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Modeling of Combined Economic and Emission Dispatch Using Improved Sand Cat Optimization Algorithm

Computers, Materials & Continua 2023年第6期75卷 6145-6160页

作者： Fadwa Alrowais Jaber S.Alzahrani Radwa Marzouk Abdullah Mohamed gouse pasha mohammed Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversityMecca24382Saudi Arabia Research Centre Future University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Combined Economic and Emission Dispatch(CEED)task forms multi-objective optimization problems to be resolved to minimize emission and fuel *** disadvantage of the conventional method is its incapability to avoid falling in local optimal,particularly when handling nonlinear and complex *** have recently received considerable attention due to their enhanced capacity to prevent local optimal solutions in addressing all the optimization problems as a black ***,this paper focuses on the design of an improved sand cat optimization algorithm based CEED(ISCOA-CEED)*** ISCOA-CEED technique majorly concen-trates on reducing fuel costs and the emission of generation ***,the presented ISCOA-CEED technique transforms the equality constraints of the CEED issue into inequality ***,the improved sand cat optimization algorithm(ISCOA)is derived from the integration of tra-ditional SCOA with the Levy Flight(LF)*** last,the ISCOA-CEED technique is applied to solve a series of 6 and 11 generators in the CEED *** experimental validation of the ISCOA-CEED technique ensured the enhanced performance of the presented ISCOA-CEED technique over other recent approaches.

关键词： Economic and emission dispatch multi-objective optimization metaheuristics fuel cost minimization sand cat optimization

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Optimal Fuzzy Logic Enabled Intrusion Detection for Secure IoT-Cloud Environment

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Computers, Materials & Continua 2023年第3期74卷 6737-6753页

作者： Fatma S.Alrayes Nuha Alshuqayran Mohamed K Nour Mesfer Al Duhayyim Abdullah Mohamed Amgad Atta Abdelmageed mohammed gouse pasha mohammed Ishfaq Yaseen Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic UniversitySaudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia Research Centre Future University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Recently,Internet of Things(IoT)devices have developed at a faster rate and utilization of devices gets considerably increased in day to day *** the benefits of IoT devices,security issues remain challenging owing to the fact that most devices do not include memory and computing resources essential for satisfactory security ***,IoT devices are vulnerable to different kinds of attacks.A single attack on networking system/device could result in considerable data to data security and *** the emergence of artificial intelligence(AI)techniques can be exploited for attack detection and classification in the IoT *** this view,this paper presents novel metaheuristics feature selection with fuzzy logic enabled intrusion detection system(MFSFL-IDS)in the IoT *** presented MFSFL-IDS approach purposes for recognizing the existence of intrusions and accomplish security in the IoT *** achieve this,the MFSFL-IDS model employs data pre-processing to transform the data into useful ***,henry gas solubility optimization(HGSO)algorithm is applied as a feature selection approach to derive useful feature ***,adaptive neuro fuzzy inference system(ANFIS)technique was utilized for the recognition and classification of intrusions in the ***,binary bat algorithm(BBA)is exploited for adjusting parameters involved in the ANFIS model.A comprehensive experimental validation of the MFSFL-IDS model is carried out using benchmark dataset and the outcomes are assessed under distinct *** experimentation outcomes highlighted the superior performance of the MFSFL-IDS model over recentapproaches with maximum accuracy of 99.80%.

关键词： Cloud computing security fuzzy logic intrusion detection internet of things metaheuristics

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Political Optimizer with Probabilistic Neural Network-Based Arabic Comparative Opinion Mining

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Intelligent Automation & Soft Computing 2023年第6期36卷 3121-3137页

作者： Najm Alotaibi Badriyya B.Al-onazi Mohamed K.Nour Abdullah Mohamed Abdelwahed Motwakel gouse pasha mohammed Ishfaq Yaseen mohammed Rizwanullah Prince Saud AlFaisal Institute for Diplomatic Studies Riyadh13369Saudi Arabia Department of Language Preparation Arabic Language Teaching InstitutePrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversityMakkah24211Saudi Arabia Research Centre Future University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharj16242Saudi Arabia

Opinion Mining(OM)studies in Arabic are limited though it is one of the most extensively-spoken languages *** the interest in OM studies in the Arabic language is growing among researchers,it needs a vast number of investigations due to the unique morphological principles of the *** OM studies experience multiple challenges owing to the poor existence of language sources and Arabic-specific linguistic *** comparative OM studies in the English language are wide and ***,comparative OM studies in the Arabic language are yet to be established and are still in a nascent *** unique features of the Arabic language make it essential to expand the studies regarding the Arabic *** contains unique featuressuchasdiacritics,elongation,*** study proposes a Political Optimizer with Probabilistic Neural Network-based Comparative Opinion Mining(POPNN-COM)model for the Arabic *** proposed POPNN-COM model aims to recognize comparative and non-comparative texts in Arabic in the context of social ***,the POPNN-COM model involves different levels of data pre-processing to transform the input data into a useful ***,the pre-processed data is fed into the PNN model for classification and recognition of the data under different class *** last,the PO algorithm is employed for fine-tuning the parameters involved in this model to achieve enhanced *** proposed POPNN-COM model was experimentally validated using two standard datasets,and the outcomes established the promising performance of the proposed POPNN-COM method over other recent approaches.

