Journal of Information and Communication Technology
Journal of Information and Communication Technology (JICT) is abstracted and indexed in Emerging Sources Citation Index (ESCI) in November 2017 and Scopus since 2011. It is a double-blind peer reviewed, international academic journal published quarterly (January, April, July and October) by Universiti Utara Malaysia.This journal covers all aspects of information and communication technology, its theories and applications. The aim of this journal is to provide coverage of the most significant research and development in the area of information and communication technology. To be accepted, a paper must be judged to be truly out standing in its field and to be of interest to a wide audience. We are particularly interested in work at the boundaries, both the boundaries of subdisciplines of information and communication technology and the boundaries between information and communication technology and other fields. This is an open access journal. The articles on this site are available in full-text and free of charge to our web visitors.
Monitoring Land Surface Temperature on Urban Expansion using Normalised Difference Vegetation Index and Google Earth Engine
Urban expansion and the corresponding increase in land surface temperature (LST) are significant environmental concerns worldwide. In Malaysia, the urbanisation rate has increased dramatically from 1970 to 2020, according to the Statistics Department. This rapid development, particularly in states such as Selangor, Johor, and Penang, has intensified surface heating and reduced ecological resilience, exacerbating the effects of climate change. Despite growing recognition of these impacts, many existing studies rely on static or predictive models that lack spatial precision and real-time monitoring. Malaysia has recorded an average LST increase since 2000, highlighting the urgent need for systematic, ICT-enabled frameworks to monitor and respond to thermal and environmental changes. This study aims to develop a scalable and replicable framework for monitoring LST changes linked to urbanisation by integrating vegetation-based indices and remote sensing technologies. The framework is demonstrated through a case study of Perak, Malaysia, which is an area experiencing accelerated urban growth and ecological stress. The study was conducted in four structured phases. First, satellite imagery from Landsat 8 and Moderate Resolution Imaging Spectroradiometer (MODIS) was collected using Google Earth Engine (GEE). Second, data preprocessing was performed to derive NDVI, Vegetation Cover Proportion (Pv), and surface Emissivity (Em). Third, spatial and temporal patterns were visualised. Finally, analysis was conducted to examine correlations between LST variation and land cover changes from 2019 to 2023. Findings reveal a consistent rise in LST and a decline in vegetation health in urbanising and deforested zones, confirming a reinforcing feedback loop of surface heat accumulation and ecological degradation. This ICT-driven framework provides a practical model for real-time environmental monitoring, supporting data-informed strategies in urban planning, reforestation, and climate adaptation, particularly in rapidly developing, climate-vulnerable contexts such as Malaysia.
Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0
AI-driven Influencer and Market Analysis: A Social Network Approach to Measure E-Commerce Relationships
Social network analysis is a process of studying social structures and relationships using graph theory and data analysis techniques. It involves mapping and measuring connections and entities in a network. However, on online selling platforms, identifying influential entities such as individuals and high-value products remains a challenge due to the complexity of customer and seller interactions. This study aims to assess seller performance and product lifetime value using AI-driven network analysis involving a measure of centrality. AI-driven network analysis utilises artificial intelligence (AI) to identify influential individuals and predict emerging trends in consumer engagement. It uses weighted degree and betweenness centrality to assess their effectiveness in identifying influential entities, including sellers, products, or organisations in a commercial network. Weighted degree centrality measures the strength and frequency of direct connections, while betweenness centrality identifies entities that act as intermediaries across different network segments. The analysis reveals that weighted degree centrality, with a value of 3190 for annual seller performance, is more closely aligned with actual sales performance and stakeholder assessments, making it a more suitable metric for supporting business decisions in this context. The findings demonstrate that AI-driven analytics enable businesses to consistently identify high-performing sellers and products based on their structural positions within the network. It contributes to the development of more targeted marketing strategies, systematic recognition of top performers, and enhanced customer engagement through data-informed decision-making. Future research may explore the integration of dynamic network modelling with multi-layered e-commerce networks, thereby increasing the depth of analysis across various platforms and industries.
A Convex Variational Interactive Model with Projection Method for Digital Images Partitioning
Digital image partitioning separates the foreground of an image from the background for subsequent analysis. In literature, the variational global model is frequently employed for digital images partitioning; however, it has been shown to underperform when the targeted object is situated near a neighbouring object. To address this issue, researchers recently devised the variational interactive model (VIM). However, because of the formulation’s non-convexity, it is sensitive to the placement of the initial contour and provides inaccurate results if the initial contour is not positioned correctly. Consequently, a new convex formulation of VIM was recently developed based on the chessboard distance function, known as the Selective Segmentation based on Chessboard distance function (SSCD) model. Although this model achieved better accuracy and efficiency in image partitioning and is less sensitive to the initial contour’s location compared to the non-convex VIM, the partitioning process is significantly slower, especially for large-sized images. This stems from the utilisation of a complex penalty term and the approximation of the regularisation term during the energy minimisation phase, which also impacts the accuracy of the partitioning results. This work contributes by proposing a new convex VIM that omits the penalty term and avoids approximating the regularisation term. Moreover, the idea of utilising the projection method is proposed to speed up the partitioning process with improved accuracy. Numerical experiments demonstrated that the proposed model achieved higher accuracy and efficiency compared to existing models. The proposed model has the potential to be formulated into a three-dimensional formulation in the future.
