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Modelling the Psychological Impact of COVID-19 in Saudi Arabia Using Machine Learning

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Computers, Materials & Continua 2021年第5期67卷 2029-2047页

作者： mohammed A.Aleid Khaled A.Z.Alyamani Mohieddine Rahmouni Theyazn H.H.Aldhyani Nizar Alsharif mohammed y.alzahrani College of Education King Faisal UniversityAl-Ahsa31982Saudi Arabia Community College in Abqaiq King Faisal UniversityAl-Ahsa31982Saudi Arabia Department of Economics and Quantitative Methods ESSECTUniversity of TunisTunisTunisia Department of Computer Engineering and Science Al-Baha UniversityAl-BahahSaudi Arabia

This article aims to assess health habits,safety behaviors,and anxiety factors in the community during the novel coronavirus disease(COVID-19)pandemic in Saudi Arabia based on primary data collected through a questionnaire with 320 *** other words,this paper aims to provide empirical insights into the correlation and the correspondence between sociodemographic factors(gender,nationality,age,citizenship factors,income,and education),and psycho-behavioral effects on individuals in response to the emergence of this new *** focus on the interaction between these variables and their effects,we suggest different methods of analysis,comprising regression trees and support vector machine regression(SVMR)*** to the regression tree results,the age variable plays a predominant role in health habits,safety behaviors,and *** health habit index,which focuses on the extent of behavioral change toward the commitment to use the health and protection methods,is highly affected by gender and age *** average monthly income is also a relevant factor but has contrasting effects during the COVID-19 pandemic *** results of the SVMR model reveal a strong positive effect of income,with R^(2) values of 99.59%,99.93%and 99.88%corresponding to health habits,safety behaviors,and anxiety.

关键词： COVID-19 health habits safety behaviors anxiety support vector machine regression regression trees

Deep Learning and Holt-Trend Algorithms for Predicting Covid-19 Pandemic

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Computers, Materials & Continua 2021年第5期67卷 2141-2160页

作者： Theyazn H.H.Aldhyani Melfi Alrasheed Mosleh Hmoud Al-Adaileh Ahmed Abdullah Alqarni mohammed y.alzahrani Ahmed H.Alahmadi Community College of Abqaiq King Faisal UniversityAl HofufSaudi Arabia Department of Quantitative Methods School of BusinessKing Faisal UniversityAl HofufSaudi Arabia Deanship of E-Learning and Distance Education King Faisal UniversityAl HofufSaudi Arabia Department of Computer Sciences and Information Technology Albaha UniversityAl BahahSaudi Arabia Department of Computer Science and Information Taibah UniversityMadinahKingdom of Saudi Arabia

The Covid-19 epidemic poses a serious public health threat to the world,where people with little or no pre-existing human immunity can be more vulnerable to its ***,developing surveillance systems for predicting the Covid-19 pandemic at an early stage could save millions of *** this study,a deep learning algorithm and a Holt-trend model are proposed to predict the *** Long-Short Term Memory(LSTM)and Holttrend algorithms were applied to predict confirmed numbers and death *** real time data used has been collected from theWorld Health Organization(WHO).In the proposed research,we have considered three countries to test the proposed model,namely Saudi Arabia,Spain and *** results suggest that the LSTM models show better performance in predicting the cases of coronavirus *** measure performance Mean squared Error(MSE),Root Mean Squared Error(RMSE),Mean error and correlation are employed to estimate the results of the proposed *** empirical results of the LSTM,using the correlation metrics,are 99.94%,99.94%and 99.91%in predicting the number of confirmed cases in the three *** far as the results of the LSTM model in predicting the number of death of Covid-19,they are 99.86%,98.876%and 99.16%with respect to Saudi Arabia,Italy and Spain ***,the experiment’s results of the Holt-Trend model in predicting the number of confirmed cases of Covid-19,using the correlation metrics,are 99.06%,99.96%and 99.94%,whereas the results of the Holt-Trend model in predicting the number of death cases are 99.80%,99.96%and 99.94%with respect to the Saudi Arabia,Italy and Spain *** empirical results indicate the efficient performance of the presented model in predicting the number of confirmed and death cases of Covid-19 in these *** findings provide better insights regarding the future of Covid-19 this pandemic in *** results were obtained by applying time series models,which

