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Artificial Intelligence Based Prostate Cancer Classification Model Using Biomedical Images

Computers, Materials & Continua 2022年第8期72卷 3799-3813页

作者： Areej A.Malibari Reem Alshahrani Fahd N.Al-Wesabi Siwar Ben Haj Hassine Mimouna Abdullah Alkhonaini anwer Mustafa Hilal Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Computer Science 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

Medical image processing becomes a hot research topic in healthcare sector for effective decision making and diagnoses of *** resonance imaging(MRI)is a widely utilized tool for the classification and detection of prostate *** the manual screening process of prostate cancer is difficult,automated diagnostic methods become *** study develops a novel Deep Learning based Prostate Cancer Classification(DTL-PSCC)model using MRI *** presented DTL-PSCC technique encompasses EfficientNet based feature extractor for the generation of a set of feature *** addition,the fuzzy k-nearest neighbour(FKNN)model is utilized for classification process where the class labels are allotted to the input MRI ***,the membership value of the FKNN model can be optimally tuned by the use of krill herd algorithm(KHA)which results in improved classification *** order to demonstrate the good classification outcome of the DTL-PSCC technique,a wide range of simulations take place on benchmark MRI *** extensive comparative results ensured the betterment of the DTL-PSCC technique over the recent methods with the maximum accuracy of 85.09%.

关键词： MRI images prostate cancer deep learning medical image processing metaheuristics krill herd algorithm

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Optimal Deep Learning Based Ransomware Detection and Classification in the Internet of Things Environment

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Computer Systems Science & Engineering 2023年第9期46卷 3087-3102页

作者： Manal Abdullah Alohali Muna Elsadig Fahd N.Al-Wesabi Mesfer Al Duhayyim anwer Mustafa Hilal Abdelwahed Motwakel 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 Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

With the advent of the Internet of Things(IoT),several devices like sensors nowadays can interact and easily share *** the IoT model is prone to security concerns as several attackers try to hit the network and make it *** such scenarios,security concern is the most *** models were intended to address these security problems;still,several emergent variants of botnet attacks like Bashlite,Mirai,and Persirai use security *** malware classification and detection in the IoT model is still a problem,as the adversary reliably generates a new variant of IoT malware and actively searches for compromise on the victim *** article develops a Sine Cosine Algorithm with Deep Learning based Ransomware Detection and Classification(SCADL-RWDC)method in an IoT *** the presented SCADL-RWDCtechnique,the major intention exists in recognizing and classifying ransomware attacks in the IoT *** SCADL-RWDC technique uses the SCA feature selection(SCA-FS)model to improve the detection ***,the SCADL-RWDC technique exploits the hybrid grey wolf optimizer(HGWO)with a gated recurrent unit(GRU)model for ransomware classification.A widespread experimental analysis is performed to exhibit the enhanced ransomware detection outcomes of the SCADL-RWDC *** comparison study reported the enhancement of the SCADL-RWDC technique over other models.

关键词： Security IoT network ransomware attack deep learning metaheuristics

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Compact Bat Algorithm with Deep Learning Model for Biomedical EEG EyeState Classification

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Computers, Materials & Continua 2022年第9期72卷 4589-4601页

作者： Souad Larabi-Marie-Sainte Eatedal Alabdulkreem Mohammad Alamgeer Mohamed K Nour anwer Mustafa Hilal Mesfer Al Duhayyim Abdelwahed Motwakel Ishfaq Yaseen Department of Computer Science College of Computer and Information SciencesPrince Sultan UniversitySaudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Information Systems College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia

Electroencephalography(EEG)eye state classification becomes an essential tool to identify the cognitive state of *** can be used in several fields such as motor imagery recognition,drug effect detection,emotion categorization,seizure detection,*** the latest advances in deep learning(DL)models,it is possible to design an accurate and prompt EEG EyeState classification *** this view,this study presents a novel compact bat algorithm with deep learning model for biomedical EEG EyeState classification(CBADL-BEESC)*** major intention of the CBADL-BEESC technique aims to categorize the presence of EEG *** CBADL-BEESC model performs feature extraction using the ALexNet model which helps to produce useful feature *** addition,extreme learning machine autoencoder(ELM-AE)model is applied to classify the EEG signals and the parameter tuning of the ELM-AE model is performed using *** experimental result analysis of the CBADL-BEESC model is carried out on benchmark results and the comparative outcome reported the supremacy of the CBADL-BEESC model over the recent methods.

