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A Comprehensive Systematic Review: Advancements in Skin Cancer Classification and Segmentation Using the ISIC Dataset

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Computer Modeling in Engineering & Sciences 2024年第9期140卷 2131-2164页

作者： Madiha Hameed Aneela Zameer muhammad asif zahoor raja Department of Computer Sciences Pakistan Institute of Engineering and Applied SciencesNilore50700Islamabad Future Technology Research Center National Yunlin University of Science and TechnologyDouliou64002TaiwanChina Directorate of IT Services University of GujratGujrat50700Pakistan

The International Skin Imaging Collaboration(ISIC)datasets are pivotal resources for researchers in machine learning for medical image analysis,especially in skin cancer *** datasets contain tens of thousands of dermoscopic photographs,each accompanied by gold-standard lesion diagnosis *** challenges associated with ISIC datasets have spurred significant advancements,with research papers reporting metrics surpassing those of human *** cancers are categorized into melanoma and non-melanoma types,with melanoma posing a greater threat due to its rapid potential for metastasis if left *** paper aims to address challenges in skin cancer detection via visual inspection and manual examination of skin lesion images,processes historically known for their *** notable advancements in machine learning and deep learning models,persistent challenges remain,largely due to the intricate nature of skin lesion *** review research on convolutional neural networks(CNNs)in skin cancer classification and segmentation,identifying issues like data duplication and augmentation *** explore the efficacy of Vision Transformers(ViTs)in overcoming these challenges within ISIC dataset *** leverage their capabilities to capture both global and local relationships within images,reducing data duplication and enhancing model ***,ViTs alleviate augmentation issues by effectively leveraging original *** a thorough examination of ViT-based methodologies,we illustrate their pivotal role in enhancing ISIC image classification and *** study offers valuable insights for researchers and practitioners looking to utilize ViTs for improved analysis of dermatological ***,this paper emphasizes the crucial role of mathematical and computational modeling processes in advancing skin cancer detection methodologies,highlighting their significance in improving algorithmic

关键词： Medical image skin cancer classification skin cancer segmentation international skin imaging collaboration convolutional neural network deep learning

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Numerical Solutions of a Novel Designed Prevention Class in the HIV Nonlinear Model

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Computer Modeling in Engineering & Sciences 2021年第10期129卷 227-251页

作者： Zulqurnain Sabir muhammad Umar muhammad asif zahoor raja Dumitru Baleanu Department of Mathematics and Statistics Hazara UniversityMansehra21120Pakistan Future Technology Research Center National Yunlin University of Science and TechnologyYunlin64002Taiwan Department of Mathematics Cankaya UniversityAnkara06790Turkey Institute of Space Science MagureleBucharest77125Romania

The presented research aims to design a new prevention class(P)in the HIV nonlinear system,i.e.,the HIPV *** numerical treatment of the newly formulated HIPV model is portrayed handled by using the strength of stochastic procedure based numerical computing schemes exploiting the artificial neural networks(ANNs)modeling legacy together with the optimization competence of the hybrid of global and local search schemes via genetic algorithms(GAs)and active-set approach(ASA),i.e.,*** optimization performances through GA-ASA are accessed by presenting an error-based fitness function designed for all the classes of the HIPV model and its corresponding initial conditions represented with nonlinear systems of *** check the exactness of the proposed stochastic scheme,the comparison of the obtained results and Adams numerical results is *** the convergence measures,the learning curves are presented based on the different contact rate ***,the statistical performances through different operators indicate the stability and reliability of the proposed stochastic scheme to solve the novel designed HIPV model.

