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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 Model.By 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 parameters.The main aim of this research is to provide a model for disease transmission using fuzzy theory,which can successfully address uncertainty in mathematical modeling.Our main emphasis is on the imprecise transmission rate parameter,utilizing a three-part description of its membership level.This enhances the representation of disease processes with greater complexity and tackles the difficulties related to quantifying uncertainty in mathematical models.We investigate equilibrium points for three separate scenarios and perform a comprehensive sensitivity analysis,providing insight into the complex correlation betweenmodel parameters and epidemic results.In order to facilitate a quantitative analysis of the fuzzy model,we propose the implementation of a resilient numerical scheme.The convergence study of the scheme demonstrates its trustworthiness,providing a conditionally positive solution,which represents a significant improvement compared to current forward Euler schemes.The numerical findings demonstrate themodel’s effectiveness in accurately representing the dynamics of disease transmission.Significantly,when the mortality coefficient rises,both the susceptible and infected populations decrease,highlighting the model’s sensitivity to important epidemiological factors.Moreover,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 individuals.This correlation offers a significant understanding of how many elements affect the consequences of an epidemic.Our objective is to enh

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

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Construction of a Computational Scheme for the Fuzzy HIV/AIDS Epidemic Model with a Nonlinear Saturated Incidence Rate

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Computer Modeling in Engineering & Sciences 2024年第2期138卷 1405-1425页

This work aimed to construct an epidemic model with fuzzy parameters.Since the classical epidemic model doesnot elaborate on the successful interaction of susceptible and infective people,the constructed fuzzy epidemicmodel discusses the more detailed versions of the interactions between infective and susceptible people.Thenext-generation matrix approach is employed to find the reproduction number of a deterministic model.Thesensitivity analysis and local stability analysis of the systemare also provided.For solving the fuzzy epidemic model,a numerical scheme is constructed which consists of three time levels.The numerical scheme has an advantage overthe existing forward Euler scheme for determining the conditions of getting the positive solution.The establishedscheme also has an advantage over existing non-standard finite difference methods in terms of order of accuracy.The stability of the scheme for the considered fuzzy model is also provided.From the plotted results,it can beobserved that susceptible people decay by rising interaction parameters.

关键词： Epidemic model fuzzy rate parameters next generation matrix local stability proposed numerical scheme

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Impact of numerous larval diets on the biology of pink bollworm Pectinophora gossypiella(Saunders)(Lepidoptera:Gelechiidae)under laboratory conditions

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Journal of Cotton Research 2024年第1期7卷 69-78页

作者： AKHTAR Shamim arif muhammad Jalal GOGI muhammad Dildar HAQ Imran-ul Department of Entomology University of Agriculture FaisalabadPunjabPakistan Ayub Agricultural Research Institute(Entomological Research Institute) FaisalabadPunjabPakistan Department of Plant Pathology University of AgricultureFaisalabadPunjabPakistan

Background Pink bollworm,Pectinophora gossypiella(Saunders)(Lepidoptera:Gelechiidae)has become a poten-tial pest of cotton by causing substantial yield losses around the world including Pakistan.Keeping in view the facts like limited research investigations,unavailability,and high cost of artificial diet’s constituents and their premixes,the present research investigations on the dietary aspect of P.gossypiella were conducted.The larvae of P.gossypiella were reared on different diets that were prepared using indigenous elements.The standard/laboratory diet com-prised of wheat germ meal 34.5 g,casein 30.0 g,agar–agar 20.0 g,sucrose 10.0 g,brewer’s yeast 5.0 g,α-cellulose 1.0 g,potassium-sorbate1.5 g,niplagin 0.5 g,decavitamin 0.01 g,choline-chloride 0.06 g,maize-oil 3.30 g,honey 2.0 g,and water 730.0 mL.Alternatives to cotton bolls and wheat germ meal were okra seed sprouts,okra fruit,cottonseed meal,and okra seed meals,which were included in the study to introduce an efficient and economic mass-rearing system.Results The larval development completed in 19.68d±0.05 d with a weight of 20.18mg±0.20 mg at the fourth instar fed on the cottonseed meal-based diet instead of wheat germ meal based diet.On the same diet,84.00%±4.00%,17.24 mg±0.03 mg,and 7.76d±0.06 d were recorded as pupae formation,pupal weight,and pupal duration,respectively.Adult emergence,76.00%±1.00%was recorded from pupae collected from larvae raised on cottonseed meal-based diet.These male and female moths lived for 40.25d±0.10 d,and 44.34d±0.11 d,respectively.Females deposited 21.28±0.04 eggs per day with the viability of 65.78%±0.14%.The larval mortal-ity at the fourth instar was 37.20%±1.36%and malformed pupation of 12.00%±1.41%was recorded.Replacement of wheat germ meal with that of local meals(cottonseed and okra seed)in the standard laboratory diet has saved 463.80 to 467.10 PKR with 1.62 to 1.63 cost economic returns,respectively.Conclusion This research is of novel nature as it provides a concise and workable system for the economic and suc-c