Copyright (c) 2025 Journal of Information and Communication Technology https://creativecommons.org/licenses/by/4.0
Robust Single Sample Per Person Face Recognition with Probabilistic Illumination Enhancement
The challenges imposed by the Single Sample Per Person face recognition problem become especially critical when face images are captured in uncontrolled environments, which typically involve variations in illumination, facial expression, pose, occlusion, and more. In particular, illumination changes caused by poor lighting conditions can significantly degrade the performance of face recognition systems. In this paper, we investigate the SSPP face recognition problem in the presence of potential illumination variation and propose a two-fold approach. Firstly, we analyse and quantify the illumination variations in face images and then normalise these variations based on a probabilistic image enhancement approach. Subsequently, the feature embedding process was performed using the deep learning-based FaceNet system, and finally, faces were classified using the classical Support Vector Machine. The performance of the proposed approach is demonstrated through comprehensive experiments compared to state-of-the-art techniques. The proposed method achieved significant accuracy improvements, attaining 96.17% and 95.68% for 20 and 30 subjects, respectively, on the Extended Yale-B dataset. The performance of the proposed method is evaluated and exhibits superiority in handling illumination compared to state-of-the-art counterparts.
The Mediating Role of Perceived Ease of Learning in Teacher Readiness to Adopt Blockchain for Educational Assessment
Traditional educational assessment systems in Malaysia face persistent issues such as data security vulnerabilities, lack of transparency, and centralised control, which collectively undermine trust, reliability, and operational efficiency. While blockchain (BC) technology has emerged as a promising solution offering decentralisation, immutability, and enhanced data security, a critical gap exists in understanding teacher readiness to adopt such innovations within the education sector. Most prior research emphasises the technical feasibility of blockchain or user acceptance in non-educational contexts, often overlooking educators’ cognitive and pedagogical challenges when learning and using unfamiliar technologies. This study investigates how key attributes of BC relative advantage (RA), compatibility (COM), and complexity (CPX) influence teacher readiness for adoption, mediated by perceived ease of learning (PEOL). A total of 750 questionnaires were distributed, resulting in 514 valid responses from post-secondary school teachers in the northern region of Malaysia, achieved through a simple and proportionate stratified random sampling technique. Partial Least Squares Structural Equation Modelling (PLS-SEM) reveals that COM and CPX significantly predict teacher readiness, while PEOL is a partial mediator. RA shows no significant direct or indirect effect, challenging assumptions about its centrality in technology adoption models. These findings contribute to the broader goals of Industrial Revolution (IR) 5.0 by highlighting the importance of human-centric, cognitively accessible technological integration strategies in education, particularly for fostering systemic reform in assessment practices.
Predictive Modeling of Software Team Performance based on Gender and Task Complexity using Logistic Regression and Decision Tree
This paper investigates the effect of gender and task complexity on software development performance, addressing a critical gap in empirical research and predictive modelling that warrants further investigation. Understanding how these factors interact is crucial for optimising task allocation and enhancing team efficiency in software engineering environments. To investigate this, data were collected from two universities involving 180 software development students with an equal representation of male and female participants. Participants were required to complete programming tasks at three levels of complexity—easy, medium, and hard—to assess their performance under varying cognitive loads. The results indicate a performance disparity based on task complexity, where male software developers consistently outperformed their female counterparts as the complexity of the tasks increased. Further analysis using predictive modelling techniques, which are logistic regression and decision tree models, revealed that logistic regression consistently achieved higher accuracy and F1 and AUC-ROC scores, demonstrating its superior predictive capability in modelling performance variations. Despite these findings, further research is necessary to explore additional interventions to help optimise task allocation based on individual strengths and expertise. Additionally, future studies should continue to challenge stereotypes and explore innovative strategies for promoting diversity and equality in software development.
Opinion Mining and Perceptions of Self-Determination in Videoconference Classrooms Using Machine Learning and Multidimensional Sentiment Frameworks
This study provides a comprehensive analysis of learner sentiments regarding learning in videoconference (VC) environments, highlighting critical areas for improving digital education. In the mixed method approach, Azure Machine Learning's (AML) three-category sentiment analysis as a quantitative approach is enhanced by a multidimensional sentiment model as a qualitative approach to analyse learner feedback. The sentiments are mapped onto the Self-Determination Theory (SDT) framework to highlight how autonomy, competence, and relatedness influence learner sentiments. Open-ended feedback to a prompt from 268 respondents across different education levels provided textual data captured through a Google form. AML provided sentiment scores that indicated the direction and type of sentiments. Combining with thematic analysis based on 5 sentiment categories enables the generation of richer and deeper insights into learner feedback. Findings indicate mixed user sentiments, with flexibility (autonomy) praised but technical issues and limited interaction (low competence and relatedness) criticised. Findings suggest enhancing interactivity, addressing technical barriers, and fostering engagement to improve learner experiences and meet diverse educational needs in digital classrooms. AML provides a broad sentiment classification with confidence scores that are more relatable and reliable. While recognising the overlapping sentiment categories in the two models, the approach offers more actionable insights for educators and platform developers. AML’s model could benefit from incorporating more granular sentiment categories to capture the complexity of user feedback better. The study enables a broader view of the user's need to provide more useful information to developers, policymakers, and instructors regarding using VC and other online platforms for teaching.