关键词： Prevention class HIV supervised neural networks infection model artificial neural networks convergence curves active-set algorithm adams results genetic algorithms

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Stochastic Computational Approach for Complex Nonlinear Ordinary Differential Equations

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Chinese Physics Letters 2011年第2期28卷 20-23页

作者： Junaid Ali Khan muhammad asif zahoor raja Ijaz Mansoor Qureshi Department of Electronic Engineering International Islamic UniversityIslamabadPakistan Department of Electrical Engineering Air UniversityIslamabadPakistan

We present an evolutionary computational approach for the solution of nonlinear ordinary differential equations(NLODEs).The mathematical modeling is performed by a feed-forward artificial neural network that defines an unsupervised *** training of these networks is achieved by a hybrid intelligent algorithm,a combination of global search with genetic algorithm and local search by pattern search *** applicability of this approach ranges from single order NLODEs,to systems of coupled differential *** illustrate the method by solving a variety of model problems and present comparisons with solutions obtained by exact methods and classical numerical *** solution is provided on a continuous finite time interval unlike the other numerical techniques with comparable *** the advent of neuroprocessors and digital signal processors the method becomes particularly interesting due to the expected essential gains in the execution speed.

关键词： technique methods ordinary

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Novel Approach for a van der Pol Oscillator in the Continuous Time Domain

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Chinese Physics Letters 2011年第11期28卷 16-19页

作者： Junaid Ali Khan muhammad asif zahoor raja Ijaz Mansoor Qureshi Department of Electronic Engineering International Islamic UniversityIslamabadPakistan

We investigate the continuous time domain numerical treatment of a van der Pol oscillator,applying the trial solution as an artificial feed-forward neural network model containing unknown adjustable *** optimization of the network is performed by simulated annealing in an unsupervised *** proposed scheme is tested successfully by its application in both non-stiff and stiff *** reliability and effectiveness is validated through comprehensive statistical *** obtained results are in good agreement with the classical RK45 method.

关键词： artificial neural stiff

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Bio-inspired computational heuristics to study models of HIV infection of CD4＋ T-cell

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International Journal of Biomathematics 2018年第2期11卷 83-114页

作者： muhammad asif zahoor raja Kiran Asma muhammad Saeed Aslam Department of Electrical Engineering COMSATS Institute of Information Technology Attoek Campus Attock Pakistan Department of Computer Sciences COMSATS Institute of Information Technology Attock Campus Attock Pakistan Pakistan Institute of Engineering and Applied Sciences Nilore Islamabad Pakistan

In this work, biologically-inspired computing framework is developed for HIV infection of CD4＋ T-cell model using feed-forward artificial neural networks （ANNs）, genetic algorithms （GAs）, sequential quadratic programming （SQP） and hybrid approach based on GA-SQP. The mathematical model for HIV infection of CD4＋ T-cells is represented with the help of initial value problems （IVPs） based on the system of ordinary differential equations （ODEs）. The ANN model for the system is constructed by exploiting its strength of universal approximation. An objective function is developed for the system through unsupervised error using ANNs in the mean square sense. Training with weights of ANNs is carried out with GAs for effective global search supported with SQP for efficient local search. The proposed scheme is evaluated on a number of scenarios for the HIV infection model by taking the different levels for infected cells, natural substitution rates of uninfected cells, and virus particles. Comparisons of the approximate solutions are made with results of Adams numerical solver to establish the correctness of the proposed scheme. Accuracy and convergence of the approach are validated through the results of statistical analysis based on the sufficient large number of independent runs.

关键词： HIV infection model bioinformatics artificial neural networks genetic algo-rithms.

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Swarming Computational Efficiency to Solve a Novel Third-Order Delay Differential Emden-Fowler System

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Computers, Materials & Continua 2022年第12期73卷 4833-4849页

作者： Wajaree Weera Zulqurnain Sabir muhammad asif zahoor raja Sakda Noinang Thongchai Botmart Department of Mathematics Faculty of ScienceKhon Kaen UniversityKhon Kaen40002Thailand Department of Mathematics and Statistics Hazara UniversityMansehraPakistan Future Technology Research Center National Yunlin University of Science and Technology123 University RoadSection 3DouliouYunlin64002Taiwan Department of Mathematics Statistics and Computer Faculty of ScienceUbon Ratchathani UniversityUbon Ratchathani34190Thailand

