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

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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Parameter Tuned machine Learning Based Emotion Recognition on Arabic Twitter Data

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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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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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Congruent Feature Selection method to Improve the Efficacy of machine Learning-Based Classification in medical Image Processing

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Computer modeling in Engineering & Sciences 2025年第1期142卷 357-384页

作者： mohd Anjum Naoufel Kraiem Hong min Ashit Kumar Dutta Yousef ibrahim Daradkeh Department of Computer Engineering Aligarh Muslim UniversityAligarh202002India College of Computer Science King Khalid UniversityAbha61413Saudi Arabia School of Computing Gachon UniversitySeongnam13120Republic of Korea Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityRiyadh13713Saudi Arabia Department of Computer Engineering and Information College of Engineering in Wadi AlddawasirPrince Sattam bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia

machine learning(mL)is increasingly applied for medical image processing with appropriate learning *** applications include analyzing images of various organs,such as the brain,lung,eye,etc.,to identify specific flaws/diseases for *** primary concern of mL applications is the precise selection of flexible image features for pattern detection and region *** of the extracted image features are irrelevant and lead to an increase in computation ***,this article uses an analytical learning paradigm to design a Congruent Feature Selection method to select the most relevant image *** process trains the learning paradigm using similarity and correlation-based features over different textural intensities and pixel *** similarity between the pixels over the various distribution patterns with high indexes is recommended for disease ***,the correlation based on intensity and distribution is analyzed to improve the feature selection ***,the more congruent pixels are sorted in the descending order of the selection,which identifies better regions than the ***,the learning paradigm is trained using intensity and region-based similarity to maximize the chances of ***,the probability of feature selection,regardless of the textures and medical image patterns,is *** process enhances the performance of mL applications for different medical image *** proposed method improves the accuracy,precision,and training rate by 13.19%,10.69%,and 11.06%,respectively,compared to other models for the selected *** mean error and selection time is also reduced by 12.56%and 13.56%,respectively,compared to the same models and dataset.

关键词： Computer vision feature selection machine learning region detection texture analysis image classification medical images

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Revolutionizing diabetic retinopathy screening and management:The role of artificial intelligence and machine learning

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World Journal of Clinical Cases 2025年第5期13卷 1-12页

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

Diabetic retinopathy(DR)remains a leading cause of vision impairment and blindness among individuals with diabetes,necessitating innovative approaches to screening and *** editorial explores the transformative potential of artificial intelligence(AI)and machine learning(mL)in revolutionizing DR *** and mL technologies have demonstrated remarkable advancements in enhancing the accuracy,efficiency,and accessibility of DR screening,helping to overcome barriers to early *** technologies leverage vast datasets to identify patterns and predict disease progression with unprecedented precision,enabling clinicians to make more informed ***,AI-driven solutions hold promise in personalizing management strategies for DR,incorpo-rating predictive analytics to tailor interventions and optimize treatment *** automating routine tasks,AI can reduce the burden on healthcare providers,allowing for a more focused allocation of resources towards complex patient *** review aims to evaluate the current advancements and applic-ations of AI and mL in DR screening,and to discuss the potential of these techno-logies in developing personalized management strategies,ultimately aiming to improve patient outcomes and reduce the global burden of *** integration of AI and mL in DR care represents a paradigm shift,offering a glimpse into the future of ophthalmic healthcare.

关键词： Diabetic retinopathy Artificial intelligence machine learning Screening management Predictive analytics Personalized medicine

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An efficient nano-adsorbent via surfactants/dual surfactants assisted ultrasonic co-precipitation method for sono-removal of monoazo and diazo anionic dyes

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Chinese Journal of Chemical Engineering 2021年第12期34卷 225-236页

作者： marwa m.ibrahim Department of Chemistry Faculty of EducationAin Shams UniversityRoxy 11711CairoEgypt

To preserve the environment for civilization,we should remove the pollutants like toxic dyes by friendly and cost efficacious *** this study,the effect of surfactants or mixed surfactants on physicochemical,optical and adsorption properties of ternary mixed oxide CeO_(2)-ZrO_(2)-Al_(2) O_3(CZA) are *** ternary mixed oxide CZA was prepared by surfactants or mixed surfactants assisted ultrasonic coprecipitation *** physicochemical and optical properties are estimated by different techniques like XRD,TEm,EDX,FTIR,S_(BET) and UV-Vis/*** CZA_T and CZA_C have hybrid shapes and high surface *** adsorption properties of ternary mixed oxides adsorbents were characterized by sono-removing anionic dyes such as Congo red(CR) and Remazol red RB-133(RR).The different factors like contact time,different dye concentrations and temperatures also *** kinetics and isotherms applications showed that,the adsorption process was followed pseudo second order kinetics and the Freundlich isotherm ***,the adsorption is spontaneous and endothermic process through the thermodynamic ***,the results showed that the ternary mixed oxide nano-adsorbent(CeO_(2)-ZrO_(2)-Al_(2) O_3) is promising and functional materials for anionic dye sweep from wastewater.

