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

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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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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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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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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Intelligent slime Mould Optimization with Deep Learning Enabled Traffic Prediction in smart Cities

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Computers, Materials & Continua 2022年第12期73卷 6563-6577页

作者： Manar Ahmed Hamza Hadeel Alsolai jaber s.alzahrani Mohammad Alamgeer Mohamed Mahmoud sayed Abu sarwar Zamani Ishfaq Yaseen Abdelwahed Motwakel Department of Computer and self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi 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 Information systems College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Computer science and Bioinformatics Singhania UniversityPacheri BariDistrict JhnujhunuRajasthanIndia Department of Architectural Engineering Faculty of Engineering and TechnologyFuture University in EgyptNew Cairo11835Egypt

Intelligent Transportation system(ITs)is one of the revolutionary technologies in smart cities that helps in reducing traffic congestion and enhancing traffic *** the help of big data and communication technologies,ITs offers real-time investigation and highly-effective traffic *** Flow Prediction(TFP)is a vital element in smart city management and is used to forecast the upcoming traffic conditions on transportation network based on past *** Network(NN)and Machine Learning(ML)models are widely utilized in resolving real-time issues since these methods are capable of dealing with adaptive data over a period of *** Learning(DL)is a kind of ML technique which yields effective performance on data classification and prediction *** this motivation,the current study introduces a novel slime Mould Optimization(sMO)model with Bidirectional Gated Recurrent Unit(BiGRU)model for Traffic Prediction(sMOBGRU-TP)in smart ***,data preprocessing is performed to normalize the input data in the range of[0,1]using minmax normalization ***,BiGRUmodel is employed for effective forecasting of traffic in smart ***,the novelty of the work lies in using sMO algorithm to effectively adjust the hyperparameters of BiGRU *** proposed sMOBGRU-TP model was experimentally validated and the simulation results established the model’s superior performance in terms of prediction compared to existing techniques.

关键词： smart cities traffic flow prediction slime mould optimization algorithm deep learning intelligent models

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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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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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Intelligent Optimization-Based Clustering with Encryption Technique for Internet of Drones Environment

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Computers, Materials & Continua 2022年第12期73卷 6617-6634页

作者： Dalia H.Elkamchouchi jaber s.alzahrani Hany Mahgoub Amal s.Mehanna Anwer Mustafa Hilal Abdelwahed Motwakel Abu sarwar Zamani Ishfaq Yaseen Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh 11671Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Computer science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Faculty of Computers and Information Computer Science DepartmentMenoufia UniversityEgypt Department of Digital Media Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo 11845Egypt Department of Computer and self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

The recent technological developments have revolutionized the functioning of Wireless sensor Network(WsN)-based industries with the development of Internet of Things(IoT).Internet of Drones(IoD)is a division under IoT and is utilized for communication amongst *** drones are naturally mobile,it undergoes frequent topological *** alterations in the topology cause route election,stability,and scalability problems in *** is considered as an effective method to transmit the images in IoD *** current study introduces an Atom search Optimization basedClusteringwith Encryption Technique for secure Internet of Drones(AsOCE-sIoD)*** key objective of the presented AsOCE-sIoD technique is to group the drones into clusters and encrypt the images captured by *** presented AsOCE-sIoD technique follows AsO-based Cluster Head(CH)and cluster construction *** addition,signcryption technique is also applied to effectually encrypt the images captured by drones in IoD *** process enables the secure transmission of images to the ground *** order to validate the efficiency of the proposed AsOCE-sIoD technique,several experimental analyses were conducted and the outcomes were inspected under different *** comprehensive comparative analysis results established the superiority of the proposed AsOCE-sIoD model over recent approaches.

关键词： Internet of drones atom search algorithm clustering encryption signcryption

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