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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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Early COVID-19 Symptoms Identification Using Hybrid Unsupervised Machine Learning Techniques

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Computers, Materials & Continua 2021年第10期69卷 747-766页

作者： Omer Ali mohamad khairi ishak Muhammad Kamran Liaquat Bhatti School of Electrical and Electronic Engineering Universiti Sains Malaysia(USM)Nibong Tebal14300Malaysia Department of Electrical Engineering NFC Institute of Engineering and Technology(NFC IET)Multan60000Pakistan

The COVID-19 virus exhibits pneumonia-like symptoms,including fever,cough,and shortness of breath,and may be *** COVID-19 contraction experiments require comprehensive clinical procedures at medical *** studies help to make a correct diagnosis of COVID-19,where the disease has already spread to the organs in most *** and early diagnosis is indispensable for providing patients with the possibility of early clinical diagnosis and slowing down the disease ***,clinical investigations in patients with COVID-19 have revealed distinct patterns of breathing relative to other diseases such as flu and cold,which are worth *** supervised Machine Learning(ML)based techniques mostly investigate clinical reports such as X-Rays and Computerized Tomography(CT)for disease *** strategy relies on a larger clinical dataset and does not focus on early symptom *** this end,an innovative hybrid unsupervised ML technique is introduced to uncover the probability of COVID-19 occurrence based on the breathing patterns and commonly reported symptoms,fever,and ***,various metrics,including body temperature,breathing and cough patterns,and physical activity,were considered in this ***,a lightweight ML algorithm based on the K-Means and Isolation Forest technique was implemented on relatively small data including 40 *** proposed technique shows an outlier detection with an accuracy of 89%,on average.

关键词： COVID-19 symptoms identification machine learning isolation forest k-means

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A Machine Learning Approach for Early COVID-19 Symptoms Identification

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Computers, Materials & Continua 2022年第2期70卷 3803-3820页

作者： Omer Ali mohamad khairi ishak Muhammad Kamran Liaquat Bhatti School of Electrical and Electronic Engineering Universiti Sains Malaysia(USM)Nibong Tebal14300PenangMalaysia Department of Electrical Engineering NFC Institute of Engineering and Technology(NFC IET)Multan60000Pakistan

Symptom identification and early detection are the first steps towards a health condition ***-19 virus causes pneumonialike symptoms such as fever,cough,and shortness of ***-19 contraction tests necessitate extensive clinical protocols in medical *** studies help with the accurate analysis of COVID-19,where the virus has already spread to the lungs in most *** majority of existing supervised machine learning-based disease detection techniques are based on clinical data like x-rays and computerized *** is heavily reliant on a larger clinical study and does not emphasize early symptom *** aim of this study is to investigate anomalies in patient physiological data for early COVID-19 symptoms *** this context,two of the most prevalent symptoms,fever and cough,were examined in a two-fold manner utilizing an unsupervised machine *** examine disease progression,physiological features from a chest-worn device were ***,a Single Vector Activity Index(SVAI)parameter is proposed to monitor the breathing and cough ***,the dataset’s variance is examined using the DBSCAN method for clustering and outlier ***,the model accuracy is evaluated to identify outliers on real-time data based on feature dissimilarities,yielding an overall detection accuracy of 90.34%.

关键词： COVID-19 symptoms identification machine learning dbscan

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Enhanced Best Fit Algorithm for Merging Small Files

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Computer Systems Science & Engineering 2023年第7期46卷 913-928页

作者： Adnan Ali Nada Masood Mirza mohamad khairi ishak School of Electrical and Electronic Engineering Universiti Sains Malaysia(USM)Nibong TebalPulau Pinang14300Malaysia University College United Arab Emirates UniversityAl AinUAE

In the Big Data era,numerous sources and environments generate massive amounts of *** enormous amount of data necessitates specialized advanced tools and procedures that effectively evaluate the information and anticipate decisions for future *** is used to process this kind of *** is known to handle vast volumes of data more efficiently than tiny amounts,which results in inefficiency in the *** study proposes a novel solution to the problem by applying the Enhanced Best Fit Merging algorithm(EBFM)that merges files depending on predefined parameters(type and size).Implementing this algorithm will ensure that the maximum amount of the block size and the generated file size will be in the same *** primary goal is to dynamically merge files with the stated criteria based on the file type to guarantee the efficacy and efficiency of the established *** procedure takes place before the files are available for the Hadoop ***,the files generated by the system are named with specific keywords to ensure there is no data loss(file overwrite).The proposed approach guarantees the generation of the fewest possible large files,which reduces the input/output memory burden and corresponds to the Hadoop framework’s *** findings show that the proposed technique enhances the framework’s performance by approximately 64%while comparing all other potential performance-impairing *** proposed approach is implementable in any environment that uses the Hadoop framework,not limited to smart cities,real-time data analysis,etc.

