Monday, June 14, 2021

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.

Arvinder Kaur & Yugal Kumar
Department of Computer Science and Information Technology, Jaypee University of Information Technology, India;
This paper presents a new diagnostic model for various diseases. In the proposed diagnostic model, a water wave optimization (WWO) algorithm is implemented for improving the diagnosis accuracy. It is observed that the WWO algorithm suffers from the absence of global best information and premature convergence problems. Hence in this work, some improvements are proposed to formulate the WWO algorithm as more promising and efficient. The global best information issue is addressed using an improved solution search equation and the aim of this is to explore the global best optimum solution. Further, a premature convergence problem is rectified using a decay operator. These improvements are incorporated in the propagation and refraction phases of the WWO algorithm. The proposed algorithm is integrated into a diagnostic model for the analysis of health care data. The proposed algorithm aims to improve the diagnosis accuracy of various diseases. The diverse disease datasets are considered for implementing the performance of the proposed diagnostic model-based on accuracy and f-score performance indicators and the existing techniques are considered for comparing the simulation results. The results confirmed that the WWO based diagnostic model achieves a higher accuracy rate as compared to existing models/techniques with most diseases/healthcare datasets. Hence, it stated that the proposed diagnostic model is more promising and efficient for the diagnosis of different diseases.   
Keywords: Computational Intelligence, Water Wave Optimization, Disease Diagnosis, Diagnostic Model, Meta-heuristic techniques

1Maha Thabet, 2Mehdi Ellouze & 3Mourad Zaied
1ISITCom, University of Sousse, Tunisia
2Faculty of Economics and Management of Sfax, Sfax University,Tunisia
3Research Team in Intelligent Machines, Gabes University Gabes, Tunisia;;
Video concept detection means describing a video with semantic concepts that correspond to the content of the video. The concepts help to retrieve video quickly. These semantic concepts describe high-level elements that depict the key information present in the content. In the last years, many efforts have been done to automate this task because the manual solution is time-consuming. Nowadays, videos come with comments. Hence, in addition to the content of the videos, the comments should be analyzed because they contain valuable data that help to retrieve videos. In this paper, we focus especially on videos shared on social media. The specificity of these videos is the presence of massive comments. We try in this paper, to exploit comments by extracting concepts from them. We aim at exploiting the comments to support the research effort working only on the visual content. Natural language processing techniques are used to analyse comments and to filter words to retain only ones that could be considered as concepts. We tested our approach on YouTube videos. The results demonstrate that the proposed approach is able to extract from the comments accurate data and concepts that can be used to make the retrieval of videos easier. It supports the research effort working on the visual and audio content of the videos.
Keywords: Keywords-based video retrieval, social media tagging, natural language processing, video concept detection.

1Azlin Ahmad, 2Rubiyah Yusof, 3Nor Saradatul Akmar Zulkifli & 4Mohd Najib Ismail
1Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, Malaysia
2Malaysia-Japan International Institute of Technology, Universiti Teknologi Malaysia, , Malaysia
3Faculty of Computer System & Software Engineering, Universiti Malaysia Pahang
4School of Computing Asia Pacific University Technology Park Malaysia,Malaysia;;; 
The data distribution issue remains an unsolved clustering problem in data mining, especially in dealing with the imbalanced dataset. The Kohonen Self-Organizing Map (KSOM) is one of the well-known clustering algorithms that can solve various problems without a pre-defined number of clusters. However, similar to other clustering algorithms, this algorithm requires sufficient data for its unsupervised learning process. The inadequate amount of class label data in a dataset significantly affects the clustering learning process, leading to inefficient and unreliable results. Many kinds of research have been done by hybridising and optimising the KSOM algorithm with various optimisation techniques. Unfortunately, the problems are still unsolved, especially the separation boundary and overlapped clusters problems. Therefore, this research proposed an improved PKSOM (iPKSOM) algorithm to solve the mentioned problem. Six different datasets; Iris, Seed, Glass, Titanic, Wdbc and Tropical Wood datasets have been chosen to investigate the effectiveness of iPKSOM algorithm. All datasets are then being observed and compared with the original KSOM results. This modification significantly impacts the clustering process by improving and refining the scatteredness of clustering data and reducing the overlapped cluster. Thus, this proposed algorithm can be implemented in clustering other complex datasets, such as high dimensional and streaming data.
Keywords: Clustering, imbalanced data, Kohonen self-organizing map, optimization, pheromone.

Izzad Ramli, Nursuriati Jamil & Noraini Seman
Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, Malaysia;;
Intonation generation in expressive speech such as storytelling is essential to produce high quality Malay language expressive speech synthesizer. Intonation generation such as explicit control has shown a good performance in terms of intelligibility with reasonably natural speech; thus, it was selected in this research. This approach modifies the prosodic features such as pitch contour, intensity, and duration to generate the intonation. However, modification of pitch contour remains a problem because the desired pitch contour is not achieved. This paper formulates an improved pitch contour algorithm to develop a modified pitch contour resembling the natural pitch contour. In this work, the syllable pitch contours of nine storytellers were extracted from their storytelling speeches to create an expressive speech syllable dataset called STORY_DATA. All the shapes of pitch contours from STORY_DATA were analyzed and clustered into the standard six main pitch contour clusters for storytelling. The clustering was done using one minus the Pearson product moment correlation. Then, an improved iterative two-steps sinusoidal pitch contour formulation was introduced to modify the pitch contours of a neutral speech into expressive pitch contour of natural speeches. Overall, the improved pitch contour formulation was able to achieve 93% high correlated matches indicating the high resemblance compared to previous pitch contour formulation at 15%. Therefore, the improved formula can be used in TTS synthesizer to produce a more natural expressive speech. We also discovered unique expressive pitch contours in Malay language and need further investigations in the future.
Keywords: Pitch contour formulation, prosody modification, speech synthesis, storytelling.