关键词： Deep learning algorithm holt-trend prediction Covid-19 machine learning

Human-Animal Affective Robot Touch Classification Using Deep Neural Network

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Computer Systems Science & Engineering 2021年第7期38卷 25-37页

作者： mohammed Ibrahim Ahmed Al-mashhadani Theyazn H.H.Aldhyani Mosleh Hmoud Al-Adhaileh Alwi M.Bamhdi mohammed y.alzahrani Fawaz Waselallah Alsaade Hasan Alkahtani AL-Iraqia University College of EducationComputer DepartmentBaghdadIraq Community College of Abqaiq King Faisal UniversityAl-AhsaSaudi Arabia Deanship of E-learning and Distance Education King Faisal UniversityAl-AhsaSaudi Arabia UMM Al-Qura University College of ComputingMakkahSaudi Arabia Department of Computer Sciences and Information Technology Albaha UniversityAl BahaSaudi Arabia College of Computer Science and Information Technology King Faisal UniversityAl-AhsaSaudi Arabia

Touch gesture recognition is an important aspect in human-robot interaction,as it makes such interaction effective and *** novelty of this study is the development of a system that recognizes human-animal affective robot touch(HAART)using a deep learning *** proposed system was used for touch gesture recognition based on a dataset provided by the Recognition of the Touch Gestures Challenge *** dataset was tested with numerous subjects performing different HAART gestures;each touch was performed on a robotic animal covered by a pressure sensor skin.A convolutional neural network algorithm is proposed to implement the touch recognition system from row inputs of the sensor *** leave-one-subject-out cross-validation method was used to validate and evaluate the proposed system.A comparative analysis between the results of the proposed system and the state-of-the-art performance is *** show that the proposed system could recognize the gestures in almost real time(after acquiring the minimum number of frames).According to the results of the leave-one-subject-out cross-validation method,the proposed algorithm could achieve a classification accuracy of 83.2%.It was also superior compared with existing systems in terms of classification ratio,touch recognition time,and data preprocessing on the same ***,the proposed system can be used in a wide range of real applications,such as image recognition,natural language recognition,and video clip classification.

关键词： Touch gesture recognition touch gesture classification deep learning

Arabic Feature-Based Text Watermarking Technique for Sensitive Detecting Tampering Attack

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Computers, Materials & Continua 2021年第9期68卷 3789-3806页

作者： Fahd N.Al-Wesabi Huda G.Iskandar Saleh alzahrani Abdelzahir Abdelmaboud mohammed Abdul Nadhem Nemri Mohammad Medani mohammed y.Alghamdi Department of Computer Science King Khalid UniversityMuhayel AseerKingdom of Saudi Arabia Faculty of Computer and IT Sana’a UniversitySana’aYemen School of Computing Faculty of EngineeringUniversiti Teknologi MalaysiaMalaysia Department of Information Systems King Khalid UniversityMuhayel AseerKingdom of Saudi Arabia Department of Computer Science Faculty of Science&Arts of BaljurshiAl-Baha UniversityKSA

In this article,a high-sensitive approach for detecting tampering attacks on transmitted Arabic-text over the Internet(HFDATAI)is proposed by integrating digital watermarking and hidden Markov model as a strategy for soft *** HFDATAI solution technically integrates and senses the watermark without modifying the original *** alphanumeric mechanism order in the first stage focused on the Markov model key secret is incorporated into an automated,null-watermarking approach to enhance the proposed approach’s efficiency,accuracy,and *** first-level order and alphanumeric Markov model technique have been used as a strategy for soft computing to analyze the text of the Arabic *** addition,the features of the interrelationship among text contexts and characteristics of watermark information extraction that is used later validated for detecting any tampering of the Arabic-text *** HFDATAI strategy was introduced based on PHP with included IDE of VS *** of four separate duration datasets in random sites illustrate the fragility,efficacy,and applicability of HFDATAI by using the three common tampering attacks i.e.,insertion,reorder,and *** HFDATAI was found to be effective,applicable,and very sensitive for detecting any possible tampering on Arabic text.

