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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

Enhanced Adaptive Brain-Computer Interface Approach for Intelligent Assistance to Disabled Peoples

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Computer Systems Science & Engineering 2023年第8期46卷 1355-1369页

作者： Ali Usman Javed Ferzund Ahmad Shaf muhammad Aamir Samar Alqhtani khlood m.mehdar Hanan Talal Halawani Hassan A.Alshamrani Abdullah A.Asiri muhammad Irfan Department of Computer Science COMSATS University IslamabadSahiwal CampusSahiwal57000Pakistan Department of Information Systems College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Anatomy Department Medicine CollegeNajran UniversityNajran61441Saudi Arabia Computer Science Department College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Radiological Sciences Department College of Applied Medical Sciences and Information SystemsNajran UniversityNajran61441Kingdom of Saudi Arabia Electrical Engineering Department College of EngineeringNajran UniversityNajran61441Saudi Arabia

Assistive devices for disabled people with the help of Brain-Computer Interaction(BCI)technology are becoming vital bio-medical *** with physical disabilities need some assistive devices to perform their daily *** these devices,higher latency factors need to be addressed ***,the main goal of this research is to implement a real-time BCI architecture with minimum latency for command *** proposed architecture is capable to communicate between different modules of the system by adopting an automotive,intelligent data processing and classification ***-sky mind wave device has been used to transfer the data to our implemented server for command ***-Net Convolutional Neural Network(TN-CNN)architecture has been proposed to recognize the brain signals and classify them into six primary mental states for data *** collection and processing are the responsibility of the central integrated server for system load *** of implemented architecture and deep learning model shows excellent *** proposed system integrity level was the minimum data loss and the accurate commands processing *** training and testing results are 99%and 93%for custom model implementation based on *** proposed real-time architecture is capable of intelligent data processing unit with fewer errors,and it will benefit assistive devices working on the local server and cloud server.

关键词： Disable person electroencephalogram convolutional neural network brain signal classification

multi-Level Deep Generative Adversarial Networks for Brain Tumor Classification on magnetic Resonance Images

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Intelligent Automation & Soft Computing 2023年第4期36卷 127-143页

作者： Abdullah A.Asiri Ahmad Shaf Tariq Ali muhammad Aamir Ali Usman muhammad Irfan Hassan A.Alshamrani khlood m.mehdar Osama m.Alshehri Samar m.Alqhtani Radiological Sciences Department College of Applied Medical SciencesNajran UniversityNajran61441Saudi Arabia Department of Computer Science COMSATS University IslamabadSahiwal CampusSahiwal57000Pakistan Electrical Engineering Department College of EngineeringNajran UniversityNajran61441Saudi Arabia Anatomy Department Medicine CollegeNajran UniversityNajranSaudi Arabia Department of Clinical Laboratory Sciences Collage of Applied Medical ScienceNajran UniversityNajranSaudi Arabia Department of Information Systems College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia

The brain tumor is an abnormal and hysterical growth of brain tissues,and the leading cause of death affected patients *** in this technol-ogy-based arena,brain tumor images with proper labeling and acquisition still have a problem with the accurate and reliable generation of realistic images of brain tumors that are completely different from the original *** artiﬁcially created medical image data would help improve the learning ability of physicians and other computer-aided systems for the generation of augmented *** over-come the highlighted issue,a Generative Adversarial Network(GAN)deep learn-ing technique in which two neural networks compete to become more accurate in creating artiﬁcially realistic data for mRI *** GAN network contains mainly two parts known as generator and ***,a generator is the convolutional neural network,and a discriminator is the deconvolutional neural *** this research,the publicly accessible Contrast-Enhanced mag-netic Resonance Imaging(CE-mRI)dataset collected from 2005-to 2020 from different hospitals in China consists of four classes has been *** proposed method is simple and achieved an accuracy of 96%.We compare our technique results with the existing results,indicating that our proposed technique outper-forms the best results associated with the existing methods.