Prediction of Rice Yields in a Changing Climate Using the Mobile Rice Yield Prediction Application
In Malaysia, rice production is crucial for national food security but faces significant challenges due to unpredictable climate patterns and climate change. Traditional prediction methods often struggle to capture the complex interaction between climatic and agricultural factors that cause a critical gap in accurately forecasting rice yields. This paper examines the Mobile Rice Yield Prediction Application (MRYPA) through a comparative analysis of two regression models: A baseline multiple linear regression model and an enhanced version incorporating polynomial and interaction terms. A 10-year dataset from the Malaysian Meteorological Department (MetMalaysia) and the Department of Statistics Malaysia (DOA) were trained and tested on varying data splits and then evaluated using performance metrics such as R-squared, Mean Absolute Error, Mean Square Error, and Root Mean Square Error. The findings reveal that while the enhanced model demonstrates marginally better predictive accuracy in smaller datasets, both models exhibit comparable performance with larger training datasets. A 50% train-test split yielded the best results for both models, achieving an R-squared value of 0.9843 for the baseline model and marginal improvements for the enhanced model. These findings underscore the potential of integrating yield and climate data into user-friendly tools, such as MRYPA, to empower stakeholders to make informed, data-driven decisions for sustainable agriculture and national food security. Future work will refine the prediction accuracy and system utility by integrating different data sources and advanced machine-learning models.
Adoption Intentions Toward AI-based Clinical Decision Support Tools: A Tam Study on Hospital Pharmacists
The increasing prevalence of antibiotic resistance leads to an alarming challenge to global healthcare, necessitating the adoption of machine learning (ML)-driven clinical decision support systems (CDSS) to enhance antimicrobial stewardship. Despite its potential, hospital pharmacist’s adoption of ML-based antibiotic resistance predictors remains limited due to usability, trust, organisational support, and perceived risk concerns. This study uses an extended version of Technology Acceptance Model (TAM) to examine the primary factors influencing pharmacists’ behavioural intention (BI) to adopt the ML-driven CDSS. A cross-sectional survey was conducted among 235 hospital pharmacists across major provinces in Indonesia, using a PLS-SEM approach to test hypothesised relationships. The findings reveal that perceived usefulness (PU) (β = 0.355, p < 0.001) is the strongest predictor of BI, followed by trust in technology (TT) (β = 0.179, p = 0.017), social influence (SI) (β = 0.184, p = 0.009), and facilitating conditions (FC) (β = 0.150, p = 0.010). Perceived risk (PR) negatively affects BI (β = -0.103, p = 0.023), highlighting concerns over AI reliability. The study underlines the need for hospital administrators to enhance IT support and training, policymakers to establish AI regulatory frameworks, and professional organisations to promote AI acceptance through peer advocacy. Clinically, increased adoption of ML-driven CDSS can improve the accuracy of antibiotic prescribing, reduce resistance rates, and enhance patient safety. It provides insights into optimising AI interventions in antimicrobial stewardship programmes and guiding future implementation strategies in hospital pharmacy practice.
Transforming Alzheimer’s Disease Diagnosis: Implementing Vision Transformer (ViT) for MRI Images Classification
Alzheimer’s disease (AD) is a progressive neurological disorder and the leading cause of dementia, accounting for 60-80% of cases. Early detection of AD is crucial for timely intervention, as the disease significantly impacts cognitive functions and daily activities. Diagnosing AD in clinical settings remains challenging due to subtle early symptoms, diverse presentations, lengthy diagnostic processes, and inconsistent criteria that heavily rely on medical expertise. Accurate and timely diagnosis during the early stages is essential for effective treatment and intervention. Modern imaging techniques, such as Magnetic Resonance Imaging (MRI), have become essential in diagnosing Alzheimer’s by providing detailed insights into structural brain changes. This study explores the application of the Vision Transformer (ViT) model for classifying MRI images of Alzheimer’s patients, focusing on enhancing accuracy and reliability through data augmentation during pre-processing. A dataset of 8,000 MRI images, categorised into four groups—non-demented, very mild demented, mild demented, and moderate demented—was used to evaluate the ViT model. The experiment achieved promising results, with an accuracy of 98.19%, sensitivity of 96.34%, specificity of 98.80%, and an F1-score of 96.37%. These findings underscore the model’s effectiveness in distinguishing between affected and unaffected individuals, minimising misdiagnosis and enabling timely clinical interventions. However, some challenges remain, particularly in the classification between “Non-Demented” and “Very Mild Demented” cases. Future research should focus on enhancing data augmentation techniques and increasing data diversity in these categories to improve the model’s performance further. The ViT model holds great potential for advancing Alzheimer’s diagnosis, offering a valuable tool for early detection and intervention in clinical settings.
Integrating Information Gain and Chi-Square for Enhanced Malware Detection Performance
Malware represents a serious and continuously evolving threat in the modern digital environment. Detecting malware is essential to safeguard devices and systems from risks such as data corruption, data theft, account compromises, and unauthorized access that could result in total system takeover. As malware has progressed from its simpler, monomorphic variants to more sophisticated forms like oligomorphic, polymorphic, and metamorphic, a machine learning-based detection system is now required, surpassing the limitations of traditional signature-based methods. Recent studies have shown that this challenge can be addressed by employing machine learning algorithms for detection. Some studies have also implemented various feature selection methods to optimize detection efficiency. However, they continue to struggle with false positives and false negatives, striving to reach zero tolerance in malware detection. This study introduces the IGCS method, a combined feature selection approach that integrates Information Gain with Chi-Square (X²) to enhance both the effectiveness and efficiency of machine learning classifiers. Using IGCS, six classifiers—Random Forest, XGBoost, kNN, Decision Tree, Logistic Regression, and Naïve Bayes—achieved higher performance scores compared to other scenarios, such as when classifiers were combined with Information Gain, Chi-Square, PCA, or even without any feature selection. As a result, Random Forest with 30 features selected by IGCS proved superior to any combination of classifiers and feature selection methods in malware detection, achieving 99.0% accuracy, recall, precision, and F1-Score. This combination also demonstrated efficiency with a 52.5% decrease in training time and a 56.9% decrease in testing time.
