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AMachine Learning Approach to Cyberbullying Detection in Arabic Tweets

Computers, Materials & Continua 2024年第7期80卷 1033-1054页

作者： Dhiaa Musleh Atta Rahman mohammed Abbas Alkherallah Menhal Kamel Al-Bohassan Mustafa mohammed Alawami Hayder Ali Alsebaa Jawad Ali Alnemer Ghazi Fayez Al-Mutairi May Issa Aldossary Dalal A.Aldowaihi Fahd Alhaidari Department of Computer Science College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Department of Computer Information Systems College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Department of Networks and Communications College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia

With the rapid growth of internet usage,a new situation has been created that enables practicing *** has increased over the past decade,and it has the same adverse effects as face-to-face bullying,like anger,sadness,anxiety,and *** the anonymity people get on the internet,they tend to bemore aggressive and express their emotions freely without considering the effects,which can be a reason for the increase in cyberbullying and it is the main motive behind the current *** study presents a thorough background of cyberbullying and the techniques used to collect,preprocess,and analyze the ***,a comprehensive review of the literature has been conducted to figure out research gaps and effective techniques and practices in cyberbullying detection in various languages,and it was deduced that there is significant room for improvement in the Arabic *** a result,the current study focuses on the investigation of shortlisted machine learning algorithms in natural language processing(NLP)for the classification of Arabic datasets duly collected from Twitter(also known as X).In this regard,support vector machine(SVM),Naive Bayes(NB),Random Forest(RF),Logistic regression(LR),Bootstrap aggregating(Bagging),Gradient Boosting(GBoost),Light Gradient Boosting Machine(LightGBM),Adaptive Boosting(AdaBoost),and eXtreme Gradient Boosting(XGBoost)were shortlisted and investigated due to their effectiveness in the similar ***,the scheme was evaluated by well-known performance measures like accuracy,precision,Recall,and ***,XGBoost exhibited the best performance with 89.95%accuracy,which is promising compared to the state-of-the-art.

关键词： Supervised machine learning ensemble learning cyberbullying Arabic tweets NLP

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The impact of foot reflexology on fatigue and sleep quality in school-aged children undergoing hemodialysis

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Journal of Integrative Nursing 2024年第2期6卷 76-82页

作者： Amal Hashem MOHAMED Mostafa SHABAN Huda Hamdy mohammed Randa Mohamed ABOBAKER Salman Hamdan ALSAQRI Rania Abd-Elnaby mohammed ALLAM Faculty of Nursing Minia UniversityMinyaEgypt College of Nursing Jouf UniversitySakakaSaudi Arabia Faculty of Nursing Cairo UniversityGizaEgypt Northern College of Nursing ARARSaudi Arabia College of Nursing University of HailHailSaudi Arabia

Objective:This study aimed to evaluate the effects of foot reflexology on fatigue and sleep quality in school-aged children receiving ***:A quasi-experimental pretest-posttest design was *** children of ages 6-12 undergoing in-center hemodialysis were *** Pittsburgh Sleep Quality Index(PSQI)and Inventory of Fatigue Symptom(IFS)scales were administered at *** then received 30 min of foot reflexology massage before hemodialysis sessions 3 days per week for 12 *** administration of the sleep and fatigue scales occurred after the intervention ***:Reflexology massage led to significant improvements in sleep quality components,including duration(0%-30%normal sleepers),efficiency(0%-50%>85%),latency(50%-0%>60 min),disturbances,and daytime *** mean PSQI score decreased from 18.2 to 9.7(P<0.05).Fatigue severity substantially decreased,with the mean IFS score improving from 105.7 to 64.1(P<0.05).Conclusion:Foot reflexology is an effective nursing intervention for reducing fatigue and improving sleep quality in children on hemodialysis,warranting integration into routine care.

