The Malaysian Journal of Computer Science (ISSN 0127-9084) is published four times a year in January, April, July and October by the Faculty of Computer Science and Information Technology, University of Malaya, since 1985. Over the years, the journal has gained popularity and the number of paper submissions has increased steadily. The rigorous reviews from the referees have helped in ensuring that the high standard of the journal is maintained. The objectives are to promote exchange of information and knowledge in research work, new inventions/developments of Computer Science and on the use of Information Technology towards the structuring of an information-rich society and to assist the academic staff from local and foreign universities, business and industrial sectors, government departments and academic institutions on publishing research results and studies in Computer Science and Information Technology through a scholarly publication. The journal is being indexed and abstracted by Clarivate Analytics' Web of Science (Q3 of Journal Citation Report Rank with impact factor 1.2) The journal is also abstracting in Elsevier's Scopus (Q3 of SCIMAGO Journal Rank) The MJCS is a recipient of the CREAM (2017) and CREME Awards (2019) by the Ministry of Higher Education Malaysia.
Faculty of Computer Science and Information Technology, University of Malaya
BYOD SECURITY POLICY COMPLIANCE IN MALAYSIAN PUBLIC UNIVERSITIES: AN INTEGRATED PMT –TPB – GDT MODEL WITH ISA MODERATION
The Bring Your Own Device (BYOD) initiative is widely implemented across various organizations today. Numerous universities in Malaysia are also adopting this approach since it reduces expenses related to maintenance and device management. This paper proposes a framework to enhance compliance with BYOD security policies in the public sector. It identifies the key factors influencing user compliance and contributes to the development of a comprehensive BYOD security policy compliance framework. Quantitative data collection was conducted in Malaysian public universities to examine user behaviors and empirically test the proposed framework. The validation process with experts was executed, and consent was received to participate in the evaluation stage. The findings confirm the effectiveness of integrating (PMT), (GDT), and (TPB) Theories in shaping compliance. All examined variables—including perceived severity, perceived threats, response efficacy, self-efficacy, and formal sanctions—were found to significantly influence attitudes, subjective norms, and behavioral control, which directly affect users’ intentions to comply. Information security awareness was validated as a moderator of both attitude and intention to comply. Overall, this study provides empirical validation of the proposed BYOD framework and offers actionable guidance for policymakers, enabling universities to mitigate BYOD related risks, strengthen compliance, and safeguard credibility amid digital transformation.
Copyright (c) 2025 Malaysian Journal of Computer Science
A STUDY ON NORMALISATION METHODS IN CITATION ANALYSIS (2016-2025)
Calculating impact factors solely from raw citation counts can be misleading because citation counts vary across disciplines and publication years. Citation analysis is a fundamental bibliometric methodology that aids in determining trends, frequency, and influence; nevertheless, normalisation methods for reducing skewness are still not well understood and are not always used. Selecting an appropriate normalisation technique to map source data to a standardised citation scale effectively is challenging. This study aims to provide new insights into which normalisation methods researchers should use when conducting citation analysis. Normalisation at the author level using Field-Weighted Citation Impact (FWCI) and Log Normalisation Citation Score (Log NCS), and the field level using Mean Normalised Citation Score (MNCS) and Mean Normalised Log Citation Score (MNLCS) are the two components of the comparative analysis presented in this work. Using citation data from six different academic fields, this study assesses how well each approach reduces skewness and creates more equitable comparisons. This study offers four contributions: it provides a structured comparison approach to author and field normalisation; it empirically demonstrates conditions under which log transformation outperforms non-log methods; it provides decision-making guidance for researchers when selecting normalisation approaches; and it validates findings across multiple disciplines. These findings are intended to improve the accuracy and validity of citation-based impact evaluations, thereby facilitating more equal academic benchmarking and collaboration.
HANDLING IMBALANCED DATA ON MULTILEVEL DEPRESSION CLASSIFICATION: CHALLENGES AND SOLUTIONS
This study addresses the challenges posed by imbalanced data in multilevel depression classification by leveraging the Adaptive Synthetic (ADASYN) technique. Subject Matter Experts (SMEs) annotate data collected from X into four categories: None, Mild, Moderate, and Severe. The imbalanced distribution, particularly with a larger group for the None category, prompts the application of ADASYN for effective data augmentation. The research framework encompasses Data Collection, Expert Data Annotation, Text Preprocessing, and Text Representation and Classification. Evaluation metrics, including Recall and F1 score, gauge the model's effectiveness in multilevel depression classification. Results showcase the efficacy of the ADASYN-enhanced model, specifically with XGBoost, demonstrating improved classification accuracy, especially for minority classes. This study contributes valuable insights to the field of multilevel depression classification, emphasizing the effectiveness of ADASYN in managing imbalanced data scenarios and showcasing the applicability of XGBoost in enhancing model performance.
