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Optimal Machine Learning Enabled Performance Monitoring for Learning Management Systems

Computer Systems Science & Engineering 2023年第3期44卷 2277-2292页

作者： Ashit Kumar Dutta mazen mushabab alqahtani Yasser Albagory Abdul Rahaman Wahab Sait Majed Alsanea Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityAd Diriyah13713RiyadhKingdom of Saudi Arabia Physical Therapy Department Majmaah UniversityMajmaah11952Kingdom of Saudi Arabia Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Kingdom of Saudi Arabia Department of Archives and Communication King Faisal UniversityAl Ahsa31982HofufKingdom of Saudi Arabia Department of Computing Arabeast CollegesRiyadh11583Kingdom of Saudi Arabia

Learning Management System(LMS)is an application software that is used in automation,delivery,administration,tracking,and reporting of courses and programs in educational *** LMS which exploits machine learning(ML)has the ability of accessing user data and exploit it for improving the learning *** recently developed artificial intelligence(AI)and ML models helps to accomplish effective performance monitoring for *** the different processes involved in ML based LMS,feature selection and classification processesfind *** this motivation,this study introduces Glowworm-based Feature Selection with Machine Learning Enabled Performance Monitoring(GSO-MFWELM)technique for *** key objective of the proposed GSO-MFWELM technique is to effectually monitor the performance in *** pro-posed GSO-MFWELM technique involves GSO-based feature selection techni-que to select the optimal ***,Weighted Extreme Learning Machine(WELM)model is applied for classification process whereas the parameters involved in WELM model are optimallyfine-tuned with the help of May-fly Optimization(MFO)*** design of GSO and MFO techniques result in reduced computation complexity and improved classification *** presented GSO-MFWELM technique was validated for its performance against benchmark dataset and the results were inspected under several *** simulation results established the supremacy of GSO-MFWELM technique over recent approaches with the maximum classification accuracy of 0.9589.

关键词： Learning management system data mining performance monitoring machine learning feature selection

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Sailfish Optimizer with EfficientNet Model for Apple Leaf Disease Detection

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Computers, Materials & Continua 2023年第1期74卷 217-233页

作者： mazen mushabab alqahtani Ashit Kumar Dutta Sultan Almotairi M.Ilayaraja Amani Abdulrahman Albraikan Fahd N.Al-Wesabi Mesfer Al Duhayyim Physical Therapy Department Majmaah UniversityMajmaah11952Saudi Arabia Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityAd Diriyah13713RiyadhSaudi Arabia Department of Natural and Applied Sciences Faculty of Community CollegeMajmaah UniversityMajmaah11952Saudi Arabia School of Computing Kalasalingam Academy of Research and EducationKrishnankoil626128India Department of Computer Sciences 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 University62529Saudi Arabia Faculty of Computer and IT Sana’a UniversitySana’a1247Yemen Department of Natural and Applied Sciences College of Community-AflajPrince Sattam Bin Abdulaziz UniversitySaudi Arabia

Recent developments in digital cameras and electronic gadgets coupled with Machine Learning(ML)and Deep Learning(DL)-based automated apple leaf disease detection models are commonly employed as reasonable alternatives to traditional visual inspection *** this background,the current paper devises an Effective Sailfish Optimizer with EfficientNet-based Apple Leaf disease detection(ESFO-EALD)*** goal of the proposed ESFO-EALD technique is to identify the occurrence of plant leaf diseases *** this scenario,Median Filtering(MF)approach is utilized to boost the quality of apple plant leaf ***,SFO with Kapur’s entropy-based segmentation technique is also utilized for the identification of the affected plant region from test ***,Adam optimizer with EfficientNet-based feature extraction and Spiking Neural Network(SNN)-based classification are employed to detect and classify the apple plant leaf images.A wide range of simulations was conducted to ensure the effective outcomes of ESFO-EALD technique on benchmark *** results reported the supremacy of the proposed ESFO-EALD approach than the existing approaches.

关键词： Agriculture computer vision image processing deep learning metaheuristics image segmentation

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