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Sustainable Energy Management with Traffic Prediction Strategy for Autonomous Vehicle Systems

Computers, Materials & Continua 2022年第8期72卷 3465-3479页

作者： Manar Ahmed Hamza Masoud Alajmi Jaber SAlzahrani Siwar Ben Haj Hassine Abdelwahed Motwakel ishfaq yaseen Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Saudi 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

Recent advancements of the intelligent transportation system(ITS)provide an effective way of improving the overall efficiency of the energy management strategy(EMSs)for autonomous vehicles(AVs).The use of AVs possesses many advantages such as congestion control,accident prevention,and ***,energy management and traffic flow prediction(TFP)still remains a challenging problem in *** complexity and uncertainties of driving situations adequately affect the outcome of the designed *** this view,this paper presents novel sustainable energy management with traffic flow prediction strategy(SEM-TPS)for *** SEM-TPS technique applies type II fuzzy logic system(T2FLS)energy management scheme to accomplish the desired engine torque based on distinct *** addition,the membership functions of the T2FLS scheme are chosen optimally using the barnacles mating optimizer(BMO).For accurate TFP,the bidirectional gated recurrent neural network(Bi-GRNN)model is used in AVs.A comprehensive experimental validation process is performed and the results are inspected with respect to several evaluation *** experimental outcomes highlighted the supreme performance of the SEM-TPS technique over the recent state of art approaches.

关键词： Sustainable energy transportation energy management traffic flow prediction soft computing deep learning

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Automated Deep Learning Based Melanoma Detection and Classification Using Biomedical Dermoscopic Images

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Computers, Materials & Continua 2023年第2期74卷 2443-2459页

作者： Amani Abdulrahman Albraikan Nadhem NEMRI Mimouna Abdullah Alkhonaini Anwer Mustafa Hilal ishfaq yaseen Abdelwahed Motwakel Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Computer Science College of Computer and Information SciencesPrince Sultan UniversitySaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Melanoma remains a serious illness which is a common formof skin *** the earlier detection of melanoma reduces the mortality rate,it is essential to design reliable and automated disease diagnosis model using dermoscopic *** recent advances in deep learning(DL)models find useful to examine the medical image and make proper *** this study,an automated deep learning based melanoma detection and classification(ADL-MDC)model is *** goal of the ADL-MDC technique is to examine the dermoscopic images to determine the existence of *** ADL-MDC technique performs contrast enhancement and data augmentation at the initial ***,the k-means clustering technique is applied for the image segmentation *** addition,Adagrad optimizer based Capsule Network(CapsNet)model is derived for effective feature extraction ***,crow search optimization(CSO)algorithm with sparse autoencoder(SAE)model is utilized for the melanoma classification *** exploitation of the Adagrad and CSO algorithm helps to properly accomplish improved performance.A wide range of simulation analyses is carried out on benchmark datasets and the results are inspected under several *** simulation results reported the enhanced performance of the ADL-MDC technique over the recent approaches.

关键词： Biomedical images dermoscopic images deep learning melanoma detection machine 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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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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Hybrid Deep Learning Enabled Intrusion Detection in Clustered IIoT Environment

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Computers, Materials & Continua 2022年第8期72卷 3763-3775页

作者： Radwa Marzouk Fadwa Alrowais Noha Negm Mimouna Abdullah Alkhonaini Manar Ahmed Hamza Mohammed Rizwanullah ishfaq yaseen Abdelwahed Motwakel Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia 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 UniversityAbha62529Saudi Arabia Department of Computer Science College of Computer and Information SciencesPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Industrial Internet of Things(IIoT)is an emerging field which connects digital equipment as well as services to physical *** detection systems(IDS)can be designed to protect the system from intrusions or *** this view,this paper presents a novel hybrid deep learning with metaheuristics enabled intrusion detection(HDL-MEID)technique for clustered IIoT *** HDL-MEID model mainly intends to organize the IIoT devices into clusters and enabled secure ***,the HDL-MEID technique designs a new chaotic mayfly optimization(CMFO)based clustering approach for the effective choice of the Cluster Heads(CH)and organize ***,equilibrium optimizer with hybrid convolutional neural network long short-term memory(HCNNLSTM)based classification model is derived to identify the existence of the intrusions in the IIoT *** experimental analysis is performed to highlight the enhanced outcomes of the HDL-MEID technique and the results were investigated under different *** experimental results highlight the supremacy of the proposed HDL-MEID technique over recent state-of-the-art techniques.

