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Energy-Efficient Routing Using Novel Optimization with Tabu Techniques for Wireless sensor Network

Computer systems science & Engineering 2023年第5期45卷 1711-1726页

作者： Manar Ahmed Hamza Aisha Hassan Abdalla Hashim Dalia H.Elkamchouchi Nadhem Nemri Jaber s.Alzahrani Amira sayed A.Aziz Mnahel Ahmed Ibrahim Abdelwahed Motwakel Department of Electrical and Computer Engineering International Islamic University MalaysiaKuala Lumpur53100Malaysia Department of Computer and self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information systems College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Digital Media Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt College of science and Humanities studies in Alquwayiyah Shaqra UniversitySaudi Arabia

Wireless sensor Network(WsN)consists of a group of limited energy source sensors that are installed in a particular region to collect data from the *** the energy-efficient data collection methods in largescale wireless sensor networks is considered to be a difficult area in the *** node clustering is a popular approach for ***,the sensor nodes are grouped to form clusters in a cluster-based WsN *** battery performance of the sensor nodes is likewise *** a result,the energy efficiency of WsNs is *** specific,the energy usage is influenced by the loads on the sensor node as well as it ranges from the Base station(Bs).Therefore,energy efficiency and load balancing are very essential in *** the proposed method,a novel Grey Wolf Improved Particle swarm Optimization with Tabu search Techniques(GW-IPsO-Ts)was *** selection of Cluster Heads(CHs)and routing path of every CH from the base station is enhanced by the proposed *** provides the best routing path and increases the lifetime and energy efficiency of the ***-to-end delay and packet loss rate have also been *** proposed GW-IPsO-Ts method enhances the evaluation of alive nodes,dead nodes,network survival index,convergence rate,and standard deviation of sensor *** to the existing algorithms,the proposed method outperforms better and improves the lifetime of the network.

关键词： Wireless sensor networks energy-efficient load balancing energy consumption network’s lifetime cluster heads grey wolf optimization tabu search particle swarm optimization

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stock Market Trading Based on Market sentiments and Reinforcement Learning

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Computers, Materials & Continua 2022年第1期70卷 935-950页

作者： K.M.Ameen suhail syam sankar Ashok s.Kumar Tsafack Nestor Naglaa F.soliman Abeer D.Algarni Walid El-shafai Fathi E.Abd El-samie Department of Computer science&Engineering NSS College of EngineeringPalakkad678008KeralaIndia Department of Electronics and Communication Engineering NSS College of EngineeringPalakkad678008KeralaIndia Unitéde Recherche de Matière Condensée D’Electronique et de Traitement du Signal(URAMACETS)Department of PhysicsUniversity of DschangP.O.Box 67DschangCameroon Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh84428Saudi Arabia Department Electronics and Electrical Communications Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt

stock market is a place,where shares of different companies are *** is a collection of buyers’and sellers’*** this digital era,analysis and prediction in the stock market have gained an essential role in shaping today’s *** market analysis can be either fundamental or *** analysis can be performed either with technical indicators or through machine learning *** this paper,we report a system that uses a Reinforcement Learning(RL)network and market sentiments to make decisions about stock market *** system uses sentiment analysis on daily market news to spot trends in stock *** sentiment analysis module generates a unified score as a measure of the daily news about *** score is then fed into the RL module as one of its *** RL section gives decisions in the form of three actions:buy,sell,or *** objective is to maximize long-term future *** have used stock data of Apple from 2006 to 2016 to interpret how sentiments affect *** stock price of any company rises,when significant positive news become available in the public *** results reveal the influence of market sentiments on forecasting of stock prices.