Copyright (c) 2025 Malaysian Journal of Computer Science https://creativecommons.org/licenses/by-sa/4.0/
ADOPTION OF MOBILE GOVERNMENT SERVICES USING AN EXTENDED UTAUT2 MODEL
The primary aim of this research is to formulate an extensive model for the adoption of mobile government (m-government) from the people's viewpoint. The research enhanced the Unified Theory of Acceptance and Use of Technology (UTAUT2) by incorporating various technological, political, and personal factors, including service diversity and quality, governmental trust, mobile internet, innovativeness, and self-efficacy. The proposed approach is subsequently evaluated using a comprehensive survey of 512 Yemeni citizens across two cities. The statistical research indicated that innovativeness was the most significant factor influencing citizens' intention to utilize m-government. The diversity and quality of services are the primary elements affecting citizens' utilization of m-government services. The PLS analysis indicates that the suggested model accounts for 58% of citizens' intention to utilize m-government services. Moreover, the proposed augmentation of UTAUT2 in this study significantly enhanced the variance elucidated for the baseline model. The implications of the discoveries are examined.
Copyright (c) 2024 Malaysian Journal of Computer Science https://creativecommons.org/licenses/by-sa/4.0/
DIGITAL SKILL ASSESSMENTS: PRE & POST COVID
This study explores the evolution of digital skills assessment between pre- and post-COVID, focusing on how the COVID-19 pandemic accelerated both the urgency and complexity of evaluating digital competence across sectors. Prior to 2020, assessment frameworks emphasised basic ICT skills that were often evaluated through standardised and summative tools. However, the global shift to remote education, telehealth, and virtual work exposed critical gaps in adaptability, contextual fluency, and digital resilience skills not captured by traditional assessments. Through a systematic narrative review of academic literature and institutional reports, this paper analyses key trends, frameworks, and innovations that have emerged in the post-pandemic era. The findings show a marked transition toward formative, adaptive, and performance-based assessments, supported by artificial intelligence, learning analytics, and gamification. These new methodologies aim to assess not only technical proficiency but also socio-emotional and ethical competencies, such as digital collaboration, online communication, and responsible use of AI tools. Sector-specific analysis reveals differentiated adaptations in education, healthcare, government, and the workforce, with increasing emphasis on dynamic, continuous, and context-sensitive evaluation models. The study highlights persistent challenges, including the digital divide, algorithmic bias, and policy fragmentation, while offering guidance for designing inclusive and scalable assessment systems. As digital ecosystems become more complex and pervasive, the ability to measure digital readiness equitably and meaningfully becomes a central priority for educational research and policy.
A DATA-DRIVEN FRAMEWORK FOR ASSESSING GOVERNMENT READINESS FOR PRIVATE BLOCKCHAIN ADOPTION
The transformative potential of private, permissioned blockchains for public service delivery is widely recognized, offering significant improvements in data integrity, transparency, and process efficiency through decentralized and unchangeable ledgers. Despite this appeal, a notable gap exists in the literature regarding a structured, data-driven framework to systematically assess government readiness for blockchain adoption. The absence of such a diagnostic tool often leads to ad-hoc, trial-and-error implementations that frequently fail to scale or integrate effectively with existing systems. This study addresses this gap by proposing and empirically validating a comprehensive, multi-dimensional framework for assessing government readiness. The methodology involved the design of a conceptual model, the development of a functional prototype, and its subsequent quantitative validation through a survey of domain experts. The findings demonstrate the framework's effectiveness in providing an integrated analysis of the technical, organizational, and environmental factors crucial for successful blockchain adoption. This research contributes to theoretical knowledge by operationalizing readiness constructs and also offers a practical, evidence-based diagnostic tool for policymakers and public managers to inform investment decisions and mitigate adoption risks.
BLOCKCHAIN-BASED INTER-ORGANIZATIONAL INFORMATION SHARING WITH CONFIDENTIALITY PROTECTION: A SYSTEMATIC LITERATURE REVIEW
Blockchain technology has emerged as a transformative solution for inter-organizational information sharing, offering enhanced security and transparency. Transparency aims to build trust, ensure data integrity, guarantee accountability, and prevent unfair practices. However, this presents a dilemma for organizations prioritizing confidentiality, making the full implementation of blockchain challenging. To address these concerns, numerous studies have explored methods for preserving confidentiality and privacy within blockchain systems. Recent research has conducted systematic reviews focusing on security and user-centric approaches to data privacy in blockchain. In this paper, we present a systematic literature review to examine techniques proposed for preserving confidentiality in inter-organizational information sharing. In total, 51 study papers were selected for synthesis. The results indicate the techniques can be categorized into seven groups: privacy-preserving, authorization, data perturbation, symmetric encryption, asymmetric encryption, isolated area, and comprehensive tool. We identified that these techniques are applicable to 11 scenarios, as outlined in this paper.
TOWARDS DIGITAL TRANSFORMATION: DEVELOPMENT OF A DIGITAL SKILLS FRAMEWORK FOR PUBLIC OFFICERS
This study aims to develop a comprehensive Digital Skills Framework for public officers to support Malaysia's digital transformation. Using a quantitative approach, expert validation confirmed the relevance of key components such as digital literacy, communication, problem-solving, and digital security (S-CVI/Ave of 0.94). The framework addresses the need for structured digital skills development to enhance public officers' effectiveness in a digital environment. While limited by its reliance on expert validation, the framework provides practical guidance for designing training programs, improving public service delivery, and supporting the state's digital agenda, contributing to more transparent and efficient governance.
