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Digital Text Document Watermarking Based Tampering Attack Detection via internet

Computer Systems Science & Engineering 2024年第3期48卷 759-771页

作者： Manal Abdullah alohali Muna Elsadig fahd n.al-wesa.i Mesfer al Duhayyim Anwer Mustafa Hilal Abdelwahed Motwakel Department of information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversityAbha62217Saudi Arabia Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversityAl-Aflaj16733Saudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Owing to the rapid increase in the interchange of text information through internet networks,the reliability and security of digital content are becoming a major research *** detection,Content authentication,and integrity verification of digital content interchanged through the internet were utilized to solve a major concern in information and communication *** authors’difficulties were tampering detection,authentication,and integrity verification of the digital *** study develops an Automated Data Mining based Digital Text Document Watermarking for Tampering Attack Detection(ADMDTW-TAD)via the *** DM concept is exploited in the presented ADMDTW-TAD technique to identify the document’s appropriate characteristics to embed larger watermark *** presented secure watermarking scheme intends to transmit digital text documents over the internet *** the watermark is embedded with no damage to the original document,it is then shared with the *** watermark extraction process is performed to get the original document *** experimen.al validation of the ADMDTW-TAD technique is carried out under varying levels of attack volumes,and the outcomes were inspected in terms of different *** simulation values indicated that the ADMDTW-TAD technique improved performance over other models.

关键词： Content authentication tampering attacks detection model security digital watermarking

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novel investigation of Stochastic Fractional Differential Equations Measles Model via the White noise and Global Derivative Operator Depending on Mittag-Leffler Kernel

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Computer Modeling in Engineering & Sciences 2024年第6期139卷 2289-2327页

作者： Saima Rashid fahd Jarad Department of Mathematics Government College UniversityFaisalabad38000Pakistan Department of natural Sciences School of Arts and SciencesLebanese American UniversityBeirut11022801Lebanon Department of Mathematics Faculty of Arts and ScienceÇnkaya UniversityAnkara06790Turkey Department of Medical Research China Medical UniversityTaichung40402TaiwanChina

Because of the features involved with their varied kernels,differential operators relying on convolution formulations have been acknowledged as effective mathematical resources for modeling real-world *** this paper,we constructed a stochastic fractional framework of measles spreading mechanisms with dual medication immunization considering the exponential decay and Mittag-Leffler *** this approach,the overall population was separated into five ***,the descriptive behavior of the system was investigated,including prerequisites for the positivity of solutions,invariant domain of the solution,presence and stability of equilibrium points,and sensitivity *** included a stochastic element in every cohort and employed linear growth and Lipschitz criteria to show the existence and uniqueness of *** numerical simulations for various fractional orders and randomization intensities are illustrated.

关键词： Measles epidemic model Atangana-Baleanu Caputo-Fabrizio differential operators existence and uniqueness qualitative analysis newton interpolating polynomial

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AMachine Learning Approach to Cyberbullying Detection in Arabic Tweets

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

作者： Dhiaa Musleh Atta Rahman Mohammed Abbas alkherallah Men.al 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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A gated recurrent unit model to predict Poisson’s ratio using deep learning

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Journal of Rock Mechanics and Geotechnical Engineering 2024年第1期16卷 123-135页

作者： fahd Saeed alakbari Mysara Eissa Mohyaldinn Mohammed Abdalla Ayoub ibnelwaleed A.Hussein.ali Samer Muhsan Syahrir Ridha Abdullah Abduljabbar Salih Petroleum Engineering Department Universiti Teknologi PETRONAS32610Bandar Seri IskandarPerakMalaysia institute of Hydrocarbon Recovery Universiti Teknologi PETRONAS32610Bandar Seri IskandarPerakMalaysia Gas Processing Center College of EngineeringQatar UniversityP.O.Box 2713DohaQatar Department of Chemical Engineering College of EngineeringQatar UniversityP.O.Box 2713DohaQatar Mechanical Engineering Department Universiti Teknologi PETRONAS32610Bandar Seri IskandarPerakMalaysia

Static Poisson’s ratio(vs)is crucial for determining geomechanical properties in petroleum applications,namely sand *** models have been used to predict vs;however,the published models were limited to specific data ranges with an average absolute percentage relative error(AAPRE)of more than 10%.The published gated recurrent unit(GRU)models do not consider trend analysis to show physical *** this study,we aim to develop a GRU model using trend analysis and three inputs for predicting n s based on a broad range of data,n s(value of 0.1627-0.4492),bulk formation density(RHOB)(0.315-2.994 g/mL),compressional time(DTc)(44.43-186.9 μs/ft),and shear time(DTs)(72.9-341.2μ s/ft).The GRU model was evaluated using different approaches,including statistical error *** GRU model showed the proper trends,and the model data ranges were wider than previous *** GRU model has the largest correlation coefficient(R)of 0.967 and the lowest AAPRE,average percent relative error(APRE),root mean square error(RMSE),and standard deviation(SD)of 3.228%,1.054%,4.389,and 0.013,respectively,compared to other *** GRU model has a high accuracy for the different datasets:training,validation,testing,and the whole datasets with R and AAPRE values were 0.981 and 2.601%,0.966 and 3.274%,0.967 and 3.228%,and 0.977 and 2.861%,*** group error analyses of all inputs show that the GRU model has less than 5% AAPRE for all input ranges,which is superior to other models that have different AAPRE values of more than 10% at various ranges of inputs.