关键词： Industrial internet of things security intrusion detection classification deep learning

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Natural Language Processing with Optimal Deep Learning-Enabled Intelligent Image Captioning System

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Computers, Materials & Continua 2023年第2期74卷 4435-4451页

作者： Radwa Marzouk Eatedal Alabdulkreem Mohamed KNour Mesfer Al Duhayyim Mahmoud Othman Abu Sarwar Zamani ishfaq yaseen Abdelwahed Motwakel Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi 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 UniversitySaudi Arabia Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversitySaudi 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

The recent developments in Multimedia Internet of Things(MIoT)devices,empowered with Natural Language Processing(NLP)model,seem to be a promising future of smart *** plays an important role in industrial models such as speech understanding,emotion detection,home automation,and so *** an image needs to be captioned,then the objects in that image,its actions and connections,and any silent feature that remains under-projected or missing from the images should be *** aim of the image captioning process is to generate a caption for *** next step,the image should be provided with one of the most significant and detailed descriptions that is syntactically as well as semantically *** this scenario,computer vision model is used to identify the objects and NLP approaches are followed to describe the *** current study develops aNatural Language Processing with Optimal Deep Learning Enabled Intelligent Image Captioning System(NLPODL-IICS).The aim of the presented NLPODL-IICS model is to produce a proper description for input *** attain this,the proposed NLPODL-IICS follows two stages such as encoding and decoding ***,at the encoding side,the proposed NLPODL-IICS model makes use of Hunger Games Search(HGS)with Neural Search Architecture Network(NASNet)*** model represents the input data appropriately by inserting it into a predefined length ***,during decoding phase,Chimp Optimization Algorithm(COA)with deeper Long Short Term Memory(LSTM)approach is followed to concatenate the description sentences 4436 CMC,2023,vol.74,no.2 produced by the *** application of HGS and COA algorithms helps in accomplishing proper parameter tuning for NASNet and LSTM models *** proposed NLPODL-IICS model was experimentally validated with the help of two benchmark *** comparative analysis confirmed the superior performance of NLPODL-IICS model over other models.

关键词： Natural language processing information retrieval image captioning deep learning metaheuristics

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Intelligent Deep Learning Model for Privacy Preserving IIoT on 6G Environment

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Computers, Materials & Continua 2022年第7期72卷 333-348页

作者： Anwer Mustafa Hilal Jaber SAlzahrani Ibrahim Abunadi Nadhem Nemri Fahd NAl-Wesabi Abdelwahed Motwakel ishfaq yaseen Abu Sarwar Zamani Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Information Systems Prince Sultan UniversityRiyadh11586Saudi Arabia Department of Information Systems King King Khalid UniversityMuhayel Aseer62529Saudi Arabia Department of Computer Science King King Khalid UniversityMuhayel Aseer62529Saudi Arabia Faculty of Computer and IT Sana’a UniversitySana’a61101Yemen

In recent times,Industrial Internet of Things(IIoT)experiences a high risk of cyber attacks which needs to be *** technology can be incorporated into IIoT system to help the entrepreneurs realize Industry 4.0 by overcoming such cyber *** blockchain-based IIoT network renders a significant support and meet the service requirements of next generation network,the performance arrived at,in existing studies still needs *** this scenario,the current research paper develops a new Privacy-Preserving Blockchain with Deep Learning model for Industrial IoT(PPBDL-IIoT)on 6G *** proposed PPBDLIIoT technique aims at identifying the existence of intrusions in ***,PPBDL-IIoT technique also involves the design of Chaos Game Optimization(CGO)with Bidirectional Gated Recurrent Neural Network(BiGRNN)technique for both detection and classification of intrusions in the ***,CGO technique is applied to fine tune the hyperparameters in BiGRNN *** algorithm is applied to optimally adjust the learning rate,epoch count,and weight decay so as to considerably improve the intrusion detection performance of BiGRNN ***,Blockchain enabled Integrity Check(BEIC)scheme is also introduced to avoid the misrouting attacks that tamper the OpenFlow rules of SDN-based IIoT *** performance of the proposed PPBDL-IIoT methodology was validated using Industrial Control System Cyber-attack(ICSCA)dataset and the outcomes were analysed under various *** experimental results highlight the supremacy of the presented PPBDL-IIoT technique than the recent state-of-the-art techniques with the higher accuracy of 91.50%.

