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Fireworks Optimization with Deep Learning-Based Arabic Handwritten Characters RecognitionModel

Computer systems science & Engineering 2024年第5期48卷 1387-1403页

作者： Abdelwahed Motwakel Badriyya B.Al-onazi jaber s.alzahrani Ayman Yafoz Mahmoud Othman Abu sarwar Zamani Ishfaq Yaseen Amgad Atta Abdelmageed Department of Computer and self Development Prince Sattam bin Abdulaziz UniversityAlKharj16278Saudi Arabia 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 UniversityMakkah24211Saudi Arabia Department of Information systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt

Handwritten character recognition becomes one of the challenging research *** studies were presented for recognizing letters of various *** availability of Arabic handwritten characters databases was *** a quarter of a billion people worldwide write and speak *** historical books and files indicate a vital data set for many Arab nationswritten in ***,Arabic handwritten character recognition(AHCR)has grabbed the attention and has become a difficult topic for pattern recognition and computer vision(CV).Therefore,this study develops fireworks optimizationwith the deep learning-based AHCR(FWODL-AHCR)*** intention of the FWODL-AHCR technique is to recognize the distinct handwritten characters in the Arabic *** initially pre-processes the handwritten images to improve their quality of ***,the RetinaNet-based deep convolutional neural network is applied as a feature extractor to produce feature ***,the deep echo state network(DEsN)model is utilized to classify handwritten ***,the FWO algorithm is exploited as a hyperparameter tuning strategy to boost recognition *** simulations in series were performed to exhibit the enhanced performance of the FWODL-AHCR *** comparison study portrayed the supremacy of the FWODL-AHCR technique over other approaches,with 99.91%and 98.94%on Hijja and AHCD datasets,respectively.

关键词： Arabic language handwritten character recognition deep learning classification parameter tuning

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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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Improved Multileader Optimization with shadow Encryption for Medical Images in IoT Environment

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Computers, Materials & Continua 2023年第2期74卷 3133-3149页

作者： Mesfer Al Duhayyim Mohammed Maray Ayman Qahmash fatma s.alrayes Nuha Alshuqayran jaber s.alzahrani Mohammed Alghamdi Abdullah Mohamed Department of Computer science College of Sciences and Humanities-AflajPrince Sattam Bin Abdulaziz UniversitySaudi Arabia 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 84428Riyadh 11671Saudi Arabia Department of Information systems College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic UniversitySaudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Information and Technology systems College of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia Research Centre Future University in EgyptNew Cairo11845Egypt

Nowadays,security plays an important role in Internet of Things(IoT)environment especially in medical services’domains like disease prediction and medical data *** healthcare sector,huge volumes of data are generated on a daily basis,owing to the involvement of advanced health care *** general terms,health care images are highly sensitive to alterations due to which any modifications in its content can result in faulty *** the same time,it is also significant to maintain the delicate contents of health care images during reconstruction ***,an encryption system is required in order to raise the privacy and security of healthcare data by not leaking any sensitive *** current study introduces Improved Multileader Optimization with shadow Image Encryption for Medical Image security(IMLOsIE-MIs)technique for IoT *** aim of the proposed IMLOsIE-MIs model is to accomplish security by generating shadows and encrypting them *** do so,the presented IMLOsIE-MIs model initially generates a set of shadows for every input medical ***,shadow image encryption process takes place with the help of Multileader Optimization(MLO)withHomomorphic Encryption(IMLO-HE)technique,where the optimal keys are generated with the help of MLO *** the receiver side,decryption process is initially carried out and shadow image reconstruction process is *** experimentation analysis was carried out on medical images and the results inferred that the proposed IMLOsIE-MIs model is an excellent performer compared to other *** comparison study outcomes demonstrate that IMLOsIE-MIs model is robust and offers high security in IoT-enabled healthcare environment.

