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Impact of COVID-19 on G20 countries:analysis of economic recession using data mining approaches

Financial Innovation 2022年第1期8卷 2208-2237页

作者： Osman Taylan abdulaziz s.alkabaa Mustafa Tahsin Yılmaz Department of Industrial Engineering Faculty of EngineeringKing Abdulaziz UniversityP.O.Box 80204Jeddah 21589Saudi Arabia

The G20 countries are the locomotives of economic growth,representing 64%of the global population and including 4.7 billion *** a monetary and market value index,real gross domestic product(GDP)is affected by several factors and reflects the economic development of *** study aimed to reveal the hidden economic patterns of G20 countries,study the complexity of related economic factors,and analyze the economic reactions taken by policymakers during the coronavirus disease of 2019(COVID-19)pandemic recession(2019–2020).In this respect,this study employed data-mining techniques of nonparametric classification tree and hierarchical clustering approaches to consider factors such as GDP/capita,industrial production,government spending,COVID-19 cases/population,patient recovery,COVID-19 death cases,number of hospital beds/1000 people,and percentage of the vaccinated population to identify clusters for G20 *** clustering approach can help policymakers measure economic indices in terms of the factors considered to identify the specific focus of influences on economic *** results exhibited significant findings for the economic effects of the COVID-19 pandemic on G20 countries,splitting them into three clusters by sharing different measurements and patterns(harmonies and variances across G20 countries).A comprehensive statistical analysis was performed to analyze endogenous and exogenous ***,the classification and regression tree method was applied to predict the associations between the response and independent factors to split the G-20 countries into different groups and analyze the economic *** such as GDP per capita and patient recovery of COVID-19 cases with values of$12,012 and 82.8%,respectively,were the most significant factors for clustering the G20 countries,with a correlation coefficient(R2)of 91.8%.The results and findings offer some crucial recommendations to handle pandemics in terms of the

关键词： Hierarchical clustering CART Economic recession Data mining COVID-19 G20 countries

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Prediction of Geopolymer Concrete Compressive strength Using Convolutional Neural Networks

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Computer Modeling in Engineering & sciences 2024年第5期139卷 1455-1486页

作者： Kolli Ramujee Pooja sadula Golla Madhu sandeep Kautish abdulaziz s.Almazyad Guojiang Xiong Ali Wagdy Mohamed Department of Civil Engineering VNR Vignana Jyothi Institute of Engineering and TechnologyHyderabadTelangana500090India Department of Information Technology VNR Vignana Jyothi Institute of Engineering and TechnologyHyderabadTelangana500090India Department of Computer science LBEF Campus(Asia Pacific University of Technology&InnovationMalaysia)Kathmandu44600Nepal Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia Guizhou Key Laboratory of Intelligent Technology in Power system College of Electrical EngineeringGuizhou UniversityGuiyang550025China Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Applied science Research Center Applied Science Private UniversityAmmanJordan

Geopolymer concrete emerges as a promising avenue for sustainable development and offers an effective solution to environmental *** attributes as a non-toxic,low-carbon,and economical substitute for conventional cement concrete,coupled with its elevated compressive strength and reduced shrinkage properties,position it as a pivotal material for diverse applications spanning from architectural structures to transportation *** this context,this study sets out the task of using machine learning(ML)algorithms to increase the accuracy and interpretability of predicting the compressive strength of geopolymer concrete in the civil engineering *** achieve this goal,a new approach using convolutional neural networks(CNNs)has been *** study focuses on creating a comprehensive dataset consisting of compositional and strength parameters of 162 geopolymer concrete mixes,all containing Class F fly *** selection of optimal input parameters is guided by two distinct *** first criterion leverages insights garnered from previous research on the influence of individual features on compressive *** second criterion scrutinizes the impact of these features within the model’s predictive *** to enhancing the CNN model’s performance is the meticulous determination of the optimal *** a systematic trial-and-error process,the study ascertains the ideal number of epochs for data division and the optimal value of k for k-fold cross-validation—a technique vital to the model’s *** model’s predictive prowess is rigorously assessed via a suite of performance metrics and comprehensive score ***,the model’s adaptability is gauged by integrating a secondary dataset into its predictive framework,facilitating a comparative evaluation against conventional prediction *** unravel the intricacies of the CNN model’s learning trajectory,a loss plot is deployed to elucidate its learning rat

