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Journal of Biosafety and Biosecurity 2024年第3期6卷 157-169页

作者： Ranjan K.Mohapatra Ahmed Mahal Pranab K.Mohapatra Ashish K.Sarangi Snehasish Mishra Meshari A.Alsuwat Nada N.Alshehri Sozan M.Abdelkhalig mohammed Garout mohammed Aljeldah Ahmad A.Alshehri Ahmed Saif mohammed abdulrahman alshahrani Ali S.Alqahtani Yahya A.Almutawif Hamza M.A.Eid Faisal M Albaqami Mohnad Abdalla Ali A.Rabaan Department of Chemistry Government College of EngineeringKeonjhar 758002OdishaIndia Department of Medical Biochemical Analysis College of Health TechnologyCihan University–ErbilErbilKurdistan RegionIraq Department of Chemistry CV Raman Global UniversityBhubaneswarOdishaIndia Department of Chemistry School of Applied SciencesCenturion University of Technology and ManagementBalangirOdishaIndia School of Biotechnology Campus-11KIIT Deemed-to-be-UniversityBhubaneswar 751024OdishaIndia Clinical Laboratory Sciences Department College of Applied Medical SciencesTaif UniversityAl-Taif 21974Saudi Arabia Internal Medicine Department King Khalid University Medical CityCollege of MedicineAbha 61481Saudi Arabia Department of Basic Medical Sciences College of MedicineAlMaarefa UniversityRiyadhSaudi Arabia Department of Community Medicine and Health Care for Pilgrims Faculty of MedicineUmm Al-Qura UniversityMakkah 21955Saudi Arabia Department of Clinical Laboratory Sciences College of Applied Medical SciencesUniversity of Hafr Al BatinHafr Al Batin 39831Saudi Arabia Department of Clinical Laboratory Sciences Faculty of Applied Medical SciencesNajran UniversityNajran 61441Saudi Arabia Department of Medical Laboratory Sciences Faculty of Applied Medical SciencesKing Khalid UniversityAbha 61481Saudi Arabia mDepartment of Medical Laboratories Technology College of Applied Medical SciencesTaibah UniversityMadinah 41411Saudi Arabia Biology Department College of ScienceIslamic University of MadinahMadinah 42351Saudi Arabia Pediatric Research Institute Children’s Hospital Affiliated to Shandong UniversityJinanShandong 250022China Molecular Diagnostic Laboratory Johns Hopkins Aramco HealthcareDhahran 31311Saudi Arabia College of Medicine Alfaisal UniversityRiyadh 11533Saudi Arabia Department of Public Health and Nutrition The University of HaripurHaripur 22610Pakistan

Outbreaks of Monkeypox(mpox)in over 100 non-endemic countries in 2022 represented a serious global health *** a neglected disease,mpox has become a global public health issue.A42R profilinlike protein from mpox(PDB ID:4QWO)represents a potential new lead for drug development and may interact with various synthetic and natural *** this report,the interaction of A42R profilinlike protein with six phytochemicals found in the medicinal plant Ficus religiosa(abundant in India)was *** on the predicted and compared protein–ligand binding energies,biological properties,IC_(50) values and toxicity,two compounds,kaempferol(C-1)and piperine(C-4),were *** characteristics and quantitative structure–activity relationship(QSAR)of these two compounds were determined,and molecular dynamics(MD)simulations were *** silico examination of the kaempferol(C-1)and piperine(C-4)interactions with A42R profilin-like protein gave best-pose ligandbinding energies of–6.98 and–5.57 kcal/mol,*** predicted IC_(50) of C-1 was 7.63 lM and 82 lM for *** data indicated that kaempferol and piperine are non-mutagenic,and the QSAR data revealed that piperlongumine(5.92)and piperine(5.25)had higher log P values than the other compounds *** simulations of A42R profilin-like protein in complex with C-1 and C-4 were performed to examine the stability of the ligand–protein ***/C and C-4 showed the highest affinity and activities,they may be suitable lead candidates for developing mpox therapeutic *** study should facilitate discovering and synthesizing innovative therapeutics to address other infectious diseases.

