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Preface: 4^th International Conference on Robotics, Intelligent Automation and Control Technologies (RIACT 2023)

AIP Conference Proceedings 2024年第1期3216卷

作者： Doss, Arockia Selvakumar Arockia Short, Michael Schilberg, Daniel Design and Automation Research Group School of Mechanical Engineering Vellore Institute of Technology Department of Mechatronics and Mechanical Engineering University of Applied Sciences School of Computing Engineering and Digital Technologies Teesside University Department of Mechatronics and Mechanical Engineering University of Applied Sciences

Preface: 4th International Conference on Robotics, Intelligent Automation and Control Technologies (RIACT 2023), AIP Conference Proceedings, Volume 3216, Issue

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Abstractive Arabic Text Summarization Using Hyperparameter Tuned Denoising Deep Neural Network

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Intelligent Automation & Soft computing 2023年第11期38卷 153-168页

作者： Ibrahim M.Alwayle Hala J.Alshahrani Saud S.Alotaibi Khaled M.Alalayah Amira Sayed A.Aziz Khadija M.Alaidarous Ibrahim Abdulrab Ahmed Manar Ahmed Hamza Department of Computer Science College of Science and ArtsSharurahNajran UniversitySharurahSaudi Arabia Department of Applied Linguistics College of LanguagesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems College of Computing and Information SystemUmm Al-Qura UniversityMakkahSaudi Arabia Department of Digital Media Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Computer Department Applied CollegeNajran UniversityNajran66462Saudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

ive Arabic Text Summarization using Hyperparameter Tuned Denoising Deep Neural Network(AATS-HTDDNN)*** presented AATS-HTDDNN technique aims to generate summaries of Arabic *** the presented AATS-HTDDNN technique,the DDNN model is utilized to generate the *** study exploits the Chameleon Swarm Optimization(CSO)algorithm to fine-tune the hyperparameters relevant to the DDNN model since it considerably affects the summarization *** phase shows the novelty of the current *** validate the enhanced summarization performance of the proposed AATS-HTDDNN model,a comprehensive experimental analysis was *** comparison study outcomes confirmed the better performance of the AATS-HTDDNN model over other approaches.

关键词： Text summarization deep learning denoising deep neural networks hyperparameter tuning Arabic language

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A Cost-Effective Approach for NDN-Based Internet of Medical Things Deployment

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Computers, Materials & Continua 2022年第1期70卷 233-249页

作者： Syed Sajid Ullah Saddam Hussain Abdu Gumaei Mohsin SAlhilal Bader Fahad Alkhamees Mueen Uddin Mabrook Al-Rakhami Department of Information Technology Hazara University MansehraKPK21120Pakistan Research Chair of Pervasive and Mobile Computing Department of Information SystemsKing Saud UniversityRiyadh11543Saudi Arabia Department of Computer Science Faculty of Applied SciencesTaiz UniversityTaiz6803Yemen Department of Information Systems College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Digital Science Faculty of ScienceUniversiti Brunei DarussalamJln Tungku linkGadongBruneiBE1410Darussalam

Nowadays,healthcare has become an important area for the Internet of Things(IoT)to automate healthcare facilities to share and use patient data anytime and anywhere with Internet *** present,the host-based Internet paradigm is used for sharing and accessing healthcare-related ***,due to the location-dependent nature,it suffers from latency,mobility,and *** this purpose,Named Data Networking(NDN)has been recommended as the future Internet paradigm to cover the shortcomings of the traditional host-based Internet ***,the novel breed lacks a secure framework for *** article constructs an NDN-Based Internet of Medical Things(NDN-IoMT)framework using a lightweight certificateless(CLC)*** adopt the Hyperelliptic Curve Cryptosystem(HCC)to reduce cost,which provides strong security using a smaller key size compared to Elliptic Curve Cryptosystem(ECC).Furthermore,we validate the safety of the proposed scheme through *** cost-efficiency,we compare the designed scheme with relevant certificateless signature *** final result shows that our proposed scheme uses minimal network ***,we deploy the given framework on NDN-IoMT.

