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Data Mining with Comprehensive Oppositional Based Learning for Rainfall Prediction

Computers, Materials & Continua 2023年第2期74卷 2725-2738页

作者： Mohammad Alamgeer Amal Al-Rasheed Ahmad Alhindi Manar Ahmed Hamza Abdelwahed Motwakel mohamed i.eldesouki Department of information Systems College of Science&Art at MahayilKing Khalid UniversityAbha62529Saudi Arabia Department of information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Science College of Computing and Information SystemMecca24382Saudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharj16278Saudi Arabia Department of information System College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAlKharj16278Saudi Arabia

Data mining process involves a number of steps fromdata collection to visualization to identify useful data from massive data *** same time,the recent advances of machine learning(ML)and deep learning(DL)models can be utilized for effectual rainfall *** this motivation,this article develops a novel comprehensive oppositionalmoth flame optimization with deep learning for rainfall prediction(COMFO-DLRP)*** proposed CMFO-DLRP model mainly intends to predict the rainfall and thereby determine the environmental ***,data pre-processing and correlation matrix(CM)based feature selection processes are carried *** addition,deep belief network(DBN)model is applied for the effective prediction of rainfall ***,COMFO algorithm was derived by integrating the concepts of comprehensive oppositional based learning(COBL)with traditional MFO ***,the COMFO algorithm is employed for the optimal hyperparameter selection of the DBN *** demonstrating the improved outcomes of the COMFO-DLRP approach,a sequence of simulations were carried out and the outcomes are assessed under distinct *** simulation outcome highlighted the enhanced outcomes of the COMFO-DLRP method on the other techniques.

关键词： Data mining rainfall prediction deep learning correlation matrix hyperparameter tuning metaheuristics

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Hyperparameter Tuned Deep Learning Enabled Cyberbullying Classification in Social Media

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Computers, Materials & Continua 2022年第12期73卷 5011-5024页

作者： Mesfer Al Duhayyim Heba G.mohamed Saud S.Alotaibi Hany Mahgoub Abdullah mohamed Abdelwahed Motwakel Abu Sarwar Zamani mohamed i.eldesouki Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia Department of Electrical Engineering College of EngineeringPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of information Systems College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversityMuhayel AseerSaudi Arabia Faculty of Computers and information Computer Science DepartmentMenoufia UniversityEgypt Research Centre Future University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAl-KharjSaudi Arabia Department of information System College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl KharjSaudi Arabia

Cyberbullying(CB)is a challenging issue in social media and it becomes important to effectively identify the occurrence of *** recently developed deep learning(DL)models pave the way to design CB classifier models with maximum *** the same time,optimal hyperparameter tuning process plays a vital role to enhance overall *** study introduces a Teacher Learning Genetic Optimization with Deep Learning Enabled Cyberbullying Classification(TLGODL-CBC)model in Social *** proposed TLGODL-CBC model intends to identify the existence and non-existence of CB in social media ***,the input data is cleaned and pre-processed to make it compatible for further *** by,independent recurrent autoencoder(iRAE)model is utilized for the recognition and classification of ***,the TLGO algorithm is used to optimally adjust the parameters related to the iRAE model and shows the novelty of the *** assuring the improved outcomes of the TLGODLCBC approach,a wide range of simulations are executed and the outcomes are investigated under several *** simulation outcomes make sure the improvements of the TLGODL-CBC model over recent approaches.

关键词： Social media deep learning cyberbullying cybersecurity hyperparameter optimization

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Optimal Kernel Extreme Learning Machine for COViD-19 Classification on Epidemiology Dataset

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Computers, Materials & Continua 2022年第11期73卷 3305-3318页

