Malaysian Journal of Computer Science

FLOOD PREDICTION USING SUPPORT VECTOR MACHINE ALGORITHM ON MOBILE APPLICATION

Afif Shyamsul Syahiran Suhaimi — 2025-08-11

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.

ENHANCING INFANT PAIN DETECTION WITH HYBRID ATTENTION MECHANISMS IN LIGHTWEIGHT MOBILENETV3 ARCHITECTURES

Anindya Apriliyanti Pravitasari — 2025-08-11

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.

ENHANCING BVAG DATA REPLICATION TRANSACTIONS WITH HIGH-PRIORITY-NEIGHBOUR FAULT TOLERANCE APPROACH

Sharifah Hafizah Sy Ahmad Ubaidillah — 2025-08-11

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%).

DETECTING REAL-TIME E-COMMERCE FRAUD WITH ADVANCED ENSEMBLE META-MODELING

Mariyam Majidha — 2025-08-11

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.

A REVIEW OF IOS MOBILE DEVICES FORENSIC AND INVESTIGATION FRAMEWORK INTEGRATED WITH MACHINE LEARNING

Ishaq Ahmed — 2024-08-01

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.

A SYSTEMATIC LITERATURE REVIEW ON ETHICAL FRAMEWORK FOR ADOPTION OF GENERATIVE ARTIFICIAL INTELLIGENCE

Artika Arista — 2025-08-11

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.

MACHINE LEARNING IN BYOD SECURITY: THREE-LAYERED ACCESS CONTROL FRAMEWORK FOR ENHANCED THREAT DETECTION AND POLICY MANAGEMENT

Aljuaid Turkea Ayedh M — 2025-08-11

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.

ENHANCED PARALLEL DEEP LEARNING FOR MALWARE DETECTION (EPDL-MD) MODEL

Madihah Mohd Saudi — 2025-08-11

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%.

HANDLING IMBALANCED DATA ON MULTILEVEL DEPRESSION CLASSIFICATION: CHALLENGES AND SOLUTIONS

Mohd Shahrul Nizam Mohd Danuri — 2025-08-11

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.