruise control system Bionic engineering

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Explainable-AI for DoS Attacks Detection in 5G Network Using Deep Learning Models 5

Explainable-AI for DoS Attacks Detection in 5G Network Using...

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5th International Conference on Intelligent Computing, Communication, Networking and Services, ICCNS 2024

作者： Albashayreh, Amjad Tashtoush, Yahya Aldosary, Abdallah Darwish, Omar Albalas, Firas The University of Jordan Department of Computer Science Amman Jordan Jordan University of Science and Technology Department of Computer Science Irbid Jordan Prince Sattam bin Abdulaziz University The Department of Computer Engineering Ar Riyadh Saudi Arabia Eastern Michigan University Information Security and Applied Computing Department YpsilantiMI48197 United States

ISBN: (纸本)9798350354690

With the emergence of the fifth-generation (5G) net-work, numerous revolutionary applications are enabled, including low-latency and machine-type communications. This great in-crease creates a broader security threat concerns, such as denial-of-service (DoS) attacks, which can disrupt network functionality. The complexity and decentralization of 5G networks create new vulnerabilities for adversaries, necessitating comprehensive security procedures to identify, mitigate, and prevent DoS attacks in 5G networks. This paper introduces a novel approach for DoS detection in 5G networks, utilizing deep learning and machine learning models, along with Local Interpretable Model-Agnostic Explanations (LIME), to interpret model predictions and identify the significant role of data features in detecting DoS attacks. The results revealed that the random forest model demonstrated superior recall of 99.98, while BiLSTM demonstrated exceptional performance with a recall of 98.02. © 2024 IEEE.

关键词： 5G mobile communication systems

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Human Gait Recognition Using Deep Learning and Improved Ant Colony Optimization

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computers, Materials & Continua 2022年第2期70卷 2113-2130页

作者： Awais Khan Muhammad Attique Khan Muhammad Younus Javed Majed Alhaisoni Usman Tariq Seifedine Kadry Jung-In Choi Yunyoung Nam Department of Computer Science HITEC University TaxilaTaxila47080Pakistan College of Computer Science and Engineering University of Ha’ilHa’ilSaudi Arabia College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz UniversityAl-KhrajSaudi Arabia Faculty of Applied Computing and Technology Noroff University CollegeKristiansandNorway Department of Applied Artificial Intelligence Ajou UniversitySuwonKorea Department of Computer Science and Engineering Soonchunhyang UniversityAsanKorea

Human gait recognition(HGR)has received a lot of attention in the last decade as an alternative biometric *** main challenges in gait recognition are the change in in-person view angle and covariant *** major covariant factors are walking while carrying a bag and walking while wearing a *** learning is a new machine learning technique that is gaining *** techniques for HGR based on deep learning are presented in the *** requirement of an efficient framework is always required for correct and quick gait *** proposed a fully automated deep learning and improved ant colony optimization(IACO)framework for HGR using video sequences in this *** proposed framework consists of four primary *** the first step,the database is normalized in a video *** the second step,two pre-trained models named ResNet101 and InceptionV3 are selected andmodified according to the dataset’s *** that,we trained both modified models using transfer learning and extracted the *** IACO algorithm is used to improve the extracted *** is used to select the best features,which are then passed to the Cubic SVM for final *** cubic SVM employs a multiclass *** experiment was carried out on three angles(0,18,and 180)of the CASIA B dataset,and the accuracy was 95.2,93.9,and 98.2 percent,respectively.A comparison with existing techniques is also performed,and the proposed method outperforms in terms of accuracy and computational time.

关键词： Gait recognition deep learning transfer learning features optimization classification

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Automatic classification of normal/AD brain MRI slices using whale-algorithm optimized hybrid image features

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Journal of Ambient Intelligence and Humanized Computing 2023年第10期14卷 14237-14248页

作者： Kadry, Seifedine Jessy, V. Elizabeth Rajinikanth, Venkatesan Crespo, Rubén González Department of Applied Data Science Noroff University College Kristiansand4612 Norway College of Engineering and Information Technology Ajman University Ajman United Arab Emirates Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon Division of Research and Innovation Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Tamil Nadu Chennai602105 India Department of Computer Science and Technology Universidad Internacional de La Rioja La Rioja Logroño Spain