作者： Saud S.Alotaibi Amal Al-Rasheed Sami Althahabi Manar Ahmed Hamza Abdullah mohamed Abu Sarwar Zamani Abdelwahed Motwakel mohamed i.eldesouki Department of information Systems College of Computing and Information SystemUmm Al-Qura UniversitySaudi 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&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Research Centre Future University in EgyptNew Cairo11745Egypt Department of information System College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Artificial intelligence(Ai)encompasses various domains such as Machine Learning(ML),Deep Learning(DL),and other cognitive technologies which have been widely applied in healthcare *** models are utilized in healthcare sector in which the machines are used to investigate and make decisions based on prediction and classification of input *** this motivation,the current study involves the design of Metaheuristic Optimization with Kernel Extreme Learning Machine for COViD-19 Prediction Model on Epidemiology Dataset,named MOKELM-CPED *** primary aim of the presented MOKELM-CPED model is to accomplish effectual COViD-19 classification outcomes using epidemiology *** the proposed MOKELM-CPED model,the data first undergoes pre-processing to transform the medical data into useful *** by,data classification process is performed by following Kernel Extreme Learning Machine(KELM)***,Symbiotic Organism Search(SOS)optimization algorithm is utilized to fine tune the KELM parameters which consequently helps in achieving high detection *** order to investigate the improved classifier outcomes of MOKELM-CPED model in an effectual manner,a comprehensive experimental analysis was conducted and the results were inspected under diverse *** outcome of the experiments infer the enhanced performance of the proposed method over recent approaches under distinct measures.

关键词： COViD-19 epidemiology dataset machine learning artificial intelligence metaheuristics healthcare

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Enhanced Crow Search with Deep Learning-Based Cyberattack Detection in SDN-ioT Environment

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intelligent Automation & Soft Computing 2023年第6期36卷 3157-3173页

作者： Abdelwahed Motwakel Fadwa Alrowais Khaled Tarmissi Radwa Marzouk Abdullah mohamed Abu Sarwar Zamani ishfaq Yaseen mohamed i.eldesouki 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 Sciences College of Computing and Information SystemUmm Al-Qura UniversityMeccaSaudi Arabia Department of information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Research Centre Future University in EgyptNew Cairo11845Egypt Department of information System College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAlKharj16436Saudi Arabia

The paradigm shift towards the internet of Things(ioT)phe-nomenon and the rise of edge-computing models provide massive poten-tial for several upcoming ioT applications like smart grid,smart energy,smart home,smart health and smart transportation ***,it also provides a sequence of novel cyber-security *** ioT networks provide several advantages,the heterogeneous nature of the network and the wide connectivity of the devices make the network easy for *** result in financial loss and data breaches for organizations and ***,it becomes crucial to secure the ioT environment from such *** this motivation,the current study introduces an effectual Enhanced Crow Search Algorithm with Deep Learning-Driven Cyberattack Detection(ECSADL-CAD)model for the Software-Defined Networking(SDN)-enabled ioT *** presented ECSADL-CAD approach aims to identify and classify the cyberattacks in the SDN-enabled ioT *** attain this,the ECSADL-CAD model initially pre-processes the *** the presented ECSADL-CAD model,the Reinforced Deep Belief Network(RDBN)model is employed for attack *** last,the ECSA-based hyperparameter tuning process gets executed to boost the overall classification outcomes.A series of simulations were conducted to validate the improved outcomes of the proposed ECSADL-CAD *** experimental outcomes confirmed the superiority of the proposed ECSADL-CAD model over other existing methodologies.

关键词： Software defined networks artificial intelligence cybersecurity deep learning internet of things

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Cuckoo Optimized Convolution Support Vector Machine for Big Health Data Processing

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Computers, Materials & Continua 2022年第11期73卷 3039-3055页

作者： Eatedal Alabdulkreem Jaber S.Alzahrani Majdy M.Eltahir Abdullah mohamed Manar Ahmed Hamza Abdelwahed Motwakel mohamed i.eldesouki Mohammed Rizwanullah Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversityMecca24382Saudi Arabia Department of information Systems College of Science&Art at MahayilKing Khalid UniversityAbha 62529Saudi Arabia Research Centre Future University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam Bin Abdulaziz UniversityAlKharj16278Saudi Arabia Department of information System College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAlKharj16278Saudi Arabia