关键词： Biomedical signals EEG EyeState classification deep learning metaheuristics

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Novel Image Encryption and Compression Scheme for IoT Environment

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Computers, Materials & Continua 2022年第4期71卷 1443-1457页

作者： Mesfer Al Duhayyim Fahd N.Al-Wesabi Radwa Marzouk Manar Ahmed Hamza anwer Mustafa Hilal Majdy M.Eltahir Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversitySaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Latest advancements made in the processing abilities of smartdevices have resulted in the designing of Intelligent Internet of Things (IoT)environment. This advanced environment enables the nodes to connect, collect, perceive, and examine useful data from its surroundings. Wireless Multimedia Surveillance Networks (WMSNs) form a vital part in IoT-assistedenvironment since it contains visual sensors that examine the surroundingsfrom a number of overlapping views by capturing the images *** IoT devices generate a massive quantity of digital media, it is thereforerequired to save the media, especially images, in a secure way. In order toachieve security, encryption techniques as well as compression techniques areemployed to reduce the amount of digital data, being communicated overthe network. Encryption Then Compression (ETC) techniques pave a wayfor secure and compact transmission of the available data to prevent unauthorized access. With this background, the current research paper presentsa new ETC technique to accomplish image security in IoT *** proposed model involves three major processes namely, IoT-based imageacquisition, encryption, and compression. The presented model involves optimal Signcryption Technique with Whale Optimization Algorithm (NMWOA)abbreviated as ST-NMWOA. The optimal key generation of signcryptiontechnique takes place with the help of NMWOA. Besides, the presented modelalso uses Discrete Fourier Transform (DFT) and Matrix Minimization (MM)algorithm-based compression technique. Extensive set of experimental analysis was conducted to validate the effective performance of the proposed *** obtained values infer that the presented model is superior in terms of bothcompression efficiency and data secrecy in resource-limited IoT environment.

关键词： Data compression image security encryption signcryption optimal key generation

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IoT-Driven Optimal Lightweight RetinaNet-Based Object Detection for Visually Impaired People

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Computer Systems Science & Engineering 2023年第7期46卷 475-489页

作者： Mesfer Alduhayyem Mrim M.Alnfiai Nabil Almalki Fahd N.Al-Wesabi anwer Mustafa Hilal Manar Ahmed Hamza Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia King Salman Center for Disability Research RiyadhAl-Hayātim 16273Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Special Education College of EducationKing Saud UniversityRiyadh12372Saudi Arabia Department of Computer Science College of Science&ArtsKing Khaled UniversityAr-Riyad 12372Saudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Visual impairment is one of the major problems among people of all age groups across the *** Impaired Persons(VIPs)require help from others to carry out their day-to-day *** they experience several problems in their daily lives,technical intervention can help them resolve the *** this background,an automatic object detection tool is the need of the hour to empower VIPs with safe *** recent advances in the Internet of Things(IoT)and Deep Learning(DL)techniques make it *** current study proposes IoT-assisted Transient Search Optimization with a Lightweight RetinaNetbased object detection(TSOLWR-ODVIP)model to help *** primary aim of the presented TSOLWR-ODVIP technique is to identify different objects surrounding VIPs and to convey the information via audio message to *** data acquisition,IoT devices are used in this ***,the Lightweight RetinaNet(LWR)model is applied to detect objects ***,the TSO algorithm is employed for fine-tuning the hyperparameters involved in the LWR ***,the Long Short-Term Memory(LSTM)model is exploited for classifying *** performance of the proposed TSOLWR-ODVIP technique was evaluated using a set of objects,and the results were examined under distinct *** comparison study outcomes confirmed that the TSOLWR-ODVIP model could effectually detect and classify the objects,enhancing the quality of life of VIPs.

关键词： Visually impaired people deep learning object detection computer vision long short-term memory transient search optimization

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An Optimized Deep Learning Model for Emotion Classification in Tweets

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Computers, Materials & Continua 2022年第3期70卷 6365-6380页

作者： Chinu Singla Fahd NAl-Wesabi Yash Singh Pathania Badria Sulaiman Alfurhood anwer Mustafa Hilal Mohammed Rizwanullah Manar Ahmed Hamza Mohammad Mahzari Department of Computer Science and Engineering Thapar Institute of Engineering and TechnologyPatialaIndia Department of Computer Science King Khalid UniversityMuhayel AseerKingdom of Saudi Arabia Faculty of Computer and IT Sana’a UniversitySana’aYemen Department of Computer Science College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversitySaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlkharjSaudi Arabia Department of English College of Science&HumanitiesPrince Sattam bin Abdulaziz UniversityAlkharjSaudi Arabia

The task of automatically analyzing sentiments from a tweet has more use now than ever due to the spectrum of emotions expressed from national leaders to the average *** this data can be critical for any *** are often expressed with different intensity and topics which can provide great insight into how something affects *** analysis in Twittermitigates the various issues of analyzing the tweets in terms of views expressed and several approaches have already been proposed for sentiment analysis in *** used for analyzing tweet emotions are also briefly presented in literature survey *** this paper,hybrid combination of different model’s LSTM-CNN have been proposed where LSTMis Long Short TermMemory andCNNrepresents ConvolutionalNeural ***,the main contribution of our work is to compare various deep learning and machine learning models and categorization based on the techniques *** main drawback of LSTM is that it’s a timeconsuming process whereas CNN do not express content information in an accurate way,thus our proposed hybrid technique improves the precision rate and helps in achieving better *** step of our mentioned technique is to preprocess the data in order to remove stop words and unnecessary data to improve the efficiency in terms of time and accuracy also it shows optimal results when it is compared with predefined approaches.