In recent years, the prevalence of Age-Related Illnesses (ARL) has been increasing among older individuals, and early recognition and treatment will result in better living conditions. It is well known that Alzheimer's Disease (AD) is among the ARD, and severe cases may result in dementia as well. It is the purpose of this study to propose a technique for distinguishing normal/AD brain MRI slices with improved accuracy utilizing the T2-modality. This scheme consists following phases: (i) Brain MRI collection and preprocessing, (ii) Deep feature extraction with the chosen scheme, (iii) Handcrafted feature extraction, (iv) Whale Algorithm (WA) based feature reduction and serial integration, and (v) binary classification using five-fold cross-validation. A total of 2000 MRI slices (1000 normal and 1000 AD class) are examined during this task using images collected from Alzheimer’s Disease Neuroimaging Initiative (ADNI). This study confirms that the proposed scheme provides a classification accuracy of > 98% when applied with the K-Nearest Classifier. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Feature extraction

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Applications for the groups ), where prime upper than 9

Iraqi Journal for Computer Science and Mathematics

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Iraqi Journal for computer science and Mathematics 2024年第1期5卷 78-84页

作者： Alameer Hadi, Lemya Abd Fiadh, Mahmood S. Jasim, Niran Sabah Eleiwy, Jabbar Abed Department of Communication Engineering University of Technology Baghdad Iraq Department of Computer Science College of Education Al-Iraqia University Baghdad Iraq Department of Mathematics College of Educational for Pure Science lbn Al-Haitham University of Baghdad Baghdad Iraq Department of Applied Sciences University of Technology Baghdad Iraq

The problem of finding the cyclic decomposition (c.d.) for the groups S.U.T.(2P), where D, where prime upper than 9 is determined in this work. Also, we compute the Artin characters (***.) and Artin indicator (***.) for the same groups, we obtain that after computing the conjugacy classes, cyclic subgroups, the ordinary character table (***.) and the rational valued character table for each group. © 2024 College of Education, Al-Iraqia University. All rights reserved.

关键词： Artin character Artin indicator Cyclic decomposition cyclic subgroups upper triangular subgroup

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Revolutionizing Urban Waste Management: Cutting-Edge Image Processing for Precise Solid Waste Classification 5

Revolutionizing Urban Waste Management: Cutting-Edge Image P...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Rama Krishna, S. Patan, Muzeeb Khan Sudarsan, J.S. Padmapriya, R. Bhagavathiperumal, S. Balaram, Allam Department of Computer Science & Engineering Hyderabad502329 India College of Engineering and Technology University of Technology and Applied Sciences Department of EEE Muscat Oman India Sathyabama Institute of Science and Technology Department of Civil Engineering Chennai India Sri Krishna College of Engineering and Technology Coimbatore India MLR Institute of Technology Department of Computer Science and Engineering Hyderabad500043 India

ISBN: (纸本)9798331530013

Due to rapid urbanization and population growth, urban waste management has become one of the major challenges faced by cities around the world. Solid waste classification and sorting are necessary for intensifying recycling, safeguarding environment and manage additional landfilling of materials that are reusable or recyclable, and are getting in landfill of solid waste in certain extent. In this study, an original ensemble model using the VGG19 and EfficientNet architectures is proposed to classify solid waste in urban area. This proposed model was applied on a dataset consists of 2,527 number of images in to six classes to increase the accuracy of classification as well as to maximize the efficiency of waste. It preprocesses the incoming data using various techniques such as image resizing, normalization and augmentation to make the dataset more diverse and robust. The ensemble mechanism performed better combining the detailed feature extraction skills of VGG19 and versatile and scalable architecture of EfficientNet. On test set, it outperformed individual models and the accuracy for the proposed model was 98.75% which closely followed, previously obtain the best result. © 2024 IEEE.

关键词： Population statistics

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Traffic-oriented reconfigurable NoC with augmented inter-port buffer sharing

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Frontiers of Information technology & Electronic Engineering 2024年第10期25卷 1322-1336页

作者： Chenglong SUN Yiming OUYANG Huaguo LIANG School of Computer and Information Engineering Fuyang Normal UniversityFuyang 236041China School of Computer Science and Information Engineering Hefei University of TechnologyHefei 230601China School of Electronic Science and Applied Physics Hefei University of TechnologyHefei 230601China

As the number of cores in a multicore system increases,the communication pressure on the interconnection network also *** network-on-chip(NoC)architecture is expected to take on the ever-expanding communication demands triggered by the ever-increasing number of *** communication behavior of the NoC architecture exhibits significant spatial–temporal variation,posing a considerable challenge for NoC *** this paper,we propose a traffic-oriented reconfigurable NoC with augmented inter-port buffer sharing to adapt to the varying traffic flows with a high ***,a modified input port is introduced to support buffer sharing between adjacent ***,the modified input port can be dynamically reconfigured to react to on-demand ***,it is ascertained that a centralized output-oriented buffer management works well with the reconfigurable input ***,this reconfiguration method can be implemented with a low overhead hardware design without imposing a great burden on the system *** experimental results show that compared to other proposals,the proposed NoC architecture can greatly reduce the packet latency and improve the saturation throughput,without incurring significant area and power overhead.