Big health data collection and storing for further analysis is a challenging task because this knowledge is big and has many *** cloud-based ioT health providers have been described in the literature ***,there are a number of issues related to time consumed and overall network performance when it comes to big data *** the existing method,less performed optimization algorithms were used for optimizing the *** the proposed method,the Chaotic Cuckoo Optimization algorithm was used for feature selection,and Convolutional Support Vector Machine(CSVM)was *** research presents a method for analyzing healthcare information that uses in future *** major goal is to take a variety of data while improving efficiency and minimizing process *** suggested method employs a hybrid method that is divided into two *** the first stage,it reduces the features by using the Chaotic Cuckoo Optimization algorithm with Levy flight,opposition-based learning,and distributor *** the second stage,CSVM is used which combines the benefits of convolutional neural network(CNN)and *** CSVM modifies CNN’s convolution product to learn hidden deep inside data *** improved economic flexibility,greater protection,greater analytics with confidentiality,and lower operating cost,the suggested approach is built on fog *** results of the experiments show that the suggested method can minimize the number of features in the datasets,enhances the accuracy by 82%,and decrease the time of the process.

关键词： Healthcare convolutional support vector machine feature selection chaotic cuckoo optimization accuracy processing time convolutional neural network

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Metaheuristic with Deep Learning Enabled Biomedical Bone Age Assessment and Classification Model

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Computers, Materials & Continua 2022年第12期73卷 5473-5489页

作者： Mesfer Al Duhayyim Areej A.Malibari Marwa Obayya mohamed K.Nour Ahmed S.Salama mohamed i.eldesouki Abu Sarwar Zamani Mohammed Rizwanullah Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia Department of industrial and Systems Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Biomedical Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Electrical Engineering Faculty of Engineering&TechnologyFuture University in EgyptNew Cairo11845Egypt Department of information System College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

The skeletal bone age assessment(BAA)was extremely implemented in development prediction and auxiliary analysis of medicinal issues.X-ray images of hands were detected from the estimation of bone age,whereas the ossification centers of epiphysis and carpal bones are important *** typical skeletal BAA approaches remove these regions for predicting the bone age,however,few of them attain suitable efficacy or *** BAA techniques with deep learning(DL)methods are reached the leading efficiency on manual and typical ***,this study introduces an intellectual skeletal bone age assessment and classification with the use of metaheuristic with deep learning(iSBAAC-MDL)*** presented iSBAAC-MDL technique majorly focuses on the identification of bone age prediction and classification *** attain this,the presented iSBAAC-MDL model derives a mask Region-related Convolutional Neural Network(Mask-RCNN)with MobileNet as baseline model to extract *** by,the whale optimization algorithm(WOA)is implemented for hyperparameter tuning of the MobileNet *** last,Deep Feed-Forward Module(DFFM)based age prediction and Radial Basis Function Neural Network(RBFNN)based stage classification approach is *** experimental evaluation of the iSBAAC-MDL model is tested using benchmark dataset and the outcomes are assessed over distinct *** experimental outcomes reported the better performances of the iSBAACMDL model over recent approaches with maximum accuracy of 0.9920.

关键词： Biomedical images bone age assessment age prediction computer vision deep learning image classification

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Privacy Preserving image Encryption with Deep Learning Based ioT Healthcare Applications

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Computers, Materials & Continua 2022年第10期73卷 1159-1175页

作者： Mohammad Alamgeer Saud S.Alotaibi Shaha Al-Otaibi Nazik Alturki Anwer Mustafa Hilal Abdelwahed Motwakel ishfaq Yaseen mohamed i.eldesouki Department of information Systems College of Science&Art at MahayilKing Khalid UniversityMuhayel Aseer62529Saudi Arabia Department of information Systems College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia Department of information System College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia

Latest developments in computing and communication technologies are enabled the design of connected healthcare system which are mainly based on ioT and Edge ***,data encryption,and deep learning(DL)models can be utilized to design efficient security solutions for ioT healthcare *** this aspect,this article introduces a Blockchain with privacy preserving image encryption and optimal deep learning(BPPiEODL)technique for ioT healthcare *** proposed BPPiE-ODL technique intends to securely transmit the encrypted medical images captured by ioT devices and performs classification process at the cloud *** proposed BPPiE-ODL technique encompasses the design of dragonfly algorithm(DFA)with signcryption technique to encrypt the medical images captured by the ioT ***,blockchain(BC)can be utilized as a distributed data saving approach for generating a ledger,which permits access to the users and prevents third party’s access to encrypted *** addition,the classification process includes SqueezeNet based feature extraction,softmax classifier(SMC),and Nadam based hyperparameter *** usage of Nadam model helps to optimally regulate the hyperparameters of the SqueezeNet *** examining the enhanced encryption as well as classification performance of the BPPiE-ODL technique,a comprehensive experimental analysis is carried *** simulation outcomes demonstrate the significant performance of the BPPiE-ODL technique on the other techniques with increased precision and accuracy of 0.9551 and 0.9813 respectively.