关键词： Static Poisson’s ratio Deep learning Gated recurrent unit(GRU) Sand control Trend analysis Geomechanical properties

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Validation of a Method for Characterization of Ethanol in Water by HS-GC-FiD to Serve the Traceability of Halal Measurements

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Green and Sustainable Chemistry 2024年第2期14卷 17-28页

作者： Adel B. Shehata Abdulrahman R. al Askar Mohammed A. al Rasheed Abdulrahman M. al Zahrany fahd A. al Kharraa Sowailem A. al Sowailem Chemistry Department/national Measurement and Calibration Center (nMCC) Saudi Standards Metrology and Quality Organization (SASO) Riyadh Kingdom of Saudi Arabia

The determination of the ethanol content in food products is of fundamen.al importance for Halal certification. in this work, an analytical method for the determination of ethanol in water by headspace gas chromatography with flame ionization detector (HS-GC-FiD) has been developed and validated for the use in characterization of ethanol reference materials. The validation study was carried out in the linear calibration range 100 - 1500 mg/kg using the niST SRM 2900, nominal 95.6%. The studied performance characteristics of the method were the limit of detection, LOD, the limit of quantification LOQ, selectivity, linearity, precision, recovery and bias. The validation results showed that the method is selective, precise, accurate and free from any significant bias. The LOD and LOQ were 1.27 and 3.86 mg/kg respectively and the estimated expanded uncertainty was 2% indicating that the method is fit for the purpose of certification of ethanol in water reference materials.

关键词： Ethanol HS-GC-FiD Validation Linearity Precision Bias

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An Optimized algorithm for Resource allocation for D2D in Heterogeneous networks

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Computers, Materials & Continua 2022年第2期70卷 2923-2936页

作者： Abdul Kadir Hamid fahd n.al-wesa.i nadhem nemri Ammar Zahary imran Khan Department of Electrical&Computer Engineering University of SharjahSharjahUnited Arab Emirates Department of Computer Science King Khalid UniversityMuhayel AseerKSA Faculty of Computer and iT Sana’a UniversitySanaaYemen Department of information Systems King Khalid UniversityMayahel AseerKSA Department of Electrical Engineering University of Engineering and TechnologyPeshawarPaki

With the emergence of 5G mobile multimedia services,end users’demand for high-speed,low-latency mobile communication network access is *** them,the device-to-device(D2D)communication is one of the considerable *** D2D communication,the data does not need to be relayed and forwarded by the base station,but under the control of the base station,a direct local link is allowed between two adjacent mobile *** flexible communicationmode reduces the processing bottlenecks and coverage blind spots of the base station,and can be widely used in dense user communication scenarios such as heterogeneous ultra-dense wireless *** of the important factors which affects the quality-of-service(QoS)of D2D communications is co-channel *** order to solve this problem of co-channel interference,this paper proposes a graph coloring based *** main idea is to utilize the weighted priority of spectrum resources and enables multiple D2D users to reuse the single cellular user *** proposed algorithm also provides simpler power *** heterogeneous pattern of interference is determined using different types of interferences and UE and the priority of color is *** results show that the proposed algorithm effectively reduced the co-channel interference,power consumption and improved the system throughput as compared with existing algorithms.

关键词： 5G device-to-device communication wireless networks spectrum

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Hand Gesture Recognition for Disabled People Using Bayesian Optimization with Transfer Learning

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intelligent Automation & Soft Computing 2023年第6期36卷 3325-3342页

作者： Fadwa alrowais Radwa Marzouk fahd n.al-wesa.i Anwer Mustafa Hilal Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Mathematics Faculty of ScienceCairo UniversityGiza12613Egypt Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Sign language recognition can be treated as one of the efficient solu-tions for disabled people to communicate with *** helps them to convey the required data by the use of sign language with no *** latest develop-ments in computer vision and image processing techniques can be accurately uti-lized for the sign recognition process by disabled *** Sign Language(ASL)detection was challenging because of the enhancing intraclass similarity and higher *** article develops a new Bayesian Optimiza-tion with Deep Learning-Driven Hand Gesture Recognition Based Sign Language Communication(BODL-HGRSLC)for Disabled *** BODL-HGRSLC technique aims to recognize the hand gestures for disabled people’s *** presented BODL-HGRSLC technique integrates the concepts of compu-ter vision(CV)and DL *** the presented BODL-HGRSLC technique,a deep convolutional neural network-based residual network(Resnet)model is applied for feature ***,the presented BODL-HGRSLC model uses Bayesian optimization for the hyperparameter tuning *** last,a bidir-ectional gated recurrent unit(BiGRU)model is exploited for the HGR procedure.A wide range of experiments was conducted to demonstrate the enhanced perfor-mance of the presented BODL-HGRSLC *** comprehensive comparison study reported the improvements of the BODL-HGRSLC model over other DL models with maximum accuracy of 99.75%.

