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Automated Arabic Text Classification Using Hyperparameter Tuned Hybrid Deep Learning Model

Computers, Materials & Continua 2023年第3期74卷 5447-5465页

作者： Badriyya B.Al-onazi saud s.alotaib saeed Masoud Alshahrani Najm alotaibi Mrim M.Alnfiai Ahmed s.salama Manar Ahmed Hamza Department of Language Preparation Arabic Language Teaching InstitutePrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information systems College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer science College of Computing and Information TechnologyShaqra UniversityShaqraSaudi Arabia Prince saud AlFaisal Institute for Diplomatic studies Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif P.O.Box 11099Taif21944Saudi Arabia Department of Electrical Engineering Faculty of Engineering&TechnologyFuture University in EgyptNew Cairo11845Egypt Department of Computer and self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

The text classification process has been extensively investigated in various languages,especially *** classification models are vital in several Natural Language Processing(NLP)*** Arabic language has a lot of *** instance,it is the fourth mostly-used language on the internet and the sixth official language of ***,there are few studies on the text classification process in Arabic.A few text classification studies have been published earlier in the Arabic *** general,researchers face two challenges in the Arabic text classification process:low accuracy and high dimensionality of the *** this study,an Automated Arabic Text Classification using Hyperparameter Tuned Hybrid Deep Learning(AATC-HTHDL)model is *** major goal of the proposed AATC-HTHDL method is to identify different class labels for the Arabic *** first step in the proposed model is to pre-process the input data to transform it into a useful *** Term Frequency-Inverse Document Frequency(TF-IDF)model is applied to extract the feature ***,the Convolutional Neural Network with Recurrent Neural Network(CRNN)model is utilized to classify the Arabic *** the final stage,the Crow search Algorithm(CsA)is applied to fine-tune the CRNN model’s hyperparameters,showing the work’s *** proposed AATCHTHDL model was experimentally validated under different parameters and the outcomes established the supremacy of the proposed AATC-HTHDL model over other approaches.

关键词： Hybrid deep learning natural language processing arabic language text classification parameter tuning

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1.56 kV/30 A vertical β-Ga2O3 schottky barrier diodes with composite edge terminations

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science China(Information sciences) 2025年

作者： Yitao FENG Hong ZHOU sami ALGHAMDI Hao FANG Xiaorong ZHANG Yanbo CHEN Guotao TIAN saud WAsLY Yue HAO Jincheng ZHANG Key Lab of Wide Band Gap semiconductor Materials and Devices School of MicroelectronicsXidian University Center of Excellence in Intelligent Engineering systems (CEIEs) King Abdulaziz University China Resources Microelectronics Limited(Wuxi)

In recent years, large-area wide-bandgap (WBG) semiconductor materials have achieved numerous notable breakthroughs [1, 2]. The ultra-WBG semiconductor β-Ga2O3,with its excellent material properties, is ideally suited for the fabrication of power devices. Moreover, it offers easily obtainable large-area substrates, with reported sizes up to 6 inches, produced through melt-grown crystal growth methods. These characteristics make β-Ga2O3 highly attractive for future low-cost commercialization. Despite these advances, most research has focused on low-current and small-size diodes, with few studies addressing high-current and large-size diodes.

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Advanced Functional Electromagnetic shielding Materials:A Review Based on Micro‑Nano structure Interface Control of Biomass Cell Walls

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Nano-Micro Letters 2025年第1期17卷 98-134页

