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A Hybrid SIr-Fuzzy Model for Epidemic Dynamics:A Numerical Study

Computer Modeling in Engineering & Sciences 2024年第6期139卷 3417-3434页

作者： muhammad Shoaib Arif Kamaleldin Abodayeh Yasir Nawaz Department of Mathematics and Sciences College of Humanities and SciencesPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Mathematics Air UniversityIslamabad44000Pakistan

This study focuses on the urgent requirement for improved accuracy in diseasemodeling by introducing a newcomputational framework called the Hybrid SIr-Fuzzy *** integrating the traditional Susceptible-Infectious-recovered(SIr)modelwith fuzzy logic,ourmethod effectively addresses the complex nature of epidemic dynamics by accurately accounting for uncertainties and imprecisions in both data and model *** main aim of this research is to provide a model for disease transmission using fuzzy theory,which can successfully address uncertainty in mathematical *** main emphasis is on the imprecise transmission rate parameter,utilizing a three-part description of its membership *** enhances the representation of disease processes with greater complexity and tackles the difficulties related to quantifying uncertainty in mathematical *** investigate equilibrium points for three separate scenarios and perform a comprehensive sensitivity analysis,providing insight into the complex correlation betweenmodel parameters and epidemic *** order to facilitate a quantitative analysis of the fuzzy model,we propose the implementation of a resilient numerical *** convergence study of the scheme demonstrates its trustworthiness,providing a conditionally positive solution,which represents a significant improvement compared to current forward Euler *** numerical findings demonstrate themodel’s effectiveness in accurately representing the dynamics of disease ***,when the mortality coefficient rises,both the susceptible and infected populations decrease,highlighting the model’s sensitivity to important epidemiological ***,there is a direct relationship between higher Holling type rate values and a decrease in the number of individuals who are infected,as well as an increase in the number of susceptible *** correlation offers a significant understanding of how many elements affect the con

关键词： Fuzzy-based model sensitivity equilibriumpoints proposed numerical scheme convergence and stability analysis

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Current and further trajectories in designing functional materials for solid oxide electrochemical cells:A review of other reviews

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Journal of Energy Chemistry 2024年第7期94卷 302-331页

作者： Stanislav Baratov Elena Filonova Anastasiya Ivanova muhammad Bilal Hanif Muneeb Irshad muhammad Zubair Khan Martin Motola Sajid rauf Dmitry Medvedev Hydrogen Energy Laboratory Ural Federal University620002 EkaterinburgRussia Institute of Natural Sciences and Mathematics Ural Federal University620002 EkaterinburgRussia Department of Inorganic Chemistry Faculty of Natural SciencesComenius University BratislavaIlkovicova 6Mlynska Dolina84215 BratislavaSlovakia State Key Laboratory for Mechanical Behavior of Materials School of Materials Science and EngineeringXi'an Jiaotong UniversityXi'an 710049ShaanxiChina Department of Physics University of Engineering and TechnologyLahore 54890Pakistan Pak-Austria Fachhochschule:Institute of Applied Sciences and Technology Mang Haripur 22621Khyber PakhtunkhwaPakistan College of Mechatronics and Control Engineering Shenzhen UniversityShenzhen 518000GuangdongChina Laboratory of Electrochemical Devices Based on Solid Oxide Proton Electrolytes Institute of High Temperature Electrochemistry620066 EkaterinburgRussia

Complex oxides are an important class of materials with enormous potential for electrochemical *** on their composition and structure,such complex oxides can exhibit either a single conductivity(oxygen-ionic or protonic,or n-type,or p-type electronic)or a combination thereof gener-ating distinct dual-conducting or even triple-conducting *** properties enable their use as diverse functional materials for solid oxide fuel cells,solid oxide electrolysis cells,permeable membranes,and gas *** literature review shows that the field of solid oxide materials and related electro-chemical cells has a significant level of research engagement,with over 8,000 publications published since *** manual analysis of such a large volume of material is ***,by examining the review articles,it is possible to identify key patterns,recent achievements,prospects,and remaining *** perform such an analysis,the present article provides,for the first time,a comprehensive summary of previous review publications that have been published since 2020,with a special focus on solid oxide materials and electrochemical ***,this study provides an important reference for researchers specializing in the fields of solid state ionics,high-temperature electrochemistry,and energyconversiontechnologies.