Mutable Composite Firefly Algorithm for Microarray-Based Cancer Classification
Microarray-based cancer biomarker detection is one of the popular trends for cancer classification. Though existing approaches have given competing performance in terms of classification accuracy and reduced feature subsets, the classification of different cancer microarray datasets still requires improvements. Recently, the swarm-based hybrid algorithms have given significant performance in cancer classification. However, the efficiency of a swarm algorithm is dominated by certain factors such as fitness value, convergence, exploration, and exploitation capabilities. Thus, a swarm-based hybrid approach is proposed for cancer classification with a new variant of the Firefly Algorithm (FA) and Correlation-based Feature Selection (CFS) filter. The slow convergence issue in the FA is resolved by non-fixed size solutions termed as mutable size solutions and a composite position update function is designed for the mutable solutions. In addition, the local optima issue is overcome by the population reinitialisation method. The proposed algorithm, named the CFS-Mutable Composite Firefly Algorithm (CFS-MCFA), is evaluated based on two metrics, namely classification accuracy and genes subset size, using a Support Vector Machine (SVM) classifier. Results show that CFS-MCFA-SVM achieved 100% accuracy with only a few biomarkers for all four cancer microarray datasets, indicating the efficiency and the competing performance of the proposed algorithm in biomarker detection for microarray-based cancer classification. Apart from that, the proposed algorithm would also contribute to cancer-related issues upon verifying the relevancy of particular genes via technical analysis from a medical perspective and would be utilised in feature selection applications.
Multi-Objective Portfolio Optimization Strategy Using the SPEA-II Algorithm
In the finance world, the precise selection and optimization of stock portfolios are of paramount importance. This study explores the application of intelligent algorithms, particularly the multi-objective of Strength Pareto Evolutionary Algorithm II (SPEA-II), alongside traditional methods to determine optimal portfolios. Using the monthly stock prices, the Markowitz model is developed, focusing on the return and semi-variance criteria. Realistic constraints are applied to formulate a multi-objective optimization problem. SPEA-II and traditional multi-objective optimization methods are used to solve this problem, resulting in a set of optimal portfolios. The results show that the SPEA-II algorithm can generate portfolios with higher returns and lower risks compared to the Markowitz model and traditional methods, taking into account the complex and nonlinear conditions of the capital market. In addition, the SPEA- II algorithm showed significant efficiency and stability across different frequencies and time periods. The study highlights that the SPEA-II algorithm can serve as an effective and efficient method for stock portfolio selection and optimization, helping investors to identify portfolios with lower risk and higher return.
Deep Learning Approach in Seismology: Enhancing Earthquake Forecasting using K-Means Clustering and LSTM Networks
Located in the subduction zone of four tectonic plates, the high occurrence of seismic events is a severe threat in Indonesia. Mitigating the adverse effects of such disasters is essential to forecast the likelihood of future earthquakes. Consequently, developing a robust method of forecasting future earthquakes is critical to facilitate prevention and mitigation efforts. A reliable earthquake prediction method is necessary to reduce the after-effects to the greatest extent possible. This study utilises historical seismic and proposes innovative data pre-processing methods using K-means clustering to build a Long Short-Term Memory (LSTM) model for earthquake forecasting to overcome high-disparity locations. Four LSTM layers are embedded with adjusted fine-tuned network hyperparameters to enhance forecasting accuracy. The results attain 0.379816, 0.616292, and 0.414586 for Mean Square Error (MSE), Root MSE, and Mean Absolute Error, respectively, providing significant insights into earthquake prediction. In addition, predicted seismic occurrences are plotted on a map to display their geographic location within the specified research region. This research provides significant value in facilitating the efficient distribution of resources, such as evacuating residents impacted by earthquakes or reinforcing buildings and infrastructure, for emergency responders and policymakers.
Unlocking AI-Powered Tools Adoption among University Students: A Fuzzy-Set Approach
This study examines, from a post-pandemic theoretical perspective, university students' continuous intention (CI) to utilise AI-powered tools for educational purposes. AI-powered tools are new and underutilised in higher education. The fact that students and teachers need knowledge to use these apps in the classroom compounds the issue. Despite this technology's recent academic introduction, nothing is known about its impacts. In order to investigate the variables that influence the continual intention to employ artificial intelligence, this study discusses the possibility of integrating the self-determination theory (SDT) and technology acceptance model (TAM) with the post-acceptance model (PAM). Three hundred forty university students were solicited to complete a questionnaire to collect data for the proposed model. A dual-stage approach uses both symmetrical assumptions from structural equation modelling with partial least squares (PLS-SEM) and asymmetrical configurations from fuzzy-set qualitative comparative analysis (fsQCA). In order to better comprehend the intricate interplay between the model's inputs and its desired output, this approach is devised. Consideration is given to the fact that various configurations of external constructs exert distinct influences on internal constructs. In Thailand, perceived usefulness (PU) and autonomy predict continued AI-powered tool use. Perceived ease of use (PEOU) did not affect continuing intention. Conclusions drawn from the configurational analysis show that no single factor adequately explains a high CI level. Rather, three distinct configurations were identified as improving CI using AI-powered tools. Overall, theoretical and practical ramifications are addressed.