关键词： Network-on-chip Reconfigurable Traffic-oriented Buffer sharing

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Typhoon Track Image Prediction Using a Comprehensive LSTM Attention Model in Deep Learning 4

Typhoon Track Image Prediction Using a Comprehensive LSTM At...

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4th International Conference on Neural Networks, Information and Communication Engineering, NNICE 2024

作者： Jiang, Yiping Yu, Yi Ma, Yuelong Zhuang, Jiahao Ye, Xianze Yuan, Yifeng Su, Guanpeng Guangdong University of Science and Technology School of Computer Science Guangdong Dongguan China University of Macau Faculty of Science and Technology Fst China South China Normal University School of Information and Optoelectronic Science and Engineering Guangdong Guangzhou China Macao Polytechnic University Faculty of Applied Sciences China Zhuhai College of Science and Technology School of Computer Guangdong Zhuhai China

ISBN: (纸本)9798350394375

Typhoons are exceptionally destructive tropical cyclones that possess the capacity to inflict significant harm upon society. For institutions engaged in risk assessment and disaster mitigation, accurately forecasting typhoon paths and trajectories is of paramount importance. In this study, we propose a Typhoon Track Image Prediction model based on a Comprehensive Long Short-Term Memory (LSTM) Attention Model in the field of Deep Learning. This model significantly improves the accuracy of typhoon track prediction. To assess its performance, we utilize typhoon data from the China Typhoon Network spanning from 1949 to 2022. The Comprehensive LSTM Attention Model is compared against other popular models such as Temporal Convolutional Networks (TCN), traditional LSTM, and Seq2Seq models for forecasting the typhoon track over a 12-hour period. The results demonstrate the superiority of our proposed model, showcasing its effectiveness in terms of superior accuracy in typhoon track prediction. © 2024 IEEE.

关键词： Long short-term memory

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Improving Oil Spill Detection in Marine Environments Through Deep Learning Approaches 4

Improving Oil Spill Detection in Marine Environments Through...

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4th International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Shanmukh, M. Phani Priya, S. Baghavathi Madeswaran, Tamilselvi Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Chennai India University of Technology and Applied Sciences Department of Information Technology Nizwa Oman

ISBN: (纸本)9798350343670

Oil spills represent a major environmental threat, particularly in marine ecosystems. Timely detection and segmentation of oil spills from satellite imagery is crucial for directing rapid response and remediation *** research concludes with an insights by developing a deep learning approach for automated detection and segmentation of oil spills from synthetic aperture radar (SAR) *** research implements and compares three algorithms PSPNET,DeepLapV3 and fully convolutional networks (FCN) with U-Net for semantic segmentation of SAR *** the networks are trained on a dataset of SAR images with pixel-wise labels indicating oil spill regions. This research also evaluates the segmentation performance using common metrics like intersection-over-union (IoU) and pixel-wise accuracy. Our results indicate that both FCN and U-Net can effectively detect and delineate oil spill regions, with U-Net demonstrating slightly higher IoU scores and with and accuracy of 95%. The deep learning models generalize well to unseen SAR images. Overall, this work demonstrates the feasibility of deep convolutional neural networks for automated analysis of SAR imagery to enable rapid oil spill response at marine environment. © 2024 IEEE.

关键词： Deep Learning Algorithms Oil Spill Detection Rapid Oil Spill Response Semantic Segmentation Synthetic Aperture Radar (SAR) Imagery

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E-Medical Administration: An Automated Healthcare Management System 4th

E-Medical Administration: An Automated Healthcare Management...

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4th International Conference on Data, Engineering, and Applications, IDEA 2022

作者： Awotunde, Joseph Bamidele Misra, Sanjay Abdullahi, Thairu Olanrewaju Agrawal, Akshat Department of Computer Science University of Ilorin Ilorin Nigeria Department of Applied Data Science Institute for Energy Technology Halden Norway Amity University Haryana Manesar India

ISBN: (纸本)9789819724505

Almost any service may now be found on the Internet, thanks to advances in web technology. Every major industry is converting and building a digital face for all of its core activities to get closer to the rising digital market. A person or company using redundancy tactics in today's society will not gain from it. All well-run and well-managed businesses today need Internet access. Information digitization is critical in healthcare because of the need to move swiftly and effectively. It lays the groundwork for the creation of software that makes it easier to go from paper documents to electronic ones. Therefore, this paper proposes an automated hospital management system. E-Medical Administration is a web-based platform that removes the need for paper prescriptions in hospitals and offers E-Medical Management, which would enhance patient management, doctor scheduling, and universal access to patient data anywhere in the hospital. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Electronic health record

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