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Human Emotions Classification Using EEG via Audiovisual Stimuli and AI

Computers, Materials & Continua 2022年第12期73卷 5075-5089页

作者： Abdullah A Asiri Akhtar Badshah Fazal Muhammad hassan a.alshamrani Khalil Ullah Khalaf a.alshamrani Samar Alqhtani Muhammad Irfan Hanan Talal Halawani Khlood M Mehdar Radiological Sciences Department College of Applied Medical SciencesNajran UniversityNajranSaudi Arabia Faculty of Computer Science and Engineering GIK Institute of Engineering Sciences and TechnologyTopi23460Pakistan Department of Electrical Engineering University of Engineering and TechnologyMardan23200Pakistan Department of Software Engineering University of MalakandDir LowerPakistan Department of Computer Science and Information Systems College of EngineeringNajran University Saudi ArabiaNajran61441Saudi Arabia Electrical Engineering Department College of EngineeringNajran University Saudi ArabiaNajran61441Saudi Arabia Computer Science Department College of Computer Science and Information SystemsNajran UniversityNajranSaudi Arabia Anatomy Department Medicine CollegeNajran UniversityNajranSaudi Arabia

Electroencephalogram(EEG)is a medical imaging technology that can measure the electrical activity of the scalp produced by the brain,measured and recorded chronologically the surface of the scalp from the *** recorded signals from the brain are rich with useful *** inference of this useful information is a challenging *** paper aims to process the EEG signals for the recognition of human emotions specifically happiness,anger,fear,sadness,and surprise in response to audiovisual *** EEG signals are recorded by placing neurosky mindwave headset on the subject’s scalp,in response to audiovisual stimuli for the mentioned *** a bandpass filter with a bandwidth of 1-100 Hz,recorded raw EEG signals are *** preprocessed signals then further analyzed and twelve selected features in different domains are *** Random forest(RF)and multilayer perceptron(MLP)algorithms are then used for the classification of the emotions through extracted *** proposed audiovisual stimuli based EEG emotion classification system shows an average classification accuracy of 80%and 88%usingMLP and RF classifiers respectively on hybrid features for experimental signals of different *** proposed model outperforms in terms of cost and accuracy.

关键词： Electroencephalography emotion classification signal processing multilayer perceptron random forest

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Heart Disease Risk Prediction Expending of Classification Algorithms

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Computers, Materials & Continua 2022年第12期73卷 6595-6616页

作者： Nisha Mary Bilal Khan Abdullah A.Asiri Fazal Muhammad Salman Khan Samar Alqhtani Khlood M.Mehdar Hanan Talal Halwani Muhammad Irfan Khalaf a.alshamrani City University of Science and Information Technology Peshawar25000Pakistan Radiology Science Department College of Applied Medical Science Najran University Kingdom of Saudi Arabia Department of Electrical Engineering University of Engineering and TechnologyMardan23200Pakistan Department of Information Systems College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Anatomy Department Medicine CollegeNajran UniversityNajranSaudi Arabia Electrical Engineering Department College of EngineeringNajran University Saudi ArabiaNajran61441Saudi Arabia

Heart disease prognosis(HDP)is a difficult undertaking that requires knowledge and expertise to predict early *** failure is on the rise as a result of today’s *** healthcare business generates a vast volume of patient records,which are challenging to manage *** it comes to data mining and machine learning,having a huge volume of data is crucial for getting meaningful *** methods for predictingHDhave been used by researchers over the last few decades,but the fundamental concern remains the uncertainty factor in the output data,aswell as the need to decrease the error rate and enhance the accuracy of HDP assessment ***,in order to discover the optimal HDP solution,this study compares multiple classification algorithms utilizing two separate heart disease datasets from the Kaggle repository and the University of California,Irvine(UCI)machine learning *** a comparative analysis,Mean Absolute Error(MAE),Relative Absolute Error(RAE),precision,recall,fmeasure,and accuracy are used to evaluate Linear Regression(LR),Decision Tree(J48),Naive Bayes(NB),Artificial Neural Network(ANN),Simple Cart(SC),Bagging,Decision Stump(DS),AdaBoost,Rep Tree(REPT),and Support Vector Machine(SVM).Overall,the SVM classifier surpasses other classifiers in terms of increasing accuracy and decreasing error rate,with RAE of 33.2631 andMAEof 0.165,the precision of 0.841,recall of 0.835,f-measure of 0.833,and accuracy of 83.49 percent for the dataset gathered from *** SC improves accuracy and reduces the error rate for the Kaggle dataset,which is 3.30%for RAE,0.016 percent for MAE,0.984%for precision,0.984 percent for recall,0.984 percent for f-measure,and 98.44%for accuracy.

