Journal of ICT Research and Applications

Enhancing Healthcare RFID Asset Tracking: A Multi-Objective Optimization Approach Considering Network Delay, False Identification, and Energy Efficiency

Sinung Suakanto — 2026-05-01

This paper discusses asset challenges detection in the healthcare industry, specifically delays and inaccuracies in asset monitoring caused by suboptimal RFID polling methods. The research question is how to determine an appropriate RFID polling interval that balances asset location accuracy, reader energy consumption, and network response time. Due to the increasing risk of mismanagement and equipment loss, an efficient approach is needed to improve asset-tracking accuracy. This study proposes a simulation-based multi-objective optimization approach by determining the optimal polling period to minimize network delay, reader energy consumption, and false identifications. Monte Carlo simulation models the stochastic movement of assets to evaluate system performance under different polling strategies. The results of one experiment showed that 100 assets, with an average moving rate of 2.48, reached the optimal scanning period of 1460 minutes. Additional experiments were conducted to analyze the sensitivity of the optimal polling interval to changes in asset population and movement rates. The contribution of this study is the development of a holistic model to determine the optimal scanning time to improve asset-tracking accuracy and reduce operational costs in RFID systems. Although evaluated in a healthcare context, the proposed framework is versatile and can also be used for other RFID-based asset monitoring scenarios with similar trade-offs.

Usability Concerns and Design Solutions in Mobile Health Applications for Older Adults

Shuhadah Othman — 2026-05-12

Mobile health applications (mHealth) are known for their potential to enhance health delivery and improve health outcomes for older adults. However, despite their broad potential, significant usability concerns persist among older adults, preventing them from gaining advantages from mHealth and hindering their adoption. This paper critically examines the key usability concerns older adults face when interacting with mHealth. This research adhered to the PRISMA guidelines in the process of retrieving and selecting articles, resulting in the review and analysis of 117 articles published between 2014 and 2024. The results highlight several prevalent usability concerns, including complex navigation, overwhelming instructions, inadequate feedback mechanisms, and limited personalization options, which overlook the diverse needs of older users. Furthermore, this study identified design solutions to address these concerns, including straightforward navigation, clear instructions, and consistent layout/design tailored to older adults. Moreover, this paper established a visual representation that maps identified usability challenges to corresponding design solutions, serving as a valuable resource for researchers, designers, and developers working on mobile health applications for older adults. Integrating these solutions into the development process can significantly improve user experience and increase engagement in health interventions.

Collision-Free Tool Path Optimization for Louvre Geometries Using an Adaptive Discrete Particle Swarm Framework

Selvi Lukman — 2026-05-01

This study proposes a novel application of Particle Swarm Optimization (PSO) for tool path planning in complex louvre geometries utilized in heat transfer systems. Unlike conventional approaches, the proposed framework explicitly integrates geometric smoothness and collision avoidance into the optimization process, as it enables the generation of continuous and non-intersecting tool trajectories. This is particularly significant as surface quality in louvre fins directly influences boundary layer disruption, which in turn affects convective heat transfer efficiency and pressure drop characteristics. By minimizing abrupt tool movements and machining-induced surface roughness, the method addresses a critical gap between manufacturing precision and thermal performance. The PSO-based approach simultaneously optimizes machining time and trajectory feasibility, ensuring safe and efficient tool movements. The experimental results demonstrated rapid convergence with the objective function significantly decreasing within the first 50 iterations and stabilizing around iteration 80. The optimized solution achieved a machining time of 0.60 minutes (36 seconds) while maintaining consistent minimum objective values throughout the process. These findings highlight the robustness and stability of the proposed method. Overall, this work contributes a novel optimization framework that bridges advanced manufacturing and thermal performance considerations, establishing PSO as an effective solution for high-precision tool path planning in complex industrial geometries.

Enhancing IoT Cybersecurity with Multi-Layer Deep Transfer Learning Approach for Intrusion Detection

Anuj Rapaka — 2026-05-26

Intrusion detection in IoT-enabled cloud environments is challenged by high-dimensional traffic, class imbalance, and limited labeled data. This paper proposes a hybrid framework combining Golden Jackal–Grey Wolf Optimization (GJO-GWO) for feature selection with a Kernel Mean Alignment Autoencoder (KMA-AE) for deep transfer learning. GJO-GWO selects a compact, discriminative feature subset, while KMA-AE aligns source and target latent representations to mitigate distribution mismatch. Experiments on the CIDDS-001 dataset achieve 90.21% accuracy and 0.90 macro-F1, with improved precision–recall for minority attacks and a 60% feature reduction. Although training is more expensive, the method attains the lowest inference time, enabling real-time deployment. Overall, the framework provides an effective and generalizable intrusion detection solution for dynamic IoT environments.

Personalizing E-Commerce Experiences: A Machine Learning Framework for Dynamic Gamification and Customer Engagement

Azizul Azman — 2026-05-26

E-commerce platforms are increasingly challenged to sustain customer engagement amidst intensifying competition. Traditional gamification approaches, characterized by static, uniform mechanics, often fail to adapt to individual user preferences, leading to diminishing returns and decreased engagement over time. These conventional methods typically employ fixed reward structures that do not account for individual user behavior, resulting in a lack of sustained engagement. This paper introduces a comprehensive machine learning (ML) framework for dynamic gamification, designed to personalize game elements in real-time based on individual user behavior patterns. The framework integrates clustering algorithms, reinforcement learning (RL), and collaborative filtering techniques to analyze user interactions and generate adaptive gamified experiences. Simulated testing, conducted using a publicly available e-commerce customer behavior dataset from Kaggle, provided insights into diverse user preferences and behaviors. Simulated results demonstrated significant improvements, including a 32% increase in daily active users, a 24% higher conversion rate, and a 30.8% improvement in 30-day customer retention. The framework addresses critical technical challenges, such as scalability, real-time processing, and ethical data usage. This research contributes to the advancement of personalized digital experiences in e-commerce, offering practical guidelines for enhancing customer engagement through AI-driven gamification.