关键词： Internet of Medical Things healthcare Named Data Networking

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ANN Based Novel Approach to Detect Node Failure in Wireless Sensor Network

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Computers, Materials & Continua 2021年第11期69卷 1447-1462页

作者： Sundresan Perumal Mujahid Tabassum Ganthan Narayana Suresh Ponnan Chinmay Chakraborty Saju Mohanan Zeeshan Basit Mohammad Tabrez Quasim Faculty of Science and Technology Universiti Sains Islam MalaysiaNilai71800Malaysia Advanced Informatics Department Razak Faculty of Technology and InformaticsUniversiti Teknologi MalaysiaKuala Lumpur54100Malaysia Department of ECE Veltech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology600062TamilnaduIndia Department Electronics&Communication Engineering Birla Institute of TechnologyMesraJharkhandIndia Faulty of Information Technology University of Technology and Applied SciencesMuscat133Oman College of Computing and Information Technology University of BishaBisha67714Saudi Arabia

A wireless sensor network(WSN)consists of several tiny sensor nodes to monitor,collect,and transmit the physical information from an environment through the wireless *** node failure is considered as one of the main issues in the WSN which creates higher packet drop,delay,and energy consumption during the *** the node failure occurred mostly due to persistent energy exhaustion during transmission of data *** this paper,Artificial Neural Network(ANN)based Node Failure Detection(NFD)is developed with cognitive radio for detecting the location of the node *** ad hoc on-demand distance vector(AODV)routing protocol is used for transmitting the data from the source node to the base ***,the Mahalanobis distance is used for detecting an adjacent node to the node failure which is used to create the routing path without any node *** performance of the proposed ANN-NFD method is analysed in terms of throughput,delivery rate,number of nodes alive,drop rate,end to end delay,energy consumption,and overhead ***,the performance of the ANN-NFD method is evaluated with the header to base station and base station to header(H2B2H)*** packet delivery rate of the ANN-NFD method is 0.92 for 150 nodes that are high when compared to the H2B2H ***,the ANN-NFD method provides data consistency during data transmission under node and battery failure.

关键词： AODV artificial neural network artificial intelligence Mahalanobis distance node failure throughput wireless sensor network

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Automatic recognition of depression based on audio and video:A review

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World Journal of Psychiatry 2024年第2期14卷 225-233页

作者： Meng-Meng Han Xing-Yun Li Xin-Yu Yi Yun-Shao Zheng Wei-Li Xia Ya-Fei Liu Qing-Xiang Wang Shandong Mental Health Center Shandong UniversityJinan 250014Shandong ProvinceChina Key Laboratory of Computing Power Network and Information Security Ministry of EducationShandong Computer Science Center(National Supercomputer Center in Jinan)Qilu University of Technology(Shandong Academy of Sciences)Jinan 250353Shandong ProvinceChina Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and TechnologyQilu University of Technology(Shandong Academy of Sciences)Jinan 250353Shandong ProvinceChina Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer ScienceJinan 250353Shandong ProvinceChina Department of Ward Two Shandong Mental Health CenterShandong UniversityJinan 250014Shandong ProvinceChina

Depression is a common mental health *** current depression detection methods,specialized physicians often engage in conversations and physiological examinations based on standardized scales as auxiliary measures for depression ***-biological markers-typically classified as verbal or non-verbal and deemed crucial evaluation criteria for depression-have not been effectively *** physicians usually require extensive training and experience to capture changes in these *** in deep learning technology have provided technical support for capturing non-biological *** researchers have proposed automatic depression estimation(ADE)systems based on sounds and videos to assist physicians in capturing these features and conducting depression *** article summarizes commonly used public datasets and recent research on audio-and video-based ADE based on three perspectives:Datasets,deficiencies in existing research,and future development directions.

关键词： Depression recognition Deep learning Automatic depression estimation System Audio processing Image processing Feature fusion Future development

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Convolutional Bi-LSTM Based Human Gait Recognition Using Video Sequences

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Computers, Materials & Continua 2021年第8期68卷 2693-2709页

作者： Javaria Amin Muhammad Almas Anjum Muhammad Sharif Seifedine Kadry Yunyoung Nam ShuiHua Wang University of Wah Wah Cantt47040Pakistan National University of Technology(NUTECH) Islamabad44000Pakistan COMSATS University Islamabad Wah CampusWah CanttPakistan Faculty of Applied Computing and Technology Noroff University CollegeKristiansandNorway Department of Computer Science and Engineering Soonchunhyang UniversityAsan31538Korea Department of Mathematics University of LeicesterLeicesterUK

Recognition of human gait is a difficult assignment,particularly for unobtrusive surveillance in a video and human identification from a large ***,a method is proposed for the classification and recognition of different types of human *** proposed approach is consisting of two *** phase I,the new model is proposed named convolutional bidirectional long short-term memory(Conv-BiLSTM)to classify the video frames of human *** this model,features are derived through convolutional neural network(CNN)named ResNet-18 and supplied as an input to the LSTM model that provided more distinguishable temporal *** phase II,the YOLOv2-squeezeNet model is designed,where deep features are extricated using the fireconcat-02 layer and fed/passed to the tinyYOLOv2 model for recognized/localized the human gaits with predicted *** proposed method achieved up to 90%correct prediction scores on CASIA-A,CASIA-B,and the CASIA-C benchmark *** proposed method achieved better/improved prediction scores as compared to the recent existing works.

