Educational Data Mining(EDM)is an emergent discipline that concen-trates on the design of self-learning and adaptive approaches.Higher education institutions have started to utilize analytical tools to improve student...Educational Data Mining(EDM)is an emergent discipline that concen-trates on the design of self-learning and adaptive approaches.Higher education institutions have started to utilize analytical tools to improve students’grades and retention.Prediction of students’performance is a difficult process owing to the massive quantity of educational data.Therefore,Artificial Intelligence(AI)techniques can be used for educational data mining in a big data environ-ment.At the same time,in EDM,the feature selection process becomes necessary in creation of feature subsets.Since the feature selection performance affects the predictive performance of any model,it is important to elaborately investigate the outcome of students’performance model related to the feature selection techni-ques.With this motivation,this paper presents a new Metaheuristic Optimiza-tion-based Feature Subset Selection with an Optimal Deep Learning model(MOFSS-ODL)for predicting students’performance.In addition,the proposed model uses an isolation forest-based outlier detection approach to eliminate the existence of outliers.Besides,the Chaotic Monarch Butterfly Optimization Algo-rithm(CBOA)is used for the selection of highly related features with low com-plexity and high performance.Then,a sailfish optimizer with stacked sparse autoencoder(SFO-SSAE)approach is utilized for the classification of educational data.The MOFSS-ODL model is tested against a benchmark student’s perfor-mance data set from the UCI repository.A wide-ranging simulation analysis por-trayed the improved predictive performance of the MOFSS-ODL technique over recent approaches in terms of different measures.Compared to other methods,experimental results prove that the proposed(MOFSS-ODL)classification model does a great job of predicting students’academic progress,with an accuracy of 96.49%.展开更多
This paper proposes Parallelized Linear Time-Variant Acceleration Coefficients and Inertial Weight of Particle Swarm Optimization algorithm(PLTVACIW-PSO).Its designed has introduced the benefits of Parallel computing ...This paper proposes Parallelized Linear Time-Variant Acceleration Coefficients and Inertial Weight of Particle Swarm Optimization algorithm(PLTVACIW-PSO).Its designed has introduced the benefits of Parallel computing into the combined power of TVAC(Time-Variant Acceleration Coefficients)and IW(Inertial Weight).Proposed algorithm has been tested against linear,non-linear,traditional,andmultiswarmbased optimization algorithms.An experimental study is performed in two stages to assess the proposed PLTVACIW-PSO.Phase I uses 12 recognized Standard Benchmarks methods to evaluate the comparative performance of the proposed PLTVACIWPSO vs.IW based Particle Swarm Optimization(PSO)algorithms,TVAC based PSO algorithms,traditional PSO,Genetic algorithms(GA),Differential evolution(DE),and,finally,Flower Pollination(FP)algorithms.In phase II,the proposed PLTVACIW-PSO uses the same 12 known Benchmark functions to test its performance against the BAT(BA)and Multi-Swarm BAT algorithms.In phase III,the proposed PLTVACIW-PSO is employed to augment the feature selection problem formedical datasets.This experimental study shows that the planned PLTVACIW-PSO outpaces the performances of other comparable algorithms.Outcomes from the experiments shows that the PLTVACIW-PSO is capable of outlining a feature subset that is capable of enhancing the classification efficiency and gives the minimal subset of the core features.展开更多
Learning Management System(LMS)is an application software that is used in automation,delivery,administration,tracking,and reporting of courses and programs in educational sector.The LMS which exploits machine learning...Learning Management System(LMS)is an application software that is used in automation,delivery,administration,tracking,and reporting of courses and programs in educational sector.The LMS which exploits machine learning(ML)has the ability of accessing user data and exploit it for improving the learning experience.The recently developed artificial intelligence(AI)and ML models helps to accomplish effective performance monitoring for LMS.Among the different processes involved in ML based LMS,feature selection and classification processesfind beneficial.In this motivation,this study introduces Glowworm-based Feature Selection with Machine Learning Enabled Performance Monitoring(GSO-MFWELM)technique for LMS.The key objective of the proposed GSO-MFWELM technique is to effectually monitor the performance in LMS.The pro-posed GSO-MFWELM technique involves GSO-based feature selection techni-que to select the optimal features.Besides,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)algorithm.The design of GSO and MFO techniques result in reduced computation complexity and improved classification performance.The presented GSO-MFWELM technique was validated for its performance against benchmark dataset and the results were inspected under several aspects.The simulation results established the supremacy of GSO-MFWELM technique over recent approaches with the maximum classification accuracy of 0.9589.展开更多
文摘Educational Data Mining(EDM)is an emergent discipline that concen-trates on the design of self-learning and adaptive approaches.Higher education institutions have started to utilize analytical tools to improve students’grades and retention.Prediction of students’performance is a difficult process owing to the massive quantity of educational data.Therefore,Artificial Intelligence(AI)techniques can be used for educational data mining in a big data environ-ment.At the same time,in EDM,the feature selection process becomes necessary in creation of feature subsets.Since the feature selection performance affects the predictive performance of any model,it is important to elaborately investigate the outcome of students’performance model related to the feature selection techni-ques.With this motivation,this paper presents a new Metaheuristic Optimiza-tion-based Feature Subset Selection with an Optimal Deep Learning model(MOFSS-ODL)for predicting students’performance.In addition,the proposed model uses an isolation forest-based outlier detection approach to eliminate the existence of outliers.Besides,the Chaotic Monarch Butterfly Optimization Algo-rithm(CBOA)is used for the selection of highly related features with low com-plexity and high performance.Then,a sailfish optimizer with stacked sparse autoencoder(SFO-SSAE)approach is utilized for the classification of educational data.The MOFSS-ODL model is tested against a benchmark student’s perfor-mance data set from the UCI repository.A wide-ranging simulation analysis por-trayed the improved predictive performance of the MOFSS-ODL technique over recent approaches in terms of different measures.Compared to other methods,experimental results prove that the proposed(MOFSS-ODL)classification model does a great job of predicting students’academic progress,with an accuracy of 96.49%.
基金funded by the Prince Sultan University,Riyadh,Saudi Arabia.
文摘This paper proposes Parallelized Linear Time-Variant Acceleration Coefficients and Inertial Weight of Particle Swarm Optimization algorithm(PLTVACIW-PSO).Its designed has introduced the benefits of Parallel computing into the combined power of TVAC(Time-Variant Acceleration Coefficients)and IW(Inertial Weight).Proposed algorithm has been tested against linear,non-linear,traditional,andmultiswarmbased optimization algorithms.An experimental study is performed in two stages to assess the proposed PLTVACIW-PSO.Phase I uses 12 recognized Standard Benchmarks methods to evaluate the comparative performance of the proposed PLTVACIWPSO vs.IW based Particle Swarm Optimization(PSO)algorithms,TVAC based PSO algorithms,traditional PSO,Genetic algorithms(GA),Differential evolution(DE),and,finally,Flower Pollination(FP)algorithms.In phase II,the proposed PLTVACIW-PSO uses the same 12 known Benchmark functions to test its performance against the BAT(BA)and Multi-Swarm BAT algorithms.In phase III,the proposed PLTVACIW-PSO is employed to augment the feature selection problem formedical datasets.This experimental study shows that the planned PLTVACIW-PSO outpaces the performances of other comparable algorithms.Outcomes from the experiments shows that the PLTVACIW-PSO is capable of outlining a feature subset that is capable of enhancing the classification efficiency and gives the minimal subset of the core features.
基金supported by the Researchers Supporting Program(TUMA-Project2021-27)Almaarefa University,RiyadhSaudi Arabia.Taif University Researchers Supporting Project number(TURSP-2020/161)Taif University,Taif,Saudi Arabia.
文摘Learning Management System(LMS)is an application software that is used in automation,delivery,administration,tracking,and reporting of courses and programs in educational sector.The LMS which exploits machine learning(ML)has the ability of accessing user data and exploit it for improving the learning experience.The recently developed artificial intelligence(AI)and ML models helps to accomplish effective performance monitoring for LMS.Among the different processes involved in ML based LMS,feature selection and classification processesfind beneficial.In this motivation,this study introduces Glowworm-based Feature Selection with Machine Learning Enabled Performance Monitoring(GSO-MFWELM)technique for LMS.The key objective of the proposed GSO-MFWELM technique is to effectually monitor the performance in LMS.The pro-posed GSO-MFWELM technique involves GSO-based feature selection techni-que to select the optimal features.Besides,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)algorithm.The design of GSO and MFO techniques result in reduced computation complexity and improved classification performance.The presented GSO-MFWELM technique was validated for its performance against benchmark dataset and the results were inspected under several aspects.The simulation results established the supremacy of GSO-MFWELM technique over recent approaches with the maximum classification accuracy of 0.9589.