INVESTIGATING THE STRESS LEVELS OF CHILDREN WITH SPECIAL NEEDS IN VIRTUAL REALITY SNOEZELEN ROOM ENVIRONMENT FOR LEARNING
As the number of children with special needs increases, the number of enrollments in special schools also increases. The common impairments faced by special children include difficulties in concentration during learning due to learning disabilities, communication problems, sensory sensitivities, and restrictive or repetitive behaviour. Some special needs children were uncomfortable wearing any equipment during learning, while developing a Snoezelen room can be expensive and requires high maintenance. A VR Snoezelen room can overcome the above problem and help special needs children feel relaxed and comfortable. This research aims to study the special needs children’s responses in the Virtual Reality (VR) Snoezelen room system for learning. This research collected data via the experimental session on the stress levels of the special needs children before and after using the system. There were 10 special needs children who participated in this research, who were diagnosed with autism spectrum disorder, Down syndrome, and learning disabilities. Each participant was given three to five sessions based on the child’s performance for them to complete the given tasks. The results show that 2 out of 10 participants had increased stress levels during first-time use of the VR Snoezelen room. After the second session, all the participants’ stress levels were reduced after using the VR system. This virtual system can increase special children’s concentration, improving their memory skills in learning while feeling relaxed when using the system.
DETECTING EMOTIONAL STATE OF DEPRESSION IN SOCIAL MEDIA POSTS USING LOGISTIC REGRESSION-RECURSIVE FEATURE ELIMINATION
Depression detection through social media has garnered widespread attention due to its potential for early intervention in mental health issues. This study aims to detect depressive users based on their content shared on social media using machine learning techniques. Given the complexity and diversity of depressive text, existing research still falls short in exploring comprehensive feature extraction techniques. To address this challenge, this study proposes an integrated framework for detecting depressive tendencies through multi-dimensional feature extraction and selection techniques. The proposed approach combines TF-IDF with N-grams, DistilBERT embeddings, and SentiWordNet to capture linguistic, semantic, and emotional features. Additionally, logistic regression-based recursive feature elimination (LR-RFE) is employed to optimize high-dimensional feature sets by reducing redundancy and emphasizing key indicators.Experiments conducted on the CLEF eRisk dataset revealed varying levels of effectiveness across individual feature extraction methods. Notably, multi-feature integration significantly enhanced classification performance, achieving an accuracy of 80.8% and an F1 score of 80.54% with the combined feature set. Feature selection further improved model efficiency and performance. These findings contribute to advancing automated depression detection and lay a foundation for developing scalable and interpretable machine learning models for mental health assessment.
ENHANCING SEMANTIC INFORMATION RETRIEVAL (SIR) THROUGH ANTONYMS EXTRACTION FOR RETRIEVING PRECISE COVID-19 INFORMATION
The Semantic Web extends the capabilities of the traditional Web by enabling machines to process and interpret data through ontology knowledgebase. Integrating ontologies into the Web facilitates more accurate and precise searches, task automation, and optimized integration between systems. This research work focuses on semantic information retrieval (SIR) for COVID-19-related queries, leveraging ontologies to generate precise search results and antonyms to reduce irrelevant results. By conducting syntactic and semantic analysis, the system expands the search query using the context derived from the ontology. The query is further refined by extracting antonyms via the ontology relations. The refined query is then submitted to the search engine to retrieve more precise results. A ranking module further filters and prioritizes the most pertinent result links. The SIR approach is novel among existing information retrieval systems in that it eliminates irrelevant search results via antonyms, rather than displaying all the retrieved results based on the query, and in that it re-ranks the results semantically. The SIR algorithm demonstrates significant performance improvements for most queries, primarily due to the semantic analysis, antonyms addition and re-ranking processes. The query dataset achieved 100% precision and 80% recall, outperforming existing search engines in these metrics.
PERFORMANCE COMPARISON OF ZERO-SHOT AND TWO-SHOT PROMPTING IN DETECTING FAKE NEWS USING LARGE LANGUAGE MODELS
Fake news detection is a highly crucial challenge in Natural Language Processing (NLP), particularly during significant social events like elections and national crises. This study uses the GPT-3.5-Turbo model to test the effectiveness of zero-shot and two-shot prompting in detecting fake news on the PolitiFact and Liar datasets. Zero-shot prompting consists of task instructions without examples, whereas two-shot prompting contains a few task-related examples. The methodology includes dataset preparation, Large Language Models (LLMs) response collection, encoding, and evaluation using metrics such as accuracy, precision, recall, and F1-score. The results show that two-shot prompting increases performance marginally across all parameters when compared to zero-shot prompting. PolitiFact’s accuracy improved from 0.286 to 0.293, while Liar’s improved from 0.220 to 0.226. Precision, recall, and F1-score also showed minor gains. However, these advances were not statistically significant and highlight the model’s difficulty with handling multi-class classification in the political domain. The GPT-3.5-Turbo model performed better on the PolitiFact dataset, suggesting variability in performance across different datasets. In conclusion, although two-shot prompting provides a slight advantage, the GPT-3.5-Turbo’s overall performance remains limited, indicating the need for more sophisticated techniques (such as advanced prompting methods or more powerful LLMs) to enhance fake news detection.