A Decade in Blockchain: A Bibliometric Reflection on the Growth and Interdisciplinary Reach of a Disruptive Technology
This study seeks to deliver a comprehensive bibliometric analysis of blockchain research undertaken from 2013 to 2023. It seeks to map the field’s evolution, highlight emerging trends, and identify key contributors and thematic areas. Understanding these aspects is crucial for guiding future research and policymaking in the rapidly growing domain of blockchain technology. The study utilises a systematic bibliometric analysis approach and the Scopus database was used for collecting data. The analysis includes keyword trends, citation patterns, author productivity, and network analysis. The results reveal significant expansion and diversification in blockchain research, with 13,369 publications and 52,009 contributing authors. Key metrics include a total citation count of 266,483 and an average of 19.93 citations per paper, underscoring the field’s scholarly impact. The study identifies core research themes such as smart contracts, security, IoT integration, and emerging topics like AI and sustainability. The findings highlight the interdisciplinary nature of blockchain research and its global distribution, with notable contributions from China and the United States. The study’s reliance on selected databases and predefined keywords may exclude relevant grey literature and industry reports. Future research should integrate these sources for a more complete view. This study provides a panoramic view of blockchain research, offering valuable insights into its development, current state, and future directions. Identifying key trends, influential contributors, and emerging themes contributes to the academic discourse and supports strategic planning for researchers, practitioners, and policymakers engaged in blockchain technology.
Copyright (c) 2024 Journal of Information and Communication Technology https://creativecommons.org/licenses/by/4.0
Impact of Balanced Exploration and Exploitation on High-dimensional Feature Selection with Hierarchical Whale Optimisation Algorithm
High-dimensional datasets (HDDs) pose significant challenges in feature selection due to their complex nature. While metaheuristic algorithms like the Whale Optimisation Algorithm (WOA) have shown promise in addressing these challenges, stability issues are least addressed. Through an extensive literature review, it was identified that stability is manifested in the equilibrium of exploration and exploitation. Hence, this study introduced the Hierarchical Whale Optimisation Algorithm (HiWOA), an approach designed to enhance the WOA’s stability and performance in HDD feature selection tasks. The HiWOA incorporates a two-phase strategy comprising a nonlinear control parameter based on the arcsine function and a hierarchical position-update mechanism adapted from the Grey Wolf Optimiser. The proposed HiWOA was evaluated through 23 benchmark optimisation functions and feature selection experiments on 11 medical HDDs. The results indicate that the HiWOA outperformed the WOA and a modified variant (mWOA) in terms of a better fitness value, a more balanced exploration-exploitation ratio, and improved classification accuracy with fewer selected features. These findings demonstrate the HiWOA’s effectiveness in enhancing stability, making it a robust solution for high-dimensional optimisation and feature selection.
Restoration and Segmentation of Old Jawi Manuscripts using Variational Image Inpainting and Active Contour Models
Old Jawi Manuscripts (OJM) are crucial to historical studies, offering insights into past societies. However, degradation from mishandling and environmental factors can impair their legibility. To preserve OJM, image inpainting and segmentation are essential for restoring corrupted areas and identifying text. Recently, the Gaussian Regularization Segmentation (GRS) model has shown effectiveness in intensity inhomogeneity grayscale image segmentation, though it was not designed for corrupted OJM images. Therefore, this study aimed to reformulate the GRS model to restore and segment text from real corrupted OJM images. The methodology begins with the incorporation of the Mumford-Shah and Bertalmio inpainting models into the GRS model as new fitting terms, resulting in the Modified Gaussian Regularization Segmentation Mumford-Shah (MGRSM) model and the Modified Gaussian Regularization Segmentation Bertalmio (MGRSB) model, respectively. MATLAB was used to implement these models, and their performance was assessed on 30 corrupted OJM samples from Malay Ethnomathematics Research Group, with expert evaluations and efficiency measured by average elapsed time. The MGRSM model achieved 38.4 percent and 12.4 percent higher overall total scores from experts in terms of segmentation accuracy compared to the GRS and MGRSB models, respectively. While the GRS model is the fastest, the MGRSM model provides superior accuracy, with an average processing time of 9.35 seconds, making it the most optimal for restoring and segmenting OJM images. This approach not only enhances the preservation of historical manuscripts but also provides a practical tool for researchers and historians in safeguarding our cultural heritage.
Reinforcement Learning Algorithm for Optimising Durian Irrigation Systems: Maximising Growth and Water Efficiency
This study presents a Reinforcement Learning-based algorithm designed to optimise irrigation for Durio Zibethinus (i.e., durian) trees, aiming to maximise tree growth and reduce water usage. Traditional irrigation methods, as well as current machine learning models, often focus only on soil moisture and weather data, neglecting critical factors like actual tree growth. This study proposed a reinforcement learning irrigation (RL-Irr) algorithm incorporating tree growth stages, soil moisture, and weather conditions to determine precise irrigation needs. The algorithm was developed by calibrating the AQUACROP model using data from actual durian plantations where rain-fed irrigation (rain-fed) was practised. Daily irrigation volumes were calculated based on real-time soil moisture, weather forecasts, and weekly tree growth measurements. The reinforcement learning method was used to optimise irrigation schedules, with rewards based on soil moisture, tree growth, rainfall, and weather conditions. The algorithm was tested using AQUACROP simulations and compared against soil moisture balance irrigation (SMB-Irr) and rain-fed. The results showed that the RL-Irr reduced water use by up to 75 percent while maintaining tree growth. These findings suggest the algorithm could significantly improve water efficiency in durian farming, though real-world applications should consider potential model limitations.