关键词： Fatigue hemodialysis massage pediatric reflexology sleep

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Multi-Objective Optimization Algorithm for Grouping Decision Variables Based on Extreme Point Pareto Frontier

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Computers, Materials & Continua 2024年第4期79卷 1281-1299页

作者： JunWang Linxi Zhang Hao Zhang Funan Peng mohammed A.El-Meligy Mohamed Sharaf Qiang Fu College of Computer Science and Technology Shenyang University of Chemical TechnologyShenyang110142China Key Laboratory of Intelligent Technology for Chemical Process Industry of Liaoning Province Shenyang University of Chemical TechnologyShenyang110142China Industrial Engineering Department College of EngineeringKing Saud UniversityP.O.Box 800Riyadh11421Saudi Arabia

The existing algorithms for solving multi-objective optimization problems fall into three main categories:Decomposition-based,dominance-based,and *** multi-objective optimization problemsmainly focus on objectives,treating decision variables as a total variable to solve the problem without consideringthe critical role of decision variables in objective *** seen,a variety of decision variable groupingalgorithms have been ***,these algorithms are relatively broad for the changes of most decisionvariables in the evolution process and are time-consuming in the process of finding the Pareto *** solvethese problems,a multi-objective optimization algorithm for grouping decision variables based on extreme pointPareto frontier(MOEA-DV/EPF)is *** algorithm adopts a preprocessing rule to solve the Paretooptimal solution set of extreme points generated by simultaneous evolution in various target directions,obtainsthe basic Pareto front surface to determine the convergence effect,and analyzes the convergence and distributioneffects of decision *** the later stages of algorithm optimization,different mutation strategies are adoptedaccording to the nature of the decision variables to speed up the rate of evolution to obtain excellent individuals,thusenhancing the performance of the *** validation of the test functions shows that this algorithmcan solve the multi-objective optimization problem more efficiently.

关键词： Multi-objective evolutionary optimization algorithm decision variables grouping extreme point pareto frontier

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Network Security Enhanced with Deep Neural Network-Based Intrusion Detection System

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Computers, Materials & Continua 2024年第7期80卷 1457-1490页

作者： Fatma S.Alrayes mohammed Zakariah Syed Umar Amin Zafar Iqbal Khan Jehad Saad Alqurni Information Systems Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia College of Computer and Information Sciences King Saud UniversityRiyadh11362Saudi Arabia College of Computer and Information Sciences Prince Sultan UniversityRiyadh11586Saudi Arabia Department of Educational Technologies College of EducationImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia

This study describes improving network security by implementing and assessing an intrusion detection system(IDS)based on deep neural networks(DNNs).The paper investigates contemporary technical ways for enhancing intrusion detection performance,given the vital relevance of safeguarding computer networks against harmful *** DNN-based IDS is trained and validated by the model using the NSL-KDD dataset,a popular benchmark for IDS *** model performs well in both the training and validation stages,with 91.30%training accuracy and 94.38%validation ***,the model shows good learning and generalization capabilities with minor losses of 0.22 in training and 0.1553 in ***,for both macro and micro averages across class 0(normal)and class 1(anomalous)data,the study evaluates the model using a variety of assessment measures,such as accuracy scores,precision,recall,and F1 *** macro-average recall is 0.9422,the macro-average precision is 0.9482,and the accuracy scores are ***,macro-averaged F1 scores of 0.9245 for class 1 and 0.9434 for class 0 demonstrate the model’s ability to precisely identify anomalies *** research also highlights how real-time threat monitoring and enhanced resistance against new online attacks may be achieved byDNN-based intrusion detection systems,which can significantly improve network *** study underscores the critical function ofDNN-based IDS in contemporary cybersecurity procedures by setting the foundation for further developments in this *** research aims to enhance intrusion detection systems by examining cooperative learning techniques and integrating up-to-date threat knowledge.

