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Exploring Generative AI Recommender Systems in E-Commerce: Model, Evaluation Metric, and Comparative Review
No full textGenerative Artificial Intelligence (GAI) is changing what can be done with Recommender Systems (RS) in e-commerce by allowing much more interactive, situationally aware, and highly tailored experiences for users. The purpose of this paper is to provide overall insight into how GAI, including Large Language Models (LLMs), Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and other emerging methods, is affecting the building and running of modern e-commerce RS. This paper classifies generative models into groups based on the type of models used, data modality, and specific domain of application. Their involvement in tasks such as personalized product ranking, content generation, and cold-start problem avoidance is discussed comprehensively as well. In addition, we also analyse innovation in design trends, practical challenges, such as explainability, real-time adaptability, computational scalability, and possible trade-offs, as well as pathways ahead through the lens of current literature and empirical systems. By contrasting GAI-RS with traditional RS, we highlight their advantages in handling several problems, such as data sparsity, generating diverse recommendations, and enabling dynamic user interaction. This paper should serve to broaden awareness among scholars and practitioners about the ever-changing convergence of GAI and intelligent recommendation structures within e-commerce, emphasizing both their transformative potential and operational complexities in practice
Editorial: Augmented Intelligence for Enabling Knowledge-Driven Decision Making
No full textMore flexible and cooperative decision-making processes are required as a result of society's digital transformation. The role of augmented intelligence, that is the synergistic fusion of artificial intelligence and human judgment in facilitating knowledge-driven strategies across various domains is examined in this thematic issue. The integration of business intelligence and software engineering, which forms the foundation for creating intelligent, scalable, and explicable systems, is essential to this investigation. The six chosen papers in this issue show how machine learning techniques can be used to mine and model both structured (such as health records indicators) and unstructured (such as product reviews, e-sports discourse, and social media text from X) data. Applications in political sentiment analysis, geopolitical opinion monitoring, risk communication related to weather, e-commerce consumer feedback, gaming community analytics, and mapping malnutrition for public health intervention are all covered in these papers. From explainability and interface design to data preprocessing and model deployment, software engineering is essential to coordinating these intelligent pipelines and guaranteeing that AI outputs are not only accurate but also practically sound. The pieces in this issue collectively demonstrate how Augmented Intelligence can transform decision-making in a rapidly changing digital society when enabled by domain-aware data pipelines and structured engineering frameworks
Conjugate Gradient Methods in Fitting Precipitation of Rainfall Data in Malaysia
No full textConjugate gradient method (CGM) is one of the most efficient numerical methods for solving unconstrained optimization problems. It is also known as an iterative method with simple formulation. The classical CGM has always been an interest to the current researchers in improving the formulation which are categorized into three-term (TTCGM), spectral (SCGM), hybrid and scaled CGM. Although there are many variations of the CGM available, choosing the most efficient and effective one for a particular problem can be a time-consuming process. In this study, spectral Hestenes-Stiefel (sHS) CGM with the greatest NOI and central processing time per unit (CPU time) is selected as the efficient method to be applied to the real-life problems in regression analysis. A data set of rainfall precipitation in Malaysia from year 2009 until 2019 is collected for data fitting purpose. The data set is transformed into a test function also defined as objective function. The approximate functions are generated from CG, Least Square, Trendline method for the relative error purpose. The estimation data for the year 2020 can be predicted using the approximate functions. The calculation of relative error of the linear and quadratic model for each method is calculated based on the estimation data for the year 2020 and its actual data. The numerical results show that the sHS CGM is a suitable and good alternative to solve the Least Square models
The Hiring and Termination Procedures for Employees in Bangladesh Under the Labour Act of 2006 and the Role of the Courts
No full textThe Bangladesh Labour Act of 2006 is the primary legislation in Bangladesh that addresses issues related to employment. The main objective of the Act is to rectify previous disparities and create a favourable working environment for workers. The legislation sets out detailed regulations concerning job conditions and services. In practice, employers often hold significant powers, including the authority to hire, terminate and dismiss employees at their discretion for various reasons. However, the Labour Act and the Labour Court are tasked with ensuring fair treatment and upholding the principles of natural justice. Specifically, the Bangladesh Labour Act empowers the Labour Courts to intervene in cases of employee termination through the grievance procedure. This study thoroughly examines, with case references, the extent to which the courts apply principles of fairness and equity in job termination cases under the Bangladeshi Labour Act of 2006. The research concludes with insights into the recruitment and termination processes in Bangladesh, highlighting the need for amendments to the current labour legislation to ensure employment stability and economic support for the workforce
Sentiment Analysis of Indonesian Nickel Downstreaming on X Using Naïve Bayes and K-Nearest Neighbors