FISH IMAGE ANALYSIS: FUSION OF MOMENT-BASED AND DIRECTIONAL FEATURES IN COLOUR SPACE
This study introduces an innovative approach to content-based image retrieval (CBIR) specifically designed for fish species identification. The proposed method integrates shape, colour, and texture features using Zernike Moments Invariant (ZMI) and Local Directional Pattern (LDP), applied to the momentgram and the hue channel of the HSV colour space. This fusion ensures invariance to transformations such as rotation, scaling, and translation, enabling robust performance on natural images with varying orientations and quality. The method was evaluated using the Fish4Knowledge dataset, consisting of 27,370 images, with 30% randomly selected as query images. Experimental results demonstrate that the proposed method achieved a mean average precision (MAP) of 84.17%, significantly outperforming comparable state-of-the-art approaches. Statistical analysis using two-tailed paired t-tests confirms its superiority. By combining global shape descriptors, local texture features, and colour properties, this method delivers a comprehensive representation of fish images. The inclusion of moment-based descriptors enhances its robustness against low-resolution images and noise. This research underscores the importance of combining diverse features within CBIR systems and offers a significant improvement in retrieval accuracy, contributing to domain-specific applications such as sustainable fisheries management and aquaculture research.
A HYBRID CONTEXTUAL EMBEDDING BASED CLUSTERING AND CLASSIFICATION TECHNIQUE FOR UNSUPERVISED IMPLICIT ASPECT CATEGORIZATION IN INDONESIAN REVIEWS
Aspect categorization is a grouping of reviews based on aspect categories that follow the review domain. The problem arises when only sentiment features appear as a clue to predict implicit aspects. On the other hand, implicit aspects play an important role in generating a summary. Without implicit aspect, we probably lose some important words needed for analyzing user’s reviews. Existing techniques face difficulties in utilizing the implicit aspects due to limited resources and computationally expensive problems. Hence, we propose an implicit aspect categorization model based on a hybrid contextual embedding-based clustering and classification technique. We developed the model using an unsupervised learning approach which is no need labelled data in training. A contextual embedding-based clustering technique generated train data from explicit sentences which will be used to classify implicit aspect categorization. Four steps of the proposed implicit aspects categorization model, i.e. preprocessing data, sentence feature selection, generating train data based on clustering, and finally categorizing implicit aspect using classification technique. We experiment with several classification techniques to get the best combination of the proposed technique (i.e. Logistic Regression, Support Vector Machine, Naïve Bayes, Decision Tree, and Random Forest). Based on the experiment, the combination of contextual embedding-based clustering and Random Forest algorithm produces higher accuracy than other classification techniques, with accuracy tent to 72.04% and F1 score in 0.6788.
FUTURE SOCIAL MEDIA USE WITH THE EMERGENCE OF AI IN MALAYSIA AND INDONESIA
This study explored the future uses of social media in Malaysia and Indonesia. There were 28.68 million social media users in Malaysia and 167 million in Indonesia. Most of the 10,211 users in the United States said that social media had a largely negative impact. Another survey found that 39% of the users in the United States posited that by 2035, uses of social media would not significantly serve the public good, and 18% said social media was evolving to a worse future for society. The objectives were to explore the potential, emerging trends, and future social media uses in Malaysia and Indonesia through content analysis, the Delphi survey, and triangulation. The content analysis analyzed 40 websites using QDA Miner and WordStat 9 for theme and case identification. The Delphi survey was constructed from existing studies and distributed to 12 experts. The findings revealed that potential social media uses comprised artificial intelligence-enhanced social commerce, socio-political attempts, and the need for control mechanisms enforced by the authorities. The emerging trends included the creation of non-traditional families and social chatbots. The preferable futures of social media use comprised augmented and virtual reality, audience analytics, conversational commerce, AI-powered chatbots, digital citizenship, and virtual campaigns against online scammers. The values of social media use included narrative control, compliance with Maqasid al-Shariah, regulated technological designs, and safety in the virtual environment. The study recommended an expanded ethical guideline for social media use and an action plan for regulating social media use in the future.
OPTIMIZING BERTSNN TO ENHANCE SOURCE-TARGET DOMAIN SIMILARITY SCORING FOR CROSS-DOMAIN SENTIMENT CLASSIFICATION OF PRODUCT REVIEWS
Cross-domain sentiment analysis (CDSA) predicts sentiment polarity in a target domain using knowledge from source domains but existing CDSA methods lack effective source domain selection strategies. This study investigates BertSNN, which combines pre-trained BERT embeddings, a Siamese neural network, and various distance metrics to measure domain similarity and optimize source domain selection for CDSA. First, we experiment with document-level (DocBERT) and sentence-level (SentenceBERT) embeddings with BiLSTM and BiLSTM + CNN neural network configurations to identify the best combination for BertSNN. Second, we explore two distance metrics—Euclidean and Manhattan—alongside shifted cosine similarity to determine the most effective choice for domain similarity scoring. Using product reviews, we test on 25 target domains, examining whether using multiple top most similar source domains improve cross-domain sentiment classification compared to a single most similar source domain. Results indicate that document-level embeddings, BiLSTM and shifted cosine similarity produce the most optimal BertSNN that can select high-quality similar source domains to train a cross-domain sentiment classifier for a target domain, beating two other traditional baseline methods (i.e., bag-of-words and TF-IDF representations). Our findings also show that using top five most similar source domains (k = 5) for training generally improves cross-domain sentiment classification performance as opposed to using a single most similar source domain (k = 1). This study contributes to CDSA by advancing the understanding of embedding choices and distance metrics within a Siamese neural network for source-target domain similarity scoring and providing actionable insights on domain selection strategies to improve sentiment analysis models.
