Finding similarities in texts is important in many areas such as information retrieval, automated article scoring, and short answer categorization. Evaluating short answers is not an easy task due to differences in natural language. Methods for calculating the similarity between texts depend on semantic or grammatical aspects. This paper discusses a method for evaluating short answers using semantic networks to represent the typical (correct) answer and students' answers. The semantic network of nodes and relationships represents the text (answers). Moreover, grammatical aspects are found  by measuring the similarity of parts of speech between the answers. In addition, finding hierarchical relationships between nodes in netwo

      The transition of customers from one telecom operator to another has a direct impact on the company's growth and revenue. Traditional classification algorithms fail to predict churn effectively. This research introduces a deep learning model for predicting customers planning to leave to another operator. The model works on a high-dimensional large-scale data set. The performance of the model was measured against other classification algorithms, such as Gaussian NB, Random Forrest, and Decision Tree in predicting churn. The evaluation was performed based on accuracy, precision, recall, F-measure, Area Under Curve (AUC), and Receiver Operating Characteristic (ROC) Curve. The proposed deep learning model performs better than othe

Sentiment analysis is one of the major fields in natural language processing whose main task is to extract sentiments, opinions, attitudes, and emotions from a subjective text. And for its importance in decision making and in people's trust with reviews on web sites, there are many academic researches to address sentiment analysis problems. Deep Learning (DL) is a powerful Machine Learning (ML) technique that has emerged with its ability of feature representation and differentiating data, leading to state-of-the-art prediction results. In recent years, DL has been widely used in sentiment analysis, however, there is scarce in its implementation in the Arabic language field. Most of the previous researches address other l

This study aims to deliver the woman’s image and to unveil on how to be introduced in the TV series. The research is based on the survey method-using content analysis tool. The research sample represented in the TV series produced by the IMN, which were displayed in 2014 and used the pattern of Margaret Gallagher to analyze the content of the series in accordance with the frame analysis theory.
The study came up with declination of the woman’s representation compared with man in Iraqi TV drama, also the study finds that the series introduced the woman according to the personal, social, political, and economic frames in a standardizing method. It focuses on the characteristics always attributed to it as showing her obedient of the

With time progress importance of hiding information become more and more and all steganography applications is like computer games between hiding and extracting data, or like thieves and police men always thieve hides from police men in different ways to keep him out of prison. The sender always hides information in new way in order not to be understood by the attackers and only the authorized receiver can open the hiding message. This paper explores our proposed random method in detail, how chooses locations of pixel in randomly , how to choose a random bit to hide information in the chosen pixel, how it different from other approaches, how applying information hiding criteria on the proposed project, and attempts to test out in code, and

A new Turbidimetric method characterized by simplicity, accuracy and speed for determination of Hydronium ion by continuous flow injection analysis. The method was based on the formation of complex Zn3[Fe(CN)6] for Zinc(II) that was eluted by Hydronium ion from cation exchanger column with Potassium hexacyanoferrate(III) for the formation of a pale yellow precipitate and this precipitate was determined using homemade Linear Array Ayah-5SX1-T-1D continuous flow injection analyser. The optimum parameters were 2.7 mL.min-1 flow rate using H2O as a carrier stream, 1.7 mL.min-1 reagent stream, 110 L sample volume and open valve for the purge of the sample segment. Data treatment shows that linear range 0.01-0.1 mol.L-1 for each acids (HClO

A new Turbidimetric method characterized by simplicity, accuracy and speed for determination of Hydronium ion by continuous flow injection analysis. The method was based on the formation of complex Zn3[Fe(CN)6] for Zinc(II) that was eluted by Hydronium ion from cation exchanger column with Potassium hexacyanoferrate(III) for the formation of a pale yellow precipitate and this precipitate was determined using homemade Linear Array Ayah-5SX1-T-1D continuous flow injection analyser. The optimum parameters were 2.7 mL.min-1 flow rate using H2O as a carrier stream, 1.7 mL.min-1 reagent stream, 110 L sample volume and open valve for the purge of the sample segment. Data treatment shows that linear range 0.01-0.1 mol.L-1 for each acids (HClO

     Temperature predicting is the utilization to forecast the condition of the temperature for an upcoming date for a given area. Temperature predictions are done by gathering quantitative data in regard to the current state of the atmosphere. In this study, a proposed hybrid method to predication the daily maximum and minimum air temperature of Baghdad city which combines standard backpropagation with simulated annealing (SA). Simulated Annealing Algorithm are used for weights optimization for recurrent multi-layer neural network system. Experimental tests had been implemented using the data of maximum and minimum air temperature for month of July of Baghdad city that got from local records of Iraqi Meteorological O