关键词： GAN network CE-mRI images convolutional neural network brain tumor classiﬁcation

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

Human Emotions Classification Using EEG via Audiovisual Stimuli and AI

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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

BotSward: Centrality measures for Graph-Based Bot Detection Using machine Learning

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Computers, materials & Continua 2023年第1期74卷 693-714页

作者： khlood Shinan Khalid Alsubhi m.Usman Ashraf Department of Computer Science College Computer Science in Al-LeithUmm Al-Qura UniversityMecca 21421Saudi Arabia Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah 21589Saudi Arabia Department of Computer Science GC Women University SialkotPakistan

The number of botnet malware attacks on Internet devices has grown at an equivalent rate to the number of Internet devices that are connected to the *** detection using machine learning(mL)with flow-based features has been extensively studied in the *** flow-based detection methods involve significant computational overhead that does not completely capture network communication patterns that might reveal other features ofmalicious ***,Graph-Based Bot Detection methods using mL have gained attention to overcome these limitations,as graphs provide a real representation of network *** purpose of this study is to build a botnet malware detection system utilizing centrality measures for graph-based botnet detection and *** propose BotSward,a graph-based bot detection system that is based on *** apply the efficient centrality measures,which are Closeness Centrality(CC),Degree Centrality(CC),and PageRank(PR),and compare them with others used in the *** efficiency of the proposed method is verified on the available Czech Technical University 13 dataset(CTU-13).The CTU-13 dataset contains 13 real botnet traffic scenarios that are connected to a command-and-control(C&C)channel and that cause malicious actions such as phishing,distributed denial-of-service(DDoS)attacks,spam attacks,*** is robust to zero-day attacks,suitable for large-scale datasets,and is intended to produce better accuracy than state-of-the-art *** proposed BotSward solution achieved 99%accuracy in botnet attack detection with a false positive rate as low as 0.0001%.

关键词： Network security botnet detection graph-based features machine learning measure centrality

Investigating of Classification Algorithms for Heart Disease Risk Prediction

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Journal of Intelligent Medicine and Healthcare

Journal of Intelligent medicine and Healthcare 2022年第1期1卷 11-31页

作者： Nisha mary Bilal Khan Abdullah A Asiri Fazal muhammad Samar Alqhtani khlood m.mehdar Hanan Talal Halwani Turki Aleyani Khalaf A Alshamrani City University of Science and Information Technology Peshawar25000Pakistan Radiology Science Department College of Applied medical Science Najran UniversityKingdom of Saudi Arabia Department of Electrical Engineering University of Engineering and TechnologyMardan23200Pakistan Computer Science Department College of Computer Science and Information SystemNajran UniversitySaudi Arabia Anatomy Department Medicine CollegeNajran UniversitySaudi Arabi

Prognosis of HD is a complex task that requires experience andexpertise to predict in the early stage. Nowadays, heart failure is rising dueto the inherent lifestyle. The healthcare industry generates dense records ofpatients, which cannot be managed manually. Such an amount of data is verysignificant in the field of data mining and machine learning when gatheringvaluable knowledge. During the last few decades, researchers have used differentapproaches for the prediction of HD, but still, the major problem is theuncertainty factor in the output data and also there is a need to reduce theerror rate and increase the accuracy of evaluation metrics for HDP. However,this study largess the comparative analysis of diverse classification algorithmsgoing on two different heart disease datasets taken from the Kaggle repositoryand University of California, Irvine (UCI) machine learning repository tofind the best solution for HDP. Going through comparative analysis, tenclassifiers;LR, J48, NB, ANN, SC, Bagging, DS, AdaBoost, REPT, and SVmare evaluated using mAE, RAE, precision, recall, f-measure, and *** overall finding indicates that for the dataset taken from UCI, the SVmclassifier performs well as compared to other classifiers in terms of increasingaccuracy and reducing error rate that is 33.2631 for RAE, and 0.165 formAE, 0.841 for precision, 0.835 for recall, 0.833 for f-measure and 83.49%for accuracy. Whereas for dataset taken from Kaggle, the SC performs well interms of increasing accuracy and reducing error rate that is 3.30% for RAE,0.016 for mAE, 0.984 for precision, 0.984 for recall, 0.984 for f-measure, and98.44% for accuracy.

关键词： Heart disease classification techniques evaluation measures