关键词： Artificial diets Cottonseed meal Laboratory rearing Pectinophora gossypiella Wheat germ meal

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Optimizing Optical Fiber Faults Detection:A Comparative Analysis of Advanced Machine Learning Approaches

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Computers, Materials & Continua 2024年第5期79卷 2697-2721页

作者： Kamlesh Kumar Soothar Yuanxiang Chen arif Hussain Magsi Cong Hu Hussain shah School of Electronics Engineering Beijing University of Posts and TelecommunicationsBeijing100876China State Key Laboratory of Integrated Services Networks Xidian UniversityXi’an710071China State Key Laboratory ofNetworking and Switching Technology Beijing University of Posts and TelecommunicationsBeijing100876China

Efficient optical network management poses significant importance in backhaul and access network communicationfor preventing service disruptions and ensuring Quality of Service(QoS)satisfaction.The emerging faultsin optical networks introduce challenges that can jeopardize the network with a variety of faults.The existingliterature witnessed various partial or inadequate solutions.On the other hand,Machine Learning(ML)hasrevolutionized as a promising technique for fault detection and prevention.Unlike traditional fault managementsystems,this research has three-fold contributions.First,this research leverages the ML and Deep Learning(DL)multi-classification system and evaluates their accuracy in detecting six distinct fault types,including fiber cut,fibereavesdropping,splicing,bad connector,bending,and PC connector.Secondly,this paper assesses the classificationdelay of each classification algorithm.Finally,this work proposes a fiber optics fault prevention algorithm thatdetermines to mitigate the faults accordingly.This work utilized a publicly available fiber optics dataset namedOTDR_Data and applied different ML classifiers,such as Gaussian Naive Bayes(GNB),Logistic Regression(LR),Support Vector Machine(SVM),K-Nearest Neighbor(KNN),Random Forest(RF),and Decision Tree(DT).Moreover,Ensemble Learning(EL)techniques are applied to evaluate the accuracy of various classifiers.In addition,this work evaluated the performance of DL-based Convolutional Neural Network and Long-Short Term Memory(CNN-LSTM)hybrid classifier.The findings reveal that the CNN-LSTM hybrid technique achieved the highestaccuracy of 99%with a delay of 360 s.On the other hand,EL techniques improved the accuracy in detecting fiberoptic faults.Thus,this research comprehensively assesses accuracy and delay metrics for various classifiers andproposes the most efficient attack detection system in fiber optics.

关键词： Fiber optics fault detection multiclassification machine learning ensemble learning

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Mechanistic insights toward identification and interaction of plant parasitic nematodes:A review

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Soil Ecology Letters 2024年第1期6卷 11-27页

作者： Bisma Jan Ali Haider shah Mudasir Ahmad Bhat arif Tasleem Jan Ishfaq Ahmad Wani Ali Asghar shah Section of Microbiology Baba Ghulam Shah Badshah UniversityRajouriJ&K185234India Department of Biotechnology Baba Ghulam Shah Badshah University RajouriJ&K185234India Department of Botany Baba Ghulam Shah Badshah University RajouriJ&K185234India Department of Zoology School of Biosciences and BiotechnologyBaba Ghulam Shah Badshah University RajouriJ&K185234India

