Journal of Information and Communication Technology

An Enhanced Ant Colony Optimisation Algorithm with the Hellinger Distance for Shariah-Compliant Securities Companies Bankruptcy Prediction

2023-10-15T08:02:31+08:00

This study addresses the challenge of applying ant colony optimisation algorithms to imbalanced datasets, focusing on a bankruptcy dataset. The application of ant colony optimization (ACO) algorithms has been limited by their performance on imbalanced datasets, particularly within bankruptcy prediction where the some of bankruptcy cases lead to skewed data distributions. Traditional ACO algorithms, including the original Ant-Miner, often fail to accurately classify minority classes, which is a critical shortcoming in the context of financial distress analysis. Hence, this study proposes an improved algorithm, the Hellinger Distance Ant-Miner (HD-AntMiner), which employs Hellinger distance as the heuristic for ants to gauge the similarity or dissimilarity between probability distributions. The effectiveness of HD-AntMiner is benchmarked against established classifiers—PART and J48—as well as the conventional Ant-Miner, using public datasets and a specialized dataset of 759 Shariah-compliant securities companies in Malaysia. Utilising the Friedman test and F-score for validation, HD-AntMiner demonstrates superior performance in handling imbalanced datasets compared to other algorithms, as affirmed by the Friedman test. The F-score analysis highlights HD-AntMiner’s excellence, achieving the highest F-score for Breast-cancer and Credit-g datasets. When applied to the Shariah-compliant dataset, HD-AntMiner is compared with Ant-Miner and validated through a t-test and F-score. The t-test results confirm HD-AntMiner’s higher accuracy than Ant-Miner, while the F-score indicates superior performance across multiple years in the Shariah-compliant dataset. Although the number of rules and conditions is not statistically significant, HD-AntMiner emerges as a robust algorithm for enhancing classification accuracy in imbalanced datasets, particularly in the context of Shariah-compliant securities prediction.

Hybrid Real-Value-Genetic-Algorithm and Extended-Nelder- Mead Algorithm for Short Term Energy Demand Prediction

2023-11-09T11:58:23+08:00

Energy consumption planning of an area is very important. It is essential to accurately predict the amount of short-term power required by an area using a highly effective prediction technique. The real-value-genetics-algorithm (RVGA) is the most effective technique that is currently used. However, the RVGA has some drawbacks, including the fact that it gets caught in premature convergence even when the search is performed over long iterations. This study proposes a hybrid prediction algorithm which comprises the RVGA and the extended-Nelder-Mead (ENM) algorithm. The ENM was implemented to speed up the search for the best among all solutions produced by the RVGA. The RVGA was configured to run under small iterations, and the ENM was used to achieve convergence. Experiments were performed on historical datasets containing the monthly electricity demand of the Gorontalo area, a region in Indonesia. The performance of the hybrid algorithm was compared to the hybrid Genetic Algorithm-Particle Swarm Optimisation (GA-PSO) and Real Coded-Genetic Algorithm (RC-GA) energy demand models based on the mean-absolute-percentage-error (MAPE), mean-square-error (MSE), root-mean-square-error (RMSE), and mean-absolute-deviation (MAD) error rates. The results showed that the proposed hybrid algorithm’s MAPE, MSE, RMSE, and MAD errors were 2.95 percent, 0.13 percent, 0.36 percent and 1.29 percent, respectively. Based on the accuracy measure obtained from this study, it implies that the RVGAENM hybrid is the best model for forecasting monthly electricity demand.