关键词： Heart disease heart disease datasets evaluation measures classification techniques

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Liver Ailment Prediction Using Random Forest Model

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Computers, Materials & Continua 2023年第1期74卷 1049-1067页

作者： Fazal Muhammad Bilal Khan Rashid Naseem Abdullah A Asiri hassan a.alshamrani Khalaf a.alshamrani Samar M Alqhtani Muhammad Irfan Khlood M Mehdar Hanan Talal Halawani Department of Electrical Engineering University of Engineering and TechnologyMardan23200Pakistan City University of Science and Information Technology PeshawarPakistan Department of IT and Computer Science Pak-Austria Fachhochschule Institute of Applied Sciences and TechnologyHaripurPakistan Radiological Sciences Department College of Applied Medical SciencesNajran UniversityNajranSaudi Arabia Department of Information Systems College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Electrical Engineering Department College of EngineeringNajran University Saudi ArabiaNajran61441Saudi Arabia Anatomy Department Medicine CollegeNajran UniversityNajranSaudi Arabia Computer Science Department College of Computer Science and Information SystemsNajran UniversityNajranSaudi Arabia

Today,liver disease,or any deterioration in one’s ability to survive,is extremely common all around the *** research has indicated that liver disease is more frequent in younger people than in older *** the liver’s capability begins to deteriorate,life can be shortened to one or two days,and early prediction of such diseases is *** several machine learning(ML)approaches,researchers analyzed a variety of models for predicting liver disorders in their early *** a result,this research looks at using the Random Forest(RF)classifier to diagnose the liver disease early *** dataset was picked from the University of California,Irvine ***’s accomplishments are contrasted to those of Multi-Layer Perceptron(MLP),Average One Dependency Estimator(A1DE),Support Vector Machine(SVM),Credal Decision Tree(CDT),Composite Hypercube on Iterated Random Projection(CHIRP),K-nearest neighbor(KNN),Naïve Bayes(NB),J48-Decision Tree(J48),and Forest by Penalizing Attributes(Forest-PA).Some of the assessment measures used to evaluate each classifier include Root Relative Squared Error(RRSE),Root Mean Squared Error(RMSE),accuracy,recall,precision,specificity,Matthew’s Correlation Coefficient(MCC),F-measure,and *** has an RRSE performance of 87.6766 and an RMSE performance of 0.4328,however,its percentage accuracy is *** widely acknowledged result of this work can be used as a starting point for subsequent *** a result,every claim that a new model,framework,or method enhances forecastingmay be benchmarked and demonstrated.

关键词： Liver ailment random forest machine learning

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Adaptive Cyber Defense Technique Based on Multiagent Reinforcement Learning Strategies

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Intelligent Automation & Soft Computing 2023年第6期36卷 2757-2771页

作者： Adel alshamrani Abdullah Alshahrani Department of Cybersecurity College of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia Department of Computer Science and Artificial Intelligence College of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia

The static nature of cyber defense systems gives attackers a sufficient amount of time to explore and further exploit the vulnerabilities of information technology *** this paper,we investigate a problem where multiagent sys-tems sensing and acting in an environment contribute to adaptive cyber *** present a learning strategy that enables multiple agents to learn optimal poli-cies using multiagent reinforcement learning(MARL).Our proposed approach is inspired by the multiarmed bandits(MAB)learning technique for multiple agents to cooperate in decision making or to work *** study a MAB approach in which defenders visit a system multiple times in an alternating fash-ion to maximize their rewards and protect their *** find that this game can be modeled from an individual player’s perspective as a restless MAB *** discover further results when the MAB takes the form of a pure birth process,such as a myopic optimal policy,as well as providing environments that offer the necessary incentives required for cooperation in multiplayer projects.