关键词： SOFCs SOECs PCFCS Electrochemistry Energy conversion Hydrogen energy,Carbon neutrality

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Attitude-adjusting dynamical behavior of cubic rover on low-gravity testbed

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Astrodynamics 2024年第1期8卷 149-159页

作者： Bowen Jiang muhammad Talha Hussain Xiangyuan Zeng School of Automation Beijing Institute of TechnologyBeijing100081China

Cubic rovers that traverse by hopping systems are promising in low-gravity *** several analyses of the control methods and mobility of the cubic rover are available,investigations of its attitude-adjusting behavior are still *** study derives the dynamic equations of the two attitude-adjusting modes of the cubic rover,referred to as walking and *** relationships between the speed threshold and rotation angle of the cubic rover were investigated in both rigid and regolith environments using a self-designed low-gravity *** studies were conducted by considering the experimental and simulated *** results of this study can be interesting for roving mission planning when exploring planetary moons and small celestial bodies.

关键词： low-gravitytestbed attitude-adjusting motion cubic rover experimental study

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Deep Learning-Based ECG Classification for Arterial Fibrillation Detection

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Computers, Materials & Continua 2024年第6期79卷 4805-4824页

作者： muhammad Sohail Irshad Tehreem Masood Arfan Jaffar muhammad rashid Sheeraz Akram Abeer Aljohani Faculty of Computer Science&Information Technology The Superior UniversityLahore54000Pakistan Intelligent Data Visual Computing research(IDVCr) Lahore54000Pakistan Department of Computer Science National University of TechnologyIslamabad45000Pakistan Information Systems Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia Department of Computer Science Applied CollegeTaibah UniversityMedina42353Saudi Arabia

The application of deep learning techniques in the medical field,specifically for Atrial Fibrillation(AFib)detection through Electrocardiogram(ECG)signals,has witnessed significant *** and timely diagnosis increases the patient’s chances of ***,issues like overfitting and inconsistent accuracy across datasets remain *** a quest to address these challenges,a study presents two prominent deep learning architectures,resNet-50 and DenseNet-121,to evaluate their effectiveness in AFib *** aim was to create a robust detection mechanism that consistently performs *** such as loss,accuracy,precision,sensitivity,and Area Under the Curve(AUC)were utilized for *** findings revealed that resNet-50 surpassed DenseNet-121 in all evaluated *** demonstrated lower loss rate 0.0315 and 0.0305 superior accuracy of 98.77%and 98.88%,precision of 98.78%and 98.89%and sensitivity of 98.76%and 98.86%for training and validation,hinting at its advanced capability for AFib *** insights offer a substantial contribution to the existing literature on deep learning applications for AFib detection from ECG *** comparative performance data assists future researchers in selecting suitable deep-learning architectures for AFib ***,the outcomes of this study are anticipated to stimulate the development of more advanced and efficient ECG-based AFib detection methodologies,for more accurate and early detection of AFib,thereby fostering improved patient care and outcomes.

关键词： Convolution neural network atrial fibrillation area under curve ECG false positive rate deep learning classification

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Study of bending angle and shadow in a new Schwarzschild-like black hole affected by plasma and non-plasma mediums

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Chinese Physics C 2024年第5期48卷 309-318页

作者： riasat Ali Tiecheng Xia muhammad Awais rimsha Babar Department of Mathematics Newtouch Center for Mathematics of Shanghai UniversityShanghai 200444China Department of Mathematics University of Engineering and Technology Lahore-54890Pakistan Department of Mathematics GC University Faisalabad Layyah CampusLayyah-31200Pakistan

In this study,we analyze the models of the deflection angle of a new Schwarzschild-like black hole(BH)and employ the optical metric of the *** achieve this,we use the Gaussian curvature of the optical metric and the Gauss-Bonnet theorem,known as the Gibbons-Werner technique,to determine the deflection ***,we examine the deflection angle in the presence of a plasma medium and the effect of the plasma medium on the deflection *** deflection angle of the BH solution in the gauged super-gravity is computed using the Keeton-Petters *** the ray-tracing technique,we investigate the shadow of the corresponding BH and analyze the plots of the deflection angle and shadow to verify the influence of the plasma and algebraic thermodynamic parameters on the deflection angle and shadow.