关键词： Machine-Learning Deep-Learning intrusion detection system security privacy deep neural network NSL-KDD Dataset

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RPL-Based IoT Networks under Decreased Rank Attack:Performance Analysis in Static and Mobile Environments

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Computers, Materials & Continua 2024年第1期78卷 227-247页

作者： Amal Hkiri Mouna Karmani Omar Ben Bahri Ahmed mohammed Murayr Fawaz Hassan Alasmari Mohsen Machhout Physic Department Electronics and Micro-Electronics LaboratoryFaculty of Sciences of MonastirMonastir5000Tunisia Department of Science and Technology College of RanyahTaif UniversityP.O.Box 11099Taif21944Saudi Arabia

The RPL(IPv6 Routing Protocol for Low-Power and Lossy Networks)protocol is essential for efficient communi-cation within the Internet of Things(IoT)*** its significance,RPL’s susceptibility to attacks remains a *** paper presents a comprehensive simulation-based analysis of the RPL protocol’s vulnerability to the decreased rank attack in both static andmobilenetwork *** employ the Random Direction Mobility Model(RDM)for mobile scenarios within the Cooja *** systematic evaluation focuses on critical performance metrics,including Packet Delivery Ratio(PDR),Average End to End Delay(AE2ED),throughput,Expected Transmission Count(ETX),and Average Power Consumption(APC).Our findings illuminate the disruptive impact of this attack on the routing hierarchy,resulting in decreased PDR and throughput,increased AE2ED,ETX,and *** results underscore the urgent need for robust security measures to protect RPL-based IoT ***,our study emphasizes the exacerbated impact of the attack in mobile scenarios,highlighting the evolving security requirements of IoT networks.

关键词： RPL decreased rank attacks mobility random direction model

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A unique computational investigation of the exact traveling wave solutions for the fractional-order Kaup-Boussinesq and generalized Hirota Satsuma coupled KdV systems arising from water waves and interaction of long waves

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Journal of Ocean Engineering and Science 2024年第5期9卷 437-453页

作者： Xiaofeng Wang Xiao-Guang Yue mohammed K.A.Kaabar Arzu Akbulut Melike Kaplan Shenzhen University ShenzhenChina Department of Computer Science and Engineering European University CyprusNicosiaCyprus Institute of Mathematical Sciences Faculty of ScienceUniversity of MalayaKuala Lumpur 50603Malaysia Department of Mathematics and Statistics Washington State UniversityPullmanWA 99163USA Jabalia Camp United Nations Relief and Works Agency(UNRWA)Palestinian Refugee CampGaza Strip JabalyaPalestine Mathematics-Computer Department Art-Science FacultyEski¸s ehir Osmangazi UniversityEski¸s ehirTurkey Department of Computer Engineering Faculty of Engineering and ArchitectureKastamonu UniversityKastamonuTurkey

A novel technique,named auxiliary equation method,is applied in this research work for obtaining new traveling wave solutions for two interesting proposed systems:the Kaup-Boussinesq system and generalized Hirota-Satsuma coupled KdV system with beta time fractional *** solutions were obtained using MAPLE *** technique shows a great potential to be applied in solving various nonlinear fractional differential equations arising from mathematical physics and ocean *** a standard equation has not been used as an auxiliary equation for this technique,different and novel solutions are obtained via this technique.

关键词： Symbolic computation Fractional differential equations Beta derivative Auxiliary equation method Solitary solutions Nonlinear equations

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Impact of Regular Blood Donation on Body Iron Stores at Saudi Blood Donors

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Open Journal of Blood Diseases 2024年第3期14卷 65-80页

作者： mohammed Qassadi Saleh mohammed Abdullah College of Nursing and Health Sciences Jazan University Jazan Kingdom of Saudi Arabia