关键词： 6G networks industrial iot blockchain security intrusion detection artificial intelligence

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Big Data Analytics with Artificial Intelligence Enabled Environmental Air Pollution Monitoring Framework

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Computers, Materials & Continua 2022年第11期73卷 3235-3250页

作者： Manar Ahmed Hamza Hadil Shaiba Radwa Marzouk Ahmad Alhindi Mashael M.Asiri ishfaq yaseen Abdelwahed Motwakel Mohammed Rizwanullah Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharj16278Saudi Arabia 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 Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia&Research and InnovationSalla Holding LimitedMakkahSaudi Arabia Department of Computer Science College of Science and ArtsKing Khalid UniversityMahayil Asir62529Saudi Arabia

Environmental sustainability is the rate of renewable resourceharvesting, pollution control, and non-renewable resource exhaustion. Airpollution is a significant issue confronted by the environment particularlyby highly populated countries like India. Due to increased population, thenumber of vehicles also continues to increase. Each vehicle has its individualemission rate;however, the issue arises when the emission rate crosses thestandard value and the quality of the air gets degraded. Owing to the technological advances in machine learning (ML), it is possible to develop predictionapproaches to monitor and control pollution using real time data. With thedevelopment of the Internet of Things (IoT) and Big Data Analytics (BDA),there is a huge paradigm shift in how environmental data are employed forsustainable cities and societies, especially by applying intelligent *** this view, this study develops an optimal AI based air quality prediction andclassification (OAI-AQPC) model in big data environment. For handling bigdata from environmental monitoring, Hadoop MapReduce tool is *** addition, a predictive model is built using the hybridization of ARIMAand neural network (NN) called ARIMA-NN to predict the pollution *** improving the performance of the ARIMA-NN algorithm, the parametertuning process takes place using oppositional swallow swarm optimization(OSSO) algorithm. Finally, Adaptive neuro-fuzzy inference system (ANFIS)classifier is used to classify the air quality into pollutant and non-pollutant.A detailed experimental analysis is performed for highlighting the betterprediction performance of the proposed ARIMA-NN method. The obtainedoutcomes pointed out the enhanced outcomes of the proposed OAI-AQPCtechnique over the recent state of art techniques.

关键词： Sustainability environmental air quality predictive model pollution monitoring statistical models artificial intelligence

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Automated Deep Learning Driven Crop Classification on Hyperspectral Remote Sensing Images

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Computers, Materials & Continua 2023年第2期74卷 3167-3181页

作者： Mesfer Al Duhayyim Hadeel Alsolai Siwar Ben Haj Hassine Jaber SAlzahrani Ahmed SSalama Abdelwahed Motwakel ishfaq yaseen Abu Sarwar Zamani Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz 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 Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura 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

Hyperspectral remote sensing/imaging spectroscopy is a novel approach to reaching a spectrum from all the places of a huge array of spatial places so that several spectral wavelengths are utilized for making coherent *** remote sensing contains acquisition of digital images from several narrow,contiguous spectral bands throughout the visible,Thermal Infrared(TIR),Near Infrared(NIR),and Mid-Infrared(MIR)regions of the electromagnetic *** order to the application of agricultural regions,remote sensing approaches are studied and executed to their benefit of continuous and ***,hyperspectral images(HSI)are considered the precise for agriculture as they can offer chemical and physical data on *** this motivation,this article presents a novel Hurricane Optimization Algorithm with Deep Transfer Learning Driven Crop Classification(HOADTL-CC)model onHyperspectralRemote Sensing *** presentedHOADTL-CC model focuses on the identification and categorization of crops on hyperspectral remote sensing *** accomplish this,the presentedHOADTL-CC model involves the design ofHOAwith capsule network(CapsNet)model for generating a set of useful feature ***,Elman neural network(ENN)model is applied to allot proper class labels into the input ***,glowworm swarm optimization(GSO)algorithm is exploited to fine tune the ENNparameters involved in this *** experimental result scrutiny of the HOADTL-CC method can be tested with the help of benchmark dataset and the results are assessed under distinct *** comparative studies stated the enhanced performance of the HOADTL-CC model over recent approaches with maximum accuracy of 99.51%.

关键词： Hyperspectral images remote sensing deep learning hurricane optimization algorithm crop classification parameter tuning

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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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