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Computational Stochastic Investigations for the Socio-Ecological Dynamics with Reef Ecosystems

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Computers, Materials & Continua 2022年第12期73卷 5589-5607页

作者： Thongchai Botmart Zulqurnain Sabir Afaf S.alwabli Salem Ben Said qasem al-mdallal Maria Emilia Camargo Wajaree Weera Department of Mathematics Faculty of ScienceKhon Kaen UniversityKhon Kaen 40002Thailand Department of Mathematical Sciences United Arab Emirates UniversityP.O.Box 15551Al AinUAE Department of Mathematics and Statistics Hazara UniversityMansehraPakistan Department of Biological Sciences Rabigh College of Science and ArtsKing Abdulaziz UniversityJeddahSaudi Arabia Graduate Program in Administration Federal University of Santa MariaSanta Maria 93458Brazil

The motive of this work is to present a computational design using the stochastic scaled conjugate gradient(SCG)neural networks(NNs)called as SCGNNs for the socio-ecological dynamics(SED)with reef ecosystems and conservation estimation.The mathematical descriptions of the SED model are provided that is dependent upon five categories,macroalgae M(v),breathing coral C(v),algal turf T(v),the density of parrotfish P(v)and the opinion of human opinion X(v).The stochastic SCGNNs process is applied to formulate the SEDmodel based on the sample statistics,testing,accreditation and training.Three different variations of the SED have been provided to authenticate the stochastic SCGNNs performance through the statics for training,accreditation,and testing are 77%,12%and 11%,respectively.The obtained numerical performances have been compared with the Runge-Kutta approach to solve the SEDmodel.The reduction of mean square error(MSE)is used to investigate the numericalmeasures through the SCGNNs for solving the SED model.The precision of the SCGNNs is validated through the comparison of the results and the absolute error performances.The reliability of the SCGNNs is performed by using the correlation values,state transitions(STs),error histograms(EHs),MSE measures and regression analysis.

关键词： Socio-ecological state conservation estimation neural networks reef ecosystems scaled conjugate gradient numerical study

Fractional Order Nonlinear Bone Remodeling Dynamics Using the Supervised Neural Network

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Computers, Materials & Continua 2023年第2期74卷 2415-2430页

作者： Narongsak Yotha Qusain Hiader Zulqurnain Sabir Muhammad Asif Zahoor Raja Salem Ben Said qasem al-mdallal Thongchai Botmart Wajaree Weera Department of Applied Mathematics and Statistics Rajamangala University of Technology IsanNakhon Ratchasima30000Thailand Department of Mathematics University of GujratGujrat50700Pakistan Department of Mathematics Hazara UniversityMansehra21120Pakistan Future Technology Research Center National Yunlin University of Science and TechnologyDouliouYunlin64002Taiwan Department of Mathematical Science College of ScienceUnited Arab Emirates UniversityAl AinAbu DhabiUAE Department of Mathematics Faculty of ScienceKhon Kaen UniversityKhon Kaen40002Thailand

This study aims to solve the nonlinear fractional-order mathematical model(FOMM)by using the normal and dysregulated bone remodeling of themyeloma bone disease(MBD).For themore precise performance of the model,fractional-order derivatives have been used to solve the disease model numerically.The FOMM is preliminarily designed to focus on the critical interactions between bone resorption or osteoclasts(OC)and bone formation or osteoblasts(OB).The connections of OC and OB are represented by a nonlinear differential system based on the cellular components,which depict stable fluctuation in the usual bone case and unstable fluctuation through the MBD.Untreated myeloma causes by increasing the OC and reducing the osteoblasts,resulting in net bone waste the tumor growth.The solutions of the FOMM will be provided by using the stochastic framework based on the Levenberg-Marquardt backpropagation(LVMBP)neural networks(NN),i.e.,LVMBPNN.The mathematical performances of three variations of the fractional-order derivative based on the nonlinear disease model using the LVMPNN.The static structural performances are 82%for investigation and 9%for both learning and certification.The performances of the LVMBPNN are authenticated by using the results of the Adams-Bashforth-Moulton mechanism.To accomplish the capability,steadiness,accuracy,and ability of the LVMBPNN,the performances of the error histograms(EHs),mean square error(MSE),recurrence,and state transitions(STs)will be provided.

