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  • Open Access


    Early Detection Glaucoma and Stargardt’s Disease Using Deep Learning Techniques

    Somasundaram Devaraj*, Senthil Kumar Arunachalam

    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1283-1299, 2023, DOI:10.32604/iasc.2023.033200

    Abstract Retinal fundus images are used to discover many diseases. Several Machine learning algorithms are designed to identify the Glaucoma disease. But the accuracy and time consumption performance were not improved. To address this problem Max Pool Convolution Neural Kuan Filtered Tobit Regressive Segmentation based Radial Basis Image Classifier (MPCNKFTRS-RBIC) Model is used for detecting the Glaucoma and Stargardt’s disease by early period using higher accuracy and minimal time. In MPCNKFTRS-RBIC Model, the retinal fundus image is considered as an input which is preprocessed in hidden layer 1 using weighted adaptive Kuan filter. Then, preprocessed retinal fundus is given for hidden… More >

  • Open Access


    Numerical Comparison of Shapeless Radial Basis Function Networks in Pattern Recognition

    Sunisa Tavaen, Sayan Kaennakham*

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 4081-4098, 2023, DOI:10.32604/cmc.2023.032329

    Abstract This work focuses on radial basis functions containing no parameters with the main objective being to comparatively explore more of their effectiveness. For this, a total of sixteen forms of shapeless radial basis functions are gathered and investigated under the context of the pattern recognition problem through the structure of radial basis function neural networks, with the use of the Representational Capability (RC) algorithm. Different sizes of datasets are disturbed with noise before being imported into the algorithm as ‘training/testing’ datasets. Each shapeless radial basis function is monitored carefully with effectiveness criteria including accuracy, condition number (of the interpolation matrix),… More >

  • Open Access


    Numerical Assessment of Nanofluid Natural Convection Using Local RBF Method Coupled with an Artificial Compressibility Model

    Muneerah Al Nuwairan1,*, Elmiloud Chaabelasri2

    CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.1, pp. 133-154, 2023, DOI:10.32604/cmes.2022.022649

    Abstract In this paper, natural heat convection inside square and equilateral triangular cavities was studied using a meshless method based on collocation local radial basis function (RBF). The nanofluids used were Cu-water or -water mixture with nanoparticle volume fractions range of . A system of continuity, momentum, and energy partial differential equations was used in modeling the flow and temperature behavior of the fluids. Partial derivatives in the governing equations were approximated using the RBF method. The artificial compressibility model was implemented to overcome the pressure velocity coupling problem that occurs in such equations. The main goal of this work was… More > Graphic Abstract

    Numerical Assessment of Nanofluid Natural Convection Using Local RBF Method Coupled with an Artificial Compressibility Model

  • Open Access


    Neuro-Based Higher Order Sliding Mode Control for Perturbed Nonlinear Systems

    Ahmed M. Elmogy1,2,*, Wael M. Elawady2

    Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 385-400, 2023, DOI:10.32604/iasc.2023.032349

    Abstract One of the great concerns when tackling nonlinear systems is how to design a robust controller that is able to deal with uncertainty. Many researchers have been working on developing such type of controllers. One of the most efficient techniques employed to develop such controllers is sliding mode control (SMC). However, the low order SMC suffers from chattering problem which harm the actuators of the control system and thus unsuitable to be used in many practical applications. In this paper, the drawbacks of low order traditional sliding mode control (FOTSMC) are resolved by presenting a novel adaptive radial basis function… More >

  • Open Access


    Structural Optimization of Metal and Polymer Ore Conveyor Belt Rollers

    João Pedro Ceniz, Rodrigo de Sá Martins, Marco Antonio Luersen*, Tiago Cousseau

    CMES-Computer Modeling in Engineering & Sciences, Vol.133, No.3, pp. 601-618, 2022, DOI:10.32604/cmes.2022.021011

    Abstract Ore conveyor belt rollers operate in harsh environments, making them prone to premature failure. Their service lives are highly dependent on the stress field and bearing misalignment angle, for which limit values are defined in a standard. In this work, an optimization methodology using metamodels based on radial basis functions is implemented to reduce the mass of two models of rollers. From a structural point of view, one of the rollers is made completely of metal, while the other also has some components made of polymeric material. The objective of this study is to develop and apply a parametric structural… More >

