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

In this paper we investigate the automatic recognition of emotion in text. We propose a new method for emotion recognition based on the PPM (PPM is short for Prediction by Partial Matching) character-based text compression scheme in order to recognize Ekman’s six basic emotions (Anger, Disgust, Fear, Happiness, Sadness, Surprise). Experimental results with three datasets show that the new method is very effective when compared with traditional word-based text classification methods. We have also found that our method works best if the sizes of text in all classes used for training are similar, and that performance significantly improves with increased data.

t. The current study was conducted on the umbilical cord blood of newborns in the Banks Hospital in Baghdad, Diyala, and Khalis in Diyala, where the study included 90 samples of blood, and samples were collected for the period from the 1st of October;2020 to The first of February;2021 AD, where the study included measuring levels of interleukin-6;Adiponectin,glucose and bilirubin in the blood, comparison study between the study variables with the child's weight (greater than 3 kg),(less or equal 3 kg),the mother's age (greater than 25 years, less or equal to 25 years),the sex of the child (male, female).The results of our study showed that there were no significant differences between the variables of the current study between the two sex

insulin-like Growth Factor 1 (IGF-1) gene has been described in several studies as a candidate gene for growth. The present study attempts to identify associations between body weight traits and polymorphisms at 279 position of 5'UTR flanking region of IGF-1 gene in broiler chickens. Three hundred broiler chickens from two breeds (Cobb 500 and Hubbard F-15) were used in this study. A single nucleotide polymorphism (SNP) at 279 position of 5'UTR region of the IGF-1 gene was identified in 20.6 and 60.3% of Cobb 500 and Hubbard F-15, respectively, using the PCR-RFLP technique. Allele frequencies were 83.87 and 42.80% for the T allele and 16.13 and 57.20% for the C allele in Cobb500 and Hubbard-15 breeds, respectively. Genotype frequencies were

Abstract

The research aims to examine the relationship between psychological flow, psychological well-being, and self-management among a sample of fine artists in the Makkah region and its governorates. The research also aims to examine the mean group differences in psychological flow, psychological well-being, and self-management due to demographic variables (sex and years of practicing arts). The sample consists of (110) male and female fine artists. The descriptive correlational approach was performed to collect the data by using the psychological flow scale developed by Payne et al (2011), which was translated by the researcher, the Oxford happiness questionnaire developed by Hills and Argyle (2002), it has t

Three-dimensional (3D) reconstruction from images is a most beneficial method of object regeneration by using a photo-realistic way that can be used in many fields. For industrial fields, it can be used to visualize the cracks within alloys or walls. In medical fields, it has been used as 3D scanner to reconstruct some human organs such as internal nose for plastic surgery or to reconstruct ear canal for fabricating a hearing aid device, and others. These applications need high accuracy details and measurement that represent the main issue which should be taken in consideration, also the other issues are cost, movability, and ease of use which should be taken into consideration. This work has presented an approach for design and construc

 Finding a path solution in a dynamic environment represents a challenge for the robotics researchers, furthermore, it is the main issue for autonomous robots and manipulators since nowadays the world is looking forward to this challenge. The collision free path for robot in an environment with moving obstacles such as different objects, humans, animals or other robots is considered as an actual problem that needs to be solved.  In addition, the local minima and sharp edges are the most common problems in all path planning algorithms. The main objective of this work is to overcome these problems by demonstrating the robot path planning and obstacle avoidance using D star (D*) algorithm based on Particle Swarm Optimization (PSO)

In recent years, social media has been increasing widely and obviously as a media for users expressing their emotions and feelings through thousands of posts and comments related to tourism companies. As a consequence, it became difficult for tourists to read all the comments to determine whether these opinions are positive or negative to assess the success of a tourism company. In this paper, a modest model is proposed to assess e-tourism companies using Iraqi dialect reviews collected from Facebook. The reviews are analyzed using text mining techniques for sentiment classification. The generated sentiment words are classified into positive, negative and neutral comments by utilizing Rough Set Theory, Naïve Bayes and K-Nearest Neighbor