关键词： Bone remodeling fractional-order myeloma disease artificial neural networks levenberg-marquardt backpropagation population cell dynamics

Buoyancy driven Flow of a Second-Grade Nanofluid flow Taking into Account the Arrhenius Activation Energy and Elastic Deformation:Models and Numerical Results

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Fluid Dynamics & Materials Processing 2021年第2期17卷 319-332页

作者： R.Kalaivanan N.VishnuGanesh qasem M.al-mdallal Department of Mathematics Vivekananda CollegeMadurai625234India PG and Research Department of Mathematics Ramakrishna Mission Vivekananda CollegeChennai600004India Department of Mathematical Sciences United Arab Emirates UniversityAl AinAbu DhabiUnited Arab Emirates

The buoyancy driven flow of a second-grade nanofluid in the presence of a binary chemical reaction is analyzed in the context of a model based on the balance equations for mass,species concentration,momentum and energy.The elastic properties of the considered fluid are taken into account.The two-dimensional slip flow of such non-Newtonian fluid over a porous flat material which is stretched vertically upwards is considered.The role played by the activation energy is accounted for through an exponent form modified Arrhenius function added to the Buongiorno model for the nanofluid concentration.The effects of thermal radiation are also examined.A similarity transformations is used to turn the problem based on partial differential equations into a system of ordinary differential equations.The resulting system is solved using a fourth order RK and shooting methods.The velocity profile,temperature profile,concentration profile,local skin friction,local Nusselt number and local Sherwood number are reported for several circumstances.The influence of the chemical reaction on the properties of the concentration and momentum boundary layers is critically discussed.

关键词： Arrhenius activation energy buoyancy effects chemical reaction elastic deformation nanofluid nonlinear thermal radiation

Adaptive Cloud Intrusion Detection System Based on Pruned Exact Linear Time Technique

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Computers, Materials & Continua 2024年第6期79卷 3725-3756页

作者： Widad Elbakri Maheyzah Md.Siraj Bander ali Saleh al-rimy Sultan Noman qasem Tawfik al-Hadhrami Faculty of Computing Universiti Teknologi MalaysiaJohor BahruJohor81310Malaysia Computer Science Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia School of Science and Technology Nottingham Trent UniversityNottinghamNG118NSUK

Cloud computing environments,characterized by dynamic scaling,distributed architectures,and complex work-loads,are increasingly targeted by malicious actors.These threats encompass unauthorized access,data breaches,denial-of-service attacks,and evolving malware variants.Traditional security solutions often struggle with the dynamic nature of cloud environments,highlighting the need for robust Adaptive Cloud Intrusion Detection Systems(CIDS).Existing adaptive CIDS solutions,while offering improved detection capabilities,often face limitations such as reliance on approximations for change point detection,hindering their precision in identifying anomalies.This can lead to missed attacks or an abundance of false alarms,impacting overall security effectiveness.To address these challenges,we propose ACIDS(Adaptive Cloud Intrusion Detection System)-PELT.This novel Adaptive CIDS framework leverages the Pruned Exact Linear Time(PELT)algorithm and a Support Vector Machine(SVM)for enhanced accuracy and efficiency.ACIDS-PELT comprises four key components:(1)Feature Selection:Utilizing a hybrid harmony search algorithm and the symmetrical uncertainty filter(HSO-SU)to identify the most relevant features that effectively differentiate between normal and anomalous network traffic in the cloud environment.(2)Surveillance:Employing the PELT algorithm to detect change points within the network traffic data,enabling the identification of anomalies and potential security threats with improved precision compared to existing approaches.(3)Training Set:Labeled network traffic data forms the training set used to train the SVM classifier to distinguish between normal and anomalous behaviour patterns.(4)Testing Set:The testing set evaluates ACIDS-PELT’s performance by measuring its accuracy,precision,and recall in detecting security threats within the cloud environment.We evaluate the performance of ACIDS-PELT using the NSL-KDD benchmark dataset.The results demonstrate that ACIDS-PELT outperforms existing cloud intrusion detection techniqu

关键词： Adaptive cloud IDS harmony search distributed denial of service(DDoS) PELT machine learning SVM ISOTCID NSL-KDD

An Ensemble Learning Based Approach for Detecting and Tracking COVID19 Rumors

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Computers, Materials & Continua 2022年第1期70卷 1721-1747页

