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Forthcoming Articles

These articles have been peer-reviewed and accepted for publication in JICT, but are pending final changes, are not yet published and may not appear here in their final order of publication until they are assigned to issues. Therefore, the content conforms to our standards but the presentation (e.g. typesetting and proof-reading) is not necessarily up to the JICT standard. Additionally, titles, authors, abstracts and keywords may change before publication.


OPTIMIZATION OF ATTRIBUTE SELECTION MODEL USING BIO-INSPIRED ALGORITHMS 
1Mohammad Aizat Basir, 2Yuhanis Yusof & 1Mohamed Saifullah Hussin
1School of Informatics and Applied Mathematics, Universiti Malaysia Terengganu,Malaysia
2School of Computing, Universiti Utara Malaysia, Malaysia
 
Abstract
Attribute selection which is also known as feature selection is an essential process that is relevant to predictive analysis. To date, various feature selection algorithms have been introduced, nevertheless they all work independently. Hence, reducing the consistency of the accuracy rate. The aim of this paper is to investigate the use of bio-inspired search algorithms in producing optimal attribute set. This is achieved in two stages; 1) create attribute selection models by combining search method and feature selection algorithms, and 2) determine an optimized attribute set by employing bio-inspired algorithms. Classification performance of the produced attribute set is analyzed based on accuracy and number of selected attributes. Experimental results conducted on six (6) public real datasets reveal that the feature selection model with the implementation of bio-inspired search algorithm consistently performs good classification (i.e higher accuracy with fewer numbers of attributes) on the selected data set. Such a finding indicates that bio-inspired algorithms can contribute in identifying the few most important features to be used in data mining model construction.
 
Keywords: Feature selection, bio-inspired algorithms, data classification, data mining.

SIMILARITY DISTANCE MEASURE AND PRIORITIZATION ALGORITHM FOR TEST CASE PRIORITIZATION IN SOFTWARE PRODUCT LINE TESTING
Shahliza Abd Halim, Dayang Norhayati Abang Jawawi & Muhammad Sahak
School of Computing, Faculty of Engineering
Universiti Teknologi Malaysia, Malaysia
 
Abstract
To achieve the goal of creating products for a specific market segment, implementation of Software Product Line (SPL) is required to fulfill specific needs of customers by managing a set of common features and exploiting the variabilities between the products. Testing product-by-product is not feasible in SPL due to the combinatorial explosion of product number, thus, Test Case Prioritization (TCP) is needed to select a few test cases which could yield high number of faults. Among the most promising TCP techniques is similarity-based TCP technique which consists of similarity distance measure and prioritization algorithm. The goal of this paper is to propose an enhanced string distance and prioritization algorithm which could reorder the test cases resulting to higher rate of fault detection. Comparative study has been done between different string distance measures and prioritization algorithms to select the best techniques for similarity-based test case prioritization. Identified enhancements have been implemented to both techniques for a better adoption of prioritizing SPL test cases. Experiment has been done in order to identify the effectiveness of enhancements done for combination of both techniques. Result shows the effectiveness of the combination where it achieved highest average fault detection rate, attained fastest execution time for highest number of test cases and accomplished 41.25% average rate of fault detection. The result proves that the combination of both techniques improve SPL testing effectiveness compared to other existing techniques.
 
Keywords: Combinatorial interaction testing, similarity distance, string based prioritization, feature model, sampling algorithm.

IDENTIFYING SKYLINES IN CLOUD DATABASES WITH INCOMPLETE DATA
Yonis Gulzar, Ali Amer Alwan Aljuboori, Norsaremah Salleh & Imad Fakhri Al Shaikhli
Kulliyyah of Information Communication and Technology
International Islamic University Malaysia, Malaysia
 
Abstract
Skyline queries is a rich area of research in the database community. Due to its great benefits, it has been integrated into many database applications including but not limited to personalized recommendation, multi-objective, decision support and decision-making systems. Many variations of skyline technique have been proposed in the literature addressing the issue of handling skyline queries in incomplete database. Nevertheless, these solutions are designed to fit with centralized incomplete database (single access). However, in many real-world database systems, this might not be the case, particularly for a database with a large amount of incomplete data distributed over various remote locations such as cloud databases. It is inadequate to directly apply skyline solutions designed for the centralized incomplete database to work on cloud due to the prohibitive cost. Thus, this paper introduces a new approach called Incomplete-data Cloud Skylines (ICS) aiming at processing skyline queries in cloud databases with incomplete data. ICS emphasizes on reducing the amount of data transfer and domination tests during skyline process. ICS incorporates sorting technique that assists in arranging the data items in a way where dominating data items will be placed at the top of the list helping in eliminate dominated data items. Besides, ICS also employs a filtering technique to prune the dominated data items before applying skyline technique. ICS comprises a technique named local skyline joiner that helps in reducing the amount of data transfer between datacenters when deriving the final skylines. It limit the amount of data items to be transferred to only those local skylines of each relation. A comprehensive experiments have been performed on both synthetic and real-life datasets, which demonstrate the effectiveness and versatility of our approach in comparison to the current existing approaches. We argue that our approach is practical and can be adopted in many contemporary cloud database systems with incomplete data to process skyline queries.
 