关键词： Monkeypox Mpox Phytochemical Molecular docking MD simulation ADMET QSAR

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Fine-Tuning Cyber Security Defenses: Evaluating Supervised Machine Learning Classifiers for Windows Malware Detection

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Computers, Materials & Continua 2024年第8期80卷 2917-2939页

作者： Islam Zada mohammed Naif Alatawi Syed Muhammad Saqlain Abdullah alshahrani Adel Alshamran Kanwal Imran Hessa Alfraihi Department of Software Engineering International Islamic UniversityIslamabad25000Pakistan Information Technology Department Faculty of Computers and Information TechnologyUniversity of TabukTabuk71491Saudi Arabia Department of Computer Science and Artificial Intelligence College of Computer Science and EngineeringUniversity of JeddahJeddah21493Saudi Arabia Department of Cybersecurity College of Computer Science and EngineeringUniversity of JeddahJeddah21493Saudi Arabia Department of Computer Science University of PeshawarPeshawar25121Pakistan Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

Malware attacks on Windows machines pose significant cybersecurity threats,necessitating effective detection and prevention *** machine learning classifiers have emerged as promising tools for malware ***,there remains a need for comprehensive studies that compare the performance of different classifiers specifically for Windows malware *** this gap can provide valuable insights for enhancing cybersecurity *** numerous studies have explored malware detection using machine learning techniques,there is a lack of systematic comparison of supervised classifiers for Windows malware *** the relative effectiveness of these classifiers can inform the selection of optimal detection methods and improve overall security *** study aims to bridge the research gap by conducting a comparative analysis of supervised machine learning classifiers for detecting malware on Windows *** objectives include Investigating the performance of various classifiers,such as Gaussian Naïve Bayes,K Nearest Neighbors(KNN),Stochastic Gradient Descent Classifier(SGDC),and Decision Tree,in detecting Windows *** the accuracy,efficiency,and suitability of each classifier for real-world malware detection *** the strengths and limitations of different classifiers to provide insights for cybersecurity practitioners and *** recommendations for selecting the most effective classifier for Windows malware detection based on empirical *** study employs a structured methodology consisting of several phases:exploratory data analysis,data preprocessing,model training,and *** data analysis involves understanding the dataset’s characteristics and identifying preprocessing *** preprocessing includes cleaning,feature encoding,dimensionality reduction,and optimization to prepare the data for *** training utilizes various

关键词： Security and privacy challenges in the context of requirements engineering supervisedmachine learning malware detection windows systems comparative analysis Gaussian Naive Bayes K Nearest Neighbors Stochastic Gradient Descent Classifier Decision Tree

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YOLO and Blockchain Technology Applied to Intelligent Transportation License Plate Character Recognition for Security

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Computers, Materials & Continua 2023年第12期77卷 3697-3722页

作者： Fares Alharbi Reem alshahrani mohammed Zakariah Amjad Aldweesh abdulrahman Abdullah Alghamdi Department of Computer Science College of Computing and ITShaqra UniversityShaqra15526Saudi Arabia Department of Computer Science College of Computers and ITTaif UniversityP.O.Box 11099Taif21944Saudi Arabia College of Computer and Information Science King Saud UniversityRiyadhSaudi Arabia

