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Parameter Tuned machine Learning Based Emotion Recognition on Arabic Twitter Data

Computer Systems Science & Engineering 2023年第9期46卷 3423-3438页

作者： ibrahim m.alwayle Badriyya B.Al-onazi Jaber S.Alzahrani Khaled m.Alalayah Khadija m.Alaidarous ibrahim Abdulrab Ahmed mahmoud Othman Abdelwahed motwakel Department of Computer Science College of Science and ArtsSharurahNajran UniversityNajran55461Saudi Arabia Department of Language Preparation Arabic Language Teaching InstitutePrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversityNajran24211Saudi Arabia Computer Department Applied CollegeNajran UniversityNajran66462Saudi 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

Arabic is one of the most spoken languages across the ***,there are fewer studies concerning Sentiment Analysis(SA)in *** recent years,the detected sentiments and emotions expressed in tweets have received significant *** substantial role played by the Arab region in international politics and the global economy has urged the need to examine the sentiments and emotions in the Arabic *** common models are available:machine Learning and lexicon-based approaches to address emotion classification *** this motivation,the current research article develops a Teaching and Learning Optimization with machine Learning Based Emotion Recognition and Classification(TLBOmL-ERC)model for Sentiment Analysis on tweets made in the Arabic *** presented TLBOmL-ERC model focuses on recognising emotions and sentiments expressed in Arabic *** attain this,the proposed TLBOmLERC model initially carries out data pre-processing and a Continuous Bag Of Words(CBOW)-based word embedding *** addition,Denoising Autoencoder(DAE)model is also exploited to categorise different emotions expressed in Arabic *** improve the efficacy of the DAE model,the Teaching and Learning-based Optimization(TLBO)algorithm is utilized to optimize the *** proposed TLBOmL-ERC method was experimentally validated with the help of an Arabic tweets *** obtained results show the promising performance of the proposed TLBOmL-ERC model on Arabic emotion classification.

关键词： Arabic language Twitter data machine learning teaching and learning-based optimization sentiment analysis emotion classification

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Learning-Based Artificial Algae Algorithm with Optimal machine Learning Enabled malware Detection

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Computer Systems Science & Engineering 2023年第9期46卷 3103-3119页

作者： Khaled m.Alalayah Fatma S.Alrayes mohamed K.Nour Khadija m.Alaidarous ibrahim m.alwayle Heba mohsen ibrahim Abdulrab Ahmed mesfer Al Duhayyim Department of Computer Science College of Science and ArtsNajran UniversitySharurahSaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Computer Department Applied CollegeNajran UniversityNajran66462Saudi Arabia Department of Computer Science College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia

malware is a‘malicious software program that performs multiple cyberattacks on the Internet,involving fraud,scams,nation-state cyberwar,and *** malicious software programs come under different classifications,namely Trojans,viruses,spyware,worms,ransomware,Rootkit,botnet malware,*** is a kind of malware that holds the victim’s data hostage by encrypting the information on the user’s computer to make it inaccessible to users and only decrypting it;then,the user pays a ransom procedure of a sum of *** prevent detection,various forms of ransomware utilize more than one mechanism in their attack flow in conjunction with machine Learning(mL)*** study focuses on designing a Learning-Based Artificial Algae Algorithm with Optimal machine Learning Enabled malware Detection(LBAAA-OmLmD)approach in Computer *** presented LBAAA-OmLmDmodelmainly aims to detect and classify the existence of ransomware and goodware in the *** accomplish this,the LBAAA-OmLmD model initially derives a Learning-Based Artificial Algae Algorithm based Feature Selection(LBAAA-FS)model to reduce the curse of dimensionality ***,the Flower Pollination Algorithm(FPA)with Echo State Network(ESN)Classification model is *** FPA model helps to appropriately adjust the parameters related to the ESN model to accomplish enhanced classifier *** experimental validation of the LBAAA-OmLmD model is tested using a benchmark dataset,and the outcomes are inspected in distinct *** comprehensive comparative examination demonstrated the betterment of the LBAAAOmLmD model over recent algorithms.