关键词： Deep learning hand gesture recognition disabled people computer vision bayesian optimization

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Charged Particles Capture Cross-section by a Weakly Charged Schwarzschild Black Hole

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Research in Astronomy and Astrophysics 2024年第8期24卷 143-148页

作者： A.M.al Zahrani A.al-Jama Physics Department King Fahd University of Petroleum and Minerals

We study the capture cross-section of charged particles by a weakly charged Schwarzschild black *** dependence of the maximum impact parameter for capture on the particle’s energy is investigated numerically for different values of the electromagnetic coupling strength between the particle and the black *** capture cross-section is then *** show that the capture cross-section is independent of the electromagnetic coupling for ultra-relativistic *** astrophysical implications of our results are discussed.

关键词： accretion accretion disks black hole physics stars: black holes

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Red Deer Optimization with Artificial intelligence Enabled image Captioning System for Visually impaired People

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Computer Systems Science & Engineering 2023年第8期46卷 1929-1945页

作者： Anwer Mustafa Hilal Fadwa alrowais fahd n.al-wesa.i Radwa Marzouk Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversityMahayilSaudi Arabia Department of information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Mathematics Faculty of ScienceCairo UniversityGiza12613Egypt

The problem of producing a natural language description of an image for describing the visual content has gained more attention in natural language processing(nLP)and computer vision(CV).it can be driven by applications like image retrieval or indexing,virtual assistants,image understanding,and support of visually impaired people(ViP).Though the ViP uses other senses,touch and hearing,for recognizing objects and events,the quality of life of those persons is lower than the standard *** image captioning generates captions that will be read loudly to the ViP,thereby realizing matters happening around *** article introduces a Red Deer Optimization with Artificial intelligence Enabled image Captioning System(RDOAi-iCS)for Visually impaired *** presented RDOAi-iCS technique aids in generating image captions for *** presented RDOAiiCS technique utilizes a neural architectural search network(nASnet)model to produce image ***,the RDOAi-iCS technique uses the radial basis function neural network(RBFnn)method to generate a textual *** enhance the performance of the RDOAi-iCS method,the parameter optimization process takes place using the RDO algorithm for nasnet and the butterfly optimization.algorithm(BOA)for the RBFnn model,showing the novelty of the *** experimen.al evaluation of the RDOAi-iCS method can be tested using a benchmark *** outcomes show the enhancements of the RDOAi-iCS method over other recent image captioning approaches.

关键词： Machine learning image captioning visually impaired people parameter tuning artificial intelligence metaheuristics

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Machine Learning Based Depression,Anxiety,and Stress Predictive Model During COViD-19 Crisis

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Computers, Materials & Continua 2022年第3期70卷 5803-5820页

作者： fahd n.al-wesa.i Hadeel alsolai Anwer Mustafa Hilal Manar Ahmed Hamza Mesfer al Duhayyim noha negm Department of Computer Science King Khalid UniversityMuhayel AseerSaudi Arabia Faculty of Computer and iT Sana’a UniversitySana’aYemen Department of information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversitySaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia Computer Science Department King Khaled UniversityKSA Faculty of Science Mathematics and Computer Science DepartmentMenoufia UniversityEgypt

Corona Virus Disease-2019(COViD-19)was reported at first in Wuhan city,China by December *** Health Organization(WHO)declared COViD-19 as a pandemic i.e.,global health crisis onMarch 11,*** outbreak of COViD-19 pandemic and subsequent lockdowns to curb the spread,not only affected the economic status of a number of countries,but it also resulted in increased levels of Depression,Anxiety,and Stress(DAS)among ***,there is a need exists to comprehend the relationship among psycho-social factors in a country that is hypothetically affected by high levels of stress and fear;with tremendously-limitingmeasures of social distancing and lockdown in force;and with high rates of new cases and *** this motivation,the current study aims at investigating theDAS levels among college students during COViD-19 lockdown since they are identified as a highly-susceptible *** current study proposes to develop intelligent Feature Subset Selection withMachine Learning-based DAS predictive(iFSSML-DAS)*** presented iFSSML-DAS model involves data preprocessing,Feature Subset Selection(FSS),classification,and parameter ***,iFSSML-DAS model uses Group Gray Wolf Optimization based FSS(GGWO-FSS)technique to reduce the curse of *** addition,Beetle Swarm Optimization based Least Square Support Vector Machine(BSO-LSSVM)model is also employed for classification in which the weight and bias parameters of the LSSVM model are optimally adjusted using BSO *** performance of the proposed iFSSML-DAS model was tested using a benchmark DASS-21 dataset and the results were investigated under different *** outcome of the study suggests the development of specialized programs to handleDAS among population so as to overcome COViD-19 crisis.

关键词： Psycho-social factors covid-19 crisis management predictive models decision making machine learning

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