Privacy and trust are significant issues in intelligent transportation systems(ITS).Data security is critical in ITS systems since sensitive user data is communicated to another user over the internet through wireless devices and routes such as radio channels,optical fiber,and blockchain *** Internet of Things(IoT)is a network of connected,interconnected *** issues occasionally arise due to the amount of data ***,they have been primarily addressed by blockchain and smart contract *** there are still security issues with smart contracts,primarily due to the complexity of writing the code,there are still many challenges to consider when designing blockchain designs for the IoT *** study uses traditional blockchain technology with the“You Only Look Once”(YOLO)object detection method to accurately locate and identify license *** YOLO and blockchain technologies used for intelligent vehicle license plate recognition are promising,they have received limited research ***-time object identification and recognition would be possible by combining a cutting-edge object detection technique with a regional convolutional neural network(RCNN)built with the tensor flow core open source *** method works reasonably well for identifying any license *** Automatic License Plate Recognition(ALPR)approach delivered outstanding results in various ***,with a recognition rate of 96.2%,our system(UFPR-ALPR)surpassed the previously used technology,consisting of 4500 frames and around 150 ***,a deep learning algorithm was trained to recognize images of license plate numbers using the UFPR-ALPR ***,the license plate’s characters were complicated for standard methods to identify because of the shifting lighting *** proposed model,however,produced beneficial outcomes.

关键词： Intelligent transportation system blockchain technology license plate recognition privacy YOLO deep learning technique ALPR

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Perceptions of emergency medicine residents on the quality of residency training in the United States and Saudi Arabia

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World Journal of Emergency Medicine 2018年第1期9卷 5-12页

作者： Ahmad Aalam Mark Zocchi Khalid Alyami Abdullah Shalabi Abdullah Bakhsh Asaad Alsufyani abdulrahman Sabbagh mohammed alshahrani Jesse M.Pines Department of Emergency Medicine King Abdulaziz University Department of Emergency Medicine The George Washington University Center for Healthcare Innovation and Policy Research The George Washington University Department of Pediatrics University of Dammam Department of Emergency Medicine King Abdulaziz Medical City National Guard Hospital Department of Emergency Medicine University of Toledo Department of Emergency Medicine King Fahad Medical City

BACKGROUND: We compare educational environments(i.e. physical, emotional and intellectual experiences) of emergency medicine(EM) residents training in the United States of America(USA) and Saudi Arabia(SA).METHODS: A cross-sectional survey study was conducted using an adapted version of the validated Postgraduate Hospital Educational Environment Measure(PHEEM) survey instrument from April 2015 through June 2016 to compare educational environments in all emergency medicine residency programs in SA and three selected programs in the USA with a history of training Saudi physicians. Overall scores were compared across programs, and for subscales(autonomy, teaching, and social Support), using chi-squared, t-tests, and analysis of ***: A total of 219 surveys were returned for 260 residents across six programs(3 SA, 3 USA), with a response rate of 84%. Program-specific response rates varied from 79%–100%. All six residencies were qualitatively rated as "more positive than negative but room for improvement". Quantitative PHEEM scores for the USA programs were significantly higher: 118.7 compared to 109.9 for SA, P=0.001. In subscales, perceptions of social support were not different between the two countries(P=0.243); however, role autonomy(P<0.001) and teaching(P=0.005) were better in USA programs. There were no significant differences by post-graduate training ***: EM residents in all three emergency medicine residency programs in SA and the three USA programs studied perceive their training as high quality in general, but with room for improvements. USA residency programs scored higher in overall quality. This was driven by more favorable perceptions of role autonomy and teaching. Understanding how residents perceive their programs may help drive targeted quality improvement efforts.

关键词： Emergency medicine residents Residency training United States of America Saudi Arabia

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Chaotic Elephant Herd Optimization with Machine Learning for Arabic Hate Speech Detection

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Intelligent Automation & Soft Computing 2024年第3期39卷 567-583页

作者： Badriyya B.Al-onazi Jaber S.Alzahrani Najm Alotaibi Hussain alshahrani Mohamed Ahmed Elfaki Radwa Marzouk Heba Mohsen Abdelwahed Motwakel Department of Language Preparation Arabic Language Teaching InstitutePrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversityMecca24382Saudi Arabia Prince Saud AlFaisal Institute for Diplomatic Studies Riyadh12735Saudi Arabia Department of Computer Science College of Computing and Information TechnologyShaqra UniversityShaqra11911Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in Egypt New CairoNew Cairo11835Egypt Department of Computer and Self Development Preparatory Year DeanshipAl-Kharj16278Saudi Arabia