关键词： Computer networks machine learning security malware detection feature selection ransomware

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Optimal Deep Learning Based Intruder Identification in Industrial Internet of Things Environment

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Computer Systems Science & Engineering 2023年第9期46卷 3121-3139页

作者： Khaled m.Alalayah Fatma S.Alrayes Jaber S.Alzahrani Khadija m.Alaidarous ibrahim m.alwayle Heba mohsen ibrahim Abdulrab Ahmed mesfer Al Duhayyim Department of Computer Science College of Science and ArtsSharurahNajran UniversitySaudi Arabia Department of Information Systems 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 UniversitySaudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Computer Department Applied CollegeNajran UniversityNajran66462Saudi Arabia Department of Computer Science College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia

With the increased advancements of smart industries,cybersecurity has become a vital growth factor in the success of industrial *** Industrial Internet of Things(IIoT)or Industry 4.0 has revolutionized the concepts of manufacturing and production *** industry 4.0,powerful IntrusionDetection Systems(IDS)play a significant role in ensuring network *** various intrusion detection techniques have been developed so far,it is challenging to protect the intricate data of *** is because conventional machine Learning(mL)approaches are inadequate and insufficient to address the demands of dynamic IIoT ***,the existing Deep Learning(DL)can be employed to identify anonymous ***,the current study proposes a Hunger Games Search Optimization with Deep Learning-Driven Intrusion Detection(HGSODLID)model for the IIoT *** presented HGSODL-ID model exploits the linear normalization approach to transform the input data into a useful *** HGSO algorithm is employed for Feature Selection(HGSO-FS)to reduce the curse of ***,Sparrow Search Optimization(SSO)is utilized with a Graph Convolutional Network(GCN)to classify and identify intrusions in the ***,the SSO technique is exploited to fine-tune the hyper-parameters involved in the GCN *** proposed HGSODL-ID model was experimentally validated using a benchmark dataset,and the results confirmed the superiority of the proposed HGSODL-ID method over recent approaches.

关键词： Industrial IoT deep learning network security intrusion detection system attribute selection smart factory

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Abstractive Arabic Text Summarization Using Hyperparameter Tuned Denoising Deep Neural Network

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Intelligent Automation & Soft Computing 2023年第11期38卷 153-168页

作者： ibrahim m.alwayle Hala J.Alshahrani Saud S.Alotaibi Khaled m.Alalayah Amira Sayed A.Aziz Khadija m.Alaidarous ibrahim Abdulrab Ahmed manar Ahmed Hamza Department of Computer Science College of Science and ArtsSharurahNajran UniversitySharurahSaudi Arabia Department of Applied Linguistics College of LanguagesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems College of Computing and Information SystemUmm Al-Qura UniversityMakkahSaudi Arabia Department of Digital media Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Computer Department Applied CollegeNajran UniversityNajran66462Saudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

ive Arabic Text Summarization using Hyperparameter Tuned Denoising Deep Neural Network(AATS-HTDDNN)*** presented AATS-HTDDNN technique aims to generate summaries of Arabic *** the presented AATS-HTDDNN technique,the DDNN model is utilized to generate the *** study exploits the Chameleon Swarm Optimization(CSO)algorithm to fine-tune the hyperparameters relevant to the DDNN model since it considerably affects the summarization *** phase shows the novelty of the current *** validate the enhanced summarization performance of the proposed AATS-HTDDNN model,a comprehensive experimental analysis was *** comparison study outcomes confirmed the better performance of the AATS-HTDDNN model over other approaches.

