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.
  
PREDICTION OF STUDENTS’ SOCIAL WELL-BEING BASED ON PERSONALITY TRAITS DETERMINANTS
*1Nur Atiqah Rochin Demong, 2Melissa Shahrom, 3Ramita Abdul Rahim, 4Emi Normalina Omar & 5Mornizan Yahya
12345Department of Technology and Supply Chain Management Studies, Faculty of Business and Management, Universiti Teknologi MARA, Malaysia
rochin@uitm.edu.my; melissa@uitm.edu.my; ramita@uitm.edu.my; emi128@uitm.edu.my; mornizan@uitm.edu.my
 

ABSTRACT

Social well-being is a field that analyses society, individual behavioural patterns, behavioural networks, and cultural elements of daily life. Social well-being develops critical thinking by understanding the social frameworks that affect humans by exposing the social basis of daily actions. For instance, when students are pleased, their academic achievement, behaviour, social integration, and happiness improve. This study predicts the effects of the Big 5 Personality Traits (Extraversion, Openness, Agreeableness, Emotional Stability, and Conscientiousness) on students’ Industry 4.0 Social Well-being level by analysing their demographic and personality traits. A dataset was gathered through a survey distributed to students in a selected institution. The accuracy of the classifier was evaluated using the WEKA tool on a dataset of 286 instances and 19 attributes, for which a confusion matrix was generated. After evaluating the results of all algorithms, it was discovered that IBk and Randomizable Filtered Classifier algorithms provide the highest accuracy with a similar percentage value of 91.26% on the social well-being readiness. The agreeableness personality trait, which represents a person’s level of pleasantness, politeness, and helpfulness, had the greatest influence on the social well-being of the students. They have a positive outlook on human behaviour and get along well with others. Since social well-being contributes to a person’s increased quality of life and happiness, improving students’ current quality of life would lead to the development of a social parameter that can assess the growth of a country and the increased happiness of families and communities.

Keywords: WEKA, Personality Traits, Social Well-being, Data Mining, Classification Algorithms

 

A COMBINED DELAY-THROUGHPUT FAIRNESS MODEL FOR OPTICAL BURST SWITCHED NETWORKS

*Van Hoa Le & Viet Minh Nhat Vo
Hue University, Vietnam
 levanhoa@hueuni.edu.vn; vvmnhat@hueuni.edu.vn
*Corresponding author

ABSTRACT

Fairness is an important feature of communication networks; it is the distribution, allocation, and provision of approximately equal or equal performance parameters such as throughput, bandwidth, loss rate, and delay. In an optical burst switched (OBS) network, fairness is considered in three aspects: distance, throughput, and delay. Studies on these three types of fairnesses have been conducted, but they have usually been considered in isolation. These fairnesses should be considered together to improve the communication performance of the entire OBS network. This paper proposes a combined delay-throughput fairness model, where burst assembly and bandwidth allocation are improved to achieve both delay fairness and throughput fairness at ingress OBS nodes. The delay fairness index and the throughput fairness index are recommended as metrics for adjusting assembly queue length and allocated bandwidth for priority flows. Simulation results show that delay and throughput fairnesses can be achieved at the same time, which improves the overall communication performance of the entire OBS network.

Keywords: OBS networks, delay fairness, throughput fairness, combination model, adaptive control.

HYBRID NEIGHBOURHOOD COMPONENT ANALYSIS WITH GRADIENT TREE BOOSTING FOR FEATURE SELECTION IN FORECASTING CRIME RATE

*1Alif Ridzuan Khairuddin, 2Razana Alwee & 3Habibollah Haron
1,2,3Applied Industrial Analytics Research Group (ALIAS), Faculty of Computing, Universiti Teknologi Malaysia, Malaysia
1aridzuan8@graduate.utm.my; 2razana@utm.my; 3habib@utm.my
*Corresponding author