In recent years,the usage of social networking sites has considerably increased in the Arab *** has empowered individuals to express their opinions,especially in ***,various organizations that operate in the Arab countries have embraced social media in their day-to-day business activities at different *** is attributed to business owners’understanding of social media’s importance for business ***,the Arabic morphology is too complicated to understand due to the availability of nearly 10,000 roots and more than 900 patterns that act as the basis for verbs and *** speech over online social networking sites turns out to be a worldwide issue that reduces the cohesion of civil *** this background,the current study develops a Chaotic Elephant Herd Optimization with Machine Learning for Hate Speech Detection(CEHOML-HSD)model in the context of the Arabic *** presented CEHOML-HSD model majorly concentrates on identifying and categorising the Arabic text into hate speech and *** attain this,the CEHOML-HSD model follows different sub-processes as discussed *** the initial stage,the CEHOML-HSD model undergoes data pre-processing with the help of the TF-IDF ***,the Support Vector Machine(SVM)model is utilized to detect and classify the hate speech texts made in the Arabic ***,the CEHO approach is employed for fine-tuning the parameters involved in *** CEHO approach is developed by combining the chaotic functions with the classical EHO *** design of the CEHO algorithm for parameter tuning shows the novelty of the work.A widespread experimental analysis was executed to validate the enhanced performance of the proposed CEHOML-HSD *** comparative study outcomes established the supremacy of the proposed CEHOML-HSD model over other approaches.

关键词： Arabic language machine learning elephant herd optimization TF-IDF vectorizer hate speech detection

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Deep Learning Empowered Cybersecurity Spam Bot Detection for Online Social Networks

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Computers, Materials & Continua 2022年第3期70卷 6257-6270页

作者： Mesfer Al Duhayyim Haya Mesfer alshahrani Fahd NAl-Wesabi mohammed Alamgeer Anwer Mustafa Hilal mohammed Rizwanullah Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversitySaudi Arabia Department of Computer Science King Khalid UniversityMuhayel AseerSaudi Arabia&Faculty of Computer and ITSana’a UniversitySana’aYemen Department of Information Systems King Khalid UniversityMuhayel AseerSaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Cybersecurity encompasses various elements such as strategies,policies,processes,and techniques to accomplish availability,confidentiality,and integrity of resource processing,network,software,and data from *** this scenario,the rising popularity of Online Social Networks(OSN)is under threat from spammers for which effective spam bot detection approaches should be *** studies have developed different approaches for the detection of spam bots in *** those techniques primarily concentrated on hand-crafted features to capture the features of malicious users while the application of Deep Learning(DL)models needs to be *** this motivation,the current research article proposes a Spam Bot Detection technique using Hybrid DL model abbreviated as *** proposed SBD-HDL technique focuses on the detection of spam bots that exist in *** technique has different stages of operations such as pre-processing,classification,and parameter ***,SBD-HDL technique hybridizes Graph Convolutional Network(GCN)with Recurrent Neural Network(RNN)model for spam bot classification *** order to enhance the detection performance of GCN-RNN model,hyperparameters are tuned using Lion Optimization Algorithm(LOA).Both hybridization of GCN-RNN and LOA-based hyperparameter tuning process make the current work,a first-of-its-kind in this *** experimental validation of the proposed SBD-HDL technique,conducted upon benchmark dataset,established the supremacy of the technique since it was validated under different measures.