关键词： new Schwarzschild-like black hole Gibbons-Werner and Keeton-Petters techniques deflection angle ray-tracing approach shadow

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Human Gait recognition for Biometrics Application Based on Deep Learning Fusion Assisted Framework

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Computers, Materials & Continua 2024年第1期78卷 357-374页

作者： Ch Avais Hanif muhammad Ali Mughal muhammad Attique Khan Nouf Abdullah Almujally Taerang Kim Jae-Hyuk Cha Department of Electrical Engineering HITEC UniversityTaxila47080Pakistan Department of Computer Science and Mathematics Lebanese American UniversityBeirutLebanon Department of Computer Science HITEC UniversityTaxila47080Pakistan Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science Hanyang UniversitySeoul04763Korea

The demand for a non-contact biometric approach for candidate identification has grown over the past ten *** on the most important biometric application,human gait analysis is a significant research topic in computer *** have paid a lot of attention to gait recognition,specifically the identification of people based on their walking patterns,due to its potential to correctly identify people far *** recognition systems have been used in a variety of applications,including security,medical examinations,identity management,and access *** systems require a complex combination of technical,operational,and definitional *** employment of gait recognition techniques and technologies has produced a number of beneficial and well-liked *** proposes a novel deep learning-based framework for human gait classification in video *** framework’smain challenge is improving the accuracy of accuracy gait classification under varying conditions,such as carrying a bag and changing *** proposed method’s first step is selecting two pre-trained deep learningmodels and training fromscratch using deep transfer ***,deepmodels have been trained using static hyperparameters;however,the learning rate is calculated using the particle swarmoptimization(PSO)***,the best features are selected from both trained models using the Harris Hawks controlled Sine-Cosine optimization *** algorithm chooses the best features,combined in a novel correlation-based fusion ***,the fused best features are categorized using medium,bi-layer,and tri-layered neural *** the publicly accessible dataset known as the CASIA-B dataset,the experimental process of the suggested technique was carried out,and an improved accuracy of 94.14% was *** achieved accuracy of the proposed method is improved by the recent state-of-the-art techniques that show the significance of this wor

关键词： Gait recognition covariant factors biometric deep learning fusion feature selection

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Seasonal variations in dissolved organic matter concentration and composition in an outdoor system for bank filtration simulation

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Journal of Environmental Sciences 2024年第1期135卷 252-261页

作者： muhammad Zeeshan Omamah Ali Shamas Tabraiz Aki Sebastian ruhl German Environment Agency Section II 3.3Schichauweg 5812307BerlinGermany Technische Universitat Berlin Water TreatmentKF4Str.des 17.Juni 13510623BerlinGermany Natural and Applied Sciences Section School of Psychology and Life SciencesCanterbury Christ Church UniversityCanterburyCT11QUUK

Dissolved organic matter(DOM)in surface waters can vary markedly in character depending on seasonal variations such as rainfall intensity,UV radiations and *** in DOM as well as temperature and rainfall intensity over the year can affect the biochemical processes occurring in bank filtration(BF).Identification and characterization of DOM in the surface water could help to optimize the water treatment and provide stable and safe drinking *** study investigated year-long variations of DOM concentrations and compositions in a surface water of a circulated outdoor pond(research facility)connected to a BF *** was dominated by humic substances and a changing pattern of DOM in surface water was observed throughout the year.A significant increase of DOM(~38%)in surface water was noted in August compared to *** fluorescent DOM showed that DOM in summer was enriched with the degradable fraction whilst non-degradable fraction was dominated in winter.A constant(1.7±0.1 mg/L)effluent DOM was recirculated in the system throughout the *** removal through BF varied between 4%to 39%and was achieved within a few meters after infiltration and significantly correlated with influent DOM concentration(r^(2)=0.82,pr,no significant(p>0.05)change in the removal of DOM was observed in two subsurface layers(upper and lower).This study highlights the presence of a constant non-degradable DOM in the bank filtrate,which was not affected by temperature,redox conditions and UV radiations.

关键词： Bank filtration Dissolved organic matter Dissolved oxygen Fluorescence spectroscopy Excitation-emission matrix

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Cervical Cancer Prediction Empowered with Federated Machine Learning

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Computers, Materials & Continua 2024年第4期79卷 963-981页

作者： muhammad Umar Nasir Omar Kassem Khalil Karamath Ateeq Bassam SaleemAllah Almogadwy M.A.Khan Khan muhammad Adnan Department of Computer Sciences Bahria University Lahore CampusLahore54000Pakistan Faculty of Information Technology Liwa CollegeAbu Dhabi20009UAE Department of Computing Skyline University CollegeSharjah999041UAE Department of Computer Science Taibah UniversityMedina42315Saudi Arabia riphah School of Computing and Innovation Faculty of ComputingRiphah International UniversityLahore54000Pakistan Department of Software Faculty of Artificial Intelligence and SoftwareGachon UniversitySeongnam13120Korea