作者： Yang shi Mingjun Wu shengbo Ge Jianzhang Li Anoud saud Alshammari Jing Luo Mohammed A.Amin Hua Qiu Jinxuan Jiang Yazeed M.Asiri Runzhou Huang Hua Hou Zeinhom M.El‑Bahy Zhanhu Guo Chong Jia Kaimeng Xu Xiangmeng Chen Co‑Innovation Center of Efficient Processing and Utilization of Forest Resources College of Materials Science and EngineeringNanjing Forestry UniversityNanjing 210037People’s Republic of China state Key Laboratory of Efficient Production of Forest Resourced Beijing Forestry UniversityQinghua East Road 35Haidian DistrictBeijing 100083People’s Republic of China Department of Physics Faculty of Sciences‑ArarNorthern Border UniversityArar 91431Saudi Arabia Department of Chemistry College of ScienceTaif UniversityP.O.Box 1109921944 TaifSaudi Arabia shaanxi Key Laboratory of Macromolecular science and Technology School of Chemistry and Chemical EngineeringNorthwestern Polytechnical UniversityXi’anShaanxi 710072People’s Republic of China Integrated Composites Lab Department of Mechanical and Construction EngineeringNorthumbria UniversityNewcastle Upon Tyne NE18STUK College of Materials science and Engineering Taiyuan University of Science and TechnologyTaiyuan 030024People’s Republic of China Department of Chemistry Faculty of ScienceAl-Azhar UniversityNasr CityCairo 11884Egypt Yunnan Provincial Key Laboratory of Wood Adhesives and Glued Products International Joint Research Center for Biomass MaterialsSouthwest Forestry UniversityKunming 650224People’s Republic of China school of science Henan Agricultural UniversityZhengzhou 450002People’s Republic of China

Research efforts on electromagnetic interference(EMI)shielding materials have begun to converge on green and sustainable biomass *** materials offer numerous advantages such as being lightweight,porous,and *** to their porous nature,interfacial compatibility,and electrical conductivity,biomass materials hold significant potential as EMI shielding *** concerted efforts on the EMI shielding of biomass materials have been reported,this research area is still relatively new compared to traditional EMI shielding *** particular,a more comprehensive study and summary of the factors influencing biomass EMI shielding materials including the pore structure adjustment,preparation process,and micro-control would be *** preparation methods and characteristics of wood,bamboo,cellulose and lignin in EMI shielding field are critically discussed in this paper,and similar biomass EMI materials are summarized and *** composite methods and fillers of various biomass materials were *** paper also highlights the mechanism of EMI shielding as well as existing prospects and challenges for development trends in this field.

关键词： Biomass materials Electromagnetic interference shielding Micro-nano structure interface control Conductivity

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Germination Quality Prognosis: Classifying spectroscopic Images of the seed samples

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Intelligent Automation & soft Computing 2023年第2期35卷 1815-1829页

作者： saud s.alotaibi Department of Information systems College of Computer and Information SystemsUmm Al-Qura UniversityMakkahSaudi Arabia

One of the most critical objectives of precision farming is to assess the germination quality of *** models contribute to thisﬁeld primarily through the use of artiﬁcial intelligence techniques such as machine learning,which present difﬁculties in feature extraction and optimization,which are critical factors in predicting accuracy with few false alarms,and another signiﬁcant dif-ﬁculty is assessing germination ***,the majority of these contri-butions make use of benchmark classiﬁcation methods that are either inept or too complex to train with the supplied *** manuscript addressed these issues by introducing a novel ensemble classiﬁcation strategy dubbed“Assessing Germination Quality of seed samples(AGQss)by Adaptive Boosting Ensemble Classiﬁcation”that learns from quantitative phase features as well as universal features in greyscale spectroscopic *** experimental inquiry illustrates the signiﬁcance of the proposed model,which outperformed the currently avail-able models when performance analysis was performed.

关键词： Precision farming ensemble classiﬁcation germination quality machine learning predictive analytics

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Internet of Things Enabled DDos Attack Detection Using Pigeon Inspired Optimization Algorithm with Deep Learning Approach

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Computers, Materials & Continua 2024年第9期80卷 4047-4064页

作者： Turki Ali Alghamdi saud s.alotaibi Department of Computer science and Artificial Intelligence College of ComputingUmm Al-Qura UniversityMakkah21955Saudi Arabia