关键词： Class F fly ash compressive strength geopolymer concrete prediction deep learning convolutional neural network

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A Lightweight Intrusion Detection system Using Convolutional Neural Network and Long short-Term Memory in Fog Computing

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Computers, Materials & Continua 2024年第9期80卷 4703-4728页

作者： Hawazen Alzahrani Tarek sheltami abdulaziz Barnawi Muhammad Imam Ansar Yaser Computer Engineering Department Interdisciplinary Research Center of Smart Mobility and LogisticsKing Fahd University of Petroleum and MineralsDhahran31261Saudi Arabia Computer Engineering Department Interdisciplinary Research Center for Intelligent Secure SystemsKing Fahd University of Petroleum and MineralsDhahran31261Saudi Arabia Transportation Research Institute(IMOB) Hasselt UniversityHasselt3500Belgium

The Internet of Things(IoT)links various devices to digital services and significantly improves the quality of our ***,as IoT connectivity is growing rapidly,so do the risks of network vulnerabilities and *** interesting Intrusion Detection systems(IDss)are presented based on machine learning(ML)techniques to overcome this *** the resource limitations of fog computing environments,a lightweight IDs is *** paper introduces a hybrid deep learning(DL)method that combines convolutional neural networks(CNN)and long short-term memory(LsTM)to build an energy-aware,anomaly-based *** test this system on a recent dataset,focusing on reducing overhead while maintaining high accuracy and a low false alarm *** compare CICIoT2023,KDD-99 and NsL-KDD datasets to evaluate the performance of the proposed IDs model based on key metrics,including latency,energy consumption,false alarm rate and detection rate *** findings show an accuracy rate over 92%and a false alarm rate below 0.38%.These results demonstrate that our system provides strong security without excessive resource *** practicality of deploying IDs with limited resources is demonstrated by the successful implementation of IDs functionality on a Raspberry Pi acting as a Fog *** proposed lightweight model,with a maximum power consumption of 6.12 W,demonstrates its potential to operate effectively on energy-limited devices such as low-power fog nodes or edge *** prioritize energy efficiency whilemaintaining high accuracy,distinguishing our scheme fromexisting *** experiments demonstrate a significant reduction in false positives,ensuring accurate identification of genuine security threats while minimizing unnecessary alerts.

关键词： Intrusion detection fog computing CNN LsTM energy consumption

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BHJO: A Novel Hybrid Metaheuristic Algorithm Combining the Beluga Whale, Honey Badger, and Jellyfish search Optimizers for solving Engineering Design Problems

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Computer Modeling in Engineering & sciences 2024年第10期141卷 219-265页

作者： Farouq Zitouni saad Harous abdulaziz s.Almazyad Ali Wagdy Mohamed Guojiang Xiong Fatima Zohra Khechiba Khadidja Â Kherchouche Department of Computer science and Information Technology Kasdi Merbah UniversityP.O.Box 30000OuarglaAlgeria Department of Computer science College of Computing and InformaticsUniversity of SharjahP.O.Box 27272SharjahUnited Arab Emirates Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Applied science Research Center Applied Science Private UniversityAmman11937Jordan Guizhou Key Laboratory of Intelligent Technology in Power system College of Electrical EngineeringGuizhou UniversityGuiyang550025China

Hybridizing metaheuristic algorithms involves synergistically combining different optimization techniques to effectively address complex and challenging optimization *** approach aims to leverage the strengths of multiple algorithms,enhancing solution quality,convergence speed,and robustness,thereby offering a more versatile and efficient means of solving intricate real-world optimization *** this paper,we introduce a hybrid algorithm that amalgamates three distinct metaheuristics:the Beluga Whale Optimization(BWO),the Honey Badger Algorithm(HBA),and the Jellyfish search(Js)*** proposed hybrid algorithm will be referred to as *** this fusion,the BHJO algorithm aims to leverage the strengths of each *** this hybridization,we thoroughly examined the exploration and exploitation capabilities of the BWO,HBA,and Js metaheuristics,as well as their ability to strike a balance between exploration and *** meticulous analysis allowed us to identify the pros and cons of each algorithm,enabling us to combine them in a novel hybrid approach that capitalizes on their respective strengths for enhanced optimization *** addition,the BHJO algorithm incorporates Opposition-Based Learning(OBL)to harness the advantages offered by this technique,leveraging its diverse exploration,accelerated convergence,and improved solution quality to enhance the overall performance and effectiveness of the hybrid ***,the performance of the BHJO algorithm was evaluated across a range of both unconstrained and constrained optimization problems,providing a comprehensive assessment of its efficacy and applicability in diverse problem ***,the BHJO algorithm was subjected to a comparative analysis with several renowned algorithms,where mean and standard deviation values were utilized as evaluation *** rigorous comparison aimed to assess the performance of the BHJOalgorithmabout its counterparts,shedd