关键词： Comparative opinion mining Arabic text social media parameter tuning machine learning political optimizer

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Modified Garden Balsan Optimization Based Machine Learning for Intrusion Detection

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Computer Systems Science & Engineering 2023年第8期46卷 1471-1485页

作者： Mesfer Al Duhayyim Jaber S.Alzahrani Hanan Abdullah Mengash Mrim M.Alnfiai Radwa Marzouk gouse pasha mohammed mohammed Rizwanullah Amgad Atta Abdelmageed Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversityAl-KharjSaudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversityAl QunfidhahSaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

The Internet of Things(IoT)environment plays a crucial role in the design of smart *** and privacy are the major challenging problems that exist in the design of IoT-enabled real-time *** susceptibilities in IoT-based systems pose security threats which affect smart environment *** detection systems(IDS)can be used for IoT environments to mitigate IoT-related security attacks which use few security *** paper introduces a modified garden balsan optimizationbased machine learning model for intrusion detection(MGBO-MLID)in the IoT cloud *** presented MGBO-MLID technique focuses on the identification and classification of intrusions in the IoT cloud ***,the presented MGBO-MLID model applies min-max normalization that can be utilized for scaling the features in a uniform *** addition,the MGBO-MLID model exploits the MGBO algorithm to choose the optimal subset of ***,the attention-based bidirectional long short-term(ABiLSTM)method can be utilized for the detection and classification of *** the final level,the Aquila optimization(AO)algorithm is applied as a hyperparameter optimizer to fine-tune the ABiLSTM *** experimental validation of the MGBO-MLID method is tested using a benchmark *** extensive comparative study reported the betterment of the MGBO-MLID algorithm over recent approaches.

关键词： Deep learning internet of things cloud computing feature selection intrusion detection

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Quantum Particle Swarm Optimization with Deep Learning-Based Arabic Tweets Sentiment Analysis

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Computers, Materials & Continua 2023年第5期75卷 2575-2591页

作者： Badriyya BAl-onazi Abdulkhaleq Q.A.Hassan Mohamed K.Nour Mesfer Al Duhayyim Abdullah Mohamed Amgad Atta Abdelmageed Ishfaq Yaseen gouse pasha mohammed Department of Language Preparation Arabic Language Teaching InstitutePrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of English College of Science and Arts at MahayilKing Khalid UniversityAbha62217Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversityMakkah24231Saudi Arabia Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversityAl-Aflaj16828Saudi Arabia Research Centre Future University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharj16436Saudi Arabia

Sentiment Analysis(SA),a Machine Learning(ML)technique,is often applied in the *** SA technique is specifically applied to the data collected from social media *** research studies conducted earlier upon the SA of the tweets were mostly aimed at automating the feature extraction *** this background,the current study introduces a novel method called Quantum Particle Swarm Optimization with Deep Learning-Based Sentiment Analysis on Arabic Tweets(QPSODL-SAAT).The presented QPSODL-SAAT model determines and classifies the sentiments of the tweets written in ***,the data pre-processing is performed to convert the raw tweets into a useful ***,the word2vec model is applied to generate the feature *** Bidirectional Gated Recurrent Unit(BiGRU)classifier is utilized to identify and classify the ***,the QPSO algorithm is exploited for the optimal finetuning of the hyperparameters involved in the BiGRU *** proposed QPSODL-SAAT model was experimentally validated using the standard *** extensive comparative analysis was conducted,and the proposed model achieved a maximum accuracy of 98.35%.The outcomes confirmed the supremacy of the proposed QPSODL-SAAT model over the rest of the approaches,such as the Surface Features(SF),Generic Embeddings(GE),Arabic Sentiment Embeddings constructed using the Hybrid(ASEH)model and the Bidirectional Encoder Representations from Transformers(BERT)model.

