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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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Sign Language Recognition and Classification Model to Enhance Quality of Disabled People

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Computers, Materials & Continua 2022年第11期73卷 3419-3432页

作者： Fadwa Alrowais Saud S.Alotaibi Sami Dhahbi radwa marzouk Abdullah Mohamed Anwer Mustafa Hilal Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Information Systems College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia University of Tunis EL Manar Higher Institute of ComputerResearch Team on Intelligent Systems in Imaging and Artificial Vision(Siiva)–Lab LimticAryanah2036Tunisia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Research Centre Future University in EgyptNew Cairo11745Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlkharjSaudi Arabia

Sign language recognition can be considered as an effective solution for disabled people to communicate with *** helps them in conveying the intended information using sign languages without any *** advancements in computer vision and image processing techniques can be leveraged to detect and classify the signs used by disabled people in an effective *** optimization algorithms can be designed in a manner such that it fine tunes the hyper parameters,used in Deep Learning(DL)models as the latter considerably impacts the classification *** this motivation,the current study designs the Optimal Deep Transfer Learning Driven Sign Language Recognition and Classification(ODTL-SLRC)model for disabled *** aim of the proposed ODTL-SLRC technique is to recognize and classify sign languages used by disabled *** proposed ODTL-SLRC technique derives EfficientNet model to generate a collection of useful feature *** addition,the hyper parameters involved in EfficientNet model are fine-tuned with the help of HGSO ***,Bidirectional Long Short Term Memory(BiLSTM)technique is employed for sign language *** proposed ODTL-SLRC technique was experimentally validated using benchmark dataset and the results were inspected under several *** comparative analysis results established the superior performance of the proposed ODTL-SLRC technique over recent approaches in terms of efficiency.

关键词： Sign language image processing computer vision disabled people deep learning parameter tuning

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Red Deer Optimization with Artificial Intelligence Enabled Image Captioning System for Visually Impaired People

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Computer Systems Science & Engineering 2023年第8期46卷 1929-1945页

作者： Anwer Mustafa Hilal Fadwa Alrowais Fahd N.Al-Wesabi radwa marzouk Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversityMahayilSaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Mathematics Faculty of ScienceCairo UniversityGiza12613Egypt

The problem of producing a natural language description of an image for describing the visual content has gained more attention in natural language processing(NLP)and computer vision(CV).It can be driven by applications like image retrieval or indexing,virtual assistants,image understanding,and support of visually impaired people(VIP).Though the VIP uses other senses,touch and hearing,for recognizing objects and events,the quality of life of those persons is lower than the standard *** Image captioning generates captions that will be read loudly to the VIP,thereby realizing matters happening around *** article introduces a Red Deer Optimization with Artificial Intelligence Enabled Image Captioning System(RDOAI-ICS)for Visually Impaired *** presented RDOAI-ICS technique aids in generating image captions for *** presented RDOAIICS technique utilizes a neural architectural search network(NASNet)model to produce image ***,the RDOAI-ICS technique uses the radial basis function neural network(RBFNN)method to generate a textual *** enhance the performance of the RDOAI-ICS method,the parameter optimization process takes place using the RDO algorithm for NasNet and the butterfly optimization algorithm(BOA)for the RBFNN model,showing the novelty of the *** experimental evaluation of the RDOAI-ICS method can be tested using a benchmark *** outcomes show the enhancements of the RDOAI-ICS method over other recent Image captioning approaches.

关键词： Machine learning image captioning visually impaired people parameter tuning artificial intelligence metaheuristics

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Hand Gesture Recognition for Disabled People Using Bayesian Optimization with Transfer Learning

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Intelligent Automation & Soft Computing 2023年第6期36卷 3325-3342页

作者： Fadwa Alrowais radwa marzouk Fahd N.Al-Wesabi Anwer Mustafa Hilal Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Mathematics Faculty of ScienceCairo UniversityGiza12613Egypt Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Sign language recognition can be treated as one of the efficient solu-tions for disabled people to communicate with *** helps them to convey the required data by the use of sign language with no *** latest develop-ments in computer vision and image processing techniques can be accurately uti-lized for the sign recognition process by disabled *** Sign Language(ASL)detection was challenging because of the enhancing intraclass similarity and higher *** article develops a new Bayesian Optimiza-tion with Deep Learning-Driven Hand Gesture Recognition Based Sign Language Communication(BODL-HGRSLC)for Disabled *** BODL-HGRSLC technique aims to recognize the hand gestures for disabled people’s *** presented BODL-HGRSLC technique integrates the concepts of compu-ter vision(CV)and DL *** the presented BODL-HGRSLC technique,a deep convolutional neural network-based residual network(ResNet)model is applied for feature ***,the presented BODL-HGRSLC model uses Bayesian optimization for the hyperparameter tuning *** last,a bidir-ectional gated recurrent unit(BiGRU)model is exploited for the HGR procedure.A wide range of experiments was conducted to demonstrate the enhanced perfor-mance of the presented BODL-HGRSLC *** comprehensive comparison study reported the improvements of the BODL-HGRSLC model over other DL models with maximum accuracy of 99.75%.