The purpose of this research is to construct an integrated neuro swarming scheme using the procedures of the artificial neural networks(ANNs)with the use of global search particle swarm optimization(PSO)along with the competent local search interior-point programming(IPP)called as *** proposed computational scheme is implemented for the numerical simulations of the third order nonlinear delay differential Emden-Fowler model(TON-DD-EFM).The TON-DD-EFM is based on two types along with the particulars of shape factor,delayed terms,and singular points.A merit function is performed using the optimization of PSOIPP to find the solutions to the *** effectiveness of the ANN-PSOIPP is certified through the comparison with the exact results for solving four examples of the *** scheme’s efficiency is observed by performing the absolute error in suitable measures found around 10−04 to 10−***,the statistical-based assessments for 100 trials are provided to compute the accuracy,stability,and constancy of the ANNPSOIPP for solving the TON-DD-EFM.

关键词： Third-order nonlinear emden-fowler system artificial neural network statistical results particle swarm optimization numerical experimentations local search programming

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Numerical Computation of SEIR Model for the Zika Virus Spreading

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Computers, Materials & Continua 2023年第4期75卷 2155-2170页

作者： Suthep Suantai Zulqurnain Sabir muhammad asif zahoor raja Watcharaporn Cholamjiak Data Science Research Center Department of MathematicsFaculty of ScienceChiang Mai UniversityChiang Mai50200Thailand Research Group in Mathematics and Applied Mathematics Faculty of ScienceChiang Mai UniversityChiang Mai50200Thailand Department of Mathematics and Statistics Hazara UniversityMansehraPakistan Department of Computer Science and Mathematics Lebanese American UniversityBeirutLebanon Future Technology Research Center National Yunlin University of Science and Technology123 University RoadSection 3DouliouYunlinspan64002Taiwan School of Science University of PhayaoPhayao 56000Thailand

The purpose of this study is to present the numerical performancesand interpretations of the SEIR nonlinear system based on the Zika virusspreading by using the stochastic neural networks based intelligent computingsolver. The epidemic form of the nonlinear system represents the four dynamicsof the patients, susceptible patients S(y), exposed patients hospitalized inhospital E(y), infected patients I(y), and recovered patients R(y), i.e., SEIRmodel. The computing numerical outcomes and performances of the systemare examined by using the artificial neural networks (ANNs) and the scaledconjugate gradient (SCG) for the training of the networks, i.e., *** correctness of the ANNs-SCG scheme is observed by comparing theproposed and reference solutions for three cases of the SEIR model to solvethe nonlinear system based on the Zika virus spreading dynamics throughthe knacks of ANNs-SCG procedure based on exhaustive *** outcomes of the ANNs-SCG algorithm are found consistently in goodagreement with standard numerical solutions with negligible errors. Moreover,the procedure’s constancy, dependability, and exactness are perceived by usingthe values of state transitions, error histogram measures, correlation, andregression analysis.

关键词： SEIR nonlinear system Zika virus artificial neural networks scaled conjugate gradient statistical measures

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A Stochastic Study of the Fractional Order Model of Waste Plastic in Oceans

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Computers, Materials & Continua 2022年第11期73卷 4441-4454页

作者： Muneerah Al Nuwairan Zulqurnain Sabir muhammad asif zahoor raja Maryam Alnami Hanan Almuslem Department of Mathematics and Statistics College of ScienceKing Faisal UniversityP.O.Box 400Al-Ahsa31982Saudi Arabia Department of Mathematics and Statistics Hazara UniversityMansehraPakistan Future Technology Research Center National Yunlin University of Science and TechnologyDouliouYunlin64002Taiwan

In this paper,a fractional order model based on the management of waste plastic in the ocean(FO-MWPO)is numerically *** mathematical form of the FO-MWPO model is categorized into three components,waste plastic,Marine debris,and *** stochastic numerical solvers using the Levenberg-Marquardt backpropagation neural networks(LMQBP-NNs)have been applied to present the numerical solutions of the FO-MWPO *** competency of the method is tested by taking three variants of the FO-MWPO model based on the fractional order *** data ratio is provided for training,testing and authorization is 77%,12%,and 11%*** exactness of LMQBP-NNs is observed by using the comparative performances of the obtained and the Adams-BashforthMoulton *** verify the competence,validity,capability,exactness,and consistency of LMQBP-NNs,the performances have been obtained using the regression,state transitions,error histograms,correlation and mean square error.