关键词： Cybersecurity spam bot data classification social networks twitter deep learning

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Optimal Deep Learning Driven Intrusion Detection in SDN-Enabled IoT Environment

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Computers, Materials & Continua 2023年第3期74卷 6587-6604页

作者： mohammed Maray Haya Mesfer alshahrani Khalid A.Alissa Najm Alotaibi Abdulbaset Gaddah AliMeree Mahmoud Othman Manar Ahmed Hamza Department of Information Systems College of Computer ScienceKing Khalid UniversityAbhaSaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia SAUDI ARAMCO Cybersecurity Chair Networks and Communications DepartmentCollege of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Prince Saud AlFaisal Institute for Diplomatic Studies RiyadhSaudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science Warwick UniversityCovetryUK Department of Computer Science Faculty of Computers and Information TechnologyFuture University in Egypt New Cairo11835Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

In recent years,wireless networks are widely used in different *** phenomenon has increased the number of Internet of Things(IoT)devices and their *** IoT has numerous advantages,the commonly-used IoT devices are exposed to cyber-attacks *** scenario necessitates real-time automated detection and the mitigation of different types of attacks in high-traffic *** Software-Defined Networking(SDN)technique and the Machine Learning(ML)-based intrusion detection technique are effective tools that can quickly respond to different types of attacks in the IoT *** Intrusion Detection System(IDS)models can be employed to secure the SDN-enabled IoT environment in this *** current study devises a Harmony Search algorithmbased Feature Selection with Optimal Convolutional Autoencoder(HSAFSOCAE)for intrusion detection in the SDN-enabled IoT *** presented HSAFS-OCAE method follows a three-stage process in which the Harmony Search Algorithm-based FS(HSAFS)technique is exploited at first for feature ***,the CAE method is leveraged to recognize and classify intrusions in the SDN-enabled IoT ***,the Artificial Fish SwarmAlgorithm(AFSA)is used to fine-tune the *** process improves the outcomes of the intrusion detection process executed by the CAE algorithm and shows the work’s *** proposed HSAFSOCAE technique was experimentally validated under different aspects,and the comparative analysis results established the supremacy of the proposed model.

关键词： Internet of things SDN controller feature selection hyperparameter tuning autoencoder

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Intelligent Deep Learning Based Automated Fish Detection Model for UWSN

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Computers, Materials & Continua 2022年第3期70卷 5871-5887页

作者： Mesfer Al Duhayyim Haya Mesfer alshahrani Fahd NAl-Wesabi mohammed Alamgeer Anwer Mustafa Hilal Manar Ahmed Hamza Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversitySaudi Arabia Department of Computer Science King Khalid UniversityMuhayel AseerSaudi Arabia&Faculty of Computer and ITSana’a UniversitySana’aYemen Department of Information Systems King Khalid UniversityMuhayel AseerSaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

An exponential growth in advanced technologies has resulted in the exploration of Ocean *** has paved the way for new opportunities that can address questions relevant to diversity,uniqueness,and difficulty of marine *** Wireless Sensor Networks(UWSNs)are widely used to leverage such opportunities while these networks include a set of vehicles and sensors to monitor the environmental *** this scenario,it is fascinating to design an automated fish detection technique with the help of underwater videos and computer vision techniques so as to estimate and monitor fish biomass in water *** models have been developed earlier for fish ***,they lack robustness to accommodate considerable differences in scenes owing to poor luminosity,fish orientation,structure of seabed,aquatic plantmovement in the background and distinctive shapes and texture of fishes from different *** this motivation,the current research article introduces an Intelligent Deep Learning based Automated Fish Detection model for UWSN,named IDLAFD-UWSN *** presented IDLAFD-UWSN model aims at automatic detection of fishes from underwater videos,particularly in blurred and crowded ***-UWSN model makes use of Mask Region Convolutional Neural Network(Mask RCNN)with Capsule Network as a baseline model for fish ***,in order to train Mask RCNN,background subtraction process using GaussianMixtureModel(GMM)model is *** model makes use of motion details of fishes in video which consequently integrates the outcome with actual image for the generation of fish-dependent candidate ***,Wavelet Kernel Extreme Learning Machine(WKELM)model is utilized as a classifier *** performance of the proposed IDLAFD-UWSN model was tested against benchmark underwater video dataset and the experimental results achieved by IDLAFD-UWSN model were promising in comparison with other state-of-the-art methods under differe

关键词： Aquaculture background subtraction deep learning fish detection marine surveillance underwater sensor networks

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Intelligent Machine Learning Based EEG Signal Classification Model