Nematode identification serves as an important param-eter to study their behaviour,importance and pathogenic-ity.Application of classical morphometric based identifica-tion methods prove to be lacking due to insufficient knowledge on morphological variations among closely related taxa.Molecular approaches such as DNA and protein-based information,microarray,probing,sequence-based methods and others have been used to supplement morphology-based methods for nematode identification.Ascarosides and certain protein-based nematode-associated molecular patterns(NAMPs),can be perceived by the host plants,and can initiate a signalling cascade.This review primarily emphasizes on an updated account of different classical and modern tools used for the identification of nematodes.Besides we also summa-rize the mechanism of some important signalling pathways which are involved in the different plant nematode interactions.Nematodes constitute most diverse and least studied group of soil inhabiting invertebrates.They are ecologically and physiologically important,however,wide range of nematodes show harmful impact on the individuals that live within their vicinity.Plant parasitic nematodes(PPNs)are transparent,pseudocoelomate,free living or parasitic microorganisms.PPNs lack morphometric identification methods due to insufficient knowl-edge on morphological variations among closely related taxa.As such,molecular approaches such as DNA and protein-based information,microarray,probing,sequence-based methods and others have been used to supplement morphology-based methods for their identification.To invade the defense response of different plant species,parasitic nematodes have evolved different molecular strategies.Ascarosides and certain protein-based nematode-associated molecular patterns(NAMPs),can be perceived by the host plants,and can initiate a signaling cascade.To overcome the host confrontation and develop certain nematode feeding sites,some members can inject effectors into the cells of susceptible hosts to reprogram the

关键词： nematode identification plant nematode interaction DNA fingerprinting nematode-associated molecular patterns signaling path-ways

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Highly selective adsorption of rhenium by amyloid-like protein material

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Science China(Technological Sciences) 2024年第5期67卷 1417-1430页

作者： muhammad arif YANG QingMin KANWAL Aisha ZHAO Jian NAWAZ Mohsan REN Hao YANG Peng Key Laboratory of Applied Surface and Colloid Chemistry Ministry of Education Xi’an Key Laboratory of Polymeric Soft Matter International Joint Research Center on Functional Fiber and Soft Smart TextileSchool of Chemistry and Chemical EngineeringShaanxi Normal UniversityXi’an710119China School of Chemistry and Chemical Engineering Northwestern Polytechnical UniversityXi’an710072China

Rhenium separation from molybdenum in molybdenite minerals and waste leachate has become an emerging challenge.Addressing this challenge,we prepared a set of protein-based alkylamine/alkylammonium salts complexes as extractants for selective uptake of rhenium from molybdenum,where the protein component turned into the insoluble amyloid-like structure when its internal disulfide bonds were reduced,namely phase-transition process.Among them,the phase-transited lysozyme and methyletrioctyleammonium chloride complex(PTL-N263)exhibited the most efficient adsorption at the alkaline condition for the electrostatic interaction between negatively charged metal ions with positively charged center(R_(4)N^(+))in N263,where negatively charged protein residues hindered the ion exchange of Cl^(-)in N263 for larger size Mo species(Mo_(7)O_(24)^(6-))than smaller size Re species(ReO^(4-)).The adsorption follows the Freundlich model and pseudo-second-order kinetics,which exhibits toplevel adsorption performance with a maximum adsorption capacity of 124 mg/g and a separation factor(β_(Re/Mo))of 2.78×10^(3)for Re.The adsorption capacity per unit area(57.2 mg/(g m^(2)))is 1.6–41 times higher than previously reported adsorbents,and the cost for adsorbing 1 g of Re(VII)is$1.07,indicating its industrial capability.This adsorption strategy can be applied to separating Re from Mo in binary solutions and industrial wastewater with other competing ions.

关键词： amyloid phase-transited lysozyme selective adsorption rhenium waste leachate solution

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Depression Intensity Classification from Tweets Using Fast Text Based Weighted Soft Voting Ensemble

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Computers, Materials & Continua 2024年第2期78卷 2047-2066页

作者： muhammad Rizwan muhammad Faheem Mushtaq Maryam Rafiq arif Mehmood Isabel de la Torre Diez Monica Gracia Villar Helena Garay Imran Ashraf Department of Artificial Intelligence The Islamia University of BahawalpurBahawalnagar63100Pakistan Department of Information Technology Khwaja Fareed University of Engineering and ITRYKhan64200Pakistan Department of Computer Science&Information Technology The Islamia University of BahawalpurBahawalnagar63100Pakistan Department of Signal Theory and Communications and Telematic Engineering Unviersity of ValladolidPaseo de Belén 15Valladolid47011Spain Faculty of Social Science and Humanities Universidad Europea del Atlántico Isabel Torres 21Santander39011Spain Department of Project Management Universidad Internacional IberoamericanaCampeche24560México Fundación Universitaria Internacional de Colombia Bogotá111611Colombia Universidad Internacional Iberoamericana AreciboPuerto Rico00613USA Universidade Internacional do Cuanza Cuito46703Angola Department of Information and Communication Engineering Yeungnam UniversityGyeongsan38541South Korea

