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A Neural Study of the Fractional Heroin Epidemic Model

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Computers, Materials & Continua 2023年第2期74卷 4453-4467页

作者： Wajaree Weera Thongchai Botmart Samina Zuhra Zulqurnain sabir Muhammad Asif Zahoor Raja Salem Ben Said Department of Mathematics Faculty of ScienceKhon Kaen UniversityKhon Kaen40002Thailand Department of Computing and Engineering Abasyn University PeshawarPakistan Department of Mathematics and Statistics Hazara UniversityMansehra21120Pakistan Future Technology Research Center National Yunlin University of Science and Technology123 University RoadSection 3DouliouYunlin64002Taiwan Department of Mathematical Science College of Science United Arab Emirates University Al ain Abu DhabiUAE

This works intends to provide numerical solutions based on the nonlinear fractional order derivatives of the classical White and Comiskey model(NFD-WCM).The fractional order derivatives have provided authentic and accurate solutions for the *** solutions of the fractional NFD-WCM are provided using the stochastic computing supervised algorithm named Levenberg-Marquard Backpropagation(LMB)based on neural networks(NNs).This regression approach combines gradient descent and Gauss-Newton iterative methods,which means finding a solution through the sequences of different *** is used to demonstrate the heroin *** has been on-growth world wide,mainly in Asia,Europe,and the *** is the fourth foremost cause of death due to taking an overdose in the *** nonlinear mathematical system NFD-WCM discusses the overall circumstance of different drug users,such as suspected groups,drug users without treatment,and drug users with *** numerical results of NFD-WCM via LMB-NNs have been substantiated through training,testing,and validation *** stability and accuracy are then checked through the statistical tool,such asmean square error(MSE),error histogram,and fitness *** suggested methodology’s strength is demonstrated by the high convergence between the reference solutions and the solutions generated by adding the efficacy of a constructed solver LMB-NNs,with accuracy levels ranging from 10?9 to 10?10.

关键词： Fractional order heroin epidemic mathematical system white-comiskey model numerical results neural networks

An Intelligence Computational Approach for the Fractional 4D Chaotic Financial Model

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Computers, Materials & Continua 2023年第2期74卷 2711-2724页

作者： Wajaree Weera Thongchai Botmart Charuwat Chantawat Zulqurnain sabir Waleed Adel Muhammad Asif Zahoor Raja Muhammad Kristiawan Department of Mathematics Faculty of ScienceKhon Kaen UniversityKhon Kaen40002Thailand Department of Mathematics and Statistics Hazara UniversityMansehraPakistan Department of Mathematical Sciences United Arab Emirates UniversityP.O.Box 15551Al AinUAE Department of Mathematics and Engineering Physics Faculty of EngineeringMansoura UniversityEgypt UniversitéFrançaise D’Egypte Ismailia Desert RoadEl-ShoroukCairoEgypt Future Technology Research Center National Yunlin University of Science and Technology123 University RoadSection 3DouliouYunlin 64002Taiwan Science of Education Universitas BengkuluBengkulu CityIndonesia

The main purpose of the study is to present a numerical approach to investigate the numerical performances of the fractional 4-D chaotic financial system using a stochastic *** stochastic procedures mainly depend on the combination of the artificial neural network(ANNs)along with the Levenberg-Marquardt Backpropagation(LMB)i.e.,ANNs-LMB *** fractional-order term is defined in the Caputo sense and three cases are solved using the proposed technique for different values of the fractional orderα.The values of the fractional order derivatives to solve the fractional 4-D chaotic financial system are used between 0 and *** data proportion is applied as 73%,15%,and 12%for training,testing,and certification to solve the chaotic fractional *** acquired results are verified through the comparison of the reference solution,which indicates the proposed technique is efficient and *** 4-D chaotic model is numerically solved by using the ANNs-LMB technique to reduce the mean square error(MSE).To authenticate the exactness,and consistency of the technique,the obtained performances are plotted in the figures of correlation measures,error histograms,and *** these figures,it can be witnessed that the provided technique is effective for solving such models to give some new insight into the physical behavior of the model.