关键词： Medical image security image encryption shadow images homomorphic encryption optimal key generation

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IoT-Cloud Assisted Botnet Detection Using Rat swarm Optimizer with Deep Learning

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Computers, Materials & Continua 2023年第2期74卷 3085-3100页

作者： saeed Masoud Alshahrani fatma s.alrayes Hamed Alqahtani jaber s.alzahrani Mohammed Maray sana Alazwari Mohamed A.shamseldin Mesfer Al Duhayyim Department of Computer science College of Computing and Information TechnologyShaqra UniversityShaqraSaudi Arabia Department of Information systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information systems College of Computer ScienceCenter of Artificial IntelligenceUnit of CybersecurityKing Khalid UniversityAbhaSaudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Information systems College of Computer ScienceKing Khalid UniversityAbhaSaudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif P.O.Box 11099Taif21944Saudi Arabia Department of Mechanical 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

Nowadays,Internet of Things(IoT)has penetrated all facets of human life while on the other hand,IoT devices are heavily prone to *** has become important to develop an accurate system that can detect malicious attacks on IoT environments in order to mitigate security *** is one of the dreadfulmalicious entities that has affected many users for the past few *** is challenging to recognize Botnet since it has excellent carrying and hidden *** approaches have been employed to identify the source of Botnet at earlier *** Learning(ML)and Deep Learning(DL)techniques are developed based on heavy influence from Botnet detection *** spite of this,it is still a challenging task to detect Botnet at early stages due to low number of features accessible from Botnet *** current study devises IoT with Cloud Assisted Botnet Detection and Classification utilizingRat swarmOptimizer with Deep Learning(BDC-RsODL)*** presented BDC-RsODL model includes a series of processes like pre-processing,feature subset selection,classification,and parameter ***,the network data is pre-processed to make it compatible for further ***,RsO algorithm is exploited for effective selection of subset of ***,Long short TermMemory(LsTM)algorithm is utilized for both identification and classification of ***,sine Cosine Algorithm(sCA)is executed for fine-tuning the hyperparameters related to LsTM *** order to validate the promising 3086 CMC,2023,vol.74,no.2 performance of BDC-RsODL system,a comprehensive comparison analysis was *** obtained results confirmed the supremacy of BDCRsODL model over recent approaches.

关键词： Internet of things cloud computing long short termmemory deep learning sine cosine algorithm feature selection

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Enhanced Gorilla Troops Optimizer with Deep Learning Enabled Cybersecurity Threat Detection

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Computer systems science & Engineering 2023年第6期45卷 3037-3052页

作者： fatma s.alrayes Najm Alotaibi jaber s.alzahrani sana Alazwari Areej Alhogail Ali M.Al-sharafi Mahmoud Othman Manar Ahmed Hamza Department of Information systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Prince saud Al Faisal Institute for Diplomatic studies RiyadhSaudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Information systems College of Computer and Information SciencesKing Saud 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 Department of Computer and self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Recent developments in computer networks and Internet of Things(IoT)have enabled easy access to *** the government and business sectors face several difficulties in resolving cybersecurity network issues,like novel attacks,hackers,internet criminals,and so ***,malware attacks and software piracy pose serious risks in compromising the security of *** can steal confidential data which results infinancial and reputational *** advent of machine learning(ML)and deep learning(DL)models has been employed to accomplish security in the IoT cloud *** article pre-sents an Enhanced Artificial Gorilla Troops Optimizer with Deep Learning Enabled Cybersecurity Threat Detection(EAGTODL-CTD)in IoT Cloud *** presented EAGTODL-CTD model encompasses the identification of the threats in the IoT cloud *** proposed EAGTODL-CTD mod-el mainly focuses on the conversion of input binaryfiles to color images,where the malware can be detected using an image classification *** EAG-TODL-CTD model pre-processes the input data to transform to a compatible *** threat detection and classification,cascaded gated recurrent unit(CGRU)model is exploited to determine class ***,EAGTO approach is employed as a hyperparameter optimizer to tune the CGRU parameters,showing the novelty of our *** performance evaluation of the EAGTODL-CTD model is assessed on a dataset comprising two class labels namely malignant and *** experimental values reported the supremacy of the EAG-TODL-CTD model with increased accuracy of 99.47%.