No full textNickel downstreaming has become one of Indonesia’s most prominent industrial policies, positioned as a pathway to economic growth and global relevance in the electric vehicle supply chain. Despite its ambitions, the policy has triggered intense debate on social media, where concerns about ecological damage and foreign dominance intersect with narratives of national pride. This study employs sentiment analysis to examine public perceptions of the policy through 337 tweets collected from X (formerly Twitter). Two machine learning algorithms, Naïve Bayes and K-Nearest Neighbors, were applied to classify sentiment into positive, negative, and neutral categories, followed by evaluation using confusion matrices, accuracy, precision, recall, and F1-score. The results show that negative sentiment dominates across both models, with Naïve Bayes achieving higher accuracy and recall, while KNN displayed strengths in precision and F1-score. Wordcloud analysis further revealed that positive sentiment is associated with industrial progress and national identity, negative sentiment emphasizes environmental risks and foreign control, and neutral sentiment reflects factual reporting of events. These findings confirm that nickel downstreaming remains a contested policy, viewed as an economic opportunity by some and as a source of social and ecological concern by many others. This study demonstrates the value of integrating sentiment analysis with policy research, as social media provides real-time insights into how citizens perceive government initiatives. The evidence highlights the importance of addressing environmental sustainability and equitable resource management to build trust and legitimacy. Sentiment analysis therefore serves not only as a tool for understanding public opinion but also as a guide for shaping more inclusive governance.
Manuscript received: 5 Aug 2025 | Revised: 8 Oct 2025 | Accepted: 15 Oct 2025 | Published: 30 Nov 202
From Signatures to AI: A Comprehensive Review of DDoS Detection Strategies in IoT & SDN
No full textIn the ever-evolving landscape of the Internet of Things (IoT) and Software-Defined Networks (SDN), the rapid growth of interconnected devices has enhanced ease and efficiency. However, this evolution has also paved the way for the ominous cyber-attack: Distributed Denial of Service (DDoS). These attacks, which make systems unavailable for legitimate users, threaten the data integrity, confidentiality, and availability in IoT and SDN infrastructure. This paper delves into the critical issue of DDoS attacks within the IoT and SDN environments, offering a comprehensive exploration of detection mechanisms by categorizing them into traditional (signature-based) and anomaly-based approaches i.e., Machine Learning (ML), Deep Learning (DL), and statistical techniques. Our key findings reveal that while signature-based methods effectively identify known attack patterns, they fall short against novel threats. In contrast, AI-based approaches, particularly ML and DL, demonstrate superior performance in detecting previously unseen attacks. However, their efficiency is highly dependent on the quality of training data and model robustness. Our comparative analysis indicates that ML and DL methods achieve higher detection rates and lower false positives in experimental settings, underscoring the importance of high-quality datasets and resilient models. By highlighting the strengths and limitations of both approaches, this study provides valuable insights for researchers and cybersecurity experts. The need for an effective and diversified DDoS detection mechanism in the developing IoT and SDN domains is evident. While conventional methods remain relevant, AI-based strategies offer a dynamic avenue for enhancing security.
Manuscript received: 24 Oct 2024 | Revised: 14 Dec 2024 | Accepted: 30 Dec 2024 | Published: 31 Mar 202
Developing a Telepresence Robot for Autism Diagnosis
No full textThe global COVID-19 pandemic posed significant challenges to the healthcare industry in maintaining continuous operations while adhering to strict physical distancing protocols. Critical functions such as delivering meals to patients, supplying medical instruments, monitoring vital signs, assisting those with impaired mobility, and ensuring accurate disease diagnoses became increasingly difficult. As the world adapts to a post-pandemic reality, robots are expected to play a more prominent role by becoming more self-reliant, adaptable, and collaborative. In response to these evolving needs, the Centre for Unmanned Technologies (CUTe) at International Islamic University Malaysia (IIUM), in collaboration with Prostrain Technologies, developed the innovative medical robot called "Medibot". Medibot, a telepresence robot, presents a promising tool for observing children's true behaviours and interactions—essential for diagnosing Autism Spectrum Disorder (ASD). Equipped with a high-resolution camera, Medibot facilitates seamless video conferencing between children and experts, enabling detailed behavioural analysis during diagnostic sessions. The presence of parents beside the child enhances comfort, while the robot's non-intrusive character encourages natural responses and interactions. Compared to traditional human-led assessments, Medibot's presence is less intimidating, potentially leading to more accurate diagnoses. Medibot’s development is underpinned by a robust ROS-based software architecture, enabling autonomous navigation in complex hospital environments while avoiding static and dynamic obstacles with high operational consistency. Extensive testing has validated its mapping and navigation capabilities, ensuring smooth and predictable movements without human intervention, making the diagnostic process less intrusive and seamless. The incorporation of telepresence technology, primarily through a teleconferencing camera for live image streaming, represents a significant advancement in remote healthcare. With applications ranging from ASD diagnosis to broader medical monitoring, Medibot exemplifies the transformative potential of telepresence robotics in expanding access to specialized care and improving patient outcomes.