ENHANCING RECOMMENDER SYSTEMS WITH DEEP REINFORCEMENT LEARNING AND KNOWLEDGE GRAPH EMBEDDINGS
Deep Reinforcement Learning (DRL), a subfield of machine learning, has shown remarkable potential in various domains, including recommender systems (RSs). This study leverages DRL to improve RS performance by effectively modeling user preferences and addressing their unique needs. A knowledge graph (KG) is constructed using product information, such as features and historical purchase data, to serve as the environment for the Markov Decision Process (MDP) within the DRL framework. The KG is enriched with embeddings to enable efficient navigation and enhance its utility. The Actor-Critic model in DRL employs these embeddings within the MDP, enabling a more accurate representation of user preferences. Central to this approach is the Representation of User Preferences via Path Embedding Propagation (RUPPEP), which serves as the study’s core contribution. Experimental results demonstrate that DRL-based RSs achieve superior performance metrics, with a 13.26% improvement in NDCG for the Amazon Cell Phones dataset and a 15.43% increase for the Amazon Beauty dataset compared to the best SOTA baseline model, highlighting their potential to advance the field of recommendation systems.
ENHANCING MULTILABEL CLASSIFICATION IN CHARGE PREDICTION USING LABEL CORRELATION AND PROBLEM TRANSFORMATION METHOD
Legal Judgment Prediction (LJP) has recently gained significant interest from both academic and legal practitioners. The majority of LJP methods focus on single label prediction problem, neglecting the real-world multilabel case. Therefore, this study aimed to classify multilabel legal cases using label correlation and problem transformation methods. Data were collected from a publicly accessible legal document in the European Court of Human Rights (ECHR) and EUR-Lex. Multilabel text classification tasks face challenges such as sample diversity, complexity, and the need for effective utilization of label correlations. In this paper, we propose a model that integrates domain specific text embedding and label correlation. Proposed model leverages label powerset as problem transformation to transform a multilabel problem to a multiclass problem by incorporating domain specific text embedding and label correlation, which enhances classification performance in charge prediction and addresses label omission issues. Extensive experiments on two legal text datasets demonstrate the model’s excellent performance. The proposed model substantially outperformed two baseline studies by attaining competitive results of 80.32%-90.09% F1-score and 0.0119-0.0210 Hamming Loss score, respectively. Meanwhile, the baseline models have attained 52%-80% F1-score and 0.0452-0.1479 Hamming Loss score. Proposed model’s performance significantly surpasses the baseline models. The significance of this study is the implementation of label correlation in label powerset problem transformation method and the application of domain specific embedding to solve multilabel classification problem in legal domain.
EVALUATING SIMILARITY MEASURES FOR MALAY NOISY TEXT NORMALIZATION: PERFORMANCE AND THRESHOLD ANALYSIS
Noisy text normalization is a critical preprocessing step in natural language processing (NLP), particularly for user-generated content (UGC) that contains a lot of slang, abbreviations, and typographical errors. This extended study investigates the performance of multiple similarity measures in normalizing Malay noisy text, addressing gaps in prior study that predominantly relied on rule-based approaches and single similarity measures. By systematically evaluating token-based, edit-based, and sequence-based similarity measures across various thresholds, this study provides a comprehensive analysis of their effectiveness and computational efficiency. The methodology comprises a two-phase experiment: an initial phase to identify optimal thresholds using a small dataset and a second phase that generalizes findings on a larger dataset. Key findings reveal that edit-based measures, such as Levenshtein Distance and Damerau-Levenshtein, consistently outperform other measures at lower thresholds, achieving normalization success rates exceeding 83%. Ratcliff/Obershelp emerged as the most effective sequence-based measure, while token-based measures like Jaccard and Cosine demonstrated limited performance. The study also highlights the critical role of threshold in balancing normalization accuracy and flexibility. Additionally, computational time analysis underscores the trade-offs between accuracy and efficiency across similarity categories. These findings pave the way for more robust and adaptable text normalization strategies, particularly for Malay language studies.