Keywords: Preference queries, query processing, skyline queries, incomplete data, cloud databases.

USER REQUIREMENT ANALYSIS OF A MOBILE AUGMENTED REALITY APPLICATION TO SUPPORT LITERACY DEVELOPMENT AMONG CHILDREN WITH HEARING IMPAIRMENTS
Shiroq Al-Megren and Aziza Almutairi
Information Technology Department, King Saud University, Saudi Arabia
 
Abstract
Literacy is fundamental for children’s growth and development, as it impacts their educational, societal, and vocational progress. However, the mapping of language to printed text is different for children with hearing impairments. When reading, a hearing-impaired child maps text to sign language (SL), which is a visual language that can benefit from technological advancements, such as augmented reality (AR). There exist several efforts that utilize AR for the purpose of advancing the educational needs of people who are hearing impaired for different SLs. Nevertheless, only a few directly elicit the visual needs of children who are hearing impaired. This paper aims to address this gap in the literature with a series of user studies to elicit user requirements for the development of an AR application that supports the literacy development of Arabic children who are hearing impaired. In these user studies, three instruments were utilized, each targeting a different group of literacy influencers: questionnaires issued to parents of children with hearing impairments, interviews with teachers, and observations of children who are deaf or hard of hearing. The findings indicate that the parents and teachers preferred Arabic SL (ArSL), pictures, and videos, whereas the children struggled with ArSL and preferred finger-spelling. These preferences highlight the importance of integrating various resources to strengthen the written Arabic and ArSL literacy of Arabic children. The findings contribute to the literature on the preferences of Arabic children who are hearing impaired, their educators, and their parents. They also show the importance of establishing requirements directly from the intended users who are disabled to proactively support their learning process. The results of the study were used in the preliminary development of Word & Sign, an AR mobile application intended to aid Arabic children who are hearing impaired in their linguistic development.
 
Keywords: Arabic sign language, augmented reality, deaf and hard of hearing, hearing impaired, deaf reading, deaf education.

MULTI-OBJECTIVES MEMETIC DISCRETE DIFFERENTIAL EVOLUTION ALGORITHM FOR SOLVING THE CONTAINER PRE-MARSHALLING PROBLEM
1Hossam M. J. Mustafa, 1Masri Ayob, 1Mohd Zakree Ahmad Nazri & 2Sawsan Abu-Taleb
1Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Malaysia  
2Prince Abdullah Ben Ghazi Faculty of Information Technology, Al-Balqa Applied University, Jordan
hasa.mustafa@gmail.com; masri@ukm.edu.my; mzan@ftsm.ukm.my; sawsan@bau.edu.jo
 
Abstract
The Container Pre-marshalling Problem (CPMP) has a significant effect of reducing the ship berthing time, and can help in increasing the terminal turnover rate. In order to solve the CPMP, this research proposes a Multi-objectives Memetic Discrete Differential Evolution algorithm (MODDE). To date, existing research in CPMP only focuses on single-objective approaches, and this is not a suitable approach due to the high effort required to validate the hard constraints of CPMP. Therefore, this work aims at addressing the effect of minimizing the number of miss-overlaid container on the total number of movements in building the final feasible bay layout, by embedding it in the multi-objectives evaluation function. The proposed algorithm combines the Discrete Differential Evolution mutation with the Memetic Algorithm evolutionary steps in order to find high quality CPMP solutions, achieve high convergence rate and avoid the premature convergence and local optima problems. In addition, it improves the exploration and exploitation capabilities of the algorithm. The standard pre-marshalling benchmark dataset (i.e. Bortfeldt-Forster) is used to evaluate the effectiveness of the proposed algorithm. The experimental results have revealed that the proposed MODDE algorithm can find good solutions on instances of the standard pre-marshalling benchmarks. This has demonstrated that using the multi-objectives approach with the combination between the Discrete Differential Evolution mutation and the Memetic Algorithm evolutionary is a suitable approach for solving multi-objectives CPMP.

Keywords: Memetic Algorithms, Differential Evolution Algorithm, Multi-objectives optimization Algorithm, Container pre-marshalling Problem.

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