关键词： Text summarization deep learning denoising deep neural networks hyperparameter tuning Arabic language

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An Adaptive Hate Speech Detection Approach Using Neutrosophic Neural Networks for Social media Forensics

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Computers, materials & Continua 2024年第4期79卷 243-262页

作者： Yasmine m.ibrahim Reem Essameldin Saad m.Darwish Department of Information Technology Institute of Graduate Studies and ResearchAlexandria UniversityAlexandria21526Egypt Faculty of Computers and Information Technology Egyptian E-Learning University(EELU)Giza12611Egypt Faculty of Computers and Data Science Alexandria UniversityAlexandria21554Egypt

Detecting hate speech automatically in social media forensics has emerged as a highly challenging task due tothe complex nature of language used in such platforms. Currently, several methods exist for classifying hatespeech, but they still suffer from ambiguity when differentiating between hateful and offensive content and theyalso lack accuracy. The work suggested in this paper uses a combination of the Whale Optimization Algorithm(WOA) and Particle Swarm Optimization (PSO) to adjust the weights of two multi-Layer Perceptron (mLPs)for neutrosophic sets classification. During the training process of the mLP, the WOA is employed to exploreand determine the optimal set of weights. The PSO algorithm adjusts the weights to optimize the performanceof the mLP as fine-tuning. Additionally, in this approach, two separate mLP models are employed. One mLPis dedicated to predicting degrees of truth membership, while the other mLP focuses on predicting degrees offalse membership. The difference between these memberships quantifies uncertainty, indicating the degree ofindeterminacy in predictions. The experimental results indicate the superior performance of our model comparedto previous work when evaluated on the Davidson dataset.

关键词： Hate speech detection whale optimization neutrosophic sets social media forensics

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Classifying rockburst with confidence:A novel conformal prediction approach

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International Journal of mining Science and Technology 2024年第1期34卷 51-64页

作者： Bemah ibrahim Isaac Ahenkorah University of South Australia UniSA STEMSA 5000Australia

The scientific community recognizes the seriousness of rockbursts and the need for effective mitigation *** literature reports various successful applications of machine learning(mL)models for rockburst assessment;however,a significant question remains unanswered:How reliable are these models,and at what confidence level are classifications made?Typically,mL models output single rockburst grade even in the face of intricate and out-of-distribution samples,without any associated confidence *** the susceptibility of mL models to errors,it becomes imperative to quantify their uncertainty to prevent consequential *** address this issue,we propose a conformal prediction(CP)framework built on traditional mL models(extreme gradient boosting and random forest)to generate valid classifications of rockburst while producing a measure of confidence for its *** proposed framework guarantees marginal coverage and,in most cases,conditional coverage on the test *** CP was evaluated on a rockburst case in the Sanshandao Gold mine in China,where it achieved high coverage and efficiency at applicable confidence ***,the CP identified several“confident”classifications from the traditional mL model as unreliable,necessitating expert verification for informed *** proposed framework improves the reliability and accuracy of rockburst assessments,with the potential to bolster user confidence.

关键词： Rockburst machine learning Uncertainty quantification Conformal prediction

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New insights into astrocyte diversity from the lens of transcriptional regulation and their implications for neurodegenerative disease treatments

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Neural Regeneration Research 2024年第11期19卷 2335-2336页

作者： ibrahim Olabayode Saliu Guoyan Zhao Department of Genetics Washington University School of MedicineSt.LouisMOUSA Department of Neurology Department of Pathology and ImmunologyWashington University School of MedicineSt.LouisMOUSA

Astrocytes are a major glial cell type in the central nervous system,and they provide trophic and metabolic support to *** addition to these roles,they play crucial roles in modulating synaptic functions,development,and pruning(Brandebura et al.,2023).Astrocytes become reactive(activated)by undergoing morphological,molecular,and functional alterations in response to neuropathology such as in injuries and neurodegenerative diseases(NDs)(Escartin et al.,2021).

关键词： degenerative injuries

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Enteric neuropathy in diabetes:Implications for gastrointestinal function

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World Journal of Gastroenterology 2024年第22期30卷 2852-2865页

作者： mona mohamed ibrahim Abdalla Department of Human Biology School of MedicineInternational Medical UniversityBukit Jalil 57000Kuala LumpurMalaysia

Diabetes,commonly known for its metabolic effects,also critically affects the enteric nervous system(ENS),which is essential in regulating gastrointestinal(GI)motility,secretion,and *** development of diabetes-induced enteric neuropathy can lead to various GI dysfunctions,such as gastroparesis and irregular bowel habits,primarily due to disruptions in the function of neuronal and glial cells within the ENS,as well as oxidative stress and *** editorial explores the pathophysiological mechanisms underlying the development of enteric neuropathy in diabetic ***,it discusses the latest advances in diagnostic approaches,emphasizing the need for early detection and intervention to mitigate GI complications in diabetic *** editorial also reviews current and emerging therapeutic strategies,focusing on pharmacological treatments,dietary management,and potential neuromodulatory ***,this editorial highlights the necessity of a multidisciplinary approach in managing enteric neuropathy in diabetes,aiming to enhance patient quality of life and address a frequently overlooked complication of this widespread disease.