关键词： Meta level features lexical mistakes sentiment analysis count vector natural language processing deep learning machine learning naive bayes

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Intelligent Machine Learning Based EEG Signal Classification Model

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Computers, Materials & Continua 2022年第4期71卷 1821-1835页

作者： Mesfer Al Duhayyim Haya Mesfer Alshahrani Fahd N.Al-Wesabi Mohammed Abdullah Al-Hagery anwer Mustafa Hilal Abu Sarwar Zaman Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversitySaudi Arabia Department of Computer Science King Khalid UniversityMuhayel AseerSaudi Arabia&Faculty of Computer and ITSana’a UniversitySana’aYemen Department of Computer Science College of ComputerQassim UniversitySaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlkharjSaudi Arabia

In recent years,Brain-Computer Interface(BCI)system gained much popularity since it aims at establishing the communication between human brain and *** systems are applied in several research areas such as neuro-rehabilitation,robots,exoeskeletons,***(EEG)is a technique commonly applied in capturing brain *** is incorporated in BCI systems since it has attractive features such as noninvasive nature,high time-resolution output,mobility and *** classification process is highly essential in decision making process and it incorporates different processes namely,feature extraction,feature selection,and *** this motivation,the current research paper presents an Intelligent Optimal Fuzzy Support Vector Machine-based EEC recognition(IOFSVM-EEG)model for BCI *** Component Analysis(ICA)technique is applied onto the proposed IOFSVM-EEG model to remove the artefacts that exist in EEG signal and to retain the meaningful EEG ***,Common Spatial Pattern(CSP)-based feature extraction technique is utilized to derive a helpful set of feature vectors from the preprocessed EEG ***,OFSVM method is applied in the classification of EEG signals,in which the parameters involved in FSVM are optimally tuned using Grasshopper Optimization Algorithm(GOA).In order to validate the enhanced EEG recognition outcomes of the proposed IOFSVM-EEG model,an extensive set of experiments was *** outcomes were examined under distinct *** experimental results highlighted the enhanced performance of the presented IOFSVM-EEG model over other state-of-the-art methods.

关键词： Brain computer interface EEG recognition human computer interface machine learning parameter tuning FSVM

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Artificial Intelligence Based Sentiment Analysis for Health Crisis Management in Smart Cities

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Computers, Materials & Continua 2022年第4期71卷 143-157页

作者： anwer Mustafa Hilal Badria Sulaiman Alfurhood Fahd N.Al-Wesabi Manar Ahmed Hamza Mesfer Al Duhayyim Huda G.Iskandar Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam Bin Abdulaziz UniversityAlkharjSaudi Arabia Department of Computer Science College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversitySaudi Arabia Department of Computer Science King Khalid UniversityMuhayel AseerSaudi Arabia Faculty of Computer and IT Sana’a UniversitySana’aYemen Department of Natural and Applied Sciences College of Community-AflajPrince Sattam Bin Abdulaziz UniversitySaudi Arabia School of Computing Faculty of EngineeringUniversiti Teknologi MalaysiaMalaysia

Smart city promotes the unification of conventional urban infrastructure and information technology (IT) to improve the quality of living andsustainable urban services in the city. To accomplish this, smart cities necessitate collaboration among the public as well as private sectors to install ITplatforms to collect and examine massive quantities of data. At the same time,it is essential to design effective artificial intelligence (AI) based tools to handlehealthcare crisis situations in smart cities. To offer proficient services to peopleduring healthcare crisis time, the authorities need to look closer towardsthem. Sentiment analysis (SA) in social networking can provide valuableinformation regarding public opinion towards government actions. With thismotivation, this paper presents a new AI based SA tool for healthcare crisismanagement (AISA-HCM) in smart cities. The AISA-HCM technique aimsto determine the emotions of the people during the healthcare crisis time, suchas COVID-19. The proposed AISA-HCM technique involves distinct operations such as pre-processing, feature extraction, and classification. Besides,brain storm optimization (BSO) with deep belief network (DBN), called BSODBN model is employed for feature extraction. Moreover, beetle antennasearch with extreme learning machine (BAS-ELM) method was utilized forclassifying the sentiments as to various classes. The use of BSO and BASalgorithms helps to effectively modify the parameters involved in the DBNand ELM models respectively. The performance validation of the AISA-HCMtechnique takes place using Twitter data and the outcomes are examinedwith respect to various measures. The experimental outcomes highlighted theenhanced performance of the AISA-HCM technique over the recent state ofart SA approaches with the maximum precision of 0.89, recall of 0.88, Fmeasure of 0.89, and accuracy of 0.94.