关键词： Global optimization hybridization of metaheuristics beluga whale optimization honey badger algorithm jellyfish search optimizer chaotic maps opposition-based learning

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static Analysis Techniques for Fixing software Defects in MPI-Based Parallel Programs

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Computers, Materials & Continua 2024年第5期79卷 3139-3173页

作者： Norah Abdullah Al-Johany sanaa Abdullah sharaf Fathy Elbouraey Eassa Reem abdulaziz Alnanih Department of Computer science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia software Engineering and Distributed system Research Group King Abdulaziz UniversityJeddah21589Saudi Arabia

The Message Passing Interface (MPI) is a widely accepted standard for parallel computing on distributed ***, MPI implementations can contain defects that impact the reliability and performance of parallelapplications. Detecting and correcting these defects is crucial, yet there is a lack of published models specificallydesigned for correctingMPI defects. To address this, we propose a model for detecting and correcting MPI defects(DC_MPI), which aims to detect and correct defects in various types of MPI communication, including blockingpoint-to-point (BPTP), nonblocking point-to-point (NBPTP), and collective communication (CC). The defectsaddressed by the DC_MPI model include illegal MPI calls, deadlocks (DL), race conditions (RC), and messagemismatches (MM). To assess the effectiveness of the DC_MPI model, we performed experiments on a datasetconsisting of 40 MPI codes. The results indicate that the model achieved a detection rate of 37 out of 40 codes,resulting in an overall detection accuracy of 92.5%. Additionally, the execution duration of the DC_MPI modelranged from 0.81 to 1.36 s. These findings show that the DC_MPI model is useful in detecting and correctingdefects in MPI implementations, thereby enhancing the reliability and performance of parallel applications. TheDC_MPImodel fills an important research gap and provides a valuable tool for improving the quality ofMPI-basedparallel computing systems.

关键词： High-performance computing parallel computing software engineering software defect message passing interface deadlock

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Robust Facial Biometric Authentication system Using Pupillary Light Reflex for Liveness Detection of Facial Images

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Computer Modeling in Engineering & sciences 2024年第4期139卷 725-739页

作者： Puja s.Prasad Adepu sree Lakshmi sandeep Kautish simar Preet singh Rajesh Kumar shrivastava abdulaziz s.Almazyad Hossam M.Zawbaa Ali Wagdy Mohamed CsE Department Geethanjali College of EngineeringHyderabadTelangana501301India Department of Computer science LBEF CampusKathmandu44600Nepal school of Computer science Engineering and Technology(sCsET) Bennett UniversityGreater Noida201310India Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia CeADAR Ireland’s Centre for AI Technological University DublinDublinD07 EWV4Ireland Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Applied science Research Center Applied Science Private UniversityAmman11937Jordan

Pupil dynamics are the important characteristics of face spoofing *** face recognition system is one of the most used biometrics for authenticating individual *** main threats to the facial recognition system are different types of presentation attacks like print attacks,3D mask attacks,replay attacks,*** proposed model uses pupil characteristics for liveness detection during the authentication *** pupillary light reflex is an involuntary reaction controlling the pupil’s diameter at different light *** proposed framework consists of two-phase *** the first phase,the pupil’s diameter is calculated by applying stimulus(light)in one eye of the subject and calculating the constriction of the pupil size on both eyes in different video *** above measurement is converted into feature space using Kohn and Clynes model-defined *** support Vector Machine is used to classify legitimate subjects when the diameter change is normal(or when the eye is alive)or illegitimate subjects when there is no change or abnormal oscillations of pupil behavior due to the presence of printed photograph,video,or 3D mask of the subject in front of the *** the second phase,we perform the facial recognition ***-invariant feature transform(sIFT)is used to find the features from the facial images,with each feature having a size of a 128-dimensional *** features are scale,rotation,and orientation invariant and are used for recognizing facial *** brute force matching algorithm is used for matching features of two different *** threshold value we considered is 0.08 for good *** analyze the performance of the framework,we tested our model in two Face antispoofing datasets named Replay attack datasets and CAsIA-sURF datasets,which were used because they contain the videos of the subjects in each sample having three modalities(RGB,IR,Depth).The CAsIA-sURF datasets showed an 89.9%Equal Err