关键词： Diabetic neuropathy Gastrointestinal Insulin resistance Diabetes Enteric nervous system Enteric neuropathy

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Bayesian and Non-Bayesian Analysis for the Sine Generalized Linear Exponential model under Progressively Censored Data

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Computer modeling in Engineering & Sciences 2024年第9期140卷 2795-2823页

作者： Naif Alotaibi A.S.Al-moisheer ibrahim Elbatal mohammed Elgarhy Ehab m.Almetwally Department of mathematics and Statistics Faculty of ScienceImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia Department of mathematics College of ScienceJouf UniversityP.O.Box 848Sakaka72351Saudi Arabia mathematics and Computer Science Department Faculty of ScienceBeni-Suef UniversityBeni-Suef62521Egypt Department of Basic Sciences Higher Institute of Administrative SciencesBelbeisEgypt Faculty of Business Administration Delta University for Science and TechnologyGamasa11152Egypt

This article introduces a novel variant of the generalized linear exponential(GLE)distribution,known as the sine generalized linear exponential(SGLE)*** SGLE distribution utilizes the sine transformation to enhance its *** updated distribution is very adaptable and may be efficiently used in the modeling of survival data and dependability *** suggested model incorporates a hazard rate function(HRF)that may display a rising,J-shaped,or bathtub form,depending on its unique *** model includes many well-known lifespan distributions as separate *** suggested model is accompanied with a range of statistical *** model parameters are examined using the techniques of maximum likelihood and Bayesian estimation using progressively censored *** order to evaluate the effectiveness of these techniques,we provide a set of simulated data for testing *** relevance of the newly presented model is shown via two real-world dataset applications,highlighting its superiority over other respected similar models.

关键词： Sine G family generalized linear failure rate progressively censored data moments maximum likelihood estimation Bayesian estimation simulation

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Thermal performance of domed roof in air-conditioned spaces

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Energy and Built Environment 2024年第2期5卷 270-287页

作者： ibrahim Reda Raouf N.Abdelmessih mohamed Steit Ehab m.mina mechanical Power Engineering Faculty of EngineeringAin Shams University1 El-Sarayat St.Cairo 11517Egypt Architectural Engineering Faculty of EngineeringAin Shams UniversityCairoEgypt

Due to their thermal performance,domed roofs are one of the passive solutions that affect energy consumption in *** thermal performance of domed roofs has been investigated in many naturally ventilated ***,few studies have discussed their performance in conditioned ***,this study introduces a computational comparison between domed and flat roofs to investigate their impact on thermal comfort inside a conditioned *** an earlier stage,field measurements were carried out inside a Bahraini mosque to acquire its indoor air conditions during the summer period of 2021,in addition to validating the computational *** findings of this study confirm that,under mechanical cooling conditions,the flat roof offers a lower indoor temperature than the domed roof by 0.4℃and 0.1℃for open and closed doors,***,the air velocity is lower by approximately 0.01 m/s for both door *** overall PmV values of the flat roof are also lower by 0.07 and 0.01,while the PPD values are lower by 0.20,and 0.34 for open and closed doors,*** on these small differences,it can be concluded that the thermal performance of both roofing systems behaves equally in conditioned ***,the air patterns are substantially different,the overall thermal performance is *** similarity drives building designers to rethink the thermal performance of the domed roofs in air-conditioned spaces with such a hot climate,regardless of their aesthetic and acoustical behaviour.

关键词： Thermal comfort Computational fluid dynamics(CFD) Predicted mean vote(PmV)and predicted percentage dissatisfied(PPD) Domed roof Flat roof

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