Cervical cancer is an intrusive cancer that imitates various women around the world. Cervical cancer ranks in thefourth position because of the leading death cause in its premature stages. The cervix which is the lower end of thevagina that connects the uterus and vagina forms a cancerous tumor very slowly. This pre-mature cancerous tumorin the cervix is deadly if it cannot be detected in the early stages. So, in this delineated study, the proposed approachuses federated machine learning with numerous machine learning solvers for the prediction of cervical cancer totrain the weights with varying neurons empowered fuzzed techniques to align the neurons, Internet of MedicalThings (IoMT) to fetch data and blockchain technology for data privacy and models protection from hazardousattacks. The proposed approach achieves the highest cervical cancer prediction accuracy of 99.26% and a 0.74%misprediction rate. So, the proposed approach shows the best prediction results of cervical cancer in its early stageswith the help of patient clinical records, and all medical professionals will get beneficial diagnosing approachesfrom this study and detect cervical cancer in its early stages which reduce the overall death ratio of women due tocervical cancer.

关键词： Cervical cancer federated machine learning neurons blockchain technology

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A convolutional neural network to detect possible hidden data in spatial domain images

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Cybersecurity 2024年第1期7卷 37-52页

作者： Jean De La Croix Ntivuguruzwa Tohari ahmad Department of Informatics Institut Teknologi Sepuluh Nopember(ITS)Kampus ITS Keputih SukoliloSurabaya60111Indonesia African Center of Excellence in the Internet of Things College of Science and TechnologyUniversity of Rwanda3900KigaliRwanda

Hiding secret data in digital multimedia has been essential to protect the ***,attackers with a steganalysis technique may break *** steganalysis methods have good results with conventional Machine Learning(ML)techniques;however,the introduction of Convolutional Neural Network(CNN),a deep learning paradigm,achieved better performance over the previously proposed ML-based *** the existing CNN-based approaches yield good results,they present performance issues in classification accuracy and stability in the network training *** research proposes a new method with a CNN architecture to improve the hidden data detection accuracy and the training phase stability in spatial domain *** proposed method comprises three phases:pre-processing,feature extraction,and ***,in the pre-processing phase,we use spatial rich model filters to enhance the noise within images altered by data hiding;secondly,in the feature extraction phase,we use two-dimensional depthwise separable convolutions to improve the signal-to-noise and regular convolutions to model local features;and finally,in the classification,we use multi-scale average pooling for local features aggregation and representability enhancement regardless of the input size variation,followed by three fully connected layers to form the final feature maps that we transform into class probabilities using the softmax *** results identify an improvement in the accuracy of the considered recent scheme ranging between 4.6 and 10.2%with reduced training time up to 30.81%.

关键词： Information security Spatial domain steganalysis Deep learning Convolutional neural network Infrastructure

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Deep Convolutional Neural Networks for Accurate Classification of Gastrointestinal Tract Syndromes

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Computers, Materials & Continua 2024年第1期78卷 1207-1225页

作者： Zahid Farooq Khan muhammad ramzan Mudassar raza muhammad Attique Khan Khalid Iqbal Taerang Kim Jae-Hyuk Cha Department of Computer Science COMSATS University IslamabadWah CampusWah47040Pakistan Department of Computer Science and Mathematics Lebanese American UniversityBeirut13-5053Lebanon Department of Computer Science HITEC UniversityTaxila47080Pakistan Department of Computer Science COMSATS University IslamabadAttock CampusAttock43600Pakistan Department of Computer Science Hanyang UniversitySeoul04763Korea

Accurate detection and classification of artifacts within the gastrointestinal(GI)tract frames remain a significant challenge in medical image *** science combined with artificial intelligence is advancing to automate the diagnosis and treatment of numerous *** to this is the development of robust algorithms for image classification and detection,crucial in designing sophisticated systems for diagnosis and *** study makes a small contribution to endoscopic image *** proposed approach involves multiple operations,including extracting deep features from endoscopy images using pre-trained neural networks such as Darknet-53 and ***,feature optimization utilizes the binary dragonfly algorithm(BDA),with the fusion of the obtained feature *** fused feature set is input into the ensemble subspace k nearest neighbors(ESKNN)*** Kvasir-V2 benchmark dataset,and the COMSATS University Islamabad(CUI)Wah private dataset,featuring three classes of endoscopic stomach images were *** assessments considered various feature selection techniques,including genetic algorithm(GA),particle swarm optimization(PSO),salp swarm algorithm(SSA),sine cosine algorithm(SCA),and grey wolf optimizer(GWO).The proposed model excels,achieving an overall classification accuracy of 98.25% on the Kvasir-V2 benchmark and 99.90% on the CUI Wah private *** approach holds promise for developing an automated computer-aided system for classifying GI tract syndromes through endoscopy images.

关键词： Feature fusion Darknet-53 Xception binary dragonfly algorithm ensemble

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