关键词： Smart city sentiment analysis artificial intelligence healthcare management metaheuristics deep learning parameter tuning

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Deep Learning Driven Arabic Text to Speech Synthesizer for Visually Challenged People

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Intelligent Automation & Soft Computing 2023年第6期36卷 2639-2652页

作者： Mrim M.Alnfiai Nabil Almalki Fahd N.Al-Wesabi Mesfer Alduhayyem anwer Mustafa Hilal Manar Ahmed Hamza King Salman Center for Disability Research Riyadh13369Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Special Education College of EducationKing Saud UniversityRiyadh12372Saudi Arabia Department of Computer Science College of Science&Arts at MuhayelKing Khaled UniversityAbha62217Saudi Arabia Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversityAl-Aflaj16733Saudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharj16242Saudi Arabia

Text-To-Speech(TTS)is a speech processing tool that is highly helpful for visually-challenged *** TTS tool is applied to transform the texts into human-like ***,it is highly challenging to accomplish the TTS out-comes for the non-diacritized text of the Arabic language since it has multiple unique features and *** special characters like gemination and diacritic signs that correspondingly indicate consonant doubling and short vowels greatly impact the precise pronunciation of the Arabic ***,such signs are not frequently used in the texts written in the Arabic language since its speakers and readers can guess them from the context *** this background,the current research article introduces an Optimal Deep Learning-driven Arab Text-to-Speech Synthesizer(ODLD-ATSS)model to help the visually-challenged people in the Kingdom of Saudi *** prime aim of the presented ODLD-ATSS model is to convert the text into speech signals for visually-challenged *** attain this,the presented ODLD-ATSS model initially designs a Gated Recurrent Unit(GRU)-based prediction model for diacritic and gemination ***,the Buckwalter code is utilized to capture,store and display the Arabic *** improve the TSS performance of the GRU method,the Aquila Optimization Algorithm(AOA)is used,which shows the novelty of the *** illustrate the enhanced performance of the proposed ODLD-ATSS model,further experi-mental analyses were *** proposed model achieved a maximum accu-racy of 96.35%,and the experimental outcomes infer the improved performance of the proposed ODLD-ATSS model over other DL-based TSS models.

关键词： Saudi Arabia visually challenged people deep learning Aquila optimizer gated recurrent unit

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Water Wave Optimization with Deep Learning Driven Smart Grid Stability Prediction

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Computers, Materials & Continua 2022年第12期73卷 6019-6035页

作者： anwer Mustafa Hilal Aisha Hassan Abdalla Hashim Heba G.Mohamed Mohammad Alamgeer Mohamed K.Nour Anas Abdelrahman Abdelwahed Motwakel Department of Electrical and Computer Engineering International Islamic University MalaysiaKuala Lumpur53100Malaysia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam Bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Electrical Engineering College of EngineeringPrincess 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 and Bioinformatics Singhania UniversityPacheri BariJhnujhunuRajasthanIndia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Mechanical Engineering Faculty of Engineering&TechnologyFuture University in EgyptNew Cairo11835Egypt

Smart Grid(SG)technologies enable the acquisition of huge volumes of high dimension and multi-class data related to electric power grid operations through the integration of advanced metering infrastructures,control systems,and communication *** SGs,user demand data is gathered and examined over the present supply criteria whereas the expenses are then informed to the clients so that they can decide about electricity *** the entire procedure is valued on the basis of time,it is essential to perform adaptive estimation of the SG’s *** advancements inMachine Learning(ML)andDeep Learning(DL)models enable the designing of effective stability prediction models in *** this background,the current study introduces a novel Water Wave Optimization with Optimal Deep Learning Driven Smart Grid Stability Prediction(WWOODL-SGSP)*** aim of the presented WWOODL-SGSP model is to predict the stability level of SGs in a proficient *** attain this,the proposed WWOODL-SGSP model initially applies normalization process to scale the data to a uniform ***,WWO algorithm is applied to choose an optimal subset of features from the pre-processed ***,Deep Belief Network(DBN)model is followed to predict the stability level of ***,Slime Mold Algorithm(SMA)is exploited to fine tune the hyperparameters involved in DBN *** order to validate the enhanced performance of the proposedWWOODL-SGSP model,a wide range of experimental analyses was *** simulation results confirmthe enhanced predictive results of WWOODL-SGSP model over other recent approaches.

关键词： Smart grid stability prediction deep learning energy systems machine learning metaheursitics

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