Introduction: One of the most frequent observations in long-term blood donation is chronic iron deficiency, which can develop into anaemia. The majority of blood screening methods employed by blood banks do not incorporate iron-status markers, which may result in potential subclinical iron deficiency. The aim of this study was to evaluate the effects of repeated blood donation on the levels of iron in the body and to guide blood donors in preventing the depletion of iron stores. Methods: Regular blood donors were categorised into distinct groups according to the number of donations they gave, and then the correlation between these groups and their bodies’ iron levels was examined. Different parameters were employed to identify iron deficiency and iron depletion in blood donors: serum ferritin, mean corpuscular volume (MCV), mean corpuscular haemoglobin (MCH), mean corpuscular haemoglobin concentration (MCHC), total iron-binding capacity (TIBC), and serum iron. Results: The study included 300 individuals who regularly and willingly donated blood. There were no iron insufficiency cases among those donating blood for the first time (Group I). However, 15.5% of individuals who had donated once before (Group II) had ferritin levels of 15 - 30 μg/dl (ng/ml), indicating reduced iron stores. The rate increased to 18% (37 out of 206 individuals) among regular blood donors (Groups III, IV, and V). Iron deficiency (depletion) prevalence among regular blood donors in Groups III, IV, and V was 5.9% (12 out of 206) and 50.4% (100 out of 206). Donors who had donated blood most frequently had the lowest levels of haematological markers MCH, MCHC, and TIBC. Provide the p-values representing the differences between the means of MCV, MCH, iron, TIBC, and ferritin levels when comparing donor groups with the control group (Group I) based on the frequency of donations. Indicate statistically significant differences where the p-value is less than 0.0125. This significance level is adjuste

关键词： Iron Deficiency Anaemia Regular Blood Donors Anaemia Volunteer Blood Donor Blood Donation

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Integrating petrophysical data into efficient iterative cluster analysis for electrofacies identification in clastic reservoirs

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Energy Geoscience 2024年第4期5卷 291-305页

作者： mohammed A.Abbas Watheq J.Al-Mudhafar Aqsa Anees David A.Wood Basrah Oil Company BasrahIraq Institute of International Rivers and Eco-Security Yunnan UniversityKunming650500China Institute for Ecological Research and Pollution Control of Plateau Lakes School of Ecology and Environmental ScienceYunnan UniversityKunming650500China DWA Energy Limited LincolnUK

Efficient iterative unsupervised machine learning involving probabilistic clustering analysis with the expectation-maximization(EM)clustering algorithm is applied to categorize reservoir facies by exploiting latent and observable well-log variables from a clastic reservoir in the Majnoon oilfield,southern *** observable well-log variables consist of conventional open-hole,well-log data and the computer-processed interpretation of gamma rays,bulk density,neutron porosity,compressional sonic,deep resistivity,shale volume,total porosity,and water saturation,from three wells located in the Nahr Umr *** latent variables include shale volume and water *** EM algorithm efficiently characterizes electrofacies through iterative machine learning to identify the local maximum likelihood estimates(MLE)of the observable and latent variables in the studied *** optimized EM model developed successfully predicts the core-derived facies classification in two of the studied *** EM model clusters the data into three distinctive reservoir electrofacies(F1,F2,and F3).F1 represents a gas-bearing electrofacies with low shale volume(Vsh)and water saturation(Sw)and high porosity and permeability values identifying it as an attractive reservoir *** results of the EM model are validated using nuclear magnetic resonance(NMR)data from the third studied well for which no cores were *** NMR results confirm the effectiveness and accuracy of the EM model in predicting *** utilization of the EM algorithm for electrofacies classification/cluster analysis is ***,the clusters it establishes are less rigidly constrained than those derived from the more commonly used K-means clustering *** EM methodology developed generates dependable electrofacies estimates in the studied reservoir intervals where core samples are not ***,once calibrated with core data in some wells,the model is suitable for

关键词： Cluster analysis Electrofacies classification Expectation-maximization(EM)algorithm Clastic reservoir Maximum likelihood estimate(MLE)

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Investigating and predicting the role of photovoltaic, wind, and hydrogen energies in sustainable global energy evolution

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Global Energy Interconnection 2024年第4期7卷 429-445页

作者： Mahmood Swadi Dheyaa Jasim Kadhim Mohamed Salem Firas mohammed Tuaimah Ammar Sabri Majeed Ali Jawad Alrubaie Department of Electrical Engineering College of EngineeringUniversity of BaghdadBaghdad 10001Iraq Libyan Authority for Scientific Research Tripoli 80045Libya School of Electrical and Electronic Engineering Universiti Sains Malaysia(USM)Nibong Tebal 14300PenangMalaysia Continuing Education Center University of BaghdadBaghdad 10001Iraq Al-Furat Al-Awsat Technical University AL-Mussaib Technical CollegeBabylonIraq