ENHANCED PARALLEL DEEP LEARNING FOR MALWARE DETECTION (EPDL-MD) MODEL
The number of cyberattacks caused by malware targeting critical sectors, such as energy systems, telecommunications, healthcare, and finance, is rapidly increasing worldwide. The evolution of malware has made detection techniques more challenging, resulting in financial losses and reduced productivity. To address this issue, this paper presents the Enhancement of Parallel Deep Learning for Malware Detection (EPDL-MD) model. This model focuses on improving the parallel convolutional neural network (CNN) architecture. The performance of the CNN is influenced by its hyperparameters, and the enhancements have led to an increase in accuracy and learning rate. The experiment utilized 176,000 malware samples, which were sourced from 86 distinct malware families and one benign family. Based on the analysis and experiments, the EPDL-MD model has achieved an impressive accuracy rate of 99%.
MACHINE LEARNING IN BYOD SECURITY: THREE-LAYERED ACCESS CONTROL FRAMEWORK FOR ENHANCED THREAT DETECTION AND POLICY MANAGEMENT
Existing access control provides a security solution to manage BYOD policies but is limited to controlling and providing adequate security. This paper comprehensively implements access control encompassing three security layers of the BYOD policy simultaneously: tactical, strategic, and operational. This system comprises the initial component and dynamic attributes for enforced access decisions. The second component consists of risk monitoring and anomaly detection algorithms. Finally, the third component employs the adaptive policy adjustment algorithm, which provides recommendations to the administration for policy updates in cases of abnormal access based on the results of the attack detection algorithm. The suggested access control solution was implemented using machine learning algorithms to detect anomalous and atypical user behavior. The experimental results obtained from the UNSW-NB15 dataset confirmed that the proposed access control could improve the anomaly detection algorithm and adaptive policy adjustment performance while reducing prediction detection time. The results demonstrated that the risk monitoring and anomaly detection algorithm, with a prediction time of 0.5 seconds and an accuracy rate of 0.95 percent, is the most effective method for monitoring attacks. Additionally, the results indicated that the accuracy of the adaptive policy adjustment algorithm was approximately 97%, with a threshold value of 0.26 being the optimal modification threshold value. The solution could enhance the detection of insider threats, access control, and policy management while at the same time making access control dynamic, adaptable, flexible, and secure.
A SYSTEMATIC LITERATURE REVIEW ON ETHICAL FRAMEWORK FOR ADOPTION OF GENERATIVE ARTIFICIAL INTELLIGENCE
Generative Artificial Intelligence (GAI) has rapidly disseminated within a brief period. Given GAI's early development and widespread presence, significant issues have arisen concerning the training and functioning of its underlying models, especially in ethical domains such as those related to ChatGPT and NLP. Thus, it is imperative to engage in more extensive discourse on the subject. Despite these advancements, GAI continues to pose substantial ethical concerns, such as the potential for the fabrication of scientific results which creates false information. These concerns underscore the necessity of addressing ethical challenges to maintain the integrity and efficacy of educational and research environments. This study aims to critically analyze the ethical challenges, limitations, and potential studies for adopting GAI systems through a systematic literature review. We utilized the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology to examine and evaluate relevant research studies. The research targeted articles published in 2023 and afterward. This systematic search was conducted from May 11 to May 17, 2024. From 712 records retrieved from nine academic databases, 9 duplicate record papers, 503 publications were determined to be incomplete and unrelated, 46 papers cannot be retrieved, and 28 are not peer-reviewed. As a result, twenty-three (23) research publications were found from 126 papers were qualified for consideration. The systematic literature review revealed that challenges related to the existing ethical framework for GAI adoption include addressing ethical concerns, establishing an evaluation model, formulating global principles for the ethical use and development of GAI-based systems, creating frameworks to regulate the ethical and responsible use of GAI, and addressing privacy and security issues, alongside the necessity for clear guidelines and ethical parameters for ethical GAI. The limitations identified in recent studies include insufficient empirical evidence and validation, a need for practical implementation, insufficient specific guidelines, insufficient evaluation metrics, and measurement instruments, and the necessity for further exploration of ethical considerations. The prospective studies within the current research framework involve the development of practical implementation standards, guidelines, and best practices; the creation of evaluation metrics; the formulation of regulatory frameworks to ensure ethical use; the collection of stakeholder perspectives; and the exploration of ethical implications alongside industry-specific analyses. Overall, this review can be used as a guide for researchers and all interested parties to encourage further research and experimentation related to ethical GAI adoption in the future.
A REVIEW OF IOS MOBILE DEVICES FORENSIC AND INVESTIGATION FRAMEWORK INTEGRATED WITH MACHINE LEARNING
The review covers key components of iOS forensics, including data acquisition, data analysis, and evidence interpretation, highlighting how machine learning algorithms can automate and optimize these processes. The paper also discusses the ethical and legal implications of deploying machine learning in forensic contexts, emphasizing the need for transparency, accountability, and privacy preservation. In recent years, the exponential growth of mobile devices, particularly iOS devices, has posed significant challenges to digital forensic investigators. The sheer volume of data and complexity of data stored on these devices require innovative approaches to efficiently extract, analyze, and interpret digital evidence. This review aims to provide a comprehensive overview of the integration of ML approaches with iOS mobile devices’ forensic and investigation framework. As mobile devices continue to play an increasingly central role in our daily lives, they have become a critical source of evidence in digital forensic investigations. Among these devices iOS-based mobile devices pose unique challenges due to their closed ecosystem and strong security measures.