关键词： sIFT pupil CAsIA-sURF pupillary light reflex replay attack dataset brute force

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Fuzzy Difference Equations in Diagnoses of Glaucoma from Retinal Images Using Deep Learning

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Computer Modeling in Engineering & sciences 2024年第4期139卷 801-816页

作者： D.Dorathy Prema Kavitha L.Francis Raj sandeep Kautish abdulaziz s.Almazyad Karam M.sallam Ali Wagdy Mohamed Department of Mathematics Voorhees CollegeVelloreTamil NaduIndia Chandigarh Group of Colleges JhanjeriMohaliPunjabIndia Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia Faculty of science and Technology School of IT and SystemsUniversity of CanberraCanberraAustralia Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Applied science Research Center Applied Science Private UniversityAmman11937Jordan

The intuitive fuzzy set has found important application in decision-making and machine *** enrich and utilize the intuitive fuzzy set,this study designed and developed a deep neural network-based glaucoma eye detection using fuzzy difference equations in the domain where the retinal images *** image detections are categorized as normal eye recognition,suspected glaucomatous eye recognition,and glaucomatous eye *** degrees associated with weighted values are calculated to determine the level of concentration between the fuzzy partition and the retinal *** proposed model was used to diagnose glaucoma using retinal images and involved utilizing the Convolutional Neural Network(CNN)and deep learning to identify the fuzzy weighted regularization between *** methodology was used to clarify the input images and make them adequate for the process of glaucoma *** objective of this study was to propose a novel approach to the early diagnosis of glaucoma using the Fuzzy Expert system(FEs)and Fuzzy differential equation(FDE).The intensities of the different regions in the images and their respective peak levels were *** the peak regions were identified,the recurrence relationships among those peaks were then *** partitioning was done due to varying degrees of similar and dissimilar concentrations in the *** and dissimilar concentration levels and spatial frequency generated a threshold image from the combined fuzzy matrix and *** distinguished between a normal and abnormal eye condition,thus detecting patients with glaucomatous eyes.

关键词： Convolutional Neural Network(CNN) glaucomatous eyes fuzzy difference equation intuitive fuzzy sets image segmentation retinal images

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Neglected congenital bilateral knee dislocation treated by quadricepsplasty with semitendinosus and sartorius transfer:A case report

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World Journal of Orthopedics 2024年第8期15卷 807-812页

作者： Osama M Qasim abdulaziz A abdulaziz Nibras K Aljabri Khalid s Albaqami Rayan M suqaty College of Medicine Umm Al-Qura UniversityMecca 21955Makkah al MukarramahSaudi Arabia Department of Orthopedic King Faisal Medical Complex TaifTaif 26514TaifSaudi Arabia Department of Orthopedic Alnoor Specialist HospitalMecca 24241Makkah al MukarramahSaudi Arabia

BACKGROUND Congenital knee dislocation(CKD)is a rare condition,which accounts for 1%of congenital hip *** can present as an isolated condition or coexist with other genetic *** options include serial casting,percutaneous quadriceps recession,and V-Y quadricepsplasty(VYQ).The pathogenesis and hereditary patterns of CKD are not fully understood,with most cases being *** is usually managed immediately after ***,in this report,the patient was neglected for 2 *** sUMMARY A 2-year-old girl with bilateral CKD after birth presented to our hospital after failed serial casting;the patient had seizures and limited access to healthcare because of her family’s low socioeconomic *** birth was noted for a breech presentation accompanied by *** delivery took a long time,requiring immediate medical *** an infant,she had chronic diseases,including a small patent ductus arteriole,multicystic dysplastic kidney disease,and *** was found to have a bilateral knee dislocation of approximately-90°on hyperextension.A multidisciplinary team was involved,and medical care was *** underwent VYQ plus semitendinosus and sartorius *** four postoperative follow-ups,her knees were regaining mobility,and she could walk for 2-3 steps without *** This report highlights the importance of early intervention and recommends extensive studies of the management in similar cases.