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Computers, Materials & Continua 2022年第4期71卷 1821-1835页

作者： Mesfer Al Duhayyim Haya Mesfer alshahrani Fahd N.Al-Wesabi mohammed Abdullah Al-Hagery Anwer Mustafa Hilal Abu Sarwar Zaman Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversitySaudi Arabia Department of Computer Science King Khalid UniversityMuhayel AseerSaudi Arabia&Faculty of Computer and ITSana’a UniversitySana’aYemen Department of Computer Science College of ComputerQassim UniversitySaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlkharjSaudi Arabia

In recent years,Brain-Computer Interface(BCI)system gained much popularity since it aims at establishing the communication between human brain and *** systems are applied in several research areas such as neuro-rehabilitation,robots,exoeskeletons,***(EEG)is a technique commonly applied in capturing brain *** is incorporated in BCI systems since it has attractive features such as noninvasive nature,high time-resolution output,mobility and *** classification process is highly essential in decision making process and it incorporates different processes namely,feature extraction,feature selection,and *** this motivation,the current research paper presents an Intelligent Optimal Fuzzy Support Vector Machine-based EEC recognition(IOFSVM-EEG)model for BCI *** Component Analysis(ICA)technique is applied onto the proposed IOFSVM-EEG model to remove the artefacts that exist in EEG signal and to retain the meaningful EEG ***,Common Spatial Pattern(CSP)-based feature extraction technique is utilized to derive a helpful set of feature vectors from the preprocessed EEG ***,OFSVM method is applied in the classification of EEG signals,in which the parameters involved in FSVM are optimally tuned using Grasshopper Optimization Algorithm(GOA).In order to validate the enhanced EEG recognition outcomes of the proposed IOFSVM-EEG model,an extensive set of experiments was *** outcomes were examined under distinct *** experimental results highlighted the enhanced performance of the presented IOFSVM-EEG model over other state-of-the-art methods.

关键词： Brain computer interface EEG recognition human computer interface machine learning parameter tuning FSVM

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Battle Royale Optimization with Fuzzy Deep Learning for Arabic Sentiment Classification

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Computer Systems Science & Engineering 2023年第8期46卷 2619-2635页

作者： Manar Ahmed Hamza Hala J.alshahrani Jaber S.Alzahrani Heba Mohsen Mohamed I.Eldesouki mohammed Rizwanullah Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Applied Linguistics College of LanguagesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversityMakkah 24211Saudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Information System College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Aspect-Based Sentiment Analysis(ABSA)on Arabic corpus has become an active research topic in recent *** refers to a fine-grained Sentiment Analysis(SA)task that focuses on the extraction of the conferred aspects and the identification of respective sentiment polarity from the provided *** of the prevailing Arabic ABSA techniques heavily depend upon dreary feature-engineering and pre-processing tasks and utilize external sources such as *** literature,concerning the Arabic language text analysis,the authors made use of regular Machine Learning(ML)techniques that rely on a group of rare sources and *** sources were used for processing and analyzing the Arabic language content like ***,an important challenge in this domain is the unavailability of sufficient and reliable *** this background,the current study introduces a new Battle Royale Optimization with Fuzzy Deep Learning for Arabic Aspect Based Sentiment Classification(BROFDL-AASC)*** aim of the presented BROFDL-AASC model is to detect and classify the sentiments in the Arabic *** the presented BROFDL-AASC model,data pre-processing is performed at first to convert the input data into a useful ***,the BROFDL-AASC model includes Discriminative Fuzzy-based Restricted Boltzmann Machine(DFRBM)model for the identification and categorization of ***,the BRO algorithm is exploited for optimal fine-tuning of the hyperparameters related to the FBRBM *** scenario establishes the novelty of current *** performance of the proposed BROFDL-AASC model was validated and the outcomes demonstrate the supremacy of BROFDL-AASC model over other existing models.

关键词： Arabic corpus aspect based sentiment analysis arabic language deep learning battle royale optimization natural language processing

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