The global shift toward next-generation energy systems is propelled by the urgent need to combat climate change and the dwindling supply of fossil *** review explores the intricate challenges and opportunities for transitioning to sustainable renewable energy sources such as solar,wind,and *** transition economically challenges traditional energy sectors while fostering new industries,promoting job growth,and sustainable economic *** transition to renewable energy demands social equity,ensuring universal access to affordable energy,and considering community *** environmental benefits include a significant reduction in greenhouse gas emissions and a lesser ecological *** study highlights the rapid growth of the global wind power market,which is projected to increase from$112.23 billion in 2022 to$278.43 billion by 2030,with a compound annual growth rate of 13.67%.In addition,the demand for hydrogen is expected to increase,significantly impacting the market with potential cost reductions and making it a critical renewable energy source owing to its affordability and zero *** 2028,renewables are predicted to account for 42%of global electricity generation,with significant contributions from wind and solar photovoltaic(PV)technology,particularly in China,the European Union,the United States,and *** developments signify a global commitment to diversifying energy sources,reducing emissions,and moving toward cleaner and more sustainable energy *** review offers stakeholders the insights required to smoothly transition to sustainable energy,setting the stage for a resilient future.

关键词： Next energy Renewable energy Sustainability Environmental benefits Economic opportunities

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Flood susceptibility mapping using a novel integration of multi-temporal sentinel-1 data and eXtreme deep learning model

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Geoscience Frontiers 2024年第3期15卷 67-84页

作者： Rami Al-Ruzouq Abdallah Shanableh Ratiranjan Jena mohammed Barakat A.Gibril Nezar Atalla Hammouri Fouad Lamghari GIS&Remote Sensing Center Research Institute of Sciences and EngineeringUniversity of SharjahSharjah 27272United Arab Emirates Civil and Environmental Engineering Department University of SharjahSharjah 27272United Arab Emirates Department of Earth and Environmental Sciences Prince El-Hassan bin Talal Faculty for Natural Resources&EnvironmentThe Hashemite UniversityZarqaJordan Fujairah Research Centre Fujairah CityAl Hilal TowerAl Hilal CityFujairahUnited Arab Emirates

Flash floods(FFs)are amongst the most devastating hazards in arid regions in response to climate change and can cause the loss of agricultural land,human lives and *** of the major challenges is the high-intensity rainfall events affecting low-lying areas that are vulnerable to *** works in this field have been conducted using ensemble machine learning models and geohydrological ***,the current advancement of eXtreme deep learning,which is named eXtreme deep factorisation machine(xDeepFM),for FF susceptibility mapping(FSM)is lacking in the *** current study introduces a new model and employs a previously unapplied approach to enhance FSM for capturing the severity of *** proposed approach has three main objectives:(i)During-and after-flood effects are assessed through flood detection techniques using Sentinel-1 data.(ii)Flood inventory is updated using remote sensing-based *** derived flood effects are implemented in the next step.(iii)An FSM map is generated using an xDeepFM ***,this study aims to apply xDeepFM to estimate susceptible areas using 13 factors in the emirates of Fujairah,*** performance metrics show a recall of 0.9488),an F1-score of 0.9107),precision of(0.8756)and an overall accuracy of 90.41%.The accuracy of the applied xDeepFM model is compared with that of traditional machine learning models,specifically the deep neural network(78%),support vector machine(85.4%)and random forest(88.75%).Random forest achieves high accuracy,which is due to its strong performance that depends on factors contribution,dataset size and quality,and available computational ***,the xDeepFM model works efficiently for complicated prediction problems having high non-collinearity and huge *** obtained map denotes that the narrow basins,lowland coastal areas and riverbank areas up to 5 km(Fujairah)are highly prone to FF,whilst the alluvial plains in Al Dhaid and hilly regio

关键词： Flood susceptibility mapping eXtreme Deep Factorisation Machine Sentinel-1 Remote sensing

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