作者： Sultan Noman qasem Mohammed al-Sarem Faisal Saeed Computer Science Department College of Computer and Information SciencesAl Imam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia Computer Science Department Faculty of Applied ScienceTaiz UniversityTaiz6803Yemen College of Computer Science and Engineering Taibah UniversityMedinaSaudi Arabia Information System Department Saba’a Region UniversityMareebYemen

Rumors regarding epidemic diseases such as COVID 19,medicines and treatments,diagnostic methods and public emergencies can have harmful impacts on health and political,social and other aspects of people’s lives,especially during emergency situations and health crises.With huge amounts of content being posted to social media every second during these situations,it becomes very difficult to detect fake news(rumors)that poses threats to the stability and sustainability of the healthcare sector.A rumor is defined as a statement for which truthfulness has not been verified.During COVID 19,people found difficulty in obtaining the most truthful news easily because of the huge amount of unverified information on social media.Several methods have been applied for detecting rumors and tracking their sources for COVID 19-related information.However,very few studies have been conducted for this purpose for the Arabic language,which has unique characteristics.Therefore,this paper proposes a comprehensive approach which includes two phases:detection and tracking.In the detection phase of the study carried out,several standalone and ensemble machine learning methods were applied on the Arcov-19 dataset.A new detection model was used which combined two models:The Genetic algorithm Based Support Vector Machine(that works on users’and tweets’features)and the stacking ensemble method(that works on tweets’texts).In the tracking phase,several similarity-based techniques were used to obtain the top 1%of similar tweets to a target tweet/post,which helped to find the source of the rumors.The experiments showed interesting results in terms of accuracy,precision,recall and F1-Score for rumor detection(the accuracy reached 92.63%),and showed interesting findings in the tracking phase,in terms of ROUGE L precision,recall and F1-Score for similarity techniques.

关键词： Rumor detection rumor tracking similarity techniques COVID-19 social media analytics

Hyper-Parameter Optimization of Semi-Supervised GANs Based-Sine Cosine algorithm for Multimedia Datasets

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Computers, Materials & Continua 2022年第10期73卷 2169-2186页

作者： Anas al-Ragehi Said Jadid Abdulkadir Amgad Muneer Safwan Sadeq qasem al-Tashi Computer and Information Sciences Department Universiti Teknologi PETRONASSeri Iskandar32610PerakMalaysia Centre for Research in Data Science(CERDAS) Universiti Teknologi PETRONASSeri Iskandar32610Malaysia Mechanical Engineering Department Universiti Teknologi PETRONASSeri Iskandar32610PerakMalaysia Department of Imaging Physics The University of Texas MD Anderson Cancer CenterHoustonTXUSA University of albaydha AlbaydhaYemen

Generative Adversarial Networks(GANs)are neural networks that allow models to learn deep representations without requiring a large amount of training data.Semi-Supervised GAN Classifiers are a recent innovation in GANs,where GANs are used to classify generated images into real and fake and multiple classes,similar to a general multi-class classifier.However,GANs have a sophisticated design that can be challenging to train.This is because obtaining the proper set of parameters for all models-generator,discriminator,and classifier is complex.As a result,training a single GAN model for different datasets may not produce satisfactory results.Therefore,this study proposes an SGAN model(Semi-Supervised GAN Classifier).First,a baseline model was constructed.The model was then enhanced by leveraging the Sine-Cosine algorithm and Synthetic Minority Oversampling Technique(SMOTE).SMOTE was used to address class imbalances in the dataset,while Sine Cosine algorithm(SCA)was used to optimize the weights of the classifier models.The optimal set of hyperparameters(learning rate and batch size)were obtained using grid manual search.Four well-known benchmark datasets and a set of evaluation measures were used to validate the proposed model.The proposed method was then compared against existing models,and the results on each dataset were recorded and demonstrated the effectiveness of the proposed model.The proposed model successfully showed improved test accuracy scores of 1%,2%,15%,and 5%on benchmarking multimedia datasets;Modified National Institute of Standards and Technology(MNIST)digits,Fashion MNIST,Pneumonia Chest X-ray,and Facial Emotion Detection Dataset,respectively.