关键词： Big data Hadoop MapReduce small file HDFS

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An Optimal Method for Speech Recognition Based on Neural Network

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Intelligent Automation & Soft Computing 2023年第5期36卷 1951-1961页

作者： mohamad khairi ishak DagØivind Madsen Fahad Ahmed Al-Zahrani School of Electrical and Electronic Engineering Universiti Sains MalaysiaNibong Tebal14300Malaysia University of South-Eastern Norway Bredalsveien 143511HønefossNorway Computer Engineering Department Umm Al-Qura UniversityMecca24381Saudi Arabia

Natural language processing technologies have become more widely available in recent years,making them more useful in everyday *** learning systems that employ accessible datasets and corporate work to serve the whole spectrum of problems addressed in computational linguistics have lately yielded a number of promising *** methods were particularly advantageous for regional languages,as they were provided with cut-ting-edge language processing tools as soon as the requisite corporate information was *** bulk of modern people are unconcerned about the importance of *** aloud,on the other hand,is an effective technique for nour-ishing feelings as well as a necessary skill in the learning *** paper pro-posed a novel approach for speech recognition based on neural *** attention mechanism isﬁrst utilized to determine the speech accuracy andﬂuency assessments,with the spectrum map as the feature extraction *** increase phoneme identiﬁcation accuracy,reading precision,for example,employs a new type of deep *** makes use of the exportchapter tool,which provides a corpus,as well as the TensorFlow framework in the experimental *** experimentalﬁndings reveal that the suggested model can more effectively assess spoken speech accuracy and readingﬂuency than the old model,and its evalua-tion model’s score outcomes are more accurate.

关键词： Machine learning neural networks speech recognition signal processing learning process ﬂuency and accuracy

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An Optimal Framework for SDN Based on Deep Neural Network

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Computers, Materials & Continua 2022年第10期73卷 1125-1140页

作者： Abdallah Abdallah mohamad khairi ishak Nor Samsiah Sani Imran Khan Fahad RAlbogamy Hirofumi Amano Samih M.Mostafa Department of Industrial Engineering School of Applied Technical Sciences German Jordanian UniversityAmman35247Jordan School of Electrical and Electronic Engineering Universiti Sains MalaysiaNibong Tebal14300Malaysia Center for Artificial Intelligence Technology Faculty of Information Science and TechnologyThe National University of Malaysia(UKM)Bangi43600SelangorMalaysia Department of Electrical Engineering University of Engineering and TechnologyPeshawar814Pakistan Turabah University College Computer Sciences ProgramTaif UniversityTaif21944Saudi Arabia Research Institute for Information Technology Kyushu UniversityFukuoka819-0395Japan Computer Science-Mathematics Department Faculty of ScienceSouth Valley UniversityQena83523Egypt

Software-defined networking(SDN)is a new paradigm that promises to change by breaking vertical integration,decoupling network control logic from the underlying routers and switches,promoting(logical)network control centralization,and introducing network ***,the controller is similarly vulnerable to a“single point of failure”,an attacker can execute a distributed denial of service(DDoS)attack that invalidates the controller and compromises the network security in *** address the problem of DDoS traffic detection in SDN,a novel detection approach based on information entropy and deep neural network(DNN)is *** approach contains a DNN-based DDoS traffic detection module and an information-based entropy initial inspection *** initial inspection module detects the suspicious network traffic by computing the information entropy value of the data packet’s source and destination Internet Protocol(IP)addresses,and then identifies it using the DDoS detection module based on *** assaults were found when suspected irregular traffic was *** reveal that the algorithm recognizes DDoS activity at a rate of more than 99%,with a much better accuracy *** false alarm rate(FAR)is much lower than that of the information entropy-based detection ***,the proposed framework can shorten the detection time and improve the resource utilization efficiency.

关键词： Deep neural network computer networks data security optimization

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Coot Optimization with Deep Learning-Based False Data Injection Attack Recognition

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Computer Systems Science & Engineering 2023年第7期46卷 255-271页

作者： T.Satyanarayana Murthy P.Udayakumar Fayadh Alenezi E.Laxmi Lydia mohamad khairi ishak Department of Information Technology Chaitanya Bharathi Institute of TechnologyHyderabadTelanganaIndia Department of Computer Science and Engineering Aditya Engineering CollegeSurampalemAndhra PradeshIndia Department of Electrical Engineering College of EngineeringJouf UniversitySaudi Arabia Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India School of Electrical and Electronic Engineering Universiti Sains MalaysiaNibong Tebal14300Malaysia