关键词： Deep learning machine learning daily market news reinforcement learning stock market

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Convolutional Deep Belief Network Based short Text Classification on Arabic Corpus

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Computer systems science & Engineering 2023年第6期45卷 3097-3113页

作者： Abdelwahed Motwakel Badriyya B.Al-onazi Jaber s.Alzahrani Radwa Marzouk Amira sayed A.Aziz Abu sarwar Zamani Ishfaq Yaseen Amgad Atta Abdelmageed1 Department of Computer and self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi 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 UniversitySaudi Arabia Department of Information systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Digital Media Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt

With a population of 440 million,Arabic language users form the rapidly growing language group on the web in terms of the number of Internet users.11 million monthly Twitter users were active and posted nearly 27.4 million tweets every *** order to develop a classification system for the Arabic lan-guage there comes a need of understanding the syntactic framework of the words thereby manipulating and representing the words for making their classification *** this view,this article introduces a Dolphin swarm Optimization with Convolutional Deep Belief Network for short Text Classification(DsOCDBN-sTC)model on Arabic *** presented DsOCDBN-sTC model majorly aims to classify Arabic short text in social *** presented DsOCDBN-sTC model encompasses preprocessing and word2vec word embedding at the preliminary ***,the DsOCDBN-sTC model involves CDBN based classification model for Arabic short *** last,the DsO technique can be exploited for optimal modification of the hyperparameters related to the CDBN *** establish the enhanced performance of the DsOCDBN-sTC model,a wide range of simulations have been *** simulation results con-firmed the supremacy of the DsOCDBN-sTC model over existing models with improved accuracy of 99.26%.

关键词： Arabic text short text classification dolphin swarm optimization deep learning

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Adolescents’perceived stress of COVID-19 and self-compassion in saudi Arabia:A cross-sectional study

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International Journal of Nursing sciences 2023年第2期10卷 215-220页

作者： sama s.Hammad Mona D.Alzhrani Hebah A.Almulla College of Nursing Imam Abdulrahman Bin Faisal UniversityDammamSaudi Arabia Nursing College King Khalid UniversityAbhaSaudi Arabia

Objectives Worldwide,the COVID-19 pandemic has significantly impacted mental health challenges for adolescent *** current study aims to examine the levels of perceived stress of COVID-19 and self-compassion and their relationship among adolescents in saudi *** This study employed a cross-sectional online survey of adolescents from secondary schools in Asir,saudi *** that we distributed online included the modified Perceived stress scale(Pss-10)and the self-Compassion scale(sCs),in addition to demographic and health-related questions.A total of 500 adolescents completed the *** Adolescents in the study reported an average moderate perceived stress level of 18.6(sD=6.67)and an average moderate self-compassion level of 3.22(sD=0.53).A significant correlation is also found between the two variables(r=−0.460,Ps negative correlation indicates that lower levels of perceived stress are significantly correlated with higher levels of *** The study results show that the perceived stress of COVID-19 is inversely correlated with self-compassion in saudi *** research is needed to explore how to enhance adolescents’*** nurses’role should be given full play in this area.

关键词： Adolescent COVID-19 Pandemics Psychological stress saudi Arabia self-compassion

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An innovative technique to solve a fractal damping Duffing-jerk oscillator

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Communications in Theoretical Physics 2023年第5期75卷 3-11页

作者： Yusry O El-Dib Nasser s Elgazery Youmna M Khattab Haifa A Alyousef Department of Mathematics Faculty of EducationAin Shams UniversityRoxyCairoEgypt Department of Physics College of SciencePrincess Nourah bint Abdulrahman UniversityPO Box 84428Riyadh 11671Saudi Arabia

The idea of the present article is to look into the nonlinear dynamics and vibration of a damping Duffing-jerk oscillator in fractal space exhibiting the non-perturbative *** a new analytical technique,namely,the modification of a He’s fractal derivative that converts the fractal derivative to the traditional derivative in continuous space,this study provides an effective and easy-to-apply procedure that is dependent on the He’s fractal derivative *** analytic approximate solution has a significant match with the results of the numerical simulation as the fractal parameter is very closer to unity,which proves the reliability of the *** behavior is discussed and illustrated *** new powerful analytical tools are developed in an attempt to obtain effective analytical tools,valid for any fractal nonlinear problems.