关键词： Knee dislocation Congenital knee dislocation V-Y quadricepsplasty Neglect Neglected knee dislocation Pediatric orthopedics Case report

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Congenital absence of the lateral meniscus:A case report

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World Journal of Orthopedics 2024年第5期15卷 477-482页

作者： Hamad Ali Alkhunayfir abdulaziz Ali AlQahtani Abdulrahman Jalwi Korkoman Department of Orthopedic surgery Prince Muhammed bin Abdulaziz HospitalRiyadh 61422Saudi Arabia Department of Orthopedic surgery Prince Sultan Military Medical CityRiyadh 12233Saudi Arabia Department of Orthopedic surgery University of BishaBisha 67714Saudi Arabia

BACKGROUND Congenital absence of the menisci is a rare anatomical variation characterized by the absence or underdevelopment of one or both menisci in the knee *** menisci are crucial in load distribution,joint stability,and shock *** the clinical presentation,diagnosis,and management of this condition is important for optimal patient *** sUMMARY A 27-year-old male with a long-standing history of knee pain underwent diagnostic arthroscopy,revealing a congenital absence of the *** patient's clinical findings,imaging results,surgical procedures,and pertinent images are *** case presents a unique aspect with the congenital absence of the meniscus,contributing valuable insights to the literature on rare anatomical *** This case of congenital absence of the menisci highlights the diagnostic challenges posed by rare *** diagnostic arthroscopy played a crucial role in identifying the absence of the meniscus and providing an explanation for the patient's persistent knee *** case underscores the importance of individualized treatment approaches,including physical therapy,for optimal management of rare meniscal *** research is warranted to explore effective management strategies for the aforementioned cases and to expand our knowledge of these rare conditions.

关键词： Lateral meniscus Arthroscopy Knee joint Menisci reformation Magnetic resonance imaging Case report

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Improving Prediction of Chronic Kidney Disease Using KNN Imputed sMOTE Features and TrioNet Model

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Computer Modeling in Engineering & sciences 2024年第6期139卷 3513-3534页

作者： Nazik Alturki abdulaziz Altamimi Muhammad Umer Oumaima saidani Amal Alshardan shtwai Alsubai Marwan Omar Imran Ashraf Department of Information systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department College of Computer science and Engineering University of Hafr Al-BatinHafarAl-Batin39524Saudi Arabia Department of Computer science&Information Technology The Islamia University of BahawalpurP.O.Box 63100BahawalpurPakistan Department of Computer science College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityP.O.Box 151Al-Kharj11942Saudi Arabia Information Technology and Management Illinois Institute of TechnologyChicagoIL 60616-3793USA Information and Communication Engineering Yeungnam UniversityGyeongsan38541Korea

Chronic kidney disease(CKD)is a major health concern today,requiring early and accurate *** learning has emerged as a powerful tool for disease detection,and medical professionals are increasingly using ML classifier algorithms to identify CKD *** study explores the application of advanced machine learning techniques on a CKD dataset obtained from the University of California,UC Irvine Machine Learning *** research introduces TrioNet,an ensemble model combining extreme gradient boosting,random forest,and extra tree classifier,which excels in providing highly accurate predictions for ***,K nearest neighbor(KNN)imputer is utilized to deal withmissing values while synthetic minority oversampling(sMOTE)is used for class-imbalance *** ascertain the efficacy of the proposed model,a comprehensive comparative analysis is conducted with various machine learning *** proposed TrioNet using KNN imputer and sMOTE outperformed other models with 98.97%accuracy for *** in-depth analysis demonstrates the model’s capabilities and underscores its potential as a valuable tool in the diagnosis of CKD.

关键词： Precisionmedicine chronic kidney disease detection sMOTE missing values healthcare KNNimputer ensemble learning

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