ABSTRACT

Crime forecasting is beneficial as it provides useful information to government and authorities in planning an efficient crime prevention measure. In most criminology studies, it is found that influence from several factors such as social, demographic, and economic significantly affects crime occurrence. Therefore, most criminology experts and researchers’ study and observe the effect of several factors on crime activities as it provides a relevant insight about possible future crime trends. Based on the literature study conducted, the applications of proper analysis in identifying significant factors that influence crime are found to be scarce and limited. Therefore, this study would like to propose a hybrid model that integrates Neighbourhood Component Analysis (NCA) with Gradient Tree Boosting (GTB) in modelling the US crime rate data. The NCA is a feature selection technique used in this study to identify the significant factors that influence crime rate. Once the significant factors were identified, an artificial intelligence technique that is GTB was implemented in modelling the crime data where the crime rate value was predicted. The performance of the proposed model was compared with other existing models using quantitative measurement error analysis. Based on the result produced, the proposed NCA-GTB model outperformed other crime models in predicting the crime rate. This was proven by the experimental result where the proposed model produced the smallest quantitative measurement error in the case study.

Keywords: Feature Selection, Artificial Intelligence, Neighbourhood Component Analysis, Gradient Tree Boosting, Crime Forecasting.

 
TAGUCHI-GREY RELATIONAL ANALYSIS METHOD FOR PARAMETERS’ TUNING OF MULTI-OBJECTIVE PARETO ANT COLONY SYSTEM ALGORITHM
*1 Shatha Abdulhadi Muthana, 2 Ku Ruhana Ku-Mahamud
1General Company of Electricity Production South Region, Ministry of Electricity, Iraq
<div 2 School of Computing, Universiti Utara Malaysia, Malaysia
2Shibaura Institute of Technology, Tokyo, Japan
*shatha_muthana@yahoo.com; ruhana@uum.edu.my
*Corresponding author

ABSTRACT

In any metaheuristic the parameters’ values have strong effect in efficiency of the algorithm’s search. The purpose of this research is to find the optimal parameters’ values for Pareto Ant Colony System (PACS) algorithm which is used to obtain solution for generator maintenance scheduling problem.  For optimal maintenance scheduling with low cost, high reliability, and low violation, the parameters’ values of PACS algorithm are tuned using Taguchi and grey relational analysis (Taguchi-GRA) method through search-based approach. The new values for the parameters are tested on two (2) systems i.e., 26-, and 36-unit systems for window with operational hours [3000-5000]. The grey relational grade (GRG) performance metric and the Friedman test are used to evaluate the algorithm’s performance. The Taguchi-GRA method that produces the new values for the algorithm’s parameters has been shown to be able to provide better multi-objective generator maintenance scheduling (GMS) solution. These values can be used as benchmark values in solving multi-objective GMS problems using the multi-objective PACS algorithm and its variants.
 
Keywords: Optimization, Scheduling, Taguchi method, Grey Relational Analysis, Generator maintenance.
 
OPTIMISED COVER SELECTION FOR AUDIO STEGANOGRAPHY USING MULTI-OBJECTIVE EVOLUTIONARY ALGORITHM
*1Muhammad Harith Noor Azam, 2Farida Ridzuan & 3M Norazizi Sham Mohd Sayuti
1,2Faculty of Science and Technology, Universiti Sains Islam Malaysia, Malaysia
3Faculty of Engineering and Built Environment, Universiti Sains Islam Malaysia, Malaysia
1mh.noorazam@gmail.com; 2farida@usim.edu.my;  3azizi@usim.edu.my
*Corresponding author

