Many undergraduate learners at English departments who study English as a foreign language are unable to speak and use language correctly in their post -graduate careers. This problem can be attributed to certain difficulties, which they faced throughout their education years that hinder their endeavors to learn. Therefore, this study aims to discover the main difficulties faced by EFL students in language learning and test the difficulty variable according to gender and college variables then find suitable solutions for enhancing learning. A questionnaire with 15 items and 5 scales were used to help in discovering the difficulties. The questionnaire was distributed to the selected sample of study which consists of 90 (male and female) students selected randomly from the 3rd and 4th year class levels at English departments from colleges of Languages and Education (Ibn-Rushd) at the University of Baghdad. The results of the study showed that EFL students face difficulties in language learning such as the role of society in discouraging English language learning, the learners’ shyness, which prevents them from speaking English in fear of making mistakes, lack of motivation, and the influence of class size and crowdedness. After analyzing the results, some recommendations and suggestions were presented to solve the problem and eliminate difficulties.
Objective: To find out any gender differences in health promoting behaviors for a sample of students attending Institute of Technology/ Baquba. Methodology: An analytical, cross-sectional study conducted randomly for a sample of students attending Technical Institute/ Baquba, during the period from 10th November 2015 to 10th April 2016. A questionnaire used to collecting data, this questionnaire was consisted from two domains related to health promoting behaviors including (Dietary domain and domain of some daily life activities like sleep duration, rise time, bedtime and walking period per week). The
The study aimed at ideutifying the impact of scieutific skills in strategy and liabits of mind amony stueuts in tenth grade . The study demanded to choose a sampie that coutaiun (42) student of the fourth grade of the secondary school who were dirided into tow groups , the first is experimental studied according to scieutific skill strategy , and the other controlling , studied according to the usualway . An achievement test has been taken that adopted staudard for mind skills as research tools that are applid after ascertaining sincerity proved at the end of the experiment .The study has reached to the conclusion that there are statistically significant differnces in farour of the experiment group in both
... Show MoreText categorization refers to the process of grouping text or documents into classes or categories according to their content. Text categorization process consists of three phases which are: preprocessing, feature extraction and classification. In comparison to the English language, just few studies have been done to categorize and classify the Arabic language. For a variety of applications, such as text classification and clustering, Arabic text representation is a difficult task because Arabic language is noted for its richness, diversity, and complicated morphology. This paper presents a comprehensive analysis and a comparison for researchers in the last five years based on the dataset, year, algorithms and the accuracy th
... Show MoreClinical keratoconus (KCN) detection is a challenging and time-consuming task. In the diagnosis process, ophthalmologists must revise demographic and clinical ophthalmic examinations. The latter include slit-lamb, corneal topographic maps, and Pentacam indices (PI). We propose an Ensemble of Deep Transfer Learning (EDTL) based on corneal topographic maps. We consider four pretrained networks, SqueezeNet (SqN), AlexNet (AN), ShuffleNet (SfN), and MobileNet-v2 (MN), and fine-tune them on a dataset of KCN and normal cases, each including four topographic maps. We also consider a PI classifier. Then, our EDTL method combines the output probabilities of each of the five classifiers to obtain a decision b
In this article, Convolution Neural Network (CNN) is used to detect damage and no damage images form satellite imagery using different classifiers. These classifiers are well-known models that are used with CNN to detect and classify images using a specific dataset. The dataset used belongs to the Huston hurricane that caused several damages in the nearby areas. In addition, a transfer learning property is used to store the knowledge (weights) and reuse it in the next task. Moreover, each applied classifier is used to detect the images from the dataset after it is split into training, testing and validation. Keras library is used to apply the CNN algorithm with each selected classifier to detect the images. Furthermore, the performa
... Show More