关键词： internet of things healthcare decision making privacy preserving blockchain deep learning

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Optimal Deep Convolutional Neural Network for Vehicle Detection in Remote Sensing images

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Computers, Materials & Continua 2023年第2期74卷 3117-3131页

作者： Saeed Masoud Alshahrani Saud S.Alotaibi Shaha Al-Otaibi mohamed Mousa Anwer Mustafa Hilal Amgad Atta Abdelmageed Abdelwahed Motwakel mohamed i.eldesouki Department of Computer Science College of Computing and Information TechnologyShaqra UniversityShaqraSaudi Arabia Department of information Systems College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi 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 Department of information System College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAlKharjSaudi Arabia

Object detection(OD)in remote sensing images(RSi)acts as a vital part in numerous civilian and military application areas,like urban planning,geographic information system(GiS),and search and rescue *** recognition from RSis remained a challenging process because of the difficulty of background data and the redundancy of recognition *** latest advancements in deep learning(DL)approaches permit the design of effectual OD *** study develops an Artificial Ecosystem Optimizer with Deep Convolutional Neural Network for Vehicle Detection(AEODCNN-VD)model on Remote Sensing *** proposed AEODCNN-VD model focuses on the identification of vehicles accurately and *** detect vehicles,the presented AEODCNN-VD model employs single shot detector(SSD)with inception network as a baseline *** addition,Multiway Feature Pyramid Network(MFPN)is used for handling objects of varying sizes in *** features from the inception model are passed into theMFPNformultiway andmultiscale feature ***,the fused features are passed into bounding box and class prediction *** enhancing the detection efficiency of the AEODCNN-VD approach,AEO based hyperparameter optimizer is used,which is stimulated by the energy transfer strategies such as production,consumption,and decomposition in an *** performance validation of the presentedmethod on benchmark datasets showed promising performance over recent DL models.

关键词： Object detection remote sensing vehicle detection artificial ecosystem optimizer convolutional neural network

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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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Manta Ray Foraging Optimization with Machine Learning Based Biomedical Data Classification

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Computers, Materials & Continua 2022年第11期73卷 3275-3290页

作者： Amal Al-Rasheed Jaber S.Alzahrani Majdy M.Eltahir Abdullah mohamed Anwer Mustafa Hilal Abdelwahed Motwakel Abu Sarwar Zamani mohamed i.eldesouki Department of information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversityMecca24382Saudi Arabia Department of information Systems College of Science&Art at MahayilKing Khalid UniversityAbha62529Saudi Arabia Research Centre Future University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of information System College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

The biomedical data classification process has received significant attention in recent times due to a massive increase in the generation of healthcare data from various *** developments of artificial intelligence(Ai)and machine learning(ML)models assist in the effectual design of medical data classification ***,this article concentrates on the development of optimal Stacked Long Short Term Memory Sequence-toSequence Autoencoder(OSAE-LSTM)model for biomedical data *** presented OSAE-LSTM model intends to classify the biomedical data for the existence of ***,the OSAE-LSTM model involves min-max normalization based pre-processing to scale the data into uniform *** by,the SAE-LSTM model is utilized for the detection and classification of diseases in biomedical *** last,manta ray foraging optimization(MRFO)algorithm has been employed for hyperparameter optimization *** utilization of MRFO algorithm assists in optimal selection of hypermeters involved in the SAE-LSTM *** simulation analysis of the OSAE-LSTM model has been tested using a set of benchmark medical datasets and the results reported the improvements of the OSAELSTM model over the other approaches under several dimensions.

关键词： Biomedical data classification deep learning manta ray foraging optimization healthcare machine learning artificial intelligence

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