关键词： Deep learning hand gesture recognition disabled people computer vision bayesian optimization

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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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Deep Consensus Network for Recycling Waste Detection in Smart Cities

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Computers, Materials & Continua 2023年第5期75卷 4191-4205页

作者： Manar Ahmed Hamza Hanan Abdullah Mengash Noha Negm radwa marzouk Abdelwahed Motwakel Abu Sarwar Zamani Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz University16278AlKharjSaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science College of Science and ArtsKing Khalid UniversityMahayil Asir62529Saudi Arabia

Recently,urbanization becomes a major concern for developing as well as developed *** to the increased urbanization,one of the important challenging issues in smart cities is waste ***,automated waste detection and classification model becomes necessary for the smart city and to accomplish better recyclable waste *** recycling of waste offers the chance of reducing the quantity of waste disposed to the land fill by minimizing the requirement of collecting raw *** study develops a novel Deep Consensus Network with Whale Optimization Algorithm for Recycling Waste Object Detection(DCNWORWOD)in Smart *** goal of the DCNWO-RWOD technique intends to properly identify and classify the objects into recyclable and non-recyclable *** proposed DCNWO-RWOD technique involves the design of deep consensus network(DCN)to detect waste objects in the input *** improving the overall object detection performance of the DCN model,the whale optimization algorithm(WOA)is ***,Na飗e Bayes(NB)classifier is used for the classification of detected waste objects into recyclable and non-recyclable *** performance validation of theDCNWO-RWOD technique takes place using the open access *** extensive comparative study reported the enhanced performance of the DCNWO-RWOD technique interms of several measures.

关键词： Smart city waste management object detection recycling deep consensus network

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Covid-19 CT Lung Image Segmentation Using Adaptive Donkey and Smuggler Optimization Algorithm

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Computers, Materials & Continua 2022年第4期71卷 1133-1152页

作者： P.Prabu K.Venkatachalam Ala Saleh Alluhaidan radwa marzouk Myriam Hadjouni Sahar A.El_Rahman Department of Computer Science CHRIST(Deemed to be University)BengaluruIndia Department of Computer Science and Engineering CHRIST(Deemed to be University)BengaluruIndia Information Systems Department College of Computer and Information SciencePrincess Nourah Bint Abdulrahman UniversityRiyadhKSA 84428Saudi Arabia Department of Mathematics Faculty of ScienceCairo UniversityGiza12613Egypt Computer Sciences Department College of Computer and Information SciencePrincess Nourah Bint Abdulrahman UniversityRiyadhSaudi Arabia Department of Electrical Engineering Faculty of Engineering-ShoubraBenha UniversityCairoEgypt

COVID’19 has caused the entire universe to be in existential healthcrisis by spreading globally in the year 2020. The lungs infection is detected inComputed Tomography (CT) images which provide the best way to increasethe existing healthcare schemes in preventing the deadly virus. Nevertheless,separating the infected areas in CT images faces various issues such as lowintensity difference among normal and infectious tissue and high changes inthe characteristics of the infection. To resolve these issues, a new inf-Net (LungInfection Segmentation Deep Network) is designed for detecting the affectedareas from the CT images automatically. For the worst segmentation results,the Edge-Attention Representation (EAR) is optimized using AdaptiveDonkey and Smuggler Optimization (ADSO). The edges which are identifiedby the ADSO approach is utilized for calculating dissimilarities. An IFCM(Intuitionistic Fuzzy C-Means) clustering approach is applied for computingthe similarity of the EA component among the generated edge maps andGround-Truth (GT) edge maps. Also, a Semi-Supervised Segmentation(SSS) structure is designed using the Randomly Selected Propagation (RP)technique and Inf-Net, which needs only less number of images and unlabelleddata. Semi-Supervised Multi-Class Segmentation (SSMCS) is designed usinga Bi-LSTM (Bi-Directional Long-Short-Term-memory), acquires all theadvantages of the disease segmentation done using Semi Inf-Net and enhancesthe execution of multi-class disease labelling. The newly designed SSMCSapproach is compared with existing U-Net++, MCS, and *** such as MAE (Mean Absolute Error), Structure measure, Specificity(Spec), Dice Similarity coefficient, Sensitivity (Sen), and Enhance-AlignmentMeasure are considered for evaluation purpose.

关键词： Adaptive donkey and snuggler optimization.bi-directional long short term memory coronavirus disease 2019 randomly selected propagation semi-supervised learning

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Quantum Artificial Intelligence Based Node Localization Technique for Wireless Networks

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Computers, Materials & Continua 2022年第10期73卷 327-342页

作者： Hanan Abdullah Mengash radwa marzouk Siwar Ben Haj Hassine Anwer Mustafa Hilal Ishfaq Yaseen Abdelwahed Motwakel Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh 11671Saudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversityAbha62529Saudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Artificial intelligence(AI)techniques have received significant attention among research communities in the field of networking,image processing,natural language processing,robotics,*** the same time,a major problem in wireless sensor networks(WSN)is node localization,which aims to identify the exact position of the sensor nodes(SN)using the known position of several anchor *** comprises a massive number of SNs and records the position of the nodes,which becomes a tedious ***,the SNs might be subjected to node mobility and the position alters with ***,a precise node localization(NL)manner is required for determining the location of the *** this view,this paper presents a new quantum bird migration optimizer-based NL(QBMA-NL)technique for *** goal of the QBMA-NL approach is for determining the position of unknown nodes in the network by the use of anchor *** QBMA-NL technique is mainly based on the mating behavior of bird species at the time of mating *** addition,an objective function is derived based on the received signal strength indicator(RSSI)and Euclidean distance from the known to unknown *** demonstrating the improved performance of the QBMA-NL technique,a wide range of simulations take place and the results reported the supreme performance over the recent NL techniques.

关键词： Artificial intelligence wireless communication wireless sensor networks metaheuristics quantum computing node localization

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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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