关键词： Fractional order ocean Adams-Bashforth-Moulton LevenbergMarquardt backpropagation numerical solutions

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Flower Pollination Heuristics for Parameter Estimation of Electromagnetic Plane Waves

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Computers, Materials & Continua 2021年第8期68卷 2529-2543页

作者： Sadiq Akbar muhammad asif zahoor raja Naveed Ishtiaq Chaudhary Fawad Zaman Hani Alquhayz Department of Electronics University of PeshawarPeshawar25120Pakistan Future Technology Research Center National Yunlin University of Science and TechnologyDouliou64002Taiwan Department of Electrical Engineering International Islamic UniversityIslamabad44000Pakistan Department of Electrical Engineering COMSATS University IslamabadIslamabad44000Pakistan Department of Computer Science and Information College of Science in ZulfiMajmaah UniversityAl-Majmaah11952Saudi Arabia

For the last few decades,the parameter estimation of electromagnetic plane waves i.e.,far field sources,impinging on antenna array geometries has attracted a lot of researchers due to their use in radar,sonar and under water acoustic *** this work,nature inspired heuristics based on the flower pollination algorithm(FPA)is designed for the estimation problem of amplitude and direction of arrival of far field sources impinging on uniform linear array(ULA).Using the approximation in mean squared error sense,a fitness function of the problem is developed and the strength of the FPA is utilized for optimization of the cost function representing scenarios for various number of sources non-coherent located in the far *** worth of the proposed FPA based nature inspired computing heuristic is established through assessment studies on fitness,histograms,cumulative distribution function and box plots *** other worthy perks of the proposed scheme include simplicity of concept,ease in the implementation,extendibility and wide range of applicability to solve complex optimization *** salient features make the proposed approach as an attractive alternative to be exploited for solving different parameter estimation problems arising in nonlinear systems,power signal modelling,image processing and fault diagnosis.

关键词： Direction of arrival flower pollination algorithm plane waves parameter estimation

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Intelligent Networks for Chaotic Fractional-Order Nonlinear Financial Model

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Computers, Materials & Continua 2022年第9期72卷 5015-5030页

作者： Prem Junswang Zulqurnain Sabir muhammad asif zahoor raja Waleed Adel Thongchai Botmart Wajaree Weera Department of Statistics Faculty of ScienceKhon Kaen UniversityKhon Kaen 40002Thailand Department of Mathematics and Statistics Hazara UniversityMansehraPakistan Department of Electrical Engineering COMSATS Institute of Information TechnologyAttockPakistan Department of Mathematics and Engineering Physics Faculty of EngineeringMansoura UniversityEgypt UniversitéFrançaise D’Egypte Ismailia Desert RoadEl-ShoroukCairoEgypt Department of Mathematics Faculty of ScienceKhon Kaen UniversityKhon Kaen 40002Thailand

The purpose of this paper is to present a numerical approach based on the artificial neural networks(ANNs)for solving a novel fractional chaotic financial model that represents the effect of memory and chaos in the presented *** method is constructed with the combination of the ANNs along with the Levenberg-Marquardt backpropagation(LMB),named the *** technique is tested for solving the novel problem for three cases of the fractional-order values and the obtained results are compared with the reference *** numbers neurons have been used to solve the fractional-order chaotic financial *** selection of the data to solve the fractional-order chaotic financial model are selected as 75%for training,10%for testing,and 15%for *** results indicate that the presented approximate solutions fit exactly with the reference solution and the method is effective and *** obtained results are testified to reduce the mean square error(MSE)for solving the fractional model and verified through the various measures including correlation,MSE,regression histogram of the errors,and state transition(ST).

关键词： Financial model chaotic fractional-order reference dataset artificial neural networks levenberg-marquardt backpropagation

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