In this research paper, the concept of hyper-spherical/hyperellipsoidal separability is introduced. Method of arriving at the optimal hypersphere (maximizing margin) separating two classes is discussed. By projecting the quantized patterns into higher dimensional space (as in encoders of error correcting code), the patterns are made hyper-spherically separable. Single/multiple layers of spheric...

To solve the problem that deep learning-based image matting algorithm cannot balance accuracy and model size, a lightweight based on learning is proposed. Considering limitation of memory computing resources, aiming at lightweight. We construct network gradually improved it. Firstly, apply detachable convolution to networks form faster stronger encoder decoder networks. The simultaneous use dep...

Power delivery network (PDN) analysis and thermal are computationally expensive tasks that essential for successful integrated circuit (IC) design. Algorithmically, both these analyses have similar computational structure complexity as they involve the solution to a partial differential equation of same form. This article converts into image-to-image sequence-to-sequence translation tasks, whic...

in this paper, a novel hybrid method based on learning algorithmof fuzzy neural network and newton-cotesmethods with positive coefficient for the solution of linear fredholm integro-differential equation of the second kindwith fuzzy initial value is presented. here neural network isconsidered as a part of large field called neural computing orsoft computing. we propose alearning algorithm from ...

BACKGROUND AND OBJECTIVE Feature reduction is an essential stage in computer aided breast cancer diagnosis systems. Multilayer neural networks can be trained to extract relevant features by encoding high-dimensional data into low-dimensional codes. Optimizing traditional auto-encoders works well only if the initial weights are close to a proper solution. They are also trained to only reduce the...

Deep neural networks often need to be trained with a large number of samples in dataset. When the training dataset are not enough, performance model will degrade. The Generative Adversarial Network (GAN) is considered effective at generating samples, and thus, expanding datasets. Consequently, this paper, we proposed novel method, called Stacked Siamese (SSGAN), for large-scale images high qual...

We propose a deep structure encoder using Volterra Neural Networks (VNNs) to seek latent representation of multi-modal data whose features are jointly captured by union subspaces. The so-called self-representation embedding the codes leads simplified fusion which is driven similarly constructed decoding. Filter architecture achieved reduction in parameter complexity primarily due controlled non...

this paper intends to offer a new iterative method based on arti cial neural networks for finding solution of a fuzzy equations system. our proposed fuzzi ed neural network is a ve-layer feedback neural network that corresponding connection weights to output layer are fuzzy numbers. this architecture of arti cial neural networks, can get a real input vector and calculates its corresponding fu...

Neural networks have recently been widely used to model some of the human activities in many areas of civil engineering applications. In the present paper, artificial neural networks (ANN) for predicting compressive strength of cubes and durability of concrete containing metakaolin with fly ash and silica fume with fly ash are developed at the age of 3, 7, 28, 56 and 90 days. For building these...