关键词： Sentiment analysis Arabic tweets quantum particle swarm optimization deep learning word embedding

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Modified Sine Cosine Optimization with Adaptive Deep Belief Network for Movie Review Classification

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Intelligent Automation & Soft Computing 2023年第7期37卷 283-300页

作者： Hala J.Alshahrani Abdulbaset Gaddah Ehab S.Alnuzaili Mesfer Al Duhayyim Heba Mohsen Ishfaq Yaseen Amgad Atta Abdelmageed gouse pasha mohammed Department of Applied Linguistics College of LanguagesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of English College of Science&Art at MahayilKing Khalid UniversityMuhayel Aseer63311Saudi Arabia Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversityAl-Aflaj16828Saudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Sentiment analysis(SA)is a growing field at the intersection of computer science and computational linguistics that endeavors to automati-cally identify the sentiment presented in *** linguistics aims to describe the fundamental methods utilized in the formation of computer methods for understanding natural *** is classified as a negative or positive assessment articulated through *** can be commonly used for the movie review classification that involves the automatic determination that a review posted online(of a movie)can be negative or positive toward the thing that has been *** learning(DL)is becoming a powerful machine learning(ML)method for dealing with the increasing demand for precise *** this motivation,this study designs a computational intelligence enabled modified sine cosine optimization with a adaptive deep belief network for movie review classification(MSCADBN-MVC)*** major intention of the MSCADBN-MVC technique is focused on the identification of sentiments that exist in the movie review ***,the MSCADBN-MVC model follows data pre-processing and the word2vec word embedding *** the classification of sentiments that exist in the movie reviews,the ADBN model is utilized in this *** last,the hyperparameter tuning of the ADBN model is carried out using the MSCA technique,which integrates the Levy flight concepts into the standard sine cosine algorithm(SCA).In order to demonstrate the significant performance of the MSCADBN-MVC model,a wide-ranging experimental analysis is performed on three different *** comprehensive study highlighted the enhancements of the MSCADBN-MVC model in the movie review classification process with maximum accuracy of 88.93%.

关键词： Computational linguistics movie review analysis sentiment analysis sentiment classification deep learning

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Gender Identification Using Marginalised Stacked Denoising Autoencoders on Twitter Data

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Intelligent Automation & Soft Computing 2023年第6期36卷 2529-2544页

作者： Badriyya B.Al-onazi Mohamed K.Nour Hassan Alshamrani Mesfer Al Duhayyim Heba Mohsen Amgad Atta Abdelmageed gouse pasha mohammed Abu Sarwar Zamani Department of Language Preparation Arabic Language Teaching InstitutePrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversityMakkah24211Saudi Arabia Department of Teachers Training Arabic Linguistics InstituteKing Saud UniversityP.O.BOX 145111RiyadhZIP 4545Saudi Arabia Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversityAl-Aflaj16733Saudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Gender analysis of Twitter could reveal significant socio-cultural differ-ences between female and male *** had been made to analyze and auto-matically infer gender formerly for more commonly spoken languages’content,but,as we now know that limited work is being undertaken for *** of the research works are done mainly for English and least amount of effort for non-English *** study for Arabic demographic inference like gen-der is relatively uncommon for social networking users,especially for ***,this study aims to design an optimal marginalized stacked denoising autoencoder for gender identification on Arabic Twitter(OMSDAE-GIAT)*** presented OMSDAE-GIAR technique mainly concentrates on the identifica-tion and classification of gender exist in the Twitter *** attain this,the OMS-DAE-GIAT model derives initial stages of data pre-processing and word ***,the MSDAE model is exploited for the identification of gender into two classes namely male and *** the final stage,the OMSDAE-GIAT technique uses enhanced bat optimization algorithm(EBOA)for parameter tuning process,showing the novelty of our *** performance validation of the OMSDAE-GIAT model is inspected against an Arabic corpus dataset and the results are measured under distinct *** comparison study reported the enhanced performance of the OMSDAE-GIAT model over other recent approaches.

关键词： Arabic twitter gender identification bat algorithm hybrid deep learning social media arabic corpus

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Optimal Deep Hybrid Boltzmann Machine Based Arabic Corpus Classification Model

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Computer Systems Science & Engineering 2023年第9期46卷 2755-2772页

作者： Mesfer Al Duhayyim Badriyya B.Al-onazi Mohamed K.Nour Ayman Yafoz Amal S.Mehanna Ishfaq Yaseen Amgad Atta Abdelmageed gouse pasha mohammed Department of Computer Science College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia Department of Language Preparation Arabic Language Teaching InstitutePrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversityMakkah 24211Saudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Digital Media Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Natural Language Processing(NLP)for the Arabic language has gained much significance in recent *** most commonly-utilized NLP task is the‘Text Classification’*** main intention is to apply the Machine Learning(ML)approaches for automatically classifying the textual files into one or more pre-defined *** ML approaches,the first and foremost crucial step is identifying an appropriate large dataset to test and train the *** of the trending ML techniques,i.e.,Deep Learning(DL)technique needs huge volumes of different types of datasets for training to yield the best *** current study designs a new Dice Optimization with a Deep Hybrid Boltzmann Machinebased Arabic Corpus Classification(DODHBM-ACC)model in this *** presented DODHBM-ACC model primarily relies upon different stages of pre-processing and the word2vec word embedding *** Arabic text classification,the DHBM technique is *** technique is a hybrid version of the Deep Boltzmann Machine(DBM)and Deep Belief Network(DBN).It has the advantage of learning the decisive intention of the classification *** adjust the hyperparameters of the DHBM technique,the Dice Optimization Algorithm(DOA)is exploited in this *** experimental analysis was conducted to establish the superior performance of the proposed DODHBM-ACC *** outcomes inferred the better performance of the proposed DODHBM-ACC model over other recent approaches.