Manuscript received: 14 Sep 2024 | Revised: 18 Dec 2024 | Accepted: 3 Jan 2025 | Published: 31 Mar 202
DRD-Net: Diabetic Retinopathy Diagnosis Using A Hybrid Convolutional Neural Network
No full textDiabetic Retinopathy (DR) has become a leading cause of blindness among diabetic patients. Accurate and timely diagnosis of DR is critical to slowing disease progression. This research proposes a Hybrid Convolutional Neural Network (CNN)-based model, named Diabetic Retinopathy Detection Network (DRD-Net). The proposed DRD-Net designed to enhance diagnostic accuracy by addressing key challenges such as gradient vanishing and lesion scale variability in fundus images. Contrast-Limited Adaptive Histogram Equalization (CLAHE) was used to enhance contrast and highlight lesions in fundus images. To increase the diversity of training samples, the proposed framework employs geometric data augmentation techniques. DRD-Net incorporates the Swish activation function along with densely connected blocks to mitigate gradient vanishing and enhancing feature propagation within the network. Additionally, the model integrates two Inception blocks to facilitate multiscale feature extraction, which is essential for detecting small Regions of Interest (RoI) in fundus images. Experimental results demonstrate that DRD-Net achieves a precision of 84.4%, recall of 84.5%, F1-score of 84.1%, and accuracy of 85.1%, outperforming several state-of-the-art models on the IDRiD dataset. These results highlight DRD-Net’s potential as an effective solution for automated DR diagnosis, contributing to more efficient and accurate DR screening.
Manuscript received:1 Mar 2025 | Revised: 23 Apr 2025 | Accepted: 5 May 2025 | Published: 30 Jul 202
The Determinant of Islamic Bank Profitability and Stability in Malaysia: DOI: https://doi.org/10.33093/ijomfa.2025.6.1.7
No full textThis study examines the determinants of profitability and stability of 14 Islamic banks in Malaysia using secondary data from 2018 until 2022. Hausman and Breush-Pagan's tests indicate that the fixed effect panel model is the best model to predict the relationships. The final fixed effect model for the profitability of Islamic banks shows that capital adequacy ratio (CAR), Z-score, and gross domestic product (GDP) have significant relationships with return on assets (ROA). CAR has a negative effect, while Z-score and GDP positively affect ROA. On the other hand, the final fixed effect model for the stability of Islamic banks shows that the liquidity ratio is negatively related to Z-score. At the same time, ROA has a positive relationship with Z-score. An optimal CAR would ensure banks meet their obligations and optimise the use of their funds to maximise profits. The study shows that Islamic banks with higher Z-score have a reduced likelihood of insolvency, suggesting that they operate in a stable environment and, as a result, generate profits and excellent performance. Additionally, the study found that Islamic banks flourish during periods of robust economic expansion. Finally, the results show a rise in return on assets (ROA) would enhance the viability of financial institutions by ensuring a steady stream of profits and a consistent performance
Bridging Innovation and Security: A Bibliometric Review of Blockchain’s Impact on Higher Educational Institution Management: DOI: https://doi.org/10.33093/ijomfa.2025.6.2.8
No full textBlockchain technology in higher education institutions (HEIs) can revolutionize academic administration by improving data security, increasing transparency, and boosting operational efficiency. However, blockchain adoption in HEIs faces technological, organizational, and regulatory challenges. A bibliometric review procedure assessed the 246 relevant studies from Scopus peer-reviewed literature on blockchain adoption’s main themes, most prominent authors, journals, and articles. The findings highlight Blockchain’s ability to streamline credential verification, automate academic processes, and reduce administrative costs. However, key barriers such as scalability limitations, interoperability issues, financial constraints, and regulatory challenges continue to hinder widespread implementation. Besides, there is a significant gap in top management support and institutional readiness, which impacts the integration of blockchain systems in educational frameworks. This study advances the scholarly discourse on blockchain implementation in academia by examining the challenges associated with its adoption and proposing strategic solutions to facilitate its effective integration. The research implies compelling insights for policymakers, educational leaders, and technology developers eager to capitalize on the revolutionary opportunities of blockchain technology in HEIs. Besides, this study is among the first to demonstrate a structured analysis of Blockchain’s impact on operational efficiency in HEIs from a technological, organizational, and environmental perspective, addressing the gap in institutional readiness