An Improved Design Guideline for Dyslexia-Friendly Applications Based on Eye-Tracking Data
Addressing dyslexia through digital product design presents distinct challenges and necessitates the development of tailored strategies or guidelines to facilitate children in reading accurately and efficiently. Hence, a design guideline was formulated by integrating Interaction Design (IxD) principles to enhance comprehension and minimise reading errors while using digital applications. Nonetheless, the existing design guidelines for dyslexia people have yet to be confirmed and updated to cater to the current and latest five IxD dimensions. Therefore, this paper presents an eye-tracking usability test that was conducted to identify usability issues pertaining to the design guideline by performing the test on an application called BacaDisleksia, which was developed based on the existing guideline. A usability test was conducted using the Tobii eye-tracking tool through an in-person and moderated session with six dyslexic children. The test reveals pertinent design issues by analysing heat maps and gaze plots. Based on these findings, this paper proposes a refined design guideline with five IxD dimensions and strategies conducive to dyslexia-friendly application design by incorporating space and time components. The results contribute to the development of comprehensive design guideline for people with dyslexia, which aligns with UNESCO’s objective of utilising technology to promote inclusion for disabled learners. This effort underscores the significance of informed design decisions in digital innovation for better-serving individuals with dyslexia and similar learning challenges.
Copyright (c) 2024 https://creativecommons.org/licenses/by/4.0
Automatic Negation Detection for Semantic Analysis in Arabic Hotel Reviews Through Lexical and Structural Features: A Supervised Classification
One significant challenge in sentiment analysis is the presence of negation, which reverses the meanings of sentences, transforming positive statements into negative ones and impacting the sentiment conveyed in the text. This issue is particularly pronounced in Arabic, a language known for its complex morphology. Detecting negation is crucial for enhancing sentiment analysis performance and various natural language processing applications. This paper presents an approach for automatically detecting negation in user-generated Arabic hotel reviews through lexical and structural features. It comprises several stages: data collection, text pre-processing, feature extraction, supervised learning classification, and evaluation. The study employed multiple supervised classification techniques, including naïve Bayes, random forest, logistic regression, support vector machines, and deep learning, to analyse lexical and structural features extracted from the dataset. The results of the experiments yielded promising outcomes, demonstrating the feasibility of the approach for practical applications. The classifiers exhibited highly comparable performance in identifying negation, with only marginal deviations in their performance metrics. Notably, the deep learning classifier consistently emerged as the top performer, achieving an exceptionally high overall accuracy rate of 99.24 percent, surpassing established benchmarks in Arabic text processing and underscoring its potential for practical applications. These findings hold significant implications for advancing Arabic text processing, particularly in sentiment analysis and related NLP tasks. The high accuracy of 99.24 percent achieved by the deep learning classifier highlights its robustness in accurately detecting negation, a critical challenge in sentiment analysis. This classifier performance demonstrates the potential to be integrated into real-world applications, such as automated review systems and opinion mining tools, where accurate sentiment interpretation is essential.
Acceptance of IoT-based E-coin Track-and-trace: A Case of the Digital Euro Project in Italy
As the world becomes increasingly digitalised, there are growing concerns about the use of big data and machine learning techniques to monitor and control citizens’ spending habits. This is particularly the case regarding Central Bank Digital Currencies (CBDC), which are being trialled by an increasing number of countries. There is a perception that such currencies could violate privacy due to the centralisation of money liability. The aim of this research is to assess whether a universal e-coin level tracking service of money and public expenditures, available to everyone and inspired by Internet of Things (IoT) architectures and standards, could instil trust in institutions while increasing the acceptance of CDBCs. The research methodology comprises three key elements: (i) the conceptualisation and implementation of an IoT-based CBDC, (ii) a qualitative, technical and compliance assessment with regard to the specific reference to the Digital Euro (D€) project, and (iii) a survey we conducted among 351 respondents to ascertain the potential for CBDC acceptance within Italy. The results demonstrate that the prototype is a viable concept despite storage limitations. Furthermore, 73.83 percent of respondents who initially expressed scepticism indicated that they would be more inclined to adopt the CDBC instrument if a universal track-and-trace tool of money were made available.