关键词： Multiarmed bandits reinforcement learning multiagents intrusion detection systems

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Gender Identification Using Marginalised Stacked Denoising Autoencoders on Twitter Data

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Intelligent Automation & Soft Computing 2023年第6期36卷 2529-2544页

作者： Badriyya B.Al-onazi Mohamed K.Nour hassan alshamrani Mesfer Al Duhayyim Heba Mohsen Amgad Atta Abdelmageed Gouse Pasha Mohammed Abu Sarwar Zamani Department of Language Preparation Arabic Language Teaching InstitutePrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversityMakkah24211Saudi Arabia Department of Teachers Training Arabic Linguistics InstituteKing Saud UniversityP.O.BOX 145111RiyadhZIP 4545Saudi Arabia Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversityAl-Aflaj16733Saudi 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

Gender analysis of Twitter could reveal significant socio-cultural differ-ences between female and male *** had been made to analyze and auto-matically infer gender formerly for more commonly spoken languages’content,but,as we now know that limited work is being undertaken for *** of the research works are done mainly for English and least amount of effort for non-English *** study for Arabic demographic inference like gen-der is relatively uncommon for social networking users,especially for ***,this study aims to design an optimal marginalized stacked denoising autoencoder for gender identification on Arabic Twitter(OMSDAE-GIAT)*** presented OMSDAE-GIAR technique mainly concentrates on the identifica-tion and classification of gender exist in the Twitter *** attain this,the OMS-DAE-GIAT model derives initial stages of data pre-processing and word ***,the MSDAE model is exploited for the identification of gender into two classes namely male and *** the final stage,the OMSDAE-GIAT technique uses enhanced bat optimization algorithm(EBOA)for parameter tuning process,showing the novelty of our *** performance validation of the OMSDAE-GIAT model is inspected against an Arabic corpus dataset and the results are measured under distinct *** comparison study reported the enhanced performance of the OMSDAE-GIAT model over other recent approaches.

关键词： Arabic twitter gender identification bat algorithm hybrid deep learning social media arabic corpus

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Deep Optimal VGG16 Based COVID-19 Diagnosis Model

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Computers, Materials & Continua 2022年第1期70卷 43-58页

作者： M.Buvana K.Muthumayil S.Senthil kumar Jamel Nebhen Sultan S.alshamrani Ihsan Ali Department of Computer Science and Engineering PSNA College of Engineering and TechnologyDindigul624622India Department of Information Technology PSNA College of Engineering and TechnologyDindigul624622India Department of Electrical and Electronics Engineering New Prince Shri Bhavani College of Engineering and TechnologyChennai600073India Prince Sattam Bin Abdulaziz University College of Computer Engineering and SciencesAlkharj11942Saudi Arabia Department of Information Technology College of Computer and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Computer System and Technology Faculty of Computer Science and Information TechnologyUniversity of MalayaKuala Lumpur50603Malaysia