  • Open Access


    Simulation of Oil-Water Flow in a Shale Reservoir Using a Radial Basis Function

    Zenglin Wang1, Liaoyuan Zhang1, Anhai Zhong2, Ran Ding2, Mingjing Lu2,3,*

    FDMP-Fluid Dynamics & Materials Processing, Vol.18, No.6, pp. 1795-1804, 2022, DOI:10.32604/fdmp.2022.020020

    Abstract Due to the difficulties associated with preprocessing activities and poor grid convergence when simulating shale reservoirs in the context of traditional grid methods, in this study an innovative two-phase oil-water seepage model is elaborated. The modes is based on the radial basis meshless approach and is used to determine the pressure and water saturation in a sample reservoir. Two-dimensional examples demonstrate that, when compared to the finite difference method, the radial basis function method produces less errors and is more accurate in predicting daily oil production. The radial basis function and finite difference methods provide errors of 5.78 percent and… More >

  • Open Access


    A Novel Radial Basis Function Neural Network Approach for ECG Signal Classification

    S. Sathishkumar1,*, R. Devi Priya2

    Intelligent Automation & Soft Computing, Vol.35, No.1, pp. 129-148, 2023, DOI:10.32604/iasc.2023.023817

    Abstract Electrocardiogram (ECG) is a diagnostic method that helps to assess and record the electrical impulses of heart. The traditional methods in the extraction of ECG features is inneffective for avoiding the computational abstractions in the ECG signal. The cardiologist and medical specialist find numerous difficulties in the process of traditional approaches. The specified restrictions are eliminated in the proposed classifier. The fundamental aim of this work is to find the R-R interval. To analyze the blockage, different approaches are implemented, which make the computation as facile with high accuracy. The information are recovered from the MIT-BIH dataset. The retrieved data… More >

  • Open Access


    Visualization Detection of Solid–Liquid Two-Phase Flow in Filling Pipeline by Electrical Capacitance Tomography Technology

    Ningbo Jing1, Mingqiao Li1, Lang Liu2,*, Yutong Shen1, Peijiao Yang1, Xuebin Qin1

    CMES-Computer Modeling in Engineering & Sciences, Vol.131, No.1, pp. 465-476, 2022, DOI:10.32604/cmes.2022.018965

    Abstract During mine filling, the caking in the pipeline and the waste rock in the filling slurry may cause serious safety accidents such as pipe blocking or explosion. Therefore, the visualization of the inner mine filling of the solid–liquid two-phase flow in the pipeline is important. This paper proposes a method based on capacitance tomography for the visualization of the solid–liquid distribution on the section of a filling pipe. A feedback network is used for electrical capacitance tomography reconstruction. This reconstruction method uses radial basis function neural network fitting to determine the relationship between the capacitance vector and medium distribution error.… More >

  • Open Access


    Thermogram Adaptive Efficient Model for Breast Cancer Detection Using Fractional Derivative Mask and Hybrid Feature Set in the IoT Environment

    Ritam Sharma1, Janki Ballabh Sharma1, Ranjan Maheshwari1, Praveen Agarwal2,3,4,5,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.130, No.2, pp. 923-947, 2022, DOI:10.32604/cmes.2022.016065

    Abstract In this paper, a novel hybrid texture feature set and fractional derivative filter-based breast cancer detection model is introduced. This paper also introduces the application of a histogram of linear bipolar pattern features (HLBP) for breast thermogram classification. Initially, breast tissues are separated by masking operation and filtered by Grmwald–Letnikov fractional derivative-based Sobel mask to enhance the texture and rectify the noise. A novel hybrid feature set using HLBP and other statistical feature sets is derived and reduced by principal component analysis. Radial basis function kernel-based support vector machine is employed for detecting the abnormality in the thermogram. The performance… More >

  • Open Access


    Forecasting of Trend-Cycle Time Series Using Hybrid Model Linear Regression

    N. Ashwini1,*, V. Nagaveni2, Manoj Kumar Singh3

    Intelligent Automation & Soft Computing, Vol.32, No.2, pp. 893-908, 2022, DOI:10.32604/iasc.2022.022231

    Abstract Forecasting for a time series signal carrying single pattern characteristics can be done properly using function mapping-based principle by a well-designed artificial neural network model. But the performances degraded very much when time series carried the mixture of different patterns characteristics. The level of difficulty increases further when there is a need to predict far time samples. Among several possible mixtures of patterns, the trend-cycle time series is having its importance because of its occurrence in many real-life applications like in electric power generation, fuel consumption and automobile sales. Over the mixed characteristics of patterns, a neural model, suffered heavily… More >

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