In this work, a simplified Artificial Neural Network (ANN) based approach for recognition of various objects is explored using multiple features. The objective is to configure and train an ANN to be capable of recognizing an object using a feature set formed by Principal Component Analysis (PCA), Frequency Domain and Discrete Cosine Transform (DCT) components. The idea is to use these varied co...

231  Abstract—The kinematic of serial manipulators comprises the study of the relations between joint variables and Cartesian variables. We distinguish two problems, commonly referred to as the direct and inverse kinematic problems. The former reduces matrix multiplications, and poses no major problem. The inverse kinematic problems, however, is more challenging, for it involves intensive vari...

In this paper we present a framework for using multi-layer perceptron (MLP) networks in nonlinear generative models trained by variational Bayesian learning. The nonlinearity is handled by linearizing it using a Gauss–Hermite quadrature at the hidden neurons. This yields an accurate approximation for cases of large posterior variance. The method can be used to derive nonlinear counterparts for ...

Handwritten signatures are the most natural way of authenticating a person’s identity. An offline signature verification system generally consists of four components: data acquisition, preprocessing, feature extraction, recognition and verification. This paper presents a method for verifying handwritten signature by using NN architecture. In proposed methods the multi-layer perceptron (MLP), mo...

AbstractFitting the continuous valid logic of the exclusive OR (XOR) problem requires more than a minimal neural network configuration. This letter shows that the simplest logic to fit the XOR problem involves boundaries that cannot be mapped by a Multi-Layer Perceptron (MLP) with just two hidden nodes. This observation calls into question rule extraction methodologies based on pruning, since t...

Problem statement: This study proposed an approach for classification of placental tissues development using ultrasound images. Approach: This approach was based to the selection of tissues, feature extraction by discrete wavelet transform and classification by neural network and especially the Multi Layer Perceptron (MLP). Results: The proposed approach was tested for ultrasound placental imag...

In this work, we proposed the use of Support Vector Machines (SVM) to predict the performance of machine learning algorithms based on features of the learning problems. This work is related to the Meta-Regression approach, which has been successfully applied to predict learning performance, supporting algorithm selection. Experiments were performed in a case study in which SVMs with different k...

The goal of this study is to investigate the influence of fatigue on Steady State Visual Evoked Potential (SSVEP) during virtual wheelchair navigation. For this purpose, an experimental environment was set based on modifiable parameters (luminosity, number of obstacles and obstacles velocities). A correlation study between SSVEP and fatigue ratings was conducted by the mean of spectral analysis...

Glomerulus segmentation in kidney tissue segments is a crucial nephropathology process used to diagnose renal diseases effectively. This study proposes novel and robust application of MLP (Multi-Layer Perceptron) based architectures for the glomeruli PAS (Periodic AcidSchiff) stained whole images effective diagnosis diseases. For challenge, proposed unique solution uses MLP-UNet Perceptron U-Ne...

The process of amygdalin extraction from plum seeds was optimized using central composite design (CCD) and multilayer perceptron (MLP). The effect of time, ethanol concentration, solid-to-liquid ratio, and temperature on the amygdalin content in the extracts was estimated using both mathematical models. The MLP 4-3-1 with exponential function in hidden layer and linear function in output layer ...