 ABSTRACT

Existing embedding techniques depend on cover audio selected by users. Unknowingly, users may make a poor cover audio selection that is not optimised in its capacity or imperceptibility features, which could reduce the effectiveness of any embedding technique used. As a trade-off exists between capacity and imperceptibility, it is crucial to produce a method focused on optimising both features. One of the searching methods commonly used to find solutions for the trade-off problem in various fields is the Multi-Objective Evolutionary Algorithm (MOEA). Therefore, this research proposes a new method for optimising cover audio selection for audio steganography using the Non-dominated Sorting Genetic Algorithm-II (NSGA-II), which falls under the MOEA Pareto dominance paradigm. The proposed method provided suggestions for cover audio to the user based on imperceptibility and capacity features. Initially, the sample difference calculation was formulated to determine the maximum capacity for each cover audio defined in the cover audio database. Next, NSGA-II is implemented to determine the optimised solutions based on the parameters provided by each chromosome. The experimental results demonstrated the effectiveness of the proposed method as it managed to dominate the solutions from the previous method selected based on one criterion only. In addition, proposed method consider the trade-off managed to select the solution as the highest priority compared to previous method, which put the same solution as low as 71 in priority ranking. In conclusion, the method optimised the cover audio selected, hence, improving the effectiveness of the audio steganography used. It can be a response to help people out there where most computer and mobile users continue to be unfamiliar with audio steganography in an age where information security is crucial.

Keywords: Audio Steganography, Cover Audio Selection, Multi-objective Optimisation Problem, Trade-off.

 
 
LOGIC MINING APPROACH: SHOPPERS PURCHASING DATA EXTRACTION VIA EVOLUTIONARY ALGORITHM 
1Mohd Shareduwan Mohd Kasihmuddin, 2Nur Shahira Abdul Halim, 3Siti Zulaikha Mohd Jamaludin, 4Mohd. Asyraf Mansor, 5Alyaa Alway, 6Nur Ezlin Zamri, 7Siti Aishah Azhar & *8Muhammad Fadhil Marsani
1,2,3,7,8School of Mathematical Sciences, Universiti Sains Malaysia, 11800 USM, Pulau Pinang, Malaysia
4,5,6School of Distance Education, Universiti Sains Malaysia, 11800 USM, Pulau Pinang, Malaysia
1shareduwan@usm.my; 2nurshahirahalim@student.usm.my; 3szulaikha.szmj@usm.my; 4asyrafman@usm.my
5alyaalway@student.usm.my; 6ezlinzamri@student.usm.my; 7saishahazhar96@student.usm.my
8fadhilmarsani@usm.my
*Corresponding author

 ABSTRACT

Online shopping is a multi-billion-dollar industry worldwide. The internet users worldwide found physical retail stores are quite tricky to do product comparison and they were dissatisfied by the extensive queues at sales counters during payment. This frustration will be eased by engaging in the online or electronic shopping platforms. This work will utilize an Artificial neural network to provide knowledge extraction to the online shopping industry that might improve their services. There are limited attempts of proposing the knowledge extraction with neural network model in the online shopping field, especially the study revolving around the online shopper purchasing intentions.  2 Satisfiability logic will be used to represent the shopping attribute and will learn to be by a special Recurrent ANN named Hopfield neural network. In order to reduce the learning complexity, a genetic algorithm will be implemented to optimize the logical rule throughout the learning phase in performing 2 Satisfiability Based Reverse Analysis method, implemented during learning phase as this method will be compared. The performance of genetic algorithm with 2 Satisfiability Based Reverse Analysis are measured according on the selected performance evaluation metrics. The simulation suggests that the proposed model outperforms the existing model in doing logic mining for the online shoppers' dataset.

Keywords: 2 satisfiability, genetic algorithm, Hopfield neural network, logic mining, online shopping.