The Perspective Classification of Balanced Scorecard with Ontology Technique
The competition among Higher Education Institutions (HEIs), such as universities, worldwide is crucial. It is imperative for universities to maintain high-quality education and achieve and maintain their world ranking. Therefore, they must utilise performance management tools in their performance improvement efforts. The Balanced Scorecard (BSC) is a quality tool for performance management in universities that helps them to enhance, increase, and maintain their overall performance and ranking. When their strategy changes, the performance indicators must be revised and rearranged in accordance with the BSC form. The information needed for decision-making cannot be immediately available because there are delays in updating the performance management system. This creates a gap between performance improvement in HEIs and the BSC. To eliminate this gap, the question we need to answer is how to automatically upload the updated BSC perspectives into the performance management system to streamline the decision-making automatically. This paper aims to present the Perspective Classification of Balanced Scorecard with Ontology (PCBSC-Onto) framework for automatically classifying performance indicators into BSC’s four perspectives using an ontological approach to support the dynamic capability of the management system’s performance. The balanced scorecard ontology and performance indicator ontology created through this research were applied in the PCBSC-Onto framework. The experiment’s results are based on the performance indicator data of a university in Thailand, and it presents how this framework and its algorithms contribute to increasing performance management ability in the HEI context. The accuracy rate of the PCBSC-Onto framework is 82.97% when compared to the accuracy of the modified Delphi method. The results reveal the accuracy of the proposed ontologies and algorithms on the data from the case study.
Revolutionising Diabetic Retinopathy Diagnosis with Modified Regularisation Long Short-Term Memory Framework
The diagnosis of Diabetic Retinopathy (DR) demands a paradigm shift towards more accurate and efficient solutions to overcome vision impairment. Therefore, the current study introduces a new Modified Regularisation Long Short-term Memory (MR-LSTM) framework approach for DR diagnosis. The proposed framework leverages the power of deep learning and provides a dynamic and robust solution for the early detection of DR, which in turn preserves a patient’s vision. The proposed framework uses a DR Debrecen Dataset from the UCI database with 21 distinct features relevant to retinal health, and employs a series of data preprocessing steps, including data cleaning, normalisation, and transformation, to ensure data quality and compatibility. The MR-LSTM framework excels at capturing temporal dependencies in sequential retinal images, offering a unique advantage in understanding the progression of DR. The MR-LSTM framework is implemented using Python libraries, and the results are compared with those of other popular models. It is observed that the MR-LSTM framework outperforms other models and achieves an accuracy of 97.12 percent and an F1 Score of 98.49. Furthermore, the Receiver Operating Characteristic (ROC) curve reveals an area under the curve of 0.97, highlighting the exceptional ability to discriminate between positive and negative cases of the proposed framework. By revolutionising DR diagnosis with the proposed MR-LSTM framework, it can achieve accurate, timely, and accessible solutions in the fight against vision-threatening conditions.
Beta Distribution Weighted Fuzzy C-Ordered-Means Clustering
The fuzzy C-ordered-means clustering (FCOM) is a fuzzy clustering algorithm that enhances robustness and clustering accuracy through the ordered mechanism based on fuzzy C-means (FCM). However, despite these improvements, the FCOM algorithm’s effectiveness remains unsatisfactory due to the significant time cost incurred by its ordered operation. To address this problem, an investigation was conducted on the ordered weighted model of the FCOM algorithm leading to proposed enhancements by introducing the beta distribution weighted fuzzy C-ordered-means clustering (BDFCOM). The BDFCOM algorithm utilises the properties of the Beta distribution to weight sample features, thus not only circumventing the time cost problem of the traditional ordered mechanism but also reducing the influence of noise. Experiments were conducted on six UCI datasets to validate the effectiveness of the BDFCOM, comparing its performance against seven other clustering algorithms using six evaluation indices. The results show that compared to the average of the other seven algorithms, BDFCOM improves about 15 percent on F1-score, 11 percent on Rand Index, 13 percent on Adjusted Rand Index, 3 percent on Fowlkes-Mallows Index and 16 percent on Jaccard Index. For the other two ordered mechanism FCM algorithms, the time consumption was also reduced by 90.15 percent on average. The proposed algorithm, which designs a new way of feature weighting for ordered mechanisms, advances the field of ordered mechanisms. And, this paper provides a new method in the application field where there is a lot of noise in the dataset.
Digital Medical Images Segmentation by Active Contour Model based on the Signed Pressure Force Function
The signed pressure force (SPF) function has recently become a popular function for guiding the curve evolution of the active contour model (ACM) for image segmentation. The aim is to extract the boundaries of digital medical images for shape and image analysis. The recent SPF-based ACM demonstrates effectiveness in image segmentation. However, it may fail if the targeted object is close to a neighbouring object. Additionally, the presence of intensity inhomogeneity and noise in medical images degrades segmentation accuracy and local target areas. Thus, we proposed a new SPF-based ACM, namely the Selective Segmentation with Signed Pressure Force 1 (SSPF1) model, by incorporating the ideas of the SPF function and the distance fitting term based on geometrical constraints. Then, the new SSPF1 model was extended by incorporating an image enhancement technique to develop our second new model, termed the Selective Segmentation with Signed Pressure Force 2 (SSPF2). Numerical results indicated that the SSPF2 model was more recommended than SSPF1 as the SSPF2 model was approximately 4.7% more accurate, as indicated by the Jaccard value and was about 112 times faster in segmenting noisy images compared to the existing selective segmentation model.