关键词： Generative adversarial networks semi-supervised generative adversarial network sine-cosine algorithm SMOTE principal component analysis grid search

A systematic review:Detecting phishing websites using data mining models

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Intelligent and Converged Networks 2023年第4期4卷 326-341页

作者： Dina Jibat Sarah Jamjoom qasem Abu al-Haija Abdallah Qusef the Department of Business Intelligence Technology Princess Sumaya University for TechnologyAmman 11941Jordan the Department of Cybersecurity Princess Sumaya University for TechnologyAmman 11941Jordan the Department of Software Engineering Princess Sumaya University for TechnologyAmman 11941Jordan

As internet technology use is on the rise globally,phishing constitutes a considerable share of the threats that may attack individuals and organizations,leading to significant losses from personal and confidential information to substantial financial losses.Thus,much research has been dedicated in recent years to developing effective and robust mechanisms to enhance the ability to trace illegitimate web pages and to distinguish them from non-phishing sites as accurately as possible.Aiming to conclude whether a universally accepted model can detect phishing attempts with 100%accuracy,we conduct a systematic review of research carried out in 2018-2021 published in well-known journals published by Elsevier,IEEE,Springer,and Emerald.Those researchers studied different Data Mining(DM)algorithms,some of which created a whole new model,while others compared the performance of several algorithms.Some studies combined two or more algorithms to enhance the detection performance.Results reveal that while most algorithms achieve accuracies higher than 90%,only some specific models can achieve 100%accurate results.

关键词： phishing data mining machine learning algorithm classification

Cooperative Relay Networks Based on the OAM Technique for 5G Applications

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Computer Systems Science & Engineering 2023年第3期44卷 1911-1919页

作者： Mohammad alkhawatrah Ahmad alamayreh Nidal qasem Department of Electronics and Communications Engineering Al-Ahliyya Amman UniversityAmmanJordan

Orbital Angular Momentum(OAM)is an intrinsic property of electro-magnetic waves.Great research has been witnessed in the last decades aiming at exploiting the OAM wave property in different areas in radio and optics.One pro-mising area of particular interest is to enhance the efficiency of the available communications spectrum.However,adopting OAM-based solutions is not priceless as these suffer from wave divergence especially when the OAM order is high.This shall limit the practical communications distance,especially in the radio regime.In this paper,we propose a cooperative OAM relaying system consisting of a source,relay,and destination.Relays help the source to transmit packets to the destination by providing an alternative connection between source and desti-nation.This cooperative solution aims on the one hand,through best-path selection,on increasing the communications range.On the other hand,through the parallel transmission orders allowed by OAM carrying waves,the system could raise its total transmission throughput.Simulation results show that combining a cooperative relay with OAM improves the system throughput compared to using each element separately.In addition,the proposed cooperative relaying OAM out-performs the cooperative relaying non-orthogonal multiple access scheme,which is a key spectrally efficient technique used in 5G technology.

关键词： 5G cooperative network OAM relay throughput

Improved Beam Steering Method Using OAM Waves

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Computer Systems Science & Engineering 2023年第7期46卷 417-431页

作者： Nidal qasem Ahmad alamayreh Department of Electronics and Communications Engineering Al-Ahliyya Amman UniversityAmmanJordan

Orbital Angular Momentum(OAM)is an intrinsic feature of electromagnetic waves which has recently found many applications in several areas in radio and optics.In this paper,we use OAM wave characteristics to present a simple method for beam steering over both elevation and azimuth planes.The design overcomes some limitations of traditional steering methods,such as limited dynamic range of steering,the design complexity,bulky size of the steering structure,the limited bandwidth of operation,and low gain.Based on OAM wave characteristics,the proposed steering method avoids design complexities by adopting a simple method for generating the OAM-carrying waves.The waves are generated by an array of Planar Circular Dipole(PCD)elements.These elements are designed to cover a wide bandwidth range between 3 and 30 GHz.The transmitting array shows an enhanced gain value from 8.5 dBi to almost 11.5 dBi at the broadside angle.Besides the enhanced PCD-based OAM generation,the novelty of the design lies in a new method of beam steering.Beam steering is then performed by controlling the electrical feeding of the PCD elements;the beam azimuthal location is managed by turning off some of the PCD elements,while the elevation is determined by changing the gradient phase of excitation for the turned-on PCD elements.Detailed analysis of the steering method is carried out by finding the mathematical model of the system and the generated waves.The performance has been verified through numerical simulators.

关键词： Antenna array bandwidth beam steering orbital angular momentum