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Deep Learning with Natural Language Processing Enabled Sentimental Analysis on Sarcasm Classification

Computer Systems Science & Engineering 2023年第3期44卷 2553-2567页

作者： abdul rahaman wahab sait Mohamad Khairi Ishak Department of Documents and Archive Center of Documents and Administrative CommunicationKing Faisal UniversityAl HofufAl-Ahsa31982Saudi Arabia School of Electrical and Electronic Engineering Engineering CampusUniversiti Sains Malaysia(USM)Nibong TebalPenang14300Malaysia

Sentiment analysis(SA)is the procedure of recognizing the emotions related to the data that exist in social *** existence of sarcasm in tex-tual data is a major challenge in the efficiency of the *** works on sarcasm detection on text utilize lexical as well as pragmatic cues namely interjection,punctuations,and sentiment shift that are vital indicators of *** the advent of deep-learning,recent works,leveraging neural networks in learning lexical and contextual features,removing the need for handcrafted *** this aspect,this study designs a deep learning with natural language processing enabled SA(DLNLP-SA)technique for sarcasm *** proposed DLNLP-SA technique aims to detect and classify the occurrence of sarcasm in the input ***,the DLNLP-SA technique holds various sub-processes namely preprocessing,feature vector conversion,and ***,the pre-processing is performed in diverse ways such as single character removal,multi-spaces removal,URL removal,stopword removal,and ***,the transformation of feature vectors takes place using the N-gram feature vector ***,mayfly optimization(MFO)with multi-head self-attention based gated recurrent unit(MHSA-GRU)model is employed for the detection and classification of *** verify the enhanced outcomes of the DLNLP-SA model,a comprehensive experimental investigation is performed on the News Headlines Dataset from Kaggle Repository and the results signified the supremacy over the existing approaches.

关键词： Sentiment analysis sarcasm detection deep learning natural language processing n-grams hyperparameter tuning

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A Novel Handcrafted with Deep Features Based Brain Tumor Diagnosis Model

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Intelligent Automation & Soft Computing 2023年第2期35卷 2057-2070页

作者： abdul rahaman wahab sait Mohamad Khairi Ishak Department of Documents and Archive Center of Documents and Administrative CommunicationKing Faisal UniversityAl Hofuf31982Al-AhsaSaudi Arabia School of Electrical and Electronic Engineering Engineering CampusUniversiti Sains Malaysia(USM)Nibong Tebal14300PenangMalaysia

In healthcare sector,image classiﬁcation is one of the crucial problems that impact the quality output from image processing *** purpose of image classiﬁcation is to categorize different healthcare images under various class labels which in turn helps in the detection and management of *** Resonance Imaging(MRI)is one of the effective non-invasive strate-gies that generate a huge and distinct number of tissue contrasts in every imaging *** technique is commonly utilized by healthcare professionals for Brain Tumor(BT)*** recent advancements in Machine Learning(ML)and Deep Learning(DL)models,it is possible to detect the tumor from images automatically,using a computer-aided *** current study focuses on the design of automated Deep Learning-based BT Detection and Classiﬁcation model using MRI images(DLBTDC-MRI).The proposed DLBTDC-MRI techni-que aims at detecting and classifying different stages of *** proposed DLBTDC-MRI technique involves medianﬁltering technique to remove the noise and enhance the quality of MRI ***,morphological operations-based image segmentation approach is also applied to determine the BT-affected regions in brain MRI ***,a fusion of handcrafted deep features using VGGNet is utilized to derive a valuable set of feature ***,Artiﬁcial Fish Swarm Optimization(AFSO)with Artiﬁcial Neural Network(ANN)model is utilized as a classiﬁer to decide the presence of *** order to assess the enhanced BT classiﬁcation performance of the proposed model,a comprehensive set of simulations was performed on benchmark dataset and the results were vali-dated under several measures.

关键词： Brain tumor medical imaging image classiﬁcation handcrafted features deep learning parameter optimization

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Autonomous Unmanned Aerial Vehicles Based Decision Support System for Weed Management

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Computers, Materials & Continua 2022年第10期73卷 899-915页

作者： Ashit Kumar Dutta Yasser Albagory abdul rahaman wahab sait Ismail Mohamed Keshta 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 Archives and Communication King Faisal UniversityAl AhsaHofuf31982Kingdom of Saudi Arabia

Recently,autonomous systems become a hot research topic among industrialists and academicians due to their applicability in different domains such as healthcare,agriculture,industrial automation,*** the interesting applications of autonomous systems,their applicability in agricultural sector becomes *** unmanned aerial vehicles(UAVs)can be used for suitable site-specific weed management(SSWM)to improve crop *** spite of substantial advancements in UAV based data collection systems,automated weed detection still remains a tedious task owing to the high resemblance of weeds to the *** recently developed deep learning(DL)models have exhibited effective performance in several data classification *** this aspect,this paper focuses on the design of autonomous UAVs with decision support system for weed management(AUAV-DSSWM)*** proposed AUAV-DSSWM technique intends to identify the weeds by the use of UAV images acquired from the target ***,the AUAV-DSSWM technique primarily performs image acquisition and image pre-processing ***,the Adam optimizer with You Only Look Once Object Detector-(YOLOv3)model is applied for the detection of *** the effective classification of weeds and crops,the poor and rich optimization(PRO)algorithm with softmax layer is *** design of Adam optimizer and PRO algorithm for the parameter tuning process results in enhanced weed detection performance.A wide range of simulations take place on UAV images and the experimental results exhibit the promising performance of the AUAV-DSSWM technique over the other recent techniques with the accy of 99.23%.