Attention-Deficit/ Hyperactivity Disorder Brain Functional Reference: Introducing an Inter-subject Consistency Measure and Dual-layer Parcellation
The latest advancements in neuroimaging techniques have contributedto studying the disordered human brain, but still, there is a lack of disease-specific brain reference to localise brain seeds and standardise the comparison across studies. The current study first evaluates the inter-subject consistency across Attention-Deficit/ Hyperactivity Disorder (ADHD) in a publicly accessible resting-state functional magnetic resonance imaging (RS-fMRI) ADHD-200 dataset by proposing a new voxels similarity index (VSI) that integrates both inter-atlases flexibility (F`) from previous studies with proposed intersubjects stability (S`) measure to improve outcomes. Secondly, the study employs a dual-layer clustering-based parcellation strategy inspired by the resultant improved Master Atlas networks to examine the ADHD cerebral cortex. The suggested approach to enhancing the ADHD cortex parcellation uses spectral clustering for global structure identification, followed by hierarchical clustering for local refinements and granularity specification. The final connectivity-driven brain reference achieved an average homogeneity of 0.63, and the enhanced Master Atlas with (S’) achieved 0.35 homogeneity, surpassing the original Master Atlas with 0.27. This discovery implies that the final brain reference offers a more accurate and reliable framework to examine the connections and functions of the brain in individuals with ADHD.
Colour Image Enhancement Model of Retinal Fundus Image for Diabetic Retinopathy Recognition
Diabetic retinopathy (DR) features are typically identified through ophthalmologist eye examinations, but these images often face challenges like low contrast, non-uniform illumination, and colour inconsistency, affecting the diagnosis accuracy. Therefore, this study introduces two novel techniques to improve image quality. One is applying colour image processing techniques to original retinal fundus images, overcoming existing algorithm limitations. Firstly, a new colour correction algorithm was proposed based on Tuned Brightness Controlled Single-Scale Retinex (TBCSSR) named Fuzzy TBCSSR Histogram Matching (fTBCSSRhm) to address the issue of colour inconsistency in the dataset. Secondly, based on hybrid particle swarm optimisation-contrast stretch (HPSOCS), the hybrid of TBCSSR and HPSOCS named eTBCSSR-HPSOCS algorithm is introduced to tackle the limitations of the standard Particle Swarm Optimisation (PSO) algorithm in HPSOCS, which is prone to local optima and exhibits low convergence rates. This technique combines the L-component of the LAB colour model with an enhanced velocity mechanism in PSO and contrast stretching (lavHPSOCS). Its goal is to fine-tune parameters automatically, reduce over-enhancement, avoid unwanted artefacts, and preserve intricate details. This approach improves optimisation by balancing exploration and exploitation and refining velocity control. The proposed algorithm underwent both qualitative and quantitative evaluations. Tests on 100 retinal fundus images from primary datasets were performed to benchmark the algorithm against three existing approaches. The results show that the qualitative performance of the proposed enhancement is more favourable to ophthalmologist specialists than other images. Quantitatively, eTBCSSR-HPSOCS outperforms others with the lowest mean squared error (MSE) of 42.72859, the highest peak signal-to-noise ratio (PSNR) of 32.768, and entropy of 0.977.
An Embedded Machine Learning-Based Spoiled Leftover Food Detection Device for Multiclass Classification
Food waste’s negative environmental repercussions are causing it to become a global concern. Several studies have examined the factors influencing food waste behaviour and management. This work was motivated by the lack of previous research on machine learning and electronic noses to detect contamination from leftover cooked food. This work proposes using machine learning algorithms and electronic nose technology to recognise and forecast the contamination in leftover cooked food. After five days of storage, the freshness of cooked leftovers was evaluated using an electronic nose combined with machine learning algorithms. Most food samples used in this work were from Malaysian’s leftover lunch and dinner dishes. Four (4) gas sensors—MQ-2, MQ-136, MQ-137, and MQ-138—are used in developing the electronic nose to identify the presence of gas in the food sample. The data from the gas sensors was analysed using machine learning methods, namely Random Forest, k-nearest Neighbors, Support Vector Machine, and Linear Discriminant Analysis. Based on the results, a multi-classification technique yielded a greater accuracy rate in classifying and identifying the level of contamination in the cooked food leftovers, with average accuracy ranging from 90 percent to 100 percent. In conclusion, the work demonstrates a novel method for using machine learning algorithms to classify, identify, and predict the contamination level of leftover cooked food, contributing to reducing food waste generated primarily by Malaysians.
Reprocolla: Requirements Prioritisation Model with Collaboration Perspectives Based on Cost-Value Approach
The process of ranking requirements in software development is made up of various criteria and numerous stakeholders, which are properly selected for the effective prioritisation of software requirements. These requirements have encountered several challenges, including lack of scalability, complexity of pairwise comparisons and biases due to the cognitive load on stakeholders. Therefore, this research aimed to investigate and find solutions to improve the software prioritisation process using the Reprocolla model. The model was built by weighing the criteria in terms of benefits, opportunities, costs, and risks (BOCR) classification, which were then calculated using the fuzzy analytic hierarchy process (FAHP) method. Furthermore, the selection of alternatives was computed using the fuzzy technique for order preference using the similarity to an ideal solution (FTOPSIS) method. The success of Reprocolla was evaluated using seven datasets based on real projects and compared with the two existing methods for prioritising requirements, FAHP and FTOPSIS. The experiment results used inferential and descriptive statistics approaches with three indicators: accuracy, time consumption, and ease of use. Based on the three indicators mentioned above, the inferential statistics showed no significant difference between the perspectives of clients and developers. Whereas, descriptive statistics found that Reprocolla is more accurate, consumes less time, and has the highest ease of use percentage. The result showed that as stakeholders’ satisfaction level increases, the software development process becomes more accurate, thereby leading to a decrease in time consumption and a rise in ease of usage. The result also showed that the development of the Reprocolla tool, a collaboration between humans and machines, enhanced the effectiveness of the requirements prioritisation process.