1,2 Abdullah Almogahed & 2Mazni Omar
1Department of Software Engineering, Taiz University, Yemen
2School of Computing, Universiti Utara Malaysia, Malaysia; 
Refactoring is a critical task in software maintenance and is commonly applied to improve system design or to cope with design defects. There are 68 different types of refactoring techniques and each technique has a particular purpose and effect. However, most prior studies have selected refactoring techniques based on its common use in academic research without getting evidence from the software industry. This is a shortcoming which points to the existence of a clear gap between academic research and the corresponding industry practices. Therefore, to bridge this gap, this study identifies the most frequently used refactoring techniques, the commonly used programming language, and methods of applying refactoring techniques in the current practices of software refactoring among software practitioners in the industry, by using an online survey. The findings from the survey reveal the most used refactoring techniques, programming language, and the methods of applying the refactoring techniques. This study contributes to the improvement of software development practices by adding empirical evidence on software refactoring used by software developers. The findings would be beneficial for researchers to develop reference models and software tools to guide the practitioners in using these refactoring techniques based on their effect on software quality attributes to improve the quality of the software systems as a whole. 
Keywords: Exploratory study, software refactoring, survey, refactoring techniques.

1,2Antipas T. Teologo Jr. & 1,3Lawrence Materum
1Department of Electronics and Communications Engineering, De La Salle University, Philippines
2Electrical and Electronics Engineering Department, FEU Institute of Technology, Philippines
3International Centre, Tokyo City University, Japan;;
Wireless multipath clustering is an important area in channel modeling, and an accurate channel model can lead to a reliable wireless environment. Finding the best technique in clustering wireless multipaths is still a challenge due to the radio channels' time-variant characteristics. Several clustering techniques have been developed which offer an improved performance but considering only one or two parameters of the multipath components. This study improves the K-PowerMeans technique by incorporating weights or loads based on the principal component analysis and utilizing the Minkowski distance metric to replace the Euclidean distance. K-PowerMeans is one of the several methods in clustering wireless propagation multipaths and has been widely studied. This improved clustering technique is applied to the indoor datasets generated from the COST2100 channel model and considers the multipath components' angular domains and their delay. The Jaccard index is used to determine the new method's accuracy performance, and results show a significant improvement in the clustering of the developed algorithm than the standard K-PowerMeans.
Keywords: Channel model, minkowski distance, multipath clustering, principal component analysis, radio wave propagation.

1Wan Mohd Yusoff Wan Yaacob, 2Nur Haryani Zakaria & 3Zahurin Mat Aji
1Department of Information Technology & Communication, Politeknik Sultan Abdul Halim Mu’adzam Shah, Malaysia
2,3School of Computing, Universiti Utara Malaysia, Malaysia;;

Nowadays, there is growing views of potentially addictive behaviors such as digital addiction especially Online Game Addiction (OGA). This study argues that all type of addictions is related to common components such as Salience, Mood Modification, Tolerance, Withdrawal, Conflict, Relapse and Problems. Despite the plethora of online games consequences, there is no standards or benchmarks used to classify between addicted and non-addicted users. Hence, this study is organized to identify the factors that contribute to OGA and examine the level of OGA especially among adolescents by utilizing the Online Game Addiction Scale (OGAS). Using the same scale, the adolescents will be classified into addicted and non-addicted categories. Driven by previous studies of conventional game addiction, we adopt all the distinct common components to measure 7 underlying criteria which related to OGA. We analyze the dimensional structure of the scale based on the samples of adolescents among students of Higher Learning Institution (HLI) in Northern Malaysia. Data were collected from 389 participants who responded to an online survey. Based on the OGAS, 35% of the participants were found to be addicted to online games based on the OGAS. In addition, the findings demonstrate a good concurrent validity as shown by the coherent associations between the time spent in playing games and the category of the games. This study contributes to the identification of factors that influence the OGA among adolescents which are significant in preventing the occurrence of other behavioral issues such as insecure cyber and emotional behavior.

Keywords: Addictive behavior, digital addiction, online game addiction, online game disorder, scale and adolescent.

1Bahaa Ahmad Masmas & 2Azlinah Mohamed
1Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA (UiTM), Malaysia
2Institute for Big Data Analytics and Artificial Intelligence, Universiti Teknologi MARA (UiTM), Malaysia; 


Emergency management systems (EMS) assist the emergency managers to resolve emergencies on hand, through analyzing the emergency characteristics, consolidating data from different departments that are involved in resolving the emergency. Many countries adopted various forms of EMSs that are specialized in resolving one type of emergency, and studies show their effectiveness in producing better decisions. However, the COVID-19 pandemic showed the lack of a comprehensive framework that could deal with different emergencies. It also revealed, the inability of the current systems to communicate with each other’s to retrieve the needed data. The aim of this study is to show the current state of the EMSs in the emergency departments, constructing framework for Knowledge-based decision support system for emergency management focusing on resolving pandemics.  Qualitative approach is adopted in this research, where the authors reviewed emergency management in general and pandemics in specific. Existing emergency management systems have been investigated. Knowledge-based and decision support systems have been explored. Approaches for integration, communication, and collaboration have been studied. As a result of this study, a comprehensive framework: a knowledge-based decision support system for the emergency departments, focusing on resolving pandemics, has been introduced and been validated with domain experts who has given insights and many suggestions for future research. While the research primary focus is to assist emergency managers in resolving covid-19 pandemic, what makes the proposed framework unique is that it adopts different approaches and techniques that enable the system to deal with various emergencies not limited to the current pandemic.
Keywords: Decision support systems, emergency management, knowledge-based system, pandemic.

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