Predicting depression intensity from microblogs and social media posts has numerous benefits and applications,including predicting early psychological disorders and stress in individuals or the general public.A major challenge in predicting depression using social media posts is that the existing studies do not focus on predicting the intensity of depression in social media texts but rather only perform the binary classification of depression and moreover noisy data makes it difficult to predict the true depression in the social media text.This study intends to begin by collecting relevant Tweets and generating a corpus of 210000 public tweets using Twitter public application programming interfaces(APIs).A strategy is devised to filter out only depression-related tweets by creating a list of relevant hashtags to reduce noise in the corpus.Furthermore,an algorithm is developed to annotate the data into three depression classes:‘Mild,’‘Moderate,’and‘Severe,’based on International Classification of Diseases-10(ICD-10)depression diagnostic criteria.Different baseline classifiers are applied to the annotated dataset to get a preliminary idea of classification performance on the corpus.Further FastText-based model is applied and fine-tuned with different preprocessing techniques and hyperparameter tuning to produce the tuned model,which significantly increases the depression classification performance to an 84%F1 score and 90%accuracy compared to baselines.Finally,a FastText-based weighted soft voting ensemble(WSVE)is proposed to boost the model’s performance by combining several other classifiers and assigning weights to individual models according to their individual performances.The proposed WSVE outperformed all baselines as well as FastText alone,with an F1 of 89%,5%higher than FastText alone,and an accuracy of 93%,3%higher than FastText alone.The proposed model better captures the contextual features of the relatively small sample class and aids in the detection of early depression intensity prediction from tweet

关键词： Depression classification deep learning FastText machine learning

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Indirect Vector Control of Linear Induction Motors Using Space Vector Pulse Width Modulation

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Computers, Materials & Continua 2023年第3期74卷 6263-6287页

作者： Arjmand Khaliq Syed Abdul Rahman Kashif Fahad Ahmad muhammad Anwar Qaisar shaheen Rizwan Akhtar muhammad arif shah Abdelzahir Abdelmaboud Electrical Engineering Department University of Engineering and TechnologyLahore54700Pakistan Department of Basic Sciences Deanship of Common First YearJouf UniversitySakakaAljouf72341Saudi Arabia Department of Information Science Division of Science and TechnologyUniversity of EducationLahore54000Pakistan Department of Computer Science and Information Technology The Islamia University of BahawalpurRahim Yar Khan Sub CampusPakistan Department of IT and Computer Science Pak-Austria Fachhochschule Institute of Applied Sciences and TechnologyHaripurPakistan Department of Information Systems King Khalid UniversityMuhayel Aseer61913Saudi Arabia

Vector control schemes have recently been used to drive linear induction motors(LIM)in high-performance applications.This trend promotes the development of precise and efficient control schemes for individual motors.This research aims to present a novel framework for speed and thrust force control of LIM using space vector pulse width modulation(SVPWM)inverters.The framework under consideration is developed in four stages.To begin,MATLAB Simulink was used to develop a detailed mathematical and electromechanical dynamicmodel.The research presents a modified SVPWM inverter control scheme.By tuning the proportional-integral(PI)controller with a transfer function,optimized values for the PI controller are derived.All the subsystems mentioned above are integrated to create a robust simulation of the LIM’s precise speed and thrust force control scheme.The reference speed values were chosen to evaluate the performance of the respective system,and the developed system’s response was verified using various data sets.For the low-speed range,a reference value of 10m/s is used,while a reference value of 100 m/s is used for the high-speed range.The speed output response indicates that themotor reached reference speed in amatter of seconds,as the delay time is between 8 and 10 s.The maximum amplitude of thrust achieved is less than 400N,demonstrating the controller’s capability to control a high-speed LIM with minimal thrust ripple.Due to the controlled speed range,the developed system is highly recommended for low-speed and high-speed and heavy-duty traction applications.