关键词： Financial model chaotic Levenberg-Marquardt Backpropagation fractional order artificial neural networks reference dataset

Robust graph‐based localization for industrial Internet of things in the presence of flipping ambiguities

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CAAI Transactions on Intelligence Technology 2023年第4期8卷 1140-1149页

作者： mian Imtiaz ul Haq Ruhul Amin Khalil Muhannad Almutiry Ahmad Sawalmeh Tanveer Ahmad Nasir Saeed Faculty of Electrical Engineering Swedish College of Engineering and TechnologyWah CanttPakistan Department of Electrical Engineering Faculty of Electrical and Computer EngineeringUniversity of Engineering and TechnologyPeshawarPakistan Department of Electrical Engineering Remote Sensing UnitNorthern Border UniversityArarSaudi Arabia Data Science and Artificial Intelligence Department‐College of Science and Information Technology Irbid National UniversityIrbadJordan Innovation Education and Research Center for On‐Device AI Software(Bk21) Department of Computer Science and EngineeringChungnam National UniversityDaejeonRepublic of Korea Department of Electrical and Communication Engineering United Arab Emirates University(UAEU)Al AinUnited Arab Emirates

Localisation of machines in harsh Industrial Internet of Things(IIoT)environment is necessary for various ***,a novel localisation algorithm is proposed for noisy range measurements in IIoT *** position of an unknown machine device in the network is estimated using the relative distances between blind machines(BMs)and anchor machines(AMs).Moreover,a more practical and challenging scenario with the erroneous position of AM is considered,which brings additional uncertainty to the final position ***,the AMs selection algorithm for the localisation of BMs in the IIoT network is *** those AMs will participate in the localisation process,which increases the accuracy of the final location ***,the closed‐form expression of the proposed greedy successive anchorization process is derived,which prevents possible local convergence,reduces computation,and achieves Cramér‐Rao lower bound accuracy for white Gaussian measurement *** results are compared with the state‐of‐the‐art and verified through numerous simulations.

关键词： Cramér‐Rao lower bound greedy successive anchorization industrial internet of things localization

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 *** 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 *** 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%,*** obtained numerical performances have been compared with the Runge-Kutta approach to solve the *** reduction of mean square error(MSE)is used to investigate the numericalmeasures through the SCGNNs for solving the SED *** precision of the SCGNNs is validated through the comparison of the results and the absolute error *** 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

An Artificial Approach for the Fractional Order Rape and Its Control Model

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Computers, Materials & Continua 2023年第2期74卷 3421-3438页

作者： Wajaree Weera Zulqurnain sabir Muhammad Asif Zahoor Raja Salem Ben Said Maria Emilia Camargo Chantapish Zamart Thongchai Botmart Department of Mathematics Faculty of ScienceKhon Kaen UniversityKhon Kaen40002Thailand Department of Mathematics Near East UniversityNicosia99138Cyprus Future Technology Research Center National Yunlin University of Science and TechnologyDouliouYunlin64002Taiwan Department of Mathematical Science College of ScienceUnited Arab Emirates UniversityAl AinAbu Dhabi15551UAE Graduate Program in Administration Federal University of Santa MariaSanta Maria93458Brazil

