با سلام خدمت کاربران در صورتی که با خطای سیستم پرداخت بانکی مواجه شدید از طریق کارت به کارت (6037997535328901 بانک ملی ناصر خنجری ) مقاله خود را دریافت کنید (تا مشکل رفع گردد).
ردیف | عنوان | نوع |
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1 |
The use of space-splitting RBF-FD technique to simulate thecontrolled synchronization of neural networks arising from brainactivity modeling in epileptic seizures
استفاده از روش تقسیم فضا RBF-FD برای شبیه سازی هماهنگ سازی کنترل شده شبکه های عصبی ناشی از مدل سازی میزان مغز در تشنج های صرع-2020 This paper investigates the behavior and synchronization of a network of reaction–diffusion neuraldynamics models using a highly efficient numerical method. In fact, the dynamical modeling, behav-ior analysis and controlled synchronization of a network of FitzHugh–Nagumo (FHN) neurons whichpromising the understanding of cognitive processing are studied by considering the unidirectional gapjunctions in the medium between two distant neurons. In this study, radial basis function generatedfinite differences (RBF-FD) technique is employed in conjunction with a suitable operator splitting tech-nique, which allows us to decouple the nonlinear partial differential equations of neural network modelsinto independent linear algebraic equations of very small dimensions. The most important advantagesof the proposed method can be high accuracy and high speed, very low computational complexity, andthe sparsity property of the matrix of the coefficients derived from its linear systems, which distinguishthe proposed method from other methods. The analyses and numerical results presented totally confirmthese claims. Keywords: Finite difference | Radial basis function | Fitzhugh–Nagumo | Operator splitting | Synchronization | Epilepsy |
مقاله انگلیسی |
2 |
Building a novel classifier based on teaching learning based optimization and radial basis function neural networks for non-imputed database with irrelevant features
ساخت طبقه بندی کننده جدید مبتنی بر آموزش بهینه سازی مبتنی بر یادگیری و شبکه های عصبی با عملکرد شعاعی برای پایگاه داده غیرمجاز با ویژگی های نامربوط-2019 This work presents a novel approach by considering teaching learning based optimization (TLBO) and
radial basis function neural networks (RBFNs) for building a classifier for the databases with missing values
and irrelevant features. The least square estimator and relief algorithm have been used for imputing
the database and evaluating the relevance of features, respectively. The preprocessed dataset is used for
developing a classifier based on TLBO trained RBFNs for generating a concise and meaningful description
for each class that can be used to classify subsequent instances with no known class label. The method is
evaluated extensively through a few bench-mark datasets obtained from UCI repository. The experimental
results confirm that our approach can be a promising tool towards constructing a classifier from the
databases with missing values and irrelevant attributes. Keywords: Pattern recognition | Imputation | Classification | Radial basis function neural networks | Teaching learning based optimization | k-Nearest neighbor |
مقاله انگلیسی |
3 |
Prediction of kidney disease stages using data mining algorithms
پیش بینی مراحل بیماری کلیه با استفاده از الگوریتم های داده کاوی-2019 Early detection and characterization are considered to be critical factors in the management and control of
chronic kidney disease. Herein, use of efficient data mining techniques is shown to reveal and extract hidden
information from clinical and laboratory patient data, which can be helpful to assist physicians in maximizing
accuracy for identification of disease severity stage. The results of applying Probabilistic Neural Networks
(PNN), Multilayer Perceptron (MLP), Support Vector Machine (SVM) and Radial Basis Function (RBF) algorithms
have been compared, and our findings show that the PNN algorithm provides better classification and prediction
performance for determining severity stage in chronic kidney disease. Keywords: Prediction of kidney disease stages | Data mining techniques | Probabilistic neural networks | Multilayer perceptron | Support vector machine | Radial basis function |
مقاله انگلیسی |
4 |
A new method for CF morphology distribution evaluation and CFRC property prediction using cascade deep learning
یک روش جدید برای ارزیابی توزیع مورفولوژی CF و پیش بینی ویژگی CFRC با استفاده از یادگیری عمیق آبشاری-2019 This work presents a deep-learning method to characterize the carbon fiber (CF) morphology distribution
in carbon fiber reinforced cement-based composites (CFRC), predict the CFRC properties, and measure the
contributions of different CF morphology distribution directly using X-ray images. Firstly, the components
of CFRC in slices of X-ray images were segmented and identified using a fully convolutional network
(FCN). Then the CF morphology distribution evaluation were conducted based on the results of
the FCN. At last, the prediction of CFRC properties was realized using a cascade deep learning algorithm
and CF morphology distribution results. The results showed that the FCN provided more reasonable segmentation
results for each component in CFRC than traditional methods. CF clustered areas and CF bundles
increased sharply with the increase of CF content, while uniformly dispersed CF areas showed the
opposite trend. The cascade deep learning provided a method to predict the CFRC properties (e.g. resistivity
and bending strength) using X-ray scanning images, which could also quantificationally measure
the contributions of different CF morphology distribution to properties of the CFRC. Therefore, the proposed
method could be regarded as a nondestructive and effective test for CFRC property evaluation. Keywords: Carbon fiber reinforced cement-based | composites | Carbon fiber distribution | Computed tomography | Deep learning | Radial basis function network |
مقاله انگلیسی |