关键词： Bi-LSTM YOLOv2 open neural network resNet-18 gait squeezeNet

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Advancing Brain Image Segmentation: A Comprehensive Exploration of Enhanced VGG16 Architecture for Precise Neuroanatomical Mapping

Advancing Brain Image Segmentation: A Comprehensive Explorat...

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computing and Data Analytics (ICCDA), International Conference on

作者： Sheeja Kumari V Wilfred Blessing N. R Suresh Palarimath Hemalatha Gunasekaran Poorana Senthilkumar S Sutherlin Subitha G Department of Computational Intelligence Saveetha School of Engineering SIMATS University Chennai India College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Sultanate of Oman College of Computing and Information Sciences University of Technology and Applied Sciences Salalah Sultanate of Oman Department of Computer Applications Dr. N. G. P Arts and Science College Coimbatore Tamil Nadu India Department of Computer Science and Engineering Stella Mary's College of Engineering Kanyakumari Dt Tamil Nadu India

ISBN: (数字)9798331542573

ISBN: (纸本)9798331542580

This study introduces a novel approach to brain image segmentation by enhancing the VGG16 architecture. Neuroanatomical mapping is a critical aspect of understanding brain structures, and our research focuses on refining the VGG16 model to achieve more accurate segmentation results. We address the intricacies of brain image analysis, optimizing the architecture for the nuanced structures inherent in neuroimaging data. Our enhanced VGG16 model is thoroughly evaluated across diverse datasets, demonstrating superior performance compared to state-of-the-art segmentation methods. The framework proves robust and efficient in accurately delineating complex brain regions. We also delve into the interpretability of the segmentation results, shedding light on the learned features that contribute to the model's efficacy. The significance of our work extends beyond improved segmentation, offering valuable insights into the interpretability of deep learning models in neuroimaging. This research contributes to the broader field of biomedical image analysis and holds promise for applications in neuroscience and medical diagnostics. The proposed enhanced VGG16 framework emerges as a potent tool for advancing our understanding of brain structures and enhancing the capabilities of neuroimaging technologies.

关键词： Neuroimaging Deep learning Measurement Image segmentation Neuroscience Biological system modeling Refining Computer architecture Brain modeling Medical diagnosis

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Advanced Energy Management in Smart Grids using Lyrebird Optimization Algorithm

Advanced Energy Management in Smart Grids using Lyrebird Opt...

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Trends in Material Science and Inventive Materials (ICTMIM), International Conference on

作者： Mahmoud Z. Mistarihi Putchakayala Yanna Reddy A.Senthilnathan R. Bagavathi Subbi Naidu Bora Prasad Yadav Kurikyala Mechanical and Industrial Engineering Department Faculty of Engineering and Computing Liwa College Abu Dhabi UAE Hijjawi Faculty for Engineering Technology Yarmouk University Jordan Department of EEE Bharat Institute of Engineering and Technology Department of EEE Dr.N.G.P Institute of Technology Coimbatore India Department of Mathematics Rathinam Technical Campus Coimbatore Rajiv Gandhi University of Knowledge Technologies Nuzvid Andhra Pradesh Department of Engineering and Technology University of Technology and Applied Sciences Ibri Oman

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

Integration of a hybrid wind turbine (HWT) and a battery energy system (BES) is expected to take place under the framework of a Smart Grid (SG) to optimize energy generation, storage, and distribution and enhance stability and efficiency. One aspect of such systems is a challenge concerning the effective management of Renewable Energy (RE) fluctuation to achieve balanced operation among renewable generation and energy demand. This paper proposes a Lyrebird Optimization Algorithm (LOA) to optimize energy demand forecasting and management within the hybrid system. The LOA is used to minimize Mean Absolute Percentage Error (MAPE) in energy demand forecasting within the SG system. By optimizing energy demand forecasting, the LOA effectively improves accuracy in energy management and forecasting within the SG. Its adaptive optimization ensures that forecasting errors are minimized across various operating conditions, enhancing overall efficiency of the energy distribution system. By then the proposed LOA method is implemented on the MATLAB platform, and its performance is evaluated by comparing it with existing optimization techniques such as Improved Grey Wolf Optimization (IGWO), Multi-Objective Optimization (MOO), Multi-Objective Wind Driven Optimization (MOWDO), and Coati Optimization Algorithm (COA). The results show that the proposed LOA method provides superior performance in minimizing MAPE, achieving the lowest MAPE value of 2.90%, indicating its enhanced accuracy in energy demand forecasting and optimization.