关键词： modification of He’s fractal derivative fractal duffing-jerk vibration stability analysis non-perturbative approach damping nonlinear fractal system

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Optical Ciphering scheme for Cancellable speaker Identification system

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Computer systems science & Engineering 2023年第4期45卷 563-578页

作者： Walid El-shafai Marwa A.Elsayed Mohsen A.Rashwan Moawad I.Dessouky Adel s.El-Fishawy Naglaa F.soliman Amel A.Alhussan Fathi EAbd El-samie Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt security Engineering Laboratory Department of Computer SciencePrince Sultan UniversityRiyadh11586Saudi Arabia Department of Electronics and Communications Faculty of EngineeringCairo UniversityCairoEgypt Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadhSaudi Arabia

Most current security and authentication systems are based on personal *** security problem is a major issue in the field of biometric *** is due to the use in databases of the original *** biometrics will forever be lost if these databases are *** privacy is the most important goal of cancelable *** order to protect privacy,therefore,cancelable biometrics should be non-invertible in such a way that no information can be inverted from the cancelable biometric templates stored in personal identification/verification *** methodology to achieve non-invertibility is the employment of non-invertible *** work suggests an encryption process for cancellable speaker identification using a hybrid encryption *** system includes the 3D Jigsaw transforms and Fractional Fourier Transform(FrFT).The proposed scheme is compared with the optical Double Random Phase Encoding(DRPE)encryption *** evaluation of simulation results of cancellable biometrics shows that the algorithm proposed is secure,authoritative,and *** encryption and cancelability effects are good and reveal good ***,it introduces recommended security and robustness levels for its utilization for achieving efficient cancellable biometrics systems.

关键词： Cancellable biometrics jigsaw transform FrFT DRPE speaker identification

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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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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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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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Machine Learning Based Cybersecurity Threat Detection for secure IoT Assisted Cloud Environment

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Computer systems science & Engineering 2023年第10期47卷 855-871页

作者： Z.Faizal Khan saeed M.Alshahrani abdulrahman Alghamdi someah Alangari Nouf Ibrahim Altamami Khalid A.Alissa sana Alazwari Mesfer Al Duhayyim Fahd N.Al-Wesabi Department of Computer Engineering College of Computing and Information TechnologyShaqra UniversityShaqraSaudi Arabia Department of Computer science College of Computing and Information TechnologyShaqra UniversityShaqraSaudi Arabia Department of Computer science College of Science and Humanities DawadmiShaqra UniversityShaqraSaudi Arabia Department of Mathematics College of EducationShaqra UniversityShaqraSaudi Arabia saudi Aramco Cybersecurity Chair Networks and Communications DepartmentCollege of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif P.O.Box 11099Taif21944Saudi Arabia Department of Computer science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversityAlaflaj16828Saudi Arabia Department of Computer science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia

The Internet of Things(IoT)is determine enormous economic openings for industries and allow stimulating innovation which obtain between domains in childcare for eldercare,in health service to energy,and in developed to *** develops a difficult problem in IoT platform whereas the presence of cyber-attack requires that *** progress of automatic devices for cyber-attack classifier and detection employing Artificial Intelligence(AI)andMachine Learning(ML)devices are crucial fact to realize security in IoT *** can be required for minimizing the issues of security based on IoT devices ***,this research proposal establishes novel mayfly optimized with Regularized Extreme Learning Machine technique called as MFO-RELM model for Cybersecurity Threat classification and detection fromthe cloud and IoT *** proposed MFORELM model provides the effective detection of cybersecurity threat which occur in the cloud and IoT *** accomplish this,the MFO-RELM technique pre-processed the actual cloud and IoT data as to meaningful ***,the proposed models will receive the pre-processing data and carry out the classifier *** boosting the efficiency of the proposed models,theMFOtechnique was utilized to *** experiential outcome of the proposed technique was tested utilizing the standard CICIDs 2017 dataset,and the outcomes are examined under distinct aspects.

关键词： Mayfly optimization machine learning artificial intelligence cybersecurity threat detection

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