Internet of Things(IoTs)provides better solutions in various fields,namely healthcare,smart transportation,home,*** Denial of service(Dos)outbreaks in IoT platforms is significant in certifying the accessibility and integrity of IoT *** learning(DL)models outperform in detecting complex,non-linear relationships,allowing them to effectually severe slight deviations fromnormal IoT activities that may designate a Dos *** uninterrupted observation and real-time detection actions of DL participate in accurate and rapid detection,permitting proactive reduction events to be executed,hence securing the IoT network’s safety and ***,this study presents pigeon-inspired optimization with a DL-based attack detection and classification(PIODL-ADC)approach in an IoT *** PIODL-ADC approach implements a hyperparameter-tuned DL method for Distributed Denial-of-service(DDos)attack detection in an IoT ***,the PIODL-ADC model utilizes Z-score normalization to scale input data into a *** handling the convolutional and adaptive behaviors of IoT,the PIODL-ADCmodel employs the pigeon-inspired optimization(PIO)method for feature selection to detect the related features,considerably enhancing the recognition’s ***,the Elman Recurrent Neural Network(ERNN)model is utilized to recognize and classify DDos ***,reptile search algorithm(RsA)based hyperparameter tuning is employed to improve the precision and robustness of the ERNN method.A series of investigational validations is made to ensure the accomplishment of the PIODL-ADC *** experimental outcome exhibited that the PIODL-ADC method shows greater accomplishment when related to existing models,with a maximum accuracy of 99.81%.

关键词： Internet of things denial of service deep learning reptile search algorithm feature selection

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A brief illustration of the official national standards for the safe useof cupping therapy (Hijama) in saudi Arabia

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Journal of Integrative Medicine 2018年第5期16卷 297-298页

作者： Tamer Aboushanab saud Alsanad National Center for Complementary and Alternative Medicine Riyadh 11662 Saudi Arabia National Center for Complementary Medicine Ministry of Health Riyadh 11662 Saudi Arabia College of Medicine AI Imam Mohammad Ibn Saud Islamic University Riyadh 11662 Saudi Arabia

Cupping therapy （Hijama in Arabic） is a popular traditional treatment especially in Asia, the Middle East and Central Europe. Traditionally, cupping therapy has been one of the most used practices in saudi Arabia. standardization of the practice of cupping therapy is a very important step to eliminate or reduce any adverse events related to cupping. This article gives a brief illustration and highlights the important points of the first accredited saudi Arabian standards for the safe use of cupping equipment and its applications.

关键词： Cupping Hijama standards safety Equipment

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Efficient Object segmentation and Recognition Using Multi-Layer Perceptron Networks

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Computers, Materials & Continua 2024年第1期78卷 1381-1398页

作者： Aysha Naseer Nouf Abdullah Almujally saud s.alotaibi Abdulwahab Alazeb Jeongmin Park Department of Computer science Air UniversityIslamabad44000Pakistan Department of Information systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Information system Department Umm Al-Qura UniversityMakkahSaudi Arabia Department of Computer science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Department of Computer Engineering Tech University of KoreaGyeonggi-do15073South Korea

Object segmentation and recognition is an imperative area of computer vision andmachine learning that identifies and separates individual objects within an image or video and determines classes or categories based on their *** proposed system presents a distinctive approach to object segmentation and recognition using Artificial Neural Networks(ANNs).The system takes RGB images as input and uses a k-means clustering-based segmentation technique to fragment the intended parts of the images into different regions and label thembased on their ***,two distinct kinds of features are obtained from the segmented images to help identify the objects of *** Artificial Neural Network(ANN)is then used to recognize the objects based on their *** were carried out with three standard datasets,MsRC,Ms COCO,and Caltech 101 which are extensively used in object recognition research,to measure the productivity of the suggested *** findings from the experiment support the suggested system’s validity,as it achieved class recognition accuracies of 89%,83%,and 90.30% on the MsRC,Ms COCO,and Caltech 101 datasets,respectively.