关键词： Watermarking soft computing text analysis hidden Markov model content authentication

A Reliable NLP Scheme for English Text Watermarking Based on Contents Interrelationship

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Computer Systems Science & Engineering 2021年第6期37卷 297-311页

作者： Fahd N.Al-Wesabi Saleh alzahrani Fuad Alyarimi mohammed Abdul Nadhem Nemri mohammed M.Almazah Department of Computer Science King Khalid UniversityMuhayel AseerKingdom of Saudi Arabia Faculty of Computer and IT Sana’a UniversitySana’aYemen Department of Information Systems King Khalid UniversityMuhayel AseerKingdom of Saudi Arabia Department of Mathematics and computer in Ibb University Yemen and Department of MathematicsKing Khalid UniversityMuhayel AseerKingdom of Saudi Arabia

In this paper,a combined approach CAZWNLP(a combined approach of zero-watermarking and natural language processing)has been developed for the tampering detection of English text exchanged through the *** third gram of alphanumeric of the Markov model has been used with text-watermarking technologies to improve the performance and accuracy of tampering detection issues which are limited by the existing works reviewed in the literature of this *** third-grade level of the Markov model has been used in this method as natural language processing technology to analyze an English text and extract the textual characteristics of the given ***,the extracted features have been utilized as watermark information and then validated with the attacked English text to detect any suspected tampering occurred on *** embedding mechanism of CAZWNLP method will be achieved logically without effects or modifying the original text document to embed a watermark *** has been implemented using VS code IDE with *** experimental and simulation results using standard datasets of varying lengths show that the proposed approach can obtain high robustness and better detection accuracy of tampering common random insertion,reorder,and deletion attacks,e.g.,Comparison results with baseline approaches also show the advantages of the proposed approach.

关键词： NLP text analysis English text watermarking robustness tampering detection

Modeling of Combined Economic and Emission Dispatch Using Improved Sand Cat Optimization Algorithm

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Computers, Materials & Continua 2023年第6期75卷 6145-6160页

作者： Fadwa Alrowais Jaber S.alzahrani Radwa Marzouk Abdullah Mohamed Gouse Pasha mohammed Department of Computer Sciences 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 UniversityMecca24382Saudi Arabia Research Centre Future University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Combined Economic and Emission Dispatch(CEED)task forms multi-objective optimization problems to be resolved to minimize emission and fuel *** disadvantage of the conventional method is its incapability to avoid falling in local optimal,particularly when handling nonlinear and complex *** have recently received considerable attention due to their enhanced capacity to prevent local optimal solutions in addressing all the optimization problems as a black ***,this paper focuses on the design of an improved sand cat optimization algorithm based CEED(ISCOA-CEED)*** ISCOA-CEED technique majorly concen-trates on reducing fuel costs and the emission of generation ***,the presented ISCOA-CEED technique transforms the equality constraints of the CEED issue into inequality ***,the improved sand cat optimization algorithm(ISCOA)is derived from the integration of tra-ditional SCOA with the Levy Flight(LF)*** last,the ISCOA-CEED technique is applied to solve a series of 6 and 11 generators in the CEED *** experimental validation of the ISCOA-CEED technique ensured the enhanced performance of the presented ISCOA-CEED technique over other recent approaches.

关键词： Economic and emission dispatch multi-objective optimization metaheuristics fuel cost minimization sand cat optimization

Improved Multileader Optimization with Shadow Encryption for Medical Images in IoT Environment

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Computers, Materials & Continua 2023年第2期74卷 3133-3149页

作者： Mesfer Al Duhayyim mohammed Maray Ayman Qahmash Fatma S.Alrayes Nuha Alshuqayran Jaber S.alzahrani mohammed Alghamdi Abdullah Mohamed Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam Bin Abdulaziz UniversitySaudi Arabia Department of Information Systems College of Computer ScienceKing Khalid UniversityAbhaSaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh 11671Saudi Arabia Department of Information Systems College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic UniversitySaudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Information and Technology Systems College of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia Research Centre Future University in EgyptNew Cairo11845Egypt

Nowadays,security plays an important role in Internet of Things(IoT)environment especially in medical services’domains like disease prediction and medical data *** healthcare sector,huge volumes of data are generated on a daily basis,owing to the involvement of advanced health care *** general terms,health care images are highly sensitive to alterations due to which any modifications in its content can result in faulty *** the same time,it is also significant to maintain the delicate contents of health care images during reconstruction ***,an encryption system is required in order to raise the privacy and security of healthcare data by not leaking any sensitive *** current study introduces Improved Multileader Optimization with Shadow Image Encryption for Medical Image Security(IMLOSIE-MIS)technique for IoT *** aim of the proposed IMLOSIE-MIS model is to accomplish security by generating shadows and encrypting them *** do so,the presented IMLOSIE-MIS model initially generates a set of shadows for every input medical ***,shadow image encryption process takes place with the help of Multileader Optimization(MLO)withHomomorphic Encryption(IMLO-HE)technique,where the optimal keys are generated with the help of MLO *** the receiver side,decryption process is initially carried out and shadow image reconstruction process is *** experimentation analysis was carried out on medical images and the results inferred that the proposed IMLOSIE-MIS model is an excellent performer compared to other *** comparison study outcomes demonstrate that IMLOSIE-MIS model is robust and offers high security in IoT-enabled healthcare environment.