DETECTING REAL-TIME E-COMMERCE FRAUD WITH ADVANCED ENSEMBLE META-MODELING
As e-commerce transactions continue to surge, the threat of fraud has escalated, posing significant challenges due to class imbalances, rapidly evolving fraud tactics, and the critical need to balance false positives and negatives. This study effectively addresses these challenges through an advanced ensemble stacking approach, integrating Support Vector Machine (SVM), Neural Network, Gradient Boosting, and AdaBoost as base models, with a Random Forest as meta-model to deliver final predictions. Using an e-commerce transaction dataset, our approach achieved 99.87% accuracy, significantly outperforming individual models. The meta-model further demonstrated 0.99 precision, 0.98 recall, and 0.99 F1-score for fraud cases (Class 1), highlighting its strong ability to accurately detect fraudulent transactions while minimizing false positives and false negatives. While SVM had the longest execution time, the Neural Network was the most efficient, and AdaBoost contributed the most to the meta-model’s predictions. Model validation was performed using Local Interpretable Model-Agnostic Explanations (LIME), highlighting Transaction Hour, Transaction Amount, and Account Age Days as key predictive features. The model was successfully deployed to a web-based application, demonstrating real-time fraud detection capabilities. This research offers a robust, interpretable method for e-commerce fraud prevention, potentially reducing financial losses and enhancing online transactions.
ENHANCING BVAG DATA REPLICATION TRANSACTIONS WITH HIGH-PRIORITY-NEIGHBOUR FAULT TOLERANCE APPROACH
In distributed systems with failure interruption, the performance of database replication transactions might become very critical. Any distributed system that enforces data replication can be impacted by this problem. The fault tolerance approach is crucial to ensure the data replication transactions are always effective and dependable despite failures. The key advantage of fault tolerance is its capacity to complete the transaction notwithstanding a failure and restore system availability. This paper proposes a fault tolerance approach namely Binary-Vote-Assignment-Grid with High-Priority-Neighbour (BVAGHPN). It improves the efficiency of the data replication transaction in term of total execution time. This approach combines BVAG data replication transaction manager with the HPN to manage the transaction in the event of disasters. Instead of waiting for the problem to be fixed in the event of disaster, BVAGHPN halts the transaction on a failure replica, remove the failing replica from the alive quorum, and proceed the transaction with other replicas based on its own rating. BVAGHPN improves the outcomes of BVAG and BVAGCR in terms of the total execution time for two cases, PR failure and NR failure. For PR failure, BVAGHPN exceeds BVAG with 69.02% and BVAGCR (54.67%), respectively. Meanwhile, for NR failure, BVAGHPN improves BVAG with 76.88% and BVAGCR (71.97%).
ENHANCING INFANT PAIN DETECTION WITH HYBRID ATTENTION MECHANISMS IN LIGHTWEIGHT MOBILENETV3 ARCHITECTURES
Creating an automated pain detection system for infants less than a year old is essential because they are unable to communicate their discomfort verbally. Conventional assessment techniques like FLACC (Face, Legs, Activity, Cry, Consolability) require considerable time and may not be effective for infants with vocal cord impairments. Utilizing infants’ facial expressions for real-time, automated pain detection presents a promising approach that facilitates rapid medical response. This study adopts a machine learning approach using infant facial expressions as input and explores the efficacy of various MobileNetV3 architectures, both Small and Large, enhanced with attention mechanisms. We introduced modifications involving 12 model variants, including the integration of CBAM (Convolutional Block Attention Module), ECA (Efficient Channel Attention), and SAM (Spatial Attention Module) attention modules, as well as hybrid attention configurations (ECA + CBAM and ECA + SAM). Training was conducted on a FLACC-based dataset comprising 56 videos collected from infants under 12 months undergoing hernia treatment at Dr. Soetomo General Hospital, Surabaya, East Java, Indonesia, from November 2011 to December 2022. The dataset is categorized into three pain levels: no pain, low/moderate pain, and severe pain. Results demonstrate that attention mechanisms significantly enhance model accuracy, with hybrid configurations consistently achieving the best performance. The ECA + CBAM hybrid configuration achieved the highest accuracy of 94.5%, representing a 5% improvement over baseline models, while also reducing misclassifications across all pain levels. However, these gains come with increased computational complexity, including higher parameter counts, greater FLOPs, longer inference times, and higher memory usage. These results indicating their robustness in real-time pain detection for infants, thereby highlighting their potential for practical clinical applications.
FLOOD PREDICTION USING SUPPORT VECTOR MACHINE ALGORITHM ON MOBILE APPLICATION
Floods are recurrent natural disasters that can have a devastating impact on societies, economies, and the environment. Therefore, it is critical to predict and manage flood situations promptly to minimize the damage they cause. However, many people are unaware of flood risks, and there are limited mobile applications that can provide timely and accurate flood predictions. This study explores the application of the Support Vector Machine (SVM) algorithm for flood prediction in a mobile application. The aim is to provide users with timely and accurate flood predictions, enabling them to make informed decisions and take necessary precautions to mitigate flood impacts. By integrating the SVM algorithm into a mobile application, users gain convenient access to flood predictions, empowering them to be better prepared for potential flooding events. The user-friendly platform delivers critical flood forecasts, ensuring individuals and communities can respond effectively to flood situations. The evaluation of the SVM algorithm's performance reveals an achieved accuracy of 66.66%. In conclusion, this study underscores the potential of the SVM algorithm for flood prediction in a mobile application. These findings contribute to the field of flood forecasting technology, paving the way for more sophisticated and effective flood prediction tools in the future.
