November 2016, No 6
Abductive Network Ensembles for Improved Prediction of Future Change-Prone Classes in Object-Oriente Print E-mail

Abductive Network Ensembles for Improved

Prediction of Future Change-Prone Classes

in Object-Oriented Software

Mojeeb Al-Khiaty1, Radwan Abdel-Aal2, and Mahmoud Elish1,3

1Information and Computer Science Department, King Fahd University of Petroleum and Minerals, Saudi Arabia

2Computer Engineering Department, King Fahd University of Petroleum and Minerals, Saudi Arabia

3Computer Science Department, Gulf University for Science and Technology, Kuwait

Abstract: Software systems are subject to a series of changes due to a variety of maintenance goals. Some parts of the software system are more prone to changes than others. These change-prone parts need to be identified so that maintenance resources can be allocated effectively. This paper proposes the use of Group Method of Data Handling (GMDH)-based abductive networks for modeling and predicting change proneness of classes in object-oriented software using both software structural properties (quantified by the C&K metrics) and software change history (quantified by a set of evolution-based metrics) as predictors. The empirical results derived from an experiment conducted on a case study of an open-source system show that the proposed approach improves the prediction accuracy as compared to statistical-based prediction models.

Keywords: Change-proneness, software metrics, abductive networks, ensemble classifiers.

Received June 2, 2015; accepted September 20, 2015

 

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Chaotic Encryption Scheme Based on a Fast Permutation and Diffusion Structure Print E-mail

Chaotic Encryption Scheme Based on a Fast Permutation and Diffusion Structure

Jean De Dieu Nkapkop1,2, Joseph Effa1, Monica Borda2, Laurent Bitjoka3, and Alidou Mohamadou4

1Department of Physics, University of Ngaoundéré, Cameroon

2Department of Communications, Technical University of Cluj-Napoca, Romania

3Department of Electrical Engineering, Energetics and Automatics, University of Ngaoundéré, Cameroon

4Department of Physics, University of Maroua, Cameroon

Abstract: The image encryption architecture presented in this paper employs a novel permutation and diffusion strategy based on the sorting of chaotic solutions of the Linear Diophantine Equation (LDE) which aims to reduce the computational time observed in Chong's permutation structure. In this scheme, firstly, the sequence generated by the combination of Piece Wise Linear Chaotic Map (PWLCM) with solutions of LDE is used as a permutation key to shuffle the sub-image. Secondly, the shuffled sub-image is masked by using diffusion scheme based on Chebyshev map. Finally, in order to improve the influence of the encrypted image to the statistical attack, the recombined image is again shuffle by using the same permutation strategy applied in the first step. The design of the proposed algorithm is simple and efficient, and based on three phases which provide the necessary properties for a secure image encryption algorithm. According to NIST randomness tests the image sequence encrypted by the proposed algorithm passes all the statistical tests with the high P-values. Extensive cryptanalysis has also been performed and results of our analysis indicate that the scheme is satisfactory in term of the superior security and high speed as compared to the existing algorithms.

Keywords: Fast and secure encryption, chaotic sequence, linear diophantine equation, NIST test.

Received May 16, 2015; accepted September 7, 2015

 

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Constructing a Lexicon of Arabic-English Named Entity using SMT and Semantic Linked Data Print E-mail

Constructing a Lexicon of Arabic-English Named

Entity using SMT and Semantic Linked Data

Emna Hkiri, Souheyl Mallat, Mounir Zrigui and Mourad Mars

Faculty of Sciences of Monastir, University of Monastir, Tunisia

Abstract: Named Entity Recognition (NER) is the problem of locating and categorizing atomic entities in a given text. In this work, we used DBpedia Linked datasets and combined existing open source tools to generate from a parallel corpus a bilingual lexicon of Named Entities (NE). To annotate NE in the monolingual English corpus, we used linked data entities by mapping them to Gate Gazetteers. In order to translate entities identified by the gate tool from the English corpus, we used moses, a Statistical Machine Translation (SMT) system. The construction of the Arabic-English NE lexicon is based on the results of moses translation. Our method is fully automatic and aims to help Natural Language Processing (NLP) tasks such as, Machine Translation (MT) information retrieval, text mining and question answering. Our lexicon contains 48753 pairs of Arabic-English NE, it is freely available for use by other researchers.

Keywords: NER, named entity translation, parallel Arabic-English lexicon, DBpedia, linked data entities, parallel corpus, SMT.

Received April 1, 2015; accepted October 7, 2015

 

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Forecasting of Chaotic Time Series Using RBF Neural Networks Optimized By Genetic Algorithms Print E-mail

Forecasting of Chaotic Time Series Using RBF

Neural Networks Optimized By Genetic Algorithms

Mohammed Awad

Faculty of Engineering and Information Technology, Arab American University, Palestine

Abstract: Time series forecasting is an important tool, which is used to support the areas of planning for both individual and organizational decisions. This problem consists of forecasting future data based on past and/or present data. This paper deals with the problem of time series forecasting from a given set of input/output data. We present a hybrid approach for time series forecasting using Radial Basis Functions Neural Network (RBFNs) and Genetic Algorithms (GAs). GAs technique proposed to optimize centers c and width r of RBFN, the weights w of RBFNs optimized used traditional algorithm. This method uses an adaptive process of optimizing the RBFN parameters depending on GAs, which improve the homogenize during the process. This proposed hybrid approach improves the forecasting performance of the time series. The performance of the proposed method evaluated on examples of short-term mackey-glass time series. The results show that forecasting by RBFNs parameters is optimized using GAs to achieve better root mean square error than algorithms that optimize RBFNs parameters found by traditional algorithms.

Keywords: Time series forecasting, RBF neural networks, genetic algorithms, hybrid approach.

Received March 17, 2015; accepted October 7, 2015

 

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Contextual Text Categorization: An Improved Stemming Algorithm to Increase the Quality of Categoriza Print E-mail

Contextual Text Categorization: An Improved Stemming Algorithm to Increase the Quality of Categorization in Arabic Text

Said Gadri and Abdelouahab Moussaoui

 Department of Computer Science, University Ferhat Abbas of Setif, Algeria

Abstract: One of the methods used to reduce the size of terms vocabulary in Arabic text categorization is to replace the different variants (forms) of words by their common root. This process is called stemming based on the extraction of the root. Therefore, the search of the root in Arabic or Arabic word root extraction is more difficult than in other languages since the Arabic language has a very different and difficult structure, that is because it is a very rich language with complex morphology. Many algorithms are proposed in this field. Some of them are based on morphological rules and grammatical patterns, thus they are quite difficult and require deep linguistic knowledge. Others are statistical, so they are less difficult and based only on some calculations. In this paper we propose an improved stemming algorithm based on the extraction of the root and the technique of n-grams which permit to return Arabic words’ stems without using any morphological rules or grammatical patterns.

Keywords: Root extraction, information retrieval, bigrams, stemming, Arabic morphological rules, feature selection.

Received February 22, 2015; accepted August 12, 2015

 

 
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