The current investigations provide the solutions of the nonlinear fractional order mathematical rape and its controlmodel using the strength of artificial neural networks(ANNs)along with the Levenberg-Marquardt backpropagation approach(LMBA),i.e.,artificial neural networks-Levenberg-Marquardt backpropagation approach(ANNs-LMBA).The fractional order investigations have been presented to find more realistic results of the mathematical form of the rape and its control *** differential mathematical form of the nonlinear fractional order mathematical rape and its control model has six classes:susceptible native girls,infected immature girls,susceptible knowledgeable girls,infected knowledgeable girls,susceptible rapist population and infective rapist *** rape and its control differential system using three different fractional order values is authenticated to perform the correctness of *** data is used to present the rape and its control differential system is designated as 70%for training,14%for authorization and 16%for *** obtained performances of the ANNs-LMBA are compared with the dataset of the Adams-Bashforth-Moulton *** substantiate the consistency,aptitude,validity,exactness,and capability of the LMBA neural networks,the obtained numerical values are provided using the state transitions(STs),correlation,regression,mean square error(MSE)and error histograms(EHs).

关键词： Rape and its control differential system neural networks fractional order levenberg-marquardt backpropagation approach reference solutions

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 *** 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 *** myeloma causes by increasing the OC and reducing the osteoblasts,resulting in net bone waste the tumor *** solutions of the FOMM will be provided by using the stochastic framework based on the Levenberg-Marquardt backpropagation(LVMBP)neural networks(NN),i.e.,*** mathematical performances of three variations of the fractional-order derivative based on the nonlinear disease model using the *** static structural performances are 82%for investigation and 9%for both learning and *** performances of the LVMBPNN are authenticated by using the results of the Adams-Bashforth-Moulton *** 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

Fractional Order Environmental and Economic Model Investigations Using Artificial Neural Network

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Computers, Materials & Continua 2023年第1期74卷 1735-1748页

作者： Wajaree Weera Chantapish Zamart Zulqurnain sabir Muhammad Asif Zahoor Raja Afaf S.Alwabli S.R.Mahmoud Supreecha Wongaree Thongchai Botmart Department of Mathematics Faculty of ScienceKhon Kaen UniversityKhon Kaen40002Thailand Department of Mathematics and Statistics Hazara UniversityMansehraPakistan Department of Mathematical Sciences UAE UniversityP.O.Box 15551Al AinUAE Future Technology Research Center National Yunlin University of Science and Technology123 University RoadSection 3DouliouYunlin64002Taiwan Department of Biological Sciences Rabigh College of Science and ArtsKing Abdulaziz UniversityRabighSaudi Arabia GRC Department Faculty of Applied StudiesJeddahKing Abdulaziz UniversityRabighSaudi Arabia Institute of General Education Udon Thani Rajabhat UniversityUdon Thani41000Thailand

The motive of these investigations is to provide the importance and significance of the fractional order(FO)derivatives in the nonlinear environmental and economic(NEE)model,i.e.,FO-NEE *** dynamics of the NEE model achieves more precise by using the form of the FO *** investigations through the non-integer and nonlinear mathematical form to define the FO-NEE model are also provided in this *** composition of the FO-NEEmodel is classified into three classes,execution cost of control,system competence of industrial elements and a new diagnostics technical exclusion *** mathematical FO-NEE system is numerically studied by using the artificial neural networks(ANNs)along with the Levenberg-Marquardt backpropagation method(ANNs-LMBM).Three different cases using the FO derivative have been examined to present the numerical performances of the FO-NEE *** data is selected to solve the mathematical FO-NEE system is executed as 70%for training and 15%for both testing and *** exactness of the proposed ANNs-LMBM is observed through the comparison of the obtained and the Adams-Bashforth-Moulton database *** ratify the aptitude,validity,constancy,exactness,and competence of the ANNs-LMBM,the numerical replications using the state transitions,regression,correlation,error histograms and mean square error are also described.

