The World of Computer Science and Information Technology Journal

7

Authors: Chandra J, Nachamai.M, Anitha S Pillai

Investment prediction is a method to decide the future values of stock indexes and commodity exchanges or trading of financial services. The aim of the model is to perform optimized prediction on commodities and stock market indexes. The investment prediction is an important task for an investor to maximize his or her return on investment. The purpose of the paper is to propose an optimized model using computational intelligence and it is a step by step method that follows an integrated approach which can solve several complex problems in predictive analytics. The integrated approach in this paper uses genetic algorithm, pearson’s correlation coefficient and multilayer perceptron adaline feed forward neural network to predict the next business day high values of stock indexes and commodities trading. As an integrated method, the model uses genetic algorithm as first step to check the data optimization, since the data is considered as an important element in data analytics. The optimized data is extracted using correlation coefficient and the classifier prediction is done with multilayer perceptron adaline feed forward neural network for making the prediction. The proposed model was implemented continuously on three months data to evaluate the performance and to check the accuracy on the NSEindia. The predicted values were checked against the next business day of original values, the predicted result is very close to the original values. The model is evaluated with the statistical parameter MRE, MMRE and the accuracy rate. In comparison with other existing methods, the current method outperforms other testing patterns. Keywords- Predictive Analytics (PA); Computational Intelligence (CI); Genetic Algorithm (GA); Pearson’s Correlation Coefficient(PCC); Multi Layer Perceptron (MLP); Adaptive Linear Element (ADALINE); Neural Network (NN).

5

Authors: Fatima T. AL-Khawaldeh

Recognizing the entailment relation showed that its influence to extract the semantic inferences in wide-ranging natural language processing domains (text summarization, question answering, etc.) and enhanced the results of their output. For Arabic language, few attempts concerns with Arabic entailment problem. This paper aims to increase the entailment accuracy for Arabic texts by resolving negation of the text-hypothesis pair and determining the polarity of the text-hypothesis pair whether it is Positive, Negative or Neutral. It is noticed that the absence of negation detection feature gives inaccurate results when detecting the entailment relation since the negation revers the truth. The negation words are considered stop words and removed from the text-hypothesis pair which may lead wrong entailment decision. Another case not solved previously, it is impossible that the positive text entails negative text and vice versa. In this paper, in order to classify the text-hypothesis pair polarity, a sentiment analysis tool is used. We show that analyzing the polarity of the text-hypothesis pair increases the entailment accuracy. to evaluate our approach we used a dataset for Arabic textual entailment (ArbTEDS) consisted of 618 text-hypothesis pairs and showed that the Arabic entailment accuracy is increased by resolving negation for entailment relation and analyzing the polarity of the text-hypothesis pair. Keywords-Arabic NLP; Recognizing Text Entailment (RTE); Sentiment Polarity; Negation; (ArbTEDS) dataset; The Entailment accuracy.

7

Authors: Suhad A. Yousif, Islam Elkabani, Venus W. Samawi, Rached Zantout

A massive amount of documents are being posted online every minute. The task of document classification requires extensive background work on the content of documents, where keyword-based matching alone may not be sufficient. Much research has been carried out in several languages that has revealed significant results. However, Arabic documents still pose a great challenge due to the nature of Arabic language. Extracting roots or stems from the breakdown of multiple Arabic words and phrases are an important task that must be completed before applying text classification. The research at hand proposes an algorithm for classifying Arabic-Text documents using semantic relations between words based on an Arabic thesaurus, mainly synonyms, hyperonyms and hyponyms. The experiments conducted in this study evaluated the results using F1-Measure and compared them to results obtained via other existing methods, such as utilizing stemmers and part-of-speech taggers, where it indicated an increment of more than 12.6% for the novel method using semantic relation over other methods. Arabic-WordNet was utilized as a thesaurus for indicating possible relations to be examined. The obtained results indicate that the domain of the semantic web reveals a variety of options for enhancing text classifications, which are highly competitive with current methods. Future work will include identifying best relations to be utilized among the available 20 relations. Keywords- Arabic Text classification; Stemmer; Part of Speech; Conceptual features; Semantic relations.

5

Authors: Alaa Alhamami, Avan Sabah Hamdi

Multi-media is one type of transferring data through the Internet, and this process becomes one of the most important types of threatened data. The threat can occur by listening or sniffing the data without giving any notice to the two parties. Therefore, we will find a specific solution to maintain the security of audio during the process of transferring between the two Mobiles. Although, there are many methods to provide secure transferring, the steganography is the best way to hide the audio inside another audio and it is the best solution to reduce the risk of intruders. The Least Significant Bit method is used in this research. The proposed algorithm and method have been used by applying two mobiles that support the android operating system, and then we choose the Skype program as a host program. The main goal of using the host program is for transmitting the audio between two devices and each device located on separate network. Keywords- Steganography; Threat; Skype; Least Significant Bit; Android Operating system; Audio.