关键词： Arabic corpus text classification machine learning deep learning dice optimization

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Multi-Versus Optimization with Deep Reinforcement Learning Enabled Affect Analysis on Arabic Corpus

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Computer Systems Science & Engineering 2023年第12期47卷 3049-3065页

作者： Mesfer Al Duhayyim Badriyya B.Al-onazi Jaber S.Alzahrani Hussain Alshahrani Mohamed Ahmed Elfaki Abdullah Mohamed Ishfaq Yaseen gouse pasha mohammed mohammed Rizwanullah Abu Sarwar Zamani Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversityAflaj16273Saudi Arabia Department of Language Preparation Arabic Language Teaching InstitutePrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversityMakkah24211Saudi Arabia Department of Computer Science College of Computing and Information TechnologyShaqra UniversityShaqraSaudi Arabia Research Centre Future University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Sentiment analysis(SA)of the Arabic language becomes important despite scarce annotated corpora and confined *** affect Analysis has become an active research zone *** still,the Arabic language lags behind adequate language sources for enabling the SA ***,Arabic still faces challenges in natural language processing(NLP)tasks because of its structure complexities,history,and distinct *** has gained lesser effort than the other *** paper developed a Multi-versus Optimization with Deep Reinforcement Learning Enabled Affect Analysis(MVODRL-AA)on Arabic *** presented MVODRL-AAmodelmajorly concentrates on identifying and classifying effects or emotions that occurred in the Arabic ***,the MVODRL-AA model follows data pre-processing and word ***,an n-gram model is utilized to generate word embeddings.A deep Q-learning network(DQLN)model is then exploited to identify and classify the effect on the Arabic *** last,the MVO algorithm is used as a hyperparameter tuning approach to adjust the hyperparameters related to the DQLN model,showing the novelty of the work.A series of simulations were carried out to exhibit the promising performance of the MVODRL-AA *** simulation outcomes illustrate the betterment of the MVODRL-AA method over the other approaches with an accuracy of 99.27%.

关键词： Arabic language Arabic corpus natural language processing affect analysis deep learning

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User Behavior Traffic Analysis Using a Simplified Memory-Prediction Framework

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Computers, Materials & Continua 2022年第2期70卷 2679-2698页

作者： Rahmat Budiarto Ahmad A.Alqarni mohammed YAlzahrani Muhammad Fermi pasha Mohamed FazilMohamed Firdhous Deris Stiawan College of Computer Science&IT Albaha UniversityAlaqiq65779-7738Saudi Arabia Malaysia School of Information Technology Monash UniversityBandar Sunway47500Malaysia Department of Information Technology University of MoratuwaMoratuwa10400Sri Lanka Faculty of Computer Science Universitas SriwijayaIndralaya30662Indonesia

As nearly half of the incidents in enterprise security have been triggered by insiders,it is important to deploy a more intelligent defense system to assist enterprises in pinpointing and resolving the incidents caused by insiders or malicious software(malware)in *** to do so may cause a serious loss of reputation as well as *** the same time,modern network traffic has dynamic patterns,high complexity,and large volumes that make it more difficult to detect malware *** ability to learn tasks sequentially is crucial to the development of artificial *** neurogenetic computation models with deep-learning techniques are able to detect complex patterns;however,the models have limitations,including catastrophic forgetfulness,and require intensive computational *** defense systems using deep-learning models require more time to learn new traffic patterns,they cannot perform fully online(on-the-fly)***,an intelligent attack/malware detection system with on-the-fly learning capability is *** this paper,a memory-prediction framework was adopted,and a simplified single cell assembled sequential hierarchical memory(***)model instead of the hierarchical temporal memory(HTM)model is proposed to speed up learning convergence to achieve onthe-fly *** *** consists of a Single Neuronal Cell(SNC)model and a simplified Sequential Hierarchical Superset(SHS)*** *** implemented as the prediction engine of a user behavior analysis tool to detect insider attacks/*** experimental results show that the proposed memory model can predict users’traffic behavior with accuracy level ranging from 72%to 83%while performing on-the-fly learning.

关键词： Machine learning memory prediction framework insider attacks user behavior analytics

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