Coronavirus(COVID-19)outbreak was first identified in Wuhan,China in December *** was tagged as a pandemic soon by the WHO being a serious public medical *** spite of the fact that the virus can be diagnosed by qRT-PCR,COVID-19 patients who are affected with pneumonia and other severe complications can only be diagnosed with the help of Chest X-Ray(CXR)and Computed Tomography(CT)*** this paper,the researchers propose to detect the presence of COVID-19 through images using Best deep learning model with various *** features like Speeded-Up Robust Features(SURF),Features from Accelerated Segment Test(FAST)and Scale-Invariant Feature Transform(SIFT)are used in the test images to detect the presence of *** optimal features are extracted from the images utilizing DeVGGCovNet(Deep optimal VGG16)model through optimal learning *** task is accomplished by exceptional mating conduct of Black Widow *** this strategy,cannibalism is *** this phase,fitness outcomes are rejected and are not satisfied by the proposed *** results acquired from real case analysis demonstrate the viability of DeVGGCovNet technique in settling true issues using obscure and testing ***16 model identifies the imagewhich has a place with which it is dependent on the distinctions in *** impact of the distinctions on labels during training stage is studied and predicted for test *** proposed model was compared with existing state-of-the-art models and the results from the proposed model for disarray grid estimates like Sen,Spec,Accuracy and F1 score were promising.

关键词： COVID 19 multi-feature extraction vgg16 optimal learning rate

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Topological Evaluation of Certain Computer Networks by Contraharmonic-Quadratic Indices

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Computers, Materials & Continua 2023年第2期74卷 3795-3810页

作者： Ahmed M.Alghamdi Khalid Hamid Muhammad Waseem Iqbal M.Usman Ashraf Abdullah Alshahrani Adel alshamrani Department of Software Engineering College of Computer Science and EngineeringUniversity of JeddahJeddah21493Saudi Arabia Department of Computer Science Superior UniversityLahore54000Pakistan Department of Software Engineering Superior UniversityLahore54000Pakistan Department of Computer Science GC Women UniversitySialkot53310Pakistan Department of Computer Science and Artificial Intelligence College of Computer Science and EngineeringUniversity of JeddahJeddah21493Saudi Arabia Department of Cybersecurity College of Computer Science and Engineering at University of JeddahJeddah21493Saudi Arabia

In various fields,different networks are used,most of the time not of a single kind;but rather a mix of at least two *** kinds of networks are called bridge networks which are utilized in interconnection networks of PC,portable networks,spine of internet,networks engaged with advanced mechanics,power generation interconnection,bio-informatics and substance intensify *** number that can be entirely calculated by a graph is called graph *** mathematical graph invariants have been portrayed and utilized for connection investigation during the latest twenty ***,no trustworthy evaluation has been embraced to pick,how much these invariants are associated with a network graph or subatomic *** this paper,it will discuss three unmistakable varieties of bridge networks with an incredible capacity of assumption in the field of computer science,chemistry,physics,drug industry,informatics and arithmetic in setting with physical and manufactured developments and networks,since Contraharmonic-quadratic invariants(CQIs)are recently presented and have different figure qualities for different varieties of bridge graphs or *** study settled the geography of bridge graphs/networks of three novel sorts with two kinds of CQI and Quadratic-Contraharmonic Indices(QCIs).The deduced results can be used for the modeling of the above-mentioned networks.

关键词： Bridge networks invariants Quadratic-Contraharmonic Indices maple network graph molecular graph

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Stability of within host HTLV-I/HIV-1 co-infection in the presence of macrophages

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International Journal of Biomathematics 2023年第1期16卷 129-156页

作者： A.M.Elaiw N.H.alshamrani E.Dahy A.A.Abdellatif Department of Mathematics Faculty of Science King Abdulaziz University P.0.Box 80203Jeddah 21589Saudi Arabia Department of Mathematics Faculty of Science Al-Azhar University Assiut BranchAssiutEgypt Department of Mathematics Faculty of Science University of Jeddah P.O.Bor 80327Jeddah 21589Saudi Arabia

The main target of both human immunodeficiency virus type 1(HIV-1)and human Tlymphotropic virus type I(HTLV-I)is the CD4+T cell which is considered the key player in the immune ***,HIV-1 has another target that is the *** present paper aims to formulate and develop a mathematical model to analyze the interaction of two viruses,HIV-1 and HTLV-I with the immune *** determine a bounded domain for the concentrations of the model's *** discuss the dynamical behavior of the model and analyze the existence and stability of the system's steady *** global asymptotic stability of all steady states is proven by utilizing the Lyapunov *** also demonstrate the dynamical behavior of the system *** significant impact of macrophages on the HTLV-I/HIV-1 co-infection dynamics is *** developed model will contribute to the understanding of HTLV-I/HIV-1 co-infection dynamics and help to choose different treatment strategies against HIV-1 and HTLV-I.