关键词： K-region fusion segmentation recognition feature extraction artificial neural network computer vision

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Propagation of waves over a rugged topography

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Journal of Ocean Engineering and science 2022年第1期7卷 14-28页

作者： Mohammad saud Afzal Lalit Kumar Department of civil engineering Indian Institute of TechnologyKharagpurIndia

The propagation of waves in shallow waters is affected by the bottom topography unlike deep water waves of the coastal *** to the interaction of the wave with bed topography,the wave transformation processes ***,diffraction,shoaling,and breaking are the wave transforma-tion processes that occur in the coastal *** significant wave height over rugged topography is a standardized statistics to denote the characteristic height of the random waves in a sea ***-fore,the objective of the present study is to predict the significant wave height over rugged *** sWAN standalone and sWAN DHH platform are used to predict significant wave height over rugged topography in Mehamn harbour,*** sWAN model results are almost similar to the lab data of Vold and Lothe(2009)for all the 22 scenarios at all the output ***,the four cases reported by Taehun(2011)and lab data from Vold and Lothe(2009)for that four cases are compared with the sWAN model *** is observed that the sWAN model results are much closer to the lab data of Vold and Lothe(2009).

关键词： Wave propagation sWAN Bathymetry significant wave height and Peak wave period

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New Optical soliton solutions of Nolinear Evolution Equation Describing Nonlinear Dispersion

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Communications in Theoretical Physics 2019年第9期71卷 1063-1068页

作者： saud Owyed M.A.Abdou Abdel-Haleem Abdel-Aty s.saha Ray Department of Mathematics College of SciencesUniversity of BishaBisha61922P.O.Box344Saudi Arabia Department of Physics College of SciencesUniversity of BishaBisha61922P.O.Box344Saudi Arabia Physics Department Faculty of SciencesAl-Azhar University71524AssiutEgypt Department of Mathematics National Institute of TechnologyRourkela769008OdishaIndia

In this work, we examine two algorithm schemes, namely, Kudryashov expansion and Auxiliary equation method for obtaining new optical soliton solutions of the discrete electrical lattice models in nonlinear scheme(salerno equation). Our solutions obtained here are include the hyperbolic, rational, and trigonometric functions. Our two used methods are proved to be effective and powerful methods in obtaining the exact solutions of nonlinear evolution equations(NLEEs).

关键词： discrete electrical lattice generalized Kudryashov expansion method auxiliary equation method exact solutions

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Automated Machine Learning Enabled Cybersecurity Threat Detection in Internet of Things Environment

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Computer systems science & Engineering 2023年第4期45卷 687-700页

作者： Fadwa Alrowais sami Althahabi saud s.alotaibi Abdullah Mohamed Manar Ahmed Hamza Radwa Marzouk Department of Computer sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Information systems College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Research Centre Future University in EgyptNew Cairo11745Egypt Department of Computer and self Development Preparatory Year DeanshipPrince Sattam Bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Mathematics Faculty of ScienceCairo UniversityGiza12613Egypt

Recently,Internet of Things(IoT)devices produces massive quantity of data from distinct sources that get transmitted over public *** becomes a challenging issue in the IoT environment where the existence of cyber threats needs to be *** development of automated tools for cyber threat detection and classification using machine learning(ML)and artificial intelligence(AI)tools become essential to accomplish security in the IoT *** is needed to minimize security issues related to IoT gadgets ***,this article introduces a new Mayfly optimization(MFO)with regularized extreme learning machine(RELM)model,named MFO-RELM for Cybersecurity Threat Detection and classification in IoT *** presented MFORELM technique accomplishes the effectual identification of cybersecurity threats that exist in the IoT *** accomplishing this,the MFO-RELM model pre-processes the actual IoT data into a meaningful *** addition,the RELM model receives the pre-processed data and carries out the classification *** order to boost the performance of the RELM model,the MFO algorithm has been employed to *** performance validation of the MFO-RELM model is tested using standard datasets and the results highlighted the better outcomes of the MFO-RELM model under distinct aspects.

关键词： Cybersecurity threats classification internet of things machine learning parameter optimization

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