关键词： Autonomous systems object detection precision agriculture unmanned aerial vehicles deep learning parameter tuning

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Intelligent Student Mental Health Assessment Model on Learning Management System

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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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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 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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Colliding Bodies Optimization with Machine Learning Based Parkinson’s Disease Diagnosis

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Computer Systems Science & Engineering 2023年第3期44卷 2195-2207页

作者： Ashit Kumar Dutta Nazik M.A.Zakari Yasser Albagory abdul rahaman wahab sait Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityAd DiriyahRiyadh13713Kingdom of Saudi Arabia Department of Nursing 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 Archives and Communication King Faisal UniversityAl AhsaHofuf31982Kingdom of Saudi Arabia

Parkinson’s disease(PD)is one of the primary vital degenerative diseases that affect the Central Nervous System among elderly *** affect their quality of life drastically and millions of seniors are diagnosed with PD every year *** models have been presented earlier to detect the PD using various types of measurement data like speech,gait patterns,*** identification of PD is important owing to the fact that the patient can offer important details which helps in slowing down the progress of *** recently-emerging Deep Learning(DL)models can leverage the past data to detect and classify *** this motivation,the current study develops a novel Colliding Bodies Optimization Algorithm with Optimal Kernel Extreme Learning Machine(CBO-OKELM)for diagnosis and classification of *** goal of the proposed CBO-OKELM technique is to identify whether PD exists or ***-OKELM technique involves the design of Colliding Bodies Optimization-based Feature Selection(CBO-FS)technique for optimal subset of *** addition,Water Strider Algorithm(WSA)with Kernel Extreme Learning Machine(KELM)model is also developed for the classification of *** algorithm is used to elect the optimal set of fea-tures whereas WSA is utilized for parameter tuning of KELM model which alto-gether helps in accomplishing the maximum PD diagnostic *** experimental analysis was conducted for CBO-OKELM technique against four benchmark datasets and the model portrayed better performance such as 95.68%,96.34%,92.49%,and 92.36%on Speech PD,Voice PD,Hand PD Mean-der,and Hand PD Spiral datasets respectively.

关键词： Parkinson’s disease colliding bodies optimization algorithm feature selection metaheuristics classification kelm model

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Intelligent Smart Grid Stability Predictive Model for Cyber-Physical Energy Systems

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Computer Systems Science & Engineering 2023年第2期44卷 1219-1231页

作者： Ashit Kumar Dutta Manal Al Faraj Yasser Albagory Mohammad zeid M Alzamil 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 Archives and Communication King Faisal UniversityAl AhsaHofuf31982Kingdom of Saudi Arabia

A cyber physical energy system(CPES)involves a combination of pro-cessing,network,and physical *** smart grid plays a vital role in the CPES model where information technology(IT)can be related to the physical *** the same time,the machine learning(ML)modelsﬁnd useful for the smart grids integrated into the CPES for effective decision ***,the smart grids using ML and deep learning(DL)models are anticipated to lessen the requirement of placing many power plants for electricity *** this aspect,this study designs optimal multi-head attention based bidirectional long short term memory(OMHA-MBLSTM)technique for smart grid stability predic-tion in *** proposed OMHA-MBLSTM technique involves three subpro-cesses such as pre-processing,prediction,and hyperparameter *** OMHA-MBLSTM technique employs min-max normalization as a pre-proces-sing ***,the MBLSTM model is applied for the prediction of stability level of the smart grids in *** the same time,the moth swarm algorithm(MHA)is utilized for optimally modifying the hyperparameters involved in the MBLSTM *** ensure the enhanced outcomes of the OMHA-MBLSTM technique,a series of simulations were carried out and the results are inspected under several *** experimental results pointed out the better outcomes of the OMHA-MBLSTM technique over the recent models.

关键词： Stability prediction smart grid cyber physical energy systems deep learning data analytics moth swarm algorithm

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Optimal Sparse Autoencoder Based Sleep Stage Classification Using Biomedical Signals

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Computer Systems Science & Engineering 2023年第2期44卷 1517-1529页

作者： Ashit Kumar Dutta Yasser Albagory Manal Al Faraj Yasir A.M.Eltahir 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 Respiratory Care College of Applied SciencesAlMaarefa UniversityAd DiriyahRiyadh13713Kingdom of Saudi Arabia Department of Archives and Communication King Faisal UniversityAl AhsaHofuf31982Kingdom of Saudi Arabia

The recently developed machine learning(ML)models have the ability to obtain high detection rate using biomedical ***,this article develops an Optimal Sparse Autoencoder based Sleep Stage Classiﬁcation Model on Electroencephalography(EEG)Biomedical Signals,named OSAE-SSCEEG *** major intention of the OSAE-SSCEEG technique is toﬁnd the sleep stage disorders using the EEG biomedical *** OSAE-SSCEEG technique primarily undergoes preprocessing using min-max data normalization ***,the classiﬁcation of sleep stages takes place using the Sparse Autoencoder with Smoothed Regularization(SAE-SR)with softmax(SM)***,the parameter optimization of the SAE-SR technique is carried out by the use of Coyote Optimization Algorithm(COA)and it leads to boosted classiﬁcation efﬁ*** order to ensure the enhanced performance of the OSAE-SSCEEG technique,a wide ranging simulation analysis is performed and the obtained results demonstrate the betterment of the OSAE-SSCEEG tech-nique over the recent methods.

关键词： Biomedical signals EEG sleep stage classiﬁcation machine learning autoencoder softmax parameter tuning

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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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