关键词： Energy consumption Renewable energy sources Accuracy Scalability Real-time systems Smart grids Wind turbines Batteries Forecasting Optimization

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Automated Spam Review Detection Using Hybrid Deep Learning on Arabic Opinions

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Computer Systems Science & Engineering 2023年第9期46卷 2947-2961页

作者： IbrahimM.Alwayle Badriyya B.Al-onazi Mohamed K.Nour Khaled M.Alalayah Khadija M.Alaidarous Ibrahim Abdulrab Ahmed Amal S.Mehanna Abdelwahed Motwakel Department of Computer Science College of Science and ArtsSharurahNajran UniversitySaudi Arabia Department of Language Preparation Arabic Language Teaching InstitutePrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Computer Department Applied CollegeNajran UniversityNajran66462Saudi Arabia Department of Digital Media Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Online reviews regarding purchasing services or products offered are the main source of users’*** gain fame or profit,generally,spam reviews are written to demote or promote certain targeted products or *** practice is called review *** the last few years,various techniques have been recommended to solve the problem of spam *** spam detection study focuses on English reviews,with a lesser interest in other *** review detection in Arabic online sources is an innovative topic despite the vast amount of data ***,this study develops an Automated Spam Review Detection using optimal Stacked Gated Recurrent Unit(SRD-OSGRU)on Arabic Opinion *** presented SRD-OSGRU model mainly intends to classify Arabic reviews into two classes:spam and ***,the presented SRD-OSGRU model follows different levels of data preprocessing to convert the actual review data into a compatible ***,unigram and bigram feature extractors are *** SGRU model is employed in this study to identify and classify Arabic spam *** the trial-and-error adjustment of hyperparameters is a tedious process,a white shark optimizer(WSO)is utilized,boosting the detection efficiency of the SGRU *** experimental validation of the SRD-OSGRU model is assessed under two datasets,namely DOSC *** extensive comparison study pointed out the enhanced performance of the SRD-OSGRU model over other recent approaches.

关键词： Arabic text spam reviews machine learning deep learning white shark optimizer

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An Artificial Intelligence Approach for Solving Stochastic Transportation Problems

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Computers, Materials & Continua 2022年第1期70卷 817-829页

作者： Prachi Agrawal Khalid Alnowibet Talari Ganesh Adel F.Alrasheedi Hijaz Ahmad Ali Wagdy Mohamed Department of Mathematics and Scientific Computing National Institute of Technology HamirpurHimachal Pradesh177005India Statistics and Operations Research Department College of ScienceKing Saud UniversityRiyadh11451Kingdom of Saudi Arabia Section of Mathematics International Telematic University UninettunoRoma00186Italy Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Wireless Intelligent Networks Center(WINC) School of Engineering and Applied SciencesNile UniversityGizaEgypt

Recent years witness a great deal of interest in artificial intelligence(AI)tools in the area of *** has developed a large number of tools to solve themost difficult search-and-optimization problems in computer science and operations ***,metaheuristic-based algorithms are a sub-field of *** study presents the use of themetaheuristic algorithm,that is,water cycle algorithm(WCA),in the transportation problem.A stochastic transportation problem is considered in which the parameters supply and demand are considered as random variables that follow the Weibull *** the parameters are stochastic,the corresponding constraints are *** are converted into deterministic constraints using the stochastic programming *** this study,we propose evolutionary algorithms to handle the difficulties of the complex high-dimensional optimization *** is influenced by the water cycle process of how streams and rivers flow toward the sea(optimal solution).WCA is applied to the stochastic transportation problem,and obtained results are compared with that of the new metaheuristic optimization algorithm,namely the neural network algorithm which is inspired by the biological nervous *** is concluded that WCA presents better results when compared with the neural network algorithm.

关键词： Artificial intelligence metaheuristic algorithm stochastic programming transportation problem water cycle algorithm weibull distribution

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