Mohd Fahmi Mohamad Amran

Intergenerational learning (IGL) has gained increasing attention as a strategy to address the limited effectiveness of elderly education, particularly in the context of digital literacy and lifelong learning. Despite growing research, the field remains fragmented, with limited synthesis translating evidence into actionable educational strategies. This study aims to systematically examine how IGL enhances elderly learning ability and to identify strategic directions for its implementation. A bibliometric-driven analytical approach was employed using Scopus-indexed publications from 2021 to 2025. The top ten most-cited articles were identified through performance analysis and subsequently analyzed using a strengths, weaknesses, opportunities, and threats (SWOT) framework. The findings reveal that IGL significantly improves digital literacy, cognitive engagement, and social well-being among elderly learners. However, challenges such as internalized ageism, low learning confidence, and the absence of structured and scalable frameworks remain critical barriers. The study also highlights opportunities for integrating IGL into formal education and community-based programs, while identifying threats related to technological change and policy limitations. This study contributes a bibliometric-informed strategic synthesis that advances understanding of IGL and provides actionable insights for educators, program designers, and policymakers in elderly education.

This study develops a predictive modeling framework for multi‐channel bus ticket demand using counter ticket sales and online sales data from an aggregator‐based e‐ticketing system in Malaysia. The study integrates historical sales data with operational and external variables, including trip frequency, vehicle registration, public and school holidays, weather indicators, and ridership trends. A SARIMAX‐based time series approach is applied together with comparative machine learning models to examine seasonal demand patterns, channel‐level differences, and price sensitivity. The findings show that counter ticket sales and online sales exhibit different demand behaviors. Counter sales are relatively less sensitive to price changes and are more influenced by trip availability and seasonal travel periods, while online sales are shaped more strongly by operational efficiency, service availability, and customer purchasing behavior. The main contribution of this study is the use of price elasticity impact as a predictive variable to improve the interpretability of demand forecasting in a multi‐channel ticketing environment. The proposed framework provides practical insights for bus operators to support pricing decisions, trip planning, and data‐driven operational strategies in public transportation ticketing systems.

Ransomware has emerged as one of the most serious and rapidly evolving cyber threats. This can potentially cause substantial financial losses and adversely affect organizational operations. In recent years, ransomware attacks have increasingly used methods such as encryption, hidden communication and advanced circumvention strategies. Most traditional intrusion detection systems (IDS) struggle to detect encrypted traffic, leading to high false alerts and low accuