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Intelligent Student Mental Health Assessment Model on Learning Management System

Computer Systems Science & Engineering 2023年第2期44卷 1853-1868页

作者： Nasser Ali Aljarallah Ashit Kumar Dutta majed alsanea Abdul Rahaman Wahab Sait AlMaarefa University Ad DiriyahRiyadh13713Kingdom of Saudi Arabia Department of Business Administration Majmaah UniversityAlMajmaah11952Kingdom of Saudi Arabia Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityAd DiriyahRiyadh13713Kingdom of Saudi Arabia Department of Computing Arabeast CollegesRiyadh11583Kingdom of Saudi Arabia Department of Archives and Communication King Faisal UniversityAl AhsaHofuf31982Kingdom of Saudi Arabia

A learning management system(LMS)is a software or web based application,commonly utilized for planning,designing,and assessing a particular learning ***,the LMS offers a method of creating and delivering content to the instructor,monitoring students’involvement,and validating their *** mental health issues become common among studies in higher education globally,it is needed to properly determine it to improve mental *** article develops a new seven spot lady bird feature selection with opti-mal sparse autoencoder(SSLBFS-OSAE)model to assess students’mental health on *** major aim of the SSLBFS-OSAE model is to determine the proper health status of the students with respect to depression,anxiety,and stress(DAS).The SSLBFS-OSAE model involves a new SSLBFS model to elect a useful set of *** addition,OSAE model is applied for the classiﬁcation of mental health conditions and the performance can be improved by the use of cuckoo search optimization(CSO)based parameter tuning *** design of CSO algorithm for optimally tuning the SAE parameters results in enhanced classiﬁca-tion *** examining the improved classiﬁer results of the SSLBFS-OSAE model,a comprehensive results analysis is done and the obtained values highlighted the supremacy of the SSLBFS model over its recent methods interms of different measures.

关键词： Learning management system mental health assessment intelligent models machine learning feature selection performance assessment

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Optimal Machine Learning Enabled Performance Monitoring for Learning Management Systems

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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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Cat Swarm with Fuzzy Cognitive Maps for Automated Soil Classification

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Computer Systems Science & Engineering 2023年第2期44卷 1419-1432页

作者： Ashit Kumar Dutta Yasser Albagory Manal Al Faraj majed alsanea Abdul Rahaman Wahab Sait Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityAd DiriyahRiyadh13713Kingdom of Saudi Arabia Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Kingdom of Saudi Arabia Department of Computing Arabeast CollegesRiyadh11583Kingdom of Saudi Arabia Department of Archives and Communication King Faisal UniversityAl AhsaHofuf31982Kingdom of Saudi Arabia

Accurate soil prediction is a vital parameter involved to decide appro-priate crop,which is commonly carried out by the *** an auto-mated soil prediction tool helps to considerably improve the efﬁcacy of the *** the same time,fuzzy logic(FL)approaches can be used for the design of predictive models,particularly,Fuzzy Cognitive Maps(FCMs)have involved the concept of uncertainty representation and cognitive *** other words,the FCM is an integration of the recurrent neural network(RNN)and FL involved in the knowledge engineering *** this aspect,this paper introduces effective fuzzy cognitive maps with cat swarm optimization for automated soil classiﬁca-tion(FCMCSO-ASC)*** goal of the FCMCSO-ASC technique is to identify and categorize seven different types of *** accomplish this,the FCMCSO-ASC technique incorporates local diagonal extrema pattern(LDEP)as a feature extractor for producing a collection of feature *** addition,the FCMCSO model is applied for soil classiﬁcation and the weight values of the FCM model are optimally adjusted by the use of CSO *** exam-ining the enhanced soil classiﬁcation outcomes of the FCMCSO-ASC technique,a series of simulations were carried out on benchmark dataset and the experimen-tal outcomes reported the enhanced performance of the FCMCSO-ASC technique over the recent techniques with maximum accuracy of 96.84%.

关键词： Soil classiﬁcation intelligent models fuzzy cognitive maps cat swarm optimization fuzzy logic

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Optimal Weighted Extreme Learning Machine for Cybersecurity Fake News Classification

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Computer Systems Science & Engineering 2023年第3期44卷 2395-2409页

作者： Ashit Kumar Dutta Basit Qureshi Yasser Albagory majed alsanea Manal Al Faraj Abdul Rahaman Wahab Sait Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityAd DiriyahRiyadh13713Kingdom of Saudi Arabia Department of Computer Science Prince Sultan UniversityRiyadh11586Kingdom of Saudi Arabia Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Kingdom of Saudi Arabia Department of Computing Arabeast CollegesRiyadh11583Kingdom of Saudi Arabia Department of Archives and Communication King Faisal UniversityAl AhsaHofuf31982Kingdom of Saudi Arabia

Fake news and its significance carried the significance of affecting diverse aspects of diverse entities,ranging from a city lifestyle to a country global relativity,various methods are available to collect and determine fake *** recently developed machine learning(ML)models can be employed for the detection and classification of fake *** study designs a novel Chaotic Ant Swarm with Weighted Extreme Learning Machine(CAS-WELM)for Cybersecurity Fake News Detection and *** goal of the CAS-WELM technique is to discriminate news into fake and *** CAS-WELM technique initially pre-processes the input data and Glove technique is used for word embed-ding ***,N-gram based feature extraction technique is derived to gen-erate feature ***,WELM model is applied for the detection and classification of fake news,in which the weight value of the WELM model can be optimally adjusted by the use of CAS *** performance validation of the CAS-WELM technique is carried out using the benchmark dataset and the results are inspected under several *** experimental results reported the enhanced outcomes of the CAS-WELM technique over the recent approaches.

关键词： Cybersecurity cybercrime fake news data classification machine learning metaheuristics

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Fuzzy with Metaheuristics Based Routing for Clustered Wireless Sensor Networks

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Intelligent Automation & Soft Computing 2023年第1期35卷 367-380页

作者： Ashit Kumar Dutta Yasser Albagory majed alsanea Abdul Rahaman Wahab Sait Hazim Saleh AlRawashdeh Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityAd DiriyahRiyadh13713Kingdom of Saudi Arabia Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Kingdom of Saudi Arabia Department of Computing University Name:Arabeast CollegesRiyadh11583Kingdom of Saudi Arabia Department of Archives and Communication King Faisal UniversityAl AhsaHofuf31982Kingdom of Saudi Arabia Department of Cyber Security Onaizah Private CollegesUnaizah51911Kingdom of Saudi Arabia

Wireless sensor network(WSN)plays a vital part in real time tracking and data collection *** incorporates a set of numerous sensor nodes(SNs)commonly utilized to observe the target *** SNs operate using an inbuilt battery and it is not easier to replace or charge ***,proper utilization of available energy in the SNs is essential to prolong the lifetime of the *** this study,an effective Type-II Fuzzy Logic with Butterfly Optimization Based Route Selection(TFL-BOARS)has been developed for clustered *** TFL-BOARS technique intends to optimally select the cluster heads(CHs)and routes in the clustered ***,the TFL-BOARS technique incorporates Type-II Fuzzy Logic(T2FL)technique with distinct input parameters namely residual energy(RE),link quality(LKQ),trust level(TRL),inter-cluster distance(ICD)and node degree(NDE)to select CHs and construct ***,the butterfly optimization algorithm based route selection(BOARS)technique is derived to select optimal set of routes in the *** addition,the BOARS technique has computed afitness function using three parameters such as communication cost,distance and *** order to demonstrate the improved energy effectiveness and prolonged lifetime of the WSN,a wide-ranging simulation analysis was implemented and the experimental results reported the supremacy of the TFL-BOARS technique.

关键词： Type II-fuzzy logic uncertainty WSN energy lifetime route selection

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Ensemble Deep Learning with Chimp Optimization Based Medical Data Classification

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Intelligent Automation & Soft Computing 2023年第2期35卷 1643-1655页

作者： Ashit Kumar Dutta Yasser Albagory majed alsanea Hamdan I.Almohammed Abdul Rahaman Wahab Sait Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityAd DiriyahRiyadh13713Kingdom of Saudi Arabia Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Kingdom of Saudi Arabia Department of Computing Arabeast CollegesRiyadh11583Kingdom of Saudi Arabia Department of Basic Medical Sciences College of Medicine.Almaarefa UniversityAlMaarefa UniversityAd DiriyahRiyadh13713Kingdom of Saudi Arabia Department of Archives and Communication King Faisal UniversityAl AhsaHofuf31982Kingdom of Saudi Arabia

Eye state classiﬁcation acts as a vital part of the biomedical sector,for instance,smart home device control,drowsy driving recognition,and so *** modiﬁcations in the cognitive levels can be reﬂected via transforming the electro-encephalogram(EEG)*** deep learning(DL)models automated extract the features and often showcased improved outcomes over the conventional clas-siﬁcation model in the recognition *** paper presents an Ensemble Deep Learning with Chimp Optimization Algorithm for EEG Eye State Classiﬁ-cation(EDLCOA-ESC).The proposed EDLCOA-ESC technique involves min-max normalization approach as a pre-processing ***,wavelet packet decomposition(WPD)technique is employed for the extraction of useful features from the EEG *** addition,an ensemble of deep sparse autoencoder(DSAE)and kernel ridge regression(KRR)models are employed for EEG Eye State classiﬁ***,hyperparameters tuning of the DSAE model takes place using COA and thereby boost the classiﬁcation results to a maximum *** extensive range of simulation analysis on the benchmark dataset is car-ried out and the results reported the promising performance of the EDLCOA-ESC technique over the recent approaches with maximum accuracy of 98.50%.

关键词： EEG eye state data classiﬁcation deep learning medical data analysis chimp optimization algorithm

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Optimal Deep Belief Network Enabled Cybersecurity Phishing Email Classification

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Computer Systems Science & Engineering 2023年第3期44卷 2701-2713页

作者： Ashit Kumar Dutta T.Meyyappan Basit Qureshi majed alsanea Anas Waleed Abulfaraj Manal M.Al Faraj Abdul Rahaman Wahab Sait Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityAd DiriyahRiyadh13713Kingdom of Saudi Arabia Department of Computer Science Alagappa UniversityKaraikudi630003India Department of Computer Science Prince Sultan UniversityRiyadh11586Kingdom of Saudi Arabia Department of Computing Arabeast CollegesRiyadh11583Kingdom of Saudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah23613Kingdom of Saudi Arabia Department of Archives and Communication King Faisal UniversityAl AhsaHofuf31982Kingdom of Saudi Arabia

Recently,developments of Internet and cloud technologies have resulted in a considerable rise in utilization of online media for day to day *** results in illegal access to users’private data and compromises *** is a popular attack which tricked the user into accessing malicious data and gaining the *** identification of phishing emails can be treated as an essential process in the domain of *** article focuses on the design of bio-geography based optimization with deep learning for Phishing Email detection and classification(BBODL-PEDC)*** major intention of the BBODL-PEDC model is to distinguish emails between legitimate and *** BBODL-PEDC model initially performs data pre-processing in three levels namely email cleaning,tokenization,and stop word ***,TF-IDF model is applied for the extraction of useful feature ***,optimal deep belief network(DBN)model is used for the email classification and its efficacy can be boosted by the BBO based hyperparameter tuning *** performance validation of the BBODL-PEDC model can be performed using benchmark dataset and the results are assessed under several *** comparative studies reported the superior outcomes of the BBODL-PEDC model over the recent approaches.

关键词： Cybersecurity phishing email data classification deep learning biogeography based optimization hyperparameter tuning

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Text Compression Based on Letter’s Prefix in the Word

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Computers, Materials & Continua 2020年第7期64卷 17-30页

作者： majed AbuSafiya Al-Ahliyya Amman University Amman19328Jordan

Huffman[Huffman(1952)]encoding is one of the most known compression *** its basic use,only one encoding is given for the same letter in text to *** this paper,a text compression algorithm that is based on Huffman encoding is *** encoding is used to give different encodings for the same letter depending on the prefix preceding it in the word.A deterministic finite automaton(DFA)that recognizes the words of the text is *** DFA records the frequencies for letters that label the *** state will correspond to one of the prefixes of the words of the *** every state,a different Huffman encoding is defined for the letters that label the transitions leaving that *** Huffman encodings are then used to encode the letters of the words in the *** algorithm was implemented and experimental study showed significant reduction in compression ratio over the basic Huffman ***,more time is needed to construct these codes.

关键词： Text compression Huffman encoding deterministic finite automata

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Factors affecting farmers'choice to adopt risk management strategies:The application of multivariate and multinomial probit models

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Journal of Integrative Agriculture 2024年第12期23卷 4250-4262页

作者： Jamal Shah majed Alharthi School of Business Department of EconomicsUniversity of DundeeDundee DD14HNUnited Kingdom Finance Department College of BusinessKing Abdulaziz UniversityRabigh 21911Saudi Arabia

This study investigates the factors that impact farmers'adoption of risk management strategies(RMS)in Pakistan during times of *** study examines farmers'adoption of RMS using both multinomial probit(MNP)and multivariate probit(MVP).Data were collected from 382 farmers sampled from four districts in KhyberPakhtunkhwa(KP)province of Pakistan via a multistage sampling *** study utilizes the MNP model,considering the assumption of Independence of Irrelevant Alternatives(IIA)and incorporating correlated error *** objective is to understand farmers'behavior in risky situations and determine if there is *** are compared with the MVP model to assess robustness and gain deeper understanding of farmers'decisionmaking *** research findings reveal that our results are robust,and farmers behave homogeneously in various RMS *** adopt RMS individually or in combination to mitigate the adverse effects of natural calamities on their *** risk-averse farmers,who perceive weather-related risks as a threat,access credits and information,and have farms close to a river are more likely to adopt RMS,irrespective of the format of the strategies ***,the predicted probabilities and correlation of the RMS and RM categories have strengthened our model *** findings provide insights into the behavior of farmers in adopting RMS which are helpful for policymakers and stakeholders in developing strategies to mitigate the impacts of natural calamities on farmers.

关键词： multinomial probit model multivariate probit model risk management strategies risk-attitude risk perception

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Singularity Formation forthe General Poiseuille Flow of Nematic Liquid Crystals

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Communications on Applied Mathematics and Computation 2023年第3期5卷 1130-1147页

作者： Geng Chen majed Sofiani Department of Mathematics University of KansasLawrenceKS 66045USA

We consider the Poiseuille flow of nematic liquid crystals via the full Ericksen-Leslie *** model is described by a coupled system consisting of a heat equation and a quasilinear wave *** this paper,we will construct an example with a finite time cusp singularity due to the quasilinearity of the wave equation,extended from an earlier resultonaspecial case.

关键词： Liquid crystals Cusp singularity Quasilinear wave equation

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