关键词： Cybersecurity computer networks threat detection internet of things cloud computing deep learning

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Optimal Deep Learning Based Intruder Identification in Industrial Internet of Things Environment

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Computer systems science & Engineering 2023年第9期46卷 3121-3139页

作者： Khaled M.Alalayah fatma s.alrayes.jaber s.alzahrani Khadija M.Alaidarous Ibrahim M.Alwayle Heba Mohsen Ibrahim Abdulrab Ahmed Mesfer Al Duhayyim Department of Computer science College of Science and ArtsSharurahNajran UniversitySaudi Arabia Department of Information systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Computer science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Computer Department Applied CollegeNajran UniversityNajran66462Saudi Arabia Department of Computer science College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia

With the increased advancements of smart industries,cybersecurity has become a vital growth factor in the success of industrial *** Industrial Internet of Things(IIoT)or Industry 4.0 has revolutionized the concepts of manufacturing and production *** industry 4.0,powerful IntrusionDetection systems(IDs)play a significant role in ensuring network *** various intrusion detection techniques have been developed so far,it is challenging to protect the intricate data of *** is because conventional Machine Learning(ML)approaches are inadequate and insufficient to address the demands of dynamic IIoT ***,the existing Deep Learning(DL)can be employed to identify anonymous ***,the current study proposes a Hunger Games search Optimization with Deep Learning-Driven Intrusion Detection(HGsODLID)model for the IIoT *** presented HGsODL-ID model exploits the linear normalization approach to transform the input data into a useful *** HGsO algorithm is employed for Feature selection(HGsO-Fs)to reduce the curse of ***,sparrow search Optimization(ssO)is utilized with a Graph Convolutional Network(GCN)to classify and identify intrusions in the ***,the ssO technique is exploited to fine-tune the hyper-parameters involved in the GCN *** proposed HGsODL-ID model was experimentally validated using a benchmark dataset,and the results confirmed the superiority of the proposed HGsODL-ID method over recent approaches.

关键词： Industrial IoT deep learning network security intrusion detection system attribute selection smart factory

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Modeling of Combined Economic and Emission Dispatch Using Improved sand Cat Optimization Algorithm

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Computers, Materials & Continua 2023年第6期75卷 6145-6160页

作者： Fadwa Alrowais jaber s.alzahrani Radwa Marzouk Abdullah Mohamed Gouse Pasha Mohammed Department of Computer sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversityMecca24382Saudi Arabia Research Centre Future University in EgyptNew Cairo11845Egypt Department of Computer and self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Combined Economic and Emission Dispatch(CEED)task forms multi-objective optimization problems to be resolved to minimize emission and fuel *** disadvantage of the conventional method is its incapability to avoid falling in local optimal,particularly when handling nonlinear and complex *** have recently received considerable attention due to their enhanced capacity to prevent local optimal solutions in addressing all the optimization problems as a black ***,this paper focuses on the design of an improved sand cat optimization algorithm based CEED(IsCOA-CEED)*** IsCOA-CEED technique majorly concen-trates on reducing fuel costs and the emission of generation ***,the presented IsCOA-CEED technique transforms the equality constraints of the CEED issue into inequality ***,the improved sand cat optimization algorithm(IsCOA)is derived from the integration of tra-ditional sCOA with the Levy Flight(LF)*** last,the IsCOA-CEED technique is applied to solve a series of 6 and 11 generators in the CEED *** experimental validation of the IsCOA-CEED technique ensured the enhanced performance of the presented IsCOA-CEED technique over other recent approaches.

关键词： Economic and emission dispatch multi-objective optimization metaheuristics fuel cost minimization sand cat optimization

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Hunger search Optimization with Hybrid Deep Learning Enabled Phishing Detection and Classification Model

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Computers, Materials & Continua 2022年第12期73卷 6425-6441页

作者： Hadil shaiba jaber s.alzahrani Majdy M.Eltahir Radwa Marzouk Heba Mohsen Manar Ahmed Hamza Department of Computer sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Information systems College of Science&Art at MahayilKing Khalid UniversitySaudi 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 EgyptNew Cairo11835Egypt Department of Computer and self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Phishing is one of the simplest ways in cybercrime to hack the reliable data of users such as passwords,account identifiers,bank details,*** general,these kinds of cyberattacks are made at users through phone calls,emails,or instant *** anti-phishing techniques,currently under use,aremainly based on source code features that need to scrape the webpage *** third party services,these techniques check the classification procedure of phishing Uniform Resource Locators(URLs).Even thoughMachine Learning(ML)techniques have been lately utilized in the identification of phishing,they still need to undergo feature engineering since the techniques are not well-versed in identifying phishing *** tremendous growth and evolution of Deep Learning(DL)techniques paved the way for increasing the accuracy of classification *** this background,the current research article presents a Hunger search Optimization with Hybrid Deep Learning enabled Phishing Detection and Classification(HsOHDL-PDC)*** presented HsOHDL-PDC model focuses on effective recognition and classification of phishing based on website *** addition,sOHDL-PDC model uses character-level embedding instead of word-level embedding since the URLs generally utilize words with no ***,a hybrid Convolutional Neural Network-Long short Term Memory(HCNN-LsTM)technique is also applied for identification and classification of *** hyperparameters involved in HCNN-LsTM model are optimized with the help of HsO algorithm which in turn produced improved *** performance of the proposed HsOHDL-PDC model was validated using different datasets and the outcomes confirmed the supremacy of the proposed model over other recent approaches.