Requirements of Enjoyable Mobile Learning Applications for Deaf Children

2023-09-14T07:22:34+08:00

In the domain of mobile learning applications for deaf children, many studies are more concerned with the development of mobile learning applications for deaf children than with determining what makes them enjoyable for this group of children. Some of these learning applications do not meet the needs of deaf children for learning since the requirements were not collected from the actual deaf children. As a result, many deaf learning applications are still not gaining much popularity among people who are deaf or hard of hearing due to the application due to the applications’ inability to meet their expectations and needs. Hence, this paper aims to identify the requirements for designing an enjoyable mobile learning application for deaf children. In achieving this goal, it is essential to identify the requirements of deaf children on mobile learning applications from their own everyday experience and support these identified needs with requirements gathered from their parents and teachers and literature. Three methods were conducted for this study to identify and synthesise these requirements: (1) Fun Sorter data collection method was conducted among the deaf children, whose ages are between 7 and 12 years old; (2) an interview involving teachers and parents of deaf children; and (3) a literature review on deaf children’s learning. The identified requirements were verified through a focused group attended by eight mobile learning developers. The finding identified six requirements: multimedia elements, games, easy-to-use, simple tasks, guidance,
and alerting. These requirements serve as crucial guidelines for mobile app developers, enabling the creation of enjoyable learning
applications designed specifically for deaf children. Furthermore, these requirements can potentially improve the learning journey of
deaf children, offering them valuable benefits and enhancing their educational experiences.

Multi-Class Multi-Level Classification of Mental Health Disorders Based on Textual Data from Social Media

2023-08-08T08:01:51+08:00

Mental health disorders pose a significant global public health challenge. Social media data provides insights into these conditions.
Analysing text can help identify indications of mental health disorders through text-based analysis. However, despite the large
number of studies on the analysis of mental health disorders, the predominant algorithm in the existing literature is the Multi-Class
Single-Level (MCSL) classification algorithm, which is often used for simple classification tasks involving a limited number of classes.
Typically, these classes are binary, representing either an unhealthy or a healthy mental state. This paper uses English text data from Reddit to classify mental health disorders. The Multi-Class Multi-Level (MCML) classification algorithm was applied to perform detailed
classification and address the limitations of the research scope using several approaches, including machine learning, deep learning, and transfer learning approaches. Two different pre-processing scenarios were proposed to handle unstructured text data, one of the most challenging aspects of classifying text from social media. The results of the experiments show that the MCML classification algorithm successfully performs detailed classification and produces promising results for each classification level. The proposed pre-processing scenario influences the performance of each classifier and improves classification accuracy. The best accuracy results were obtained for the Robustly Optimised BERT Pre-training Approach (RoBERTa) classifier at level 1 and level 2 classifications, namely 0.98 and 0.85, respectively. Overall, the MCML classification algorithm is proven to be used as a benchmark for early detection of text-based mental health disorders.

Online Machine Learning from Non-stationary Data Streams in the Presence of Concept Drift and Class Imbalance: A Systematic Review

2023-12-31T10:00:00+08:00

In IoT environment applications generate continuous non-stationary data streams with in-built problems of concept drift and class imbalance which cause classifier performance degradation. The imbalanced data affects the classifier during concept detection and concept adaptation. In general, for concept detection, a separate mechanism is added in parallel with the classifier to detect the concept drift called a drift detector. For concept adaptation, the classifier updates itself or trains a new classifier to replace the older one. In case, the data stream faces a class imbalance issue, the classifier may not properly adapt to the latest concept. In this survey, we study how the existing work addresses the issues of class imbalance and concept drift while learning from nonstationary
data streams. We further highlight the limitation of existing work and challenges caused by other factors of class imbalance along
with concept drift in data stream classification. Results of our survey found that, out of 1110 studies, by using our inclusion and exclusion criteria, we were able to narrow the pool of articles down to 35 that directly addressed our study objectives. The study found that issues such as multiple concept drift types, dynamic class imbalance ratio, and multi-class imbalance in presence of concept drift are still open for further research. We also observed that, while major research efforts have been dedicated to resolving concept drift and class imbalance, not much attention has been given to with-in-class imbalance, rear examples, and borderline instances when they exist with concept drift in multi-class data. This paper concludes with some suggested future directions.