关键词： Environmental and economic model artificial neural networks fractional order nonlinear Levenberg-Marquardt backpropagation

SUMO-1 modification of FEN1 facilitates its interaction with Rad9-Radl-husl to counteract DNA replication stress

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Journal of Molecular Cell Biology 2018年第5期10卷 460-474页

作者： Xiaoli Xu Rongyi Shi Li Zheng Zhigang Guo Liangyan Wang mian Zhou Ye Zhao Bing Tian Khue Truong Yuan Chen Binghui Shen Yuejin Hua Hong Xu Institute of Nuclear-Agricultural Sciences Zhejiang University Hangzhou 310029 China Department of Cancer Genetics and Epigenetics City of Hope National Medical Center and Beckman Research Institute Duarte CA 91010 USA Jiangsu Key Laboratory for Molecular and Medical Biotechnology and College of Life Sciences Nanjing Normal University Nanjing 210046 China Department of Molecular Medicine City of Hope National Medical Center and Beckman Research Institute Duarte CA 91010 USA

Human flap endonuclease 1 （FEN1） is a structure-specific, multi-functional endonuclease essential for DNA replication and repair. We and others have shown that during DNA replication, FEN1 processes Okazaki fragments via its interaction with the proliferating cell nuclear antigen （PCNA）. Alternatively, in response to DNA damage, FEN1 interacts with the PCNA-like Radg-Radl-husl complex instead of PCNA to engage in DNA repair activities, such as homology-directed repair of stalled DNA replication forks. However, it is unclear how FEN1 is able to switch between these interactions and its roles in DNA replication and DNA repair. Here, we report that FEN1 undergoes SUMOylation by SUMO-1 in response to DNA replication fork-staUing agents, such as UV irradiation, hydroxyurea, and mitomycin C. This DNA damage-induced SUMO-1 modification promotes the interaction of FEN1 with the Radg-Rad1-husl complex. Furthermore, we found that FEN1 mutations that prevent its SUMO-1 modification also impair its ability to interact with hus1 and to rescue stalled replication forks. These impairments lead to the accumulation of DNA damage and heightened sensitivity to fork-staUing agents. Altogether, our findings suggest an important role of the SUMO-1 modification of FEN1 in regulating its roles in DNA replication and repair.

关键词： flap endonuclease 1 Rad9-Rad1-hus1 complex replication stress SUMOylation

Stochastic Framework for Solving the Prey-Predator Delay Differential Model of Holling Type-Ⅲ

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Computers, Materials & Continua 2023年第3期 5915-5930页

作者： Naret Ruttanaprommarin Zulqurnain sabir Rafaél Artidoro Sandoval Nez Emad Az-Zo’bi Wajaree Weera Thongchai Botmart Chantapish Zamart Department of Science and Mathematics Faculty of Industry and TechnologyRajamangala University of Technology Isan Sakonnakhon CampusSakonnakhon47160Thailand Department of Mathematics and Statistics Hazara UniversityMansehraPakistan Department of Mathematical Sciences United Arab Emirates UniversityP.O.Box 15551Al AinUAE Universidad Nacional Autonoma de Chota CajamarcaPeru Department of Mathematics and Statistics Mutah UniversityMutah-Al Karak-Jordan Department of Mathematics Faculty of ScienceKhon Kaen UniversityKhon Kaen40002Thailand

The current research aims to implement the numerical resultsfor the Holling third kind of functional response delay differential modelutilizing a stochastic framework based on Levenberg-Marquardt backpropagationneural networks (LVMBPNNs). The nonlinear model depends uponthree dynamics, prey, predator, and the impact of the recent past. Threedifferent cases based on the delay differential system with the Holling 3^(rd) type of the functional response have been used to solve through the proposedLVMBPNNs solver. The statistic computing framework is provided byselecting 12%, 11%, and 77% for training, testing, and verification. Thirteennumbers of neurons have been used based on the input, hidden, and outputlayers structure for solving the delay differential model with the Holling 3rdtype of functional response. The correctness of the proposed stochastic schemeis observed by using the comparison performances of the proposed and referencedata-based Adam numerical results. The authentication and precision ofthe proposed solver are approved by analyzing the state transitions, regressionperformances, correlation actions, mean square error, and error histograms.

关键词： Holling 3^(rd)type delay factor mathematical model neural networks levenberg-marquardt backpropagation