A three-stage learning algorithm for deep multilayer perceptron (DMLP) with effective weight initialisation based on sparse auto-encoder is proposed in this paper, which aims to overcome difficulties in training deep neural networks with limited training data in high-dimensional feature space. At the first stage, unsupervised learning is adopted using sparse auto-encoder to obtain the initial weights of the feature extraction layers of the DMLP. At the second stage, error back-propagation is used to train the DMLP by fixing the weights obtained at the first stage for its feature extraction layers. At the third stage, all the weights of the DMLP obtained at the second stage are refined by error back-propagation. Network structures an

This paper presents a cognition path planning with control algorithm design for a nonholonomic wheeled mobile robot based on Particle Swarm Optimization (PSO) algorithm. The aim of this work is to propose the circular roadmap (CRM) method to plan and generate optimal path with free navigation as well as to propose a nonlinear MIMO-PID-MENN controller in order to track the wheeled mobile robot on the reference path. The PSO is used to find an online tune the control parameters of the proposed controller to get the best torques actions for the wheeled mobile robot. The numerical simulation results based on the Matlab package show that the proposed structure has a precise and highly accurate distance of the generated refere

The objective of the study: is to investigate the correlations between the HER2 neu gene status with the clinicopathological parameters of infiltrative breast carcinoma. A total of seventy four Iraqi breast cancer patients were collected from one center (Department of Public Health) paraffin blocks were collected from histopathology department central public health laboratories, Bagdad, Iraq from 2014-2015. The cases which has been taken included invasive ductal and invasive lobular carcinoma type Women age were ranged from 24-80 years old. Evaluation of Her-2/neu gene amplification status was done using FISH and CISH techniques that showed a significant correlations with clinicopathological parameters.

Cutaneous leishmaniasis (CL) is one of the most prevalent cutaneous parasitic protozoan infections in Iraq; characterized by a chronic infection and granulomatous disease that invades the skin. Type 1 immune was predominates in CL patients with exacerbated production of pro-inflammatory cytokine, therefore this study aimed to evaluate serum level of interferon gamma (IFN-γ) and monokine induce by interferon gamma (MIG/CXCl9) as a useful markers of disease development in patients during different stage of infection (<1 month .. early , 1-6 month.. chronic and >6 months.. late). The result showed that there was an early effort to eliminate the parasite proliferation which illustrated by a high significant increase of both IFN-γ

Linear regression is one of the most important statistical tools through which it is possible to know the relationship between the response variable and one variable (or more) of the independent variable(s), which is often used in various fields of science. Heteroscedastic is one of the linear regression problems, the effect of which leads to inaccurate conclusions. The problem of heteroscedastic may be accompanied by the presence of extreme outliers in the independent variables (High leverage points) (HLPs), the presence of (HLPs) in the data set result unrealistic estimates and misleading inferences. In this paper, we review some of the robust

This research aims to removes dyes from waste water by adsorption using banana peels. The conduct experiment done by banana powder and banana gel to compare between them and find out which one is the most efficient in adsorption. Studying the effects different factors on adsorption material and calculate the best removal efficiency to get rid of the methylene blue dye (MB).