关键词： Viral co-infection CD4+T cells macrophages Lyapunov function global stability

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Cancellable Multi-Biometric Feature Veins Template Generation Based on SHA-3 Hashing

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Computers, Materials & Continua 2023年第1期74卷 733-749页

作者： Salwa M.Serag Eldin Ahmed Sedik Sultan S.alshamrani Ahmed M.Ayoup Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Robotics and Intelligent Machine Faculty of Artificial IntelligentKafrelsheikh UniversityEgypt Department of Information Technology College of Computer and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Electrical Communications Engineering Faculty of EngineeringMinia UniversityMinia61111Egypt

In this paper,a novel cancellable biometrics technique calledMulti-Biometric-Feature-Hashing(MBFH)is *** MBFH strategy is utilized to actualize a single direction(non-invertibility)biometric *** is a typical model security conspire that is distinguished in the utilization of this protection insurance framework in numerous sorts of biometric feature strategies(retina,palm print,Hand Dorsum,fingerprint).A more robust and accurate multilingual biological structure in expressing human loneliness requires a different format to record clients with inseparable comparisons from individual biographical *** may raise worries about their utilization and security when these spread out designs are subverted as everybody is acknowledged for another biometric *** proposed structure comprises of four sections:input multi-biometric acquisition,feature extraction,Multi-Exposure Fusion(MEF)and secure hashing calculation(SHA-3).Multimodal biometrics systems that are more powerful and precise in human-unmistakable evidence require various configurations to store a comparative customer that can be contrasted with biometric wellsprings of *** top words,biometrics graphs can’t be denied and change to another request for positive Identifications(IDs)while *** biometrics is may be the special procedure used to recognize this issue.

关键词： Feature extraction multi-biometrics SHA-3 MEF

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Effective Diagnosis of Lung Cancer via Various Data-Mining Techniques

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Intelligent Automation & Soft Computing 2023年第4期36卷 415-428页

作者： Subramanian Kanageswari D.Gladis Irshad Hussain Sultan S.alshamrani Abdullah Alshehri Bharathiar University Coimbatore641046India Bharathi Women’s College Chennai600108India Faculty of Electrical and Computer Engineering University of Engineering and TechnologyPeshawar25000PeshawarPakistan Department of Information Technology College of Computer and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Information Technology Al Baha UniversityP.O.Box 1988Al Baha65431Saudi Arabia

One of the leading cancers for both genders worldwide is lung *** occurrence of lung cancer has fully augmented since the early 19th *** this manuscript,we have discussed various data mining techniques that have been employed for cancer *** to air pollution has been related to various adverse health *** work is subject to analysis of various air pollutants and associated health hazards and intends to evaluate the impact of air pollution caused by lung *** have introduced data mining in lung cancer to air pollution,and our approach includes preprocessing,data mining,testing and evaluation,and knowledge ***,we will eradicate the noise and irrelevant data,and following that,we will join the multiple informed sources into a common *** that source,we will designate the information relevant to our investigation to be regained from that *** that,we will convert the designated data into a suitable mining *** patterns are abstracted by utilizing a relational suggestion rule mining *** patterns have revealed information,and this information is categorized with the help of an Auto Associative Neural Network classiﬁcation method(AANN).The proposed method is compared with the existing method in various *** conclusion,the projected Auto associative neural network and relational suggestion rule mining methods accomplish a high accuracy status.

关键词： Relational association rule mining auto associative neural network preprocessing data mining biological neural network

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