CONCEPT-GUIDED GAN WITH DISENTANGLED REPRESENTATIONS FOR EXPLAINABLE PLANT DISEASE IMAGE SYNTHESIS
Generative Adversarial Networks (GANs) have advanced image synthesis and are widely used to augment training data for deep Convolutional Neural Networks (CNNs). However, in scientific domains like plant disease identification, interpretability and morphological control are essential. Existing GANs typically rely on pixel-level feedback from the discriminator and function as black-box architectures, often learning entangled representations that limit control over high-level morphological features like leaf shape and disease patterns. This study introduces CXAI-GAN, an explainable GAN that integrates Concept-based Explainable AI (C-XAI) to generate biologically realistic images with explicit morphological control. The generator is modified to disentangle and encode three interpretable concepts: leaf shape, surface texture, and disease pattern. Unlike post-hoc Explainable AI (XAI) methods (e.g., LIME, SHAP) that reveal what features matter numerically, CXAI-GAN explains why outputs are generated through concept learning. CXAI-GAN achieves strong performance with an FID of 19.64, SSIM of 0.955, and PSNR of 34.91. Fine-grained evaluations show high fidelity: shape similarity (HDS 0.973), texture alignment (VPS 0.999), and local SSIM 0.937. In a binary classification task of visually similar grape diseases, CXAI-GAN improved accuracy by 10% and reached 96.7% with synthetic training. These results demonstrate CXAI-GAN’s effectiveness in generating interpretable, high-quality images for downstream scientific tasks.
COMPARATIVE ANALYSIS OF MISSING DATA IMPUTATION METHODS FOR FLOOD FEATURES FROM LANGAT RIVER IN SELANGOR, MALAYSIA
Flooding poses serious risks to lives, infrastructure, and ecosystems, underscoring the need for accurate forecasting. However, missing values in hydrological datasets—often caused by equipment failure or extreme weather—can compromise forecast reliability. This study evaluates five imputation techniques: Last Observation Carried Forward, Next Observation Carried Backward, Linear Interpolation, Spline Interpolation, and K-Nearest Neighbours, to identify the most effective method for reconstructing missing flood-related data. Using temperature, humidity, and water level records from the Langat River, Selangor, Malaysia, each method’s performance was assessed via Root Mean Square Error. Results show that Linear Interpolation generally yields the lowest error, while Next Observation Carried Backward performs best when missing data is minimal (1.20%).
SMOTE-ENN-LR: LEVERAGING MACHINE LEARNING FOR BREAST CANCER CLASSIFICATION IN MICROARRAY GENE EXPRESSION WITH EXPLAINABLE AI
Breast cancer continues to be a major public health issue worldwide, ranking as the second leading cause of cancer-related deaths among women. Effective early detection and classification are crucial for improving survival rates, yet they are complicated by the challenges posed by imbalanced datasets in microarray gene expression analysis. These imbalances can significantly affect the predictive power and reliability of traditional classification models, underscoring the need for more sophisticated analytical techniques. This study introduces an approach, the SMOTE-ENN-LR method, which combines the Synthetic Minority Over-sampling Technique (SMOTE) with Edited Nearest Neighbors (ENN) for noise removal and Logistic Regression (LR) to accurately classify breast cancer based on microarray data. The SMOTE technique is utilized to over-sample the minority cases in the dataset, thereby addressing the issue of underrepresentation. Simultaneously, the ENN method is employed to clean the data by removing mislabeled instances and noise, which are often prevalent in over-sampled datasets. The cleaned and stable dataset is used to train a LR model, optimizing its ability to discern between cancerous (Abnormal) and non-cancerous (Normal) gene expression profiles effectively. Our comprehensive evaluation shows that the SMOTE-ENN-LR method attained a remarkable classification accuracy of 97.14%, outperforming contemporary state-of-the-art methods. This significant enhancement in accuracy highlights the potential of combining advanced data preprocessing techniques with robust statistical learning models to tackle the inherent challenges of microarray data analysis. Further, we employ Local Interpretable Model-agnostic Explanations (LIME) and SHAP (SHapley Additive exPlanations) to offer an understandings into our model’s decision-making process, enhancing the predictions’ transparency and interpretability. Moreover, the success of the SMOTE-ENN-LR method in this study paves the way for its application in other areas of medical diagnostics where similar data imbalances may impact the accuracy and effectiveness of disease classification. These results substantiate the effectiveness of the SMOTE-ENN-LR approach in managing the complexities of imbalanced microarray gene expression data, proposing a promising path for upcoming research in medical bioinformatics and precision medicine.