The recent developments in smart cities pose major security issues for the Internet of Things(IoT)*** security issues directly result from inappropriate security management protocols and their implementation by IoT gadget ***-attackers take advantage of such gadgets’vulnerabilities through various attacks such as injection and Distributed Denial of Service(DDoS)*** this background,Intrusion Detection(ID)is the only way to identify the attacks and mitigate their *** recent advancements in Machine Learning(ML)and Deep Learning(DL)models are useful in effectively classifying *** current research paper introduces a new Coot Optimization Algorithm with a Deep Learning-based False Data Injection Attack Recognition(COADL-FDIAR)model for the IoT *** presented COADL-FDIAR technique aims to identify false data injection attacks in the IoT *** accomplish this,the COADL-FDIAR model initially preprocesses the input data and selects the features with the help of the Chi-square *** detect and classify false data injection attacks,the Stacked Long Short-Term Memory(SLSTM)model is exploited in this ***,the COA algorithm effectively adjusts the SLTSM model’s hyperparameters effectively and accomplishes a superior recognition *** proposed COADL-FDIAR model was experimentally validated using a standard dataset,and the outcomes were scrutinized under distinct *** comparative analysis results assured the superior performance of the proposed COADL-FDIAR model over other recent approaches with a maximum accuracy of 98.84%.

关键词： False data injection attack security internet of things deep learning coot optimization algorithm

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Enhanced Metaheuristics with Trust Aware Route Selection for Wireless Sensor Networks

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Computer Systems Science & Engineering 2023年第8期46卷 1431-1445页

作者： A.Francis Saviour Devaraj T.Satyanarayana Murthy Fayadh Alenezi E.Laxmi Lydia mohamad Adzhar Md Zawawi mohamad khairi ishak Department of IT The University of Technology and Applied Sciences-IbriPO Box:466IbriPostal Code:516Sultanate of Oman Chaitanya Bharathi Institute of Technology HyderabadTelanganaIndia Department of Electrical Engineering College of EngineeringJouf UniversitySaudi Arabia Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India School of Electrical and Electronic Engineering Engineering CampusUniversiti Sains MalaysiaNibong Tebal14300PenangMalaysia

Recently,a trust system was introduced to enhance security and cooperation between nodes in wireless sensor networks(WSN).In routing,the trust system includes or avoids nodes related to the estimated trust values in the routing *** article introduces Enhanced Metaheuristics with Trust Aware Secure Route Selection Protocol(EMTA-SRSP)for *** presented EMTA-SRSP technique majorly involves the optimal selection of routes in *** accomplish this,the EMTA-SRSP technique involves the design of an oppositional Aquila optimization algorithm to choose safe routes for data *** the clustering process,the nodes with maximum residual energy will be considered cluster heads(CHs).In addition,the OAOA technique gets executed to choose optimal routes based on objective functions with multiple parameters such as energy,distance,and trust *** experimental validation of the EMTA-SRSP technique is tested,and the results exhibited a better performance of the EMTA-SRSP technique over other approaches.

关键词： Security wireless sensor networks trust factor routing protocol privacy

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Earthworm Optimization with Improved SqueezeNet Enabled Facial Expression Recognition Model

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Computer Systems Science & Engineering 2023年第8期46卷 2247-2262页

作者： N.Sharmili Saud Yonbawi Sultan Alahmari E.Laxmi Lydia mohamad khairi ishak Hend Khalid Alkahtani Ayman Aljarbouh Samih M.Mostafa Computer Science and Engineering Department Gayatri Vidya Parishad College of Engineering for WomenVisakhapatnamAndhra PradeshIndia Department of Software Engineering College of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia King Abdul Aziz City for Science and Technology RiyadhKingdom of Saudi Arabia Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India School of Electrical and Electronic Engineering Engineering CampusUniversiti Sains Malaysia(USM)Nibong TebalPenang14300Malaysia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11564Saudi Arabia Department of Computer Science University of Central AsiaNaryn722600Kyrgyzstan Faculty of Computers and Information South Valley UniversityQena83523Egypt

Facial expression recognition(FER)remains a hot research area among computer vision researchers and still becomes a challenge because of high intraclass *** techniques for this problem depend on hand-crafted features,namely,LBP,SIFT,and HOG,along with that a classifier trained on a database of videos or *** execute perform well on image datasets captured in a controlled condition;however not perform well in the more challenging dataset,which has partial faces and image ***,many studies presented an endwise structure for facial expression recognition by utilizing DL ***,this study develops an earthworm optimization with an improved SqueezeNet-based FER(EWOISN-FER)*** presented EWOISN-FER model primarily applies the contrast-limited adaptive histogram equalization(CLAHE)technique as a pre-processing *** addition,the improved SqueezeNet model is exploited to derive an optimal set of feature vectors,and the hyperparameter tuning process is performed by the stochastic gradient boosting(SGB)***,EWO with sparse autoencoder(SAE)is employed for the FER process,and the EWO algorithm appropriately chooses the SAE ***-ranging experimental analysis is carried out to examine the performance of the proposed *** experimental outcomes indicate the supremacy of the presented EWOISN-FER technique.

关键词： Facial expression recognition deep learning computer vision earthworm optimization hyperparameter optimization

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