关键词： Uniform resource locators phishing cyberattacks machine learning deep learning hyperparameter optimization

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Feature subset selection with Artificial Intelligence-Based Classification Model for Biomedical Data

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Computers, Materials & Continua 2022年第9期72卷 4267-4281页

作者： jaber s.alzahrani Reem M.Alshehri Mohammad Alamgeer Anwer Mustafa Hilal Abdelwahed Motwakel Ishfaq Yaseen Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Computer science College of Computing and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Information systems College of Science&Art at MahayilKing Khalid UniversityMahayil62529Saudi Arabia Department of Computer and self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharj62529Saudi Arabia

Recently,medical data classification becomes a hot research topic among healthcare professionals and research communities,which assist in the disease diagnosis and decision making *** latest developments of artificial intelligence(AI)approaches paves a way for the design of effective medical data classification *** the same time,the existence of numerous features in the medical dataset poses a curse of dimensionality *** resolving the issues,this article introduces a novel feature subset selection with artificial intelligence based classification model for biomedical data(Fss-AICBD)*** Fss-AICBD technique intends to derive a useful set of features and thereby improve the classifier ***,the Fss-AICBD technique undergoes min-max normalization technique to prevent data *** addition,the information gain(IG)approach is applied for the optimal selection of feature ***,group search optimizer(GsO)with deep belief network(DBN)model is utilized for biomedical data classification where the hyperparameters of the DBN model can be optimally tuned by the GsO *** choice of IG and GsO approaches results in promising medical data classification *** experimental result analysis of the Fss-AICBD technique takes place using different benchmark healthcare *** simulation results reported the enhanced outcomes of the Fss-AICBD technique interms of several measures.

关键词： Medical data classification feature selection deep learning healthcare sector artificial intelligence

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Biomedical Osteosarcoma Image Classification Using Elephant Herd Optimization and Deep Learning

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Computers, Materials & Continua 2022年第12期73卷 6443-6459页

作者： Areej A.Malibari jaber s.alzahrani Marwa Obayya Noha Negm Mohammed Abdullah Al-Hagery Ahmed s.salama Anwer Mustafa Hilal Department of Industrial and systems Engineering College of EngineeringPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Biomedical Engineering College of EngineeringPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Faculty of science Mathematics and Computer Science DepartmentMenoufia UniversityEgypt Department of Computer science College of ComputerQassim UniversitySaudi 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

Osteosarcoma is a type of malignant bone tumor that is reported across the *** advancements in Machine Learning(ML)and Deep Learning(DL)models enable the detection and classification of malignancies in biomedical *** this regard,the current study introduces a new Biomedical Osteosarcoma Image Classification using Elephant Herd Optimization and Deep Transfer Learning(BOIC-EHODTL)*** presented BOIC-EHODTL model examines the biomedical images to diagnose distinct kinds of *** the initial stage,Gabor Filter(GF)is applied as a pre-processing technique to get rid of the noise from *** addition,Adam optimizer with MixNet model is also employed as a feature extraction technique to generate feature ***,EHOalgorithm is utilized along with Adaptive Neuro-Fuzzy Classifier(ANFC)model for recognition and categorization of *** algorithm is utilized to fine-tune the parameters involved in ANFC model which in turn helps in accomplishing improved classification *** design of EHO with ANFC model for classification of osteosarcoma is the novelty of current *** order to demonstrate the improved performance of BOIC-EHODTL model,a comprehensive comparison was conducted between the proposed and existing models upon benchmark dataset and the results confirmed the better performance of BOIC-EHODTL model over recent methodologies.

关键词： Biomedical imaging osteosarcoma classification deep transfer learning parameter tuning fuzzy logic

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