关键词： Medical image security image encryption shadow images homomorphic encryption optimal key generation

The User Analysis of Amazon Using Artificial Intelligence at Customer Churn

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Journal of Data Analysis and Information Processing 2024年第1期12卷 40-48页

作者： mohammed Ali alzahrani Information Technology Department Computing and ICT Faculty Taif University Taif Saudi Arabia

Customer churns remains a key focus in this research, using artificial intelligence-based technique of machine learning. Research is based on the feature-based analysis four main features were used that are selected on the basis of our customer churn to deduct the meaning full analysis of the data set. Data-set is taken from the Kaggle that is about the fine food review having more than half a million records in it. This research remains on feature based analysis that is further concluded using confusion matrix. In this research we are using confusion matrix to conclude the customer churn results. Such specific analysis helps e-commerce business for real time growth in their specific products focusing more sales and to analyze which product is getting outage. Moreover, after applying the techniques, Support Vector Machine and K-Nearest Neighbour perform better than the random forest in this particular scenario. Using confusion matrix for obtaining the results three things are obtained that are precision, recall and accuracy. The result explains feature-based analysis on fine food reviews, Amazon at customer churn Support Vector Machine performed better as in overall comparison.

关键词： Customer Churn Machine Learning Amazon Fine Food Reviews Data Science Artificial Intelligence

Classification of Electroencephalogram Signals Using LSTM and SVM Based on Fast Walsh-Hadamard Transform

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Computers, Materials & Continua 2023年第6期75卷 5271-5286页

作者： Saeed Mohsen Sherif S.M.Ghoneim mohammed S.Alzaidi Abdullah alzahrani Ashraf Mohamed Ali Hassan Electronics and Communications Engineering Department Al-Madinah Higher Institute for Engineering and TechnologyGiza12947Egypt Department of Artificial Intelligence Engineering Faculty of Computer Science and EngineeringKing Salman International University(KSIU)South Sinai46511Egypt Department of Electrical Engineering College of EngineeringTaif UniversityTaif21944Saudi Arabia Department of Electronics and Communications Engineering October High Institute for Engineering and Technology6th of October CityGiza12596Egypt

Classification of electroencephalogram(EEG)signals for humans can be achieved via artificial intelligence(AI)***,the EEG signals associated with seizure epilepsy can be detected to distinguish between epileptic and non-epileptic *** this perspective,an automated AI technique with a digital processing method can be used to improve these *** paper proposes two classifiers:long short-term memory(LSTM)and support vector machine(SVM)for the classification of seizure and non-seizure EEG *** classifiers are applied to a public dataset,namely the University of Bonn,which consists of 2 classes–seizure and *** addition,a fast Walsh-Hadamard Transform(FWHT)technique is implemented to analyze the EEG signals within the recurrence space of the ***,Hadamard coefficients of the EEG signals are obtained via the ***,the FWHT is contributed to generate an efficient derivation of seizure EEG recordings from non-seizure EEG ***,a k-fold cross-validation technique is applied to validate the performance of the proposed *** LSTM classifier provides the best performance,with a testing accuracy of 99.00%.The training and testing loss rates for the LSTM are 0.0029 and 0.0602,respectively,while the weighted average precision,recall,and F1-score for the LSTM are 99.00%.The results of the SVM classifier in terms of accuracy,sensitivity,and specificity reached 91%,93.52%,and 91.3%,*** computational time consumed for the training of the LSTM and SVM is 2000 and 2500 s,*** results show that the LSTM classifier provides better performance than SVM in the classification of EEG ***,the proposed classifiers provide high classification accuracy compared to previously published classifiers.

关键词： Electroencephalogram LSTM SVM fast Walsh-Hadamard transform seizure accuracy sensitivity specificity