Background/Objectives: The purpose of this study was to classify Alzheimer’s disease (AD) patients from Normal Control (NC) patients using Magnetic Resonance Imaging (MRI). Methods/Statistical analysis: The performance evolution is carried out for 346 MR images from Alzheimer's Neuroimaging Initiative (ADNI) dataset. The classifier Deep Belief Network (DBN) is used for the function of classification. The network is trained using a sample training set, and the weights produced are then used to check the system's recognition capability. Findings: As a result, this paper presented a novel method of automated classification system for AD determination. The suggested method offers good performance of the experiments carried out show that the

The inverse kinematic equation for a robot is very important to the control robot’s motion and position. The solving of this equation is complex for the rigid robot due to the dependency of this equation on the joint configuration and structure of robot link. In light robot arms, where the flexibility exists, the solving of this problem is more complicated than the rigid link robot because the deformation variables (elongation and bending) are present in the forward kinematic equation. The finding of an inverse kinematic equation needs to obtain the relation between the joint angles and both of the end-effector position and deformations variables. In this work, a neural network has been proposed to solve the problem of inverse kinemati

Whenever, the Internet of Things (IoT) applications and devices increased, the capability of the its access frequently stressed. That can lead a significant bottleneck problem for network performance in different layers of an end point to end point (P2P) communication route. So, an appropriate characteristic (i.e., classification) of the time changing traffic prediction has been used to solve this issue. Nevertheless, stills remain at great an open defy. Due to of the most of the presenting solutions depend on machine learning (ML) methods, that though give high calculation cost, where they are not taking into account the fine-accurately flow classification of the IoT devices is needed. Therefore, this paper presents a new model bas

Abstract

This research aim to overcome the problem of dimensionality by using the methods of non-linear regression, which reduces the root of the average square error (RMSE), and is called the method of projection pursuit regression (PPR), which is one of the methods for reducing dimensions that work to overcome the problem of dimensionality (curse of dimensionality), The (PPR) method is a statistical technique that deals with finding the most important projections in multi-dimensional data , and With each finding projection , the data is reduced by linear compounds overall the projection. The process repeated to produce good projections until the best projections are obtained. The main idea of the PPR is to model

This paper proposes improving the structure of the neural controller based on the identification model for nonlinear systems. The goal of this work is to employ the structure of the Modified Elman Neural Network (MENN) model into the NARMA-L2 structure instead of Multi-Layer Perceptron (MLP) model in order to construct a new hybrid neural structure that can be used as an identifier model and a nonlinear controller for the SISO linear or nonlinear systems. Two learning algorithms are used to adjust the parameters weight of the hybrid neural structure with its serial-parallel configuration; the first one is supervised learning algorithm based Back Propagation Algorithm (BPA) and the second one is an intelligent algorithm n