关键词： CeO_(2)-ZrO_(2)-Al_(2)O_3 Surfactants Ultrasonic co-precipitation method Optical properties Wastewater Adsorption

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Automated Spam Review Detection Using Hybrid Deep Learning on Arabic Opinions

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Computer Systems Science & Engineering 2023年第9期46卷 2947-2961页

作者： ibrahimm.alwayle Badriyya B.Al-onazi mohamed K.Nour Khaled m.Alalayah Khadija m.Alaidarous ibrahim Abdulrab Ahmed Amal S.mehanna Abdelwahed motwakel Department of Computer Science College of Science and ArtsSharurahNajran UniversitySaudi 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 UniversitySaudi Arabia Computer Department Applied CollegeNajran UniversityNajran66462Saudi 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

Online reviews regarding purchasing services or products offered are the main source of users’*** gain fame or profit,generally,spam reviews are written to demote or promote certain targeted products or *** practice is called review *** the last few years,various techniques have been recommended to solve the problem of spam *** spam detection study focuses on English reviews,with a lesser interest in other *** review detection in Arabic online sources is an innovative topic despite the vast amount of data ***,this study develops an Automated Spam Review Detection using optimal Stacked Gated Recurrent Unit(SRD-OSGRU)on Arabic Opinion *** presented SRD-OSGRU model mainly intends to classify Arabic reviews into two classes:spam and ***,the presented SRD-OSGRU model follows different levels of data preprocessing to convert the actual review data into a compatible ***,unigram and bigram feature extractors are *** SGRU model is employed in this study to identify and classify Arabic spam *** the trial-and-error adjustment of hyperparameters is a tedious process,a white shark optimizer(WSO)is utilized,boosting the detection efficiency of the SGRU *** experimental validation of the SRD-OSGRU model is assessed under two datasets,namely DOSC *** extensive comparison study pointed out the enhanced performance of the SRD-OSGRU model over other recent approaches.

关键词： Arabic text spam reviews machine learning deep learning white shark optimizer

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Summary of the current guidelines for managing iatrogenic colorectal perforations and the evolving role of endoluminal vacuum therapy

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World Journal of Clinical Cases 2025年第6期13卷 1-8页

作者： Ahmed Tawheed ibrahim Halil Bahcecioglu mehmet Yalniz mubin Ozercan Ali Cagri Oral mohamed El-Kassas Department of Endemic medicine Faculty of MedicineHelwan UniversityCairo 11795Egypt Department of Gastroenterology Firat UniversityElazig 23119Türkiye Liver Disease Research Center College of MedicineKing Saud UniversityRiyadh 7805Saudi Arabia

Colonoscopy represents a safe procedure that is widely used in medical practice either to diagnose or treat various gastrointestinal *** the last few years,the incidence rate of perforations in colonoscopic procedures has increased,especially in therapeutic *** recent advancements in endoscopic techniques and gastrointestinal tumoral resection procedures such as endoscopic mucosal resection,endoscopic full-thickness resection,and endoscopic submucosal dissection(ESD)could be a risk factor for this increased *** incidence rate of mortality of serious colonoscopic perforations is 7.1%.The management plan for these perforations starts with conservative treatment in mild cases,endoscopic closure,and surgical management in severe ***,endoluminal vacuum therapy was found to be effective in the management of colorectal perforations and this has been reported in multiple case *** editorial provides an overview of the current guidelines for the management of iatrogenic colorectal *** insights are from the perspectives of endoscopists and *** also present a management algorithm based on the guidelines of the European Society of Gastrointestinal Endoscopy,the American Gastroenterological Association,and the World Society of Emergency *** also discussed in brief the use of endoluminal vacuum therapy in colorectal perforations.

关键词： Colon Rectum Iatrogenic colonic perforation Colonoscopy Guidelines Endovacuum Endolumical vacuum therapy Endoscopic management

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molecular characterization of yolk proteins in the female crab Neptunus pelagicus(***-Edwards,1861) from the mediterranean Sea of Alexandria,Egypt

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Journal of Oceanology and Limnology 2020年第2期38卷 438-453页

作者： ibrahim G ELGHAZALY m Department of Zoology Faculty of ScienceAlexandria UniversityAlexandria 21547Egypt Department of Zoology Faculty of ScienceDamnhour UniversityEl Behara 22516Egypt

This study aimed to characterize the morphological changes in the ovary of the female crab Neptunus pelagicus and to identify specific fractions of vitelloginin and vitelline molecules during primary and secondary *** of the blue crab were collected from the mediterranean Sea of Alexandria monthly during *** and oocytes in primary and secondary vitellogenesis were detached and treated for histological *** polyacrylamide gel electrophoresis(PAGE)Bis-Tris Gels was applied to identify vitelloginin(VN)and vitelline(VL)*** Analyses were done by *** initial degenerate primers were built regarding the conserved amino acid domains of the yolk *** and secondary vitellogeneses consisted of 8 *** fraction with molecular weight 550 kDa was identified in the hemolymph in secondary *** protein fractions(VLI&VLII)were identified in secondary vitellogenic *** electrophoresis performed with extract of stage I oocyte showed two protein fractions with molecular weights 550 kDa and 460 *** stage II and III oocyte,4 subunits were presented of 180,195,140 and 120 kDa in VLI and 2 subunits with molecular weight of 110 kDa and 95 kDa in *** two fractions in stage V oocyte presented with molecular weights of 380 kDa and 360 *** blot analysis proved that both fractions were of four major polypeptide subunits with molecular weight of 180,125,90 and 85 kDa in each of the two *** hybridization signal obtained by the Northern blot was detected in the hepatopancreas during ovarian cycle and in the ovary during secondary *** result of the reverse transcription-polymerase chain reaction(RT-PCR)analysis showed that the mRNA that encodes the C-terminal region of the VN cDNA was found in the ovary in secondary vitellogenesis and in the hepatopancreas.

关键词： Neptunus pelagicus vitellogenesis vitelloginin and vitelline native polyacrylamide gel electrophoresis(PAGE)Bis-Tris Gels PAGE-SDS western blot analysis reverse transcription-polymerase chain reaction(RT-PCR)

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