关键词： Space vector pulse width modulation linear induction motor proportional-integral controller indirect vector control electromechanical dynamic modeling

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Improving Thyroid Disorder Diagnosis via Ensemble Stacking and Bidirectional Feature Selection

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Computers, Materials & Continua 2024年第3期78卷 4225-4241页

作者： muhammad Armghan Latif Zohaib Mushtaq Saad arif Sara Rehman muhammad Farrukh Qureshi Nagwan Abdel Samee Maali Alabdulhafith Yeong Hyeon Gu Mohammed A.Al-masni Department of Computer and Information System Cleveland State UniversityOhio44115USA Department of Electrical Electronics and Computer SystemsCollege of Engineering and TechnologyUniversity of SargodhaSargodha40100Pakistan Department of Mechanical Engineering College of EngineeringKing Faisal UniversityAl-Ahsa31982Saudi Arabia Department of Biomedical Engineering Riphah International UniversityIslamabad44000Pakistan Department of Electrical Engineering Riphah International UniversityIslamabad44000Pakistan Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Artificial Intelligence College of Software and Convergence TechnologySejong UniversitySeoul05006Korea

Thyroid disorders represent a significant global health challenge with hypothyroidism and hyperthyroidism as two common conditions arising from dysfunction in the thyroid gland.Accurate and timely diagnosis of these disorders is crucial for effective treatment and patient care.This research introduces a comprehensive approach to improve the accuracy of thyroid disorder diagnosis through the integration of ensemble stacking and advanced feature selection techniques.Sequential forward feature selection,sequential backward feature elimination,and bidirectional feature elimination are investigated in this study.In ensemble learning,random forest,adaptive boosting,and bagging classifiers are employed.The effectiveness of these techniques is evaluated using two different datasets obtained from the University of California Irvine-Machine Learning Repository,both of which undergo preprocessing steps,including outlier removal,addressing missing data,data cleansing,and feature reduction.Extensive experimentation demonstrates the remarkable success of proposed ensemble stacking and bidirectional feature elimination achieving 100%and 99.86%accuracy in identifying hyperthyroidism and hypothyroidism,respectively.Beyond enhancing detection accuracy,the ensemble stacking model also demonstrated a streamlined computational complexity which is pivotal for practical medical applications.It significantly outperformed existing studies with similar objectives underscoring the viability and effectiveness of the proposed scheme.This research offers an innovative perspective and sets the platform for improved thyroid disorder diagnosis with broader implications for healthcare and patient well-being.

关键词： Ensemble learning random forests boosting dimensionality reduction machine learning smart healthcare computer aided diagnosis

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Securing Cloud-Encrypted Data:Detecting Ransomware-as-a-Service(RaaS)Attacks through Deep Learning Ensemble

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Computers, Materials & Continua 2024年第4期79卷 857-873页

作者： Amardeep Singh Hamad Ali Abosaq Saad arif Zohaib Mushtaq muhammad Irfan Ghulam Abbas Arshad Ali Alanoud Al Mazroa School of Computer Information Sciences University of CumberlandsWilliamsburgKentucky40769USA Computer Science Department College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Department of Mechanical Engineering College of EngineeringKing Faisal UniversityAl-Ahsa31982Saudi Arabia Department of Electrical Electronics and Computer SystemsCollege of Engineering and TechnologyUniversity of SargodhaSargodha40100Pakistan Electrical Engineering Department College of EngineeringNajran UniversityNajran61441Saudi Arabia Department of Electrical Engineering The University of LahoreLahore54000Pakistan Faculty of Computer and Information Systems Islamic University of MadinahMadinah42351Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

Data security assurance is crucial due to the increasing prevalence of cloud computing and its widespread use across different industries,especially in light of the growing number of cybersecurity threats.A major and everpresent threat is Ransomware-as-a-Service(RaaS)assaults,which enable even individuals with minimal technical knowledge to conduct ransomware operations.This study provides a new approach for RaaS attack detection which uses an ensemble of deep learning models.For this purpose,the network intrusion detection dataset“UNSWNB15”from the Intelligent Security Group of the University of New South Wales,Australia is analyzed.In the initial phase,the rectified linear unit-,scaled exponential linear unit-,and exponential linear unit-based three separate Multi-Layer Perceptron(MLP)models are developed.Later,using the combined predictive power of these three MLPs,the RansoDetect Fusion ensemble model is introduced in the suggested methodology.The proposed ensemble technique outperforms previous studieswith impressive performance metrics results,including 98.79%accuracy and recall,98.85%precision,and 98.80%F1-score.The empirical results of this study validate the ensemble model’s ability to improve cybersecurity defenses by showing that it outperforms individual MLPmodels.In expanding the field of cybersecurity strategy,this research highlights the significance of combined deep learning models in strengthening intrusion detection systems against sophisticated cyber threats.

关键词： Cloud encryption RaaS ensemble threat detection deep learning cybersecurity

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