 
 
MODELING AND FORECASTING TREND: TOP FIVE CRYPTOCURRENCIES
*1,2Nurazlina Abdul Rashid & 1Mohd Tahir Bin Ismail
1School of Mathematical Sciences, Universiti Sains Malaysia, 11800 USM, Pulau Pinang, Malaysia
2Mathematical Sciences Studies, College of Computing, Informatics and Media, Universiti Teknologi Mara (UiTM) Cawangan Kedah, 08400 Merbok, Kedah, Malaysia
1,2azlina150@uitm.edu.my; 1m.tahir@usm.my
*Corresponding author

 ABSTRACT

The prediction of cryptocurrency prices is a hot topic among academics. However, in the real world, predicting the cryptocurrency price accurately can be challenging. Numerous studies have been undertaken in order to determine the best model for successful prediction. However, they lacked correct results because they avoided identifying the critical features. Nevertheless, it is important to remember that trends are critical features in time series to get data information. To the best of our knowledge, a dearth of research demonstrates that the cryptocurrency trend comprises linear and nonlinear patterns. Therefore, this study attempts to fill this gap and focus on modeling and forecasting trends in cryptocurrency. This study examines the linear and nonlinear dependency trend patterns of the top five cryptocurrency close prices. The weekly historical data of each cryptocurrency is taken at different periods due to the availability of data on the system. To achieve this goal, this study checked the results by plotting based on residual trend and diagnostic statistic checking using three deterministic methods: linear trend regression, quadratic trend, and exponential trend. Based on the minimum Akaike’s Information Criteria (AIC), the result shows that the top five cryptocurrencies’ close price data series contain both nonlinear and linear trend patterns. The information of this study will assist traders and investors in comprehending the trend of the top five cryptocurrencies and hence choosing the suitable model to predict cryptocurrency prices. Additionally, the ability to accurately measure the forecast will protect investors from losing their investment.

Keywords: bitcoin, cryptocurrency, linear, nonlinear, trend. 

 
 
IMPROVING VISUAL STYLE CLASSIFICATION INFORMATION IN DIGITAL GAMES USING INTERCODER RELIABILITY ASSESSMENT
*1Jazmi Izwan Jamal, 2Mohd Hafizuddin Mohd Yusof, 3Lim Kok Yoong & 4Jamia Azdina Jamal
1Future Creative School, Faculty of Animation and Multimedia, Akademi Seni Budaya dan Warisan Kebangsaan (ASWARA), Malaysia
2School of Creative Media, Bahrain Polytechnic, Bahrain
3Faculty of Creative Multimedia, Multimedia University, Malaysia
4Centre for Drug and Herbal Development, Faculty of Pharmacy, Universiti Kebangsaan Malaysia, Malaysia
1jazmi@aswara.edu.my; 2mohamed.hafizuddin@polytechnic.bh; 3kylim@mmu.edu.my; 4jamia@ukm.edu.my
*Corresponding author

 ABSTRACT

The digital gaming community appreciates visual style information in digital games as it facilitates information seeking. However, learned scholars have discovered that the digital game visual style classification is inconsistent and easily modified, potentially limiting the information and leading to inaccurate visual terminologies during information discovery. Therefore, this cross-sectional study was performed to assess multiple visual style classification terms and their definitions among Malaysian game developers using the closed-card sorting exercise. A total of seven professional game developers participated in the online survey that comprised thirty-five digital game case studies using a card-sorting technique to classify nineteen visual style classification terms, including psychedelic, text, illusionism, photorealism, televisualism, handicraft, caricature, cel-shaded, comic books (anime), watercolour, Lego, minimalism, pixel art, silhouette, bright, dark, maplike, colourful, and black and white. The Fleiss’ kappa intercoder reliability assessment was performed to measure the coders’ agreement on visual style classification, followed by the think-aloud protocol descriptive analysis to gather assessment insights in to the visual style descriptions. Based on the results, the intercoder reliability test achieved a significantly moderate agreement. The professional game developers agreed on eighteen visual styles and rejected the bright visual style classification due to its overlapping description with the colourful visual style. The definition of ten visual style classifications was improved from the existing Video Game Metadata Schema (VGMS) description, contributing to the digital game’s coherence and consistency. Further improving visual style classification information for machine-learning-based recommendation systems for digital game distribution platforms and digital archiving.

Keywords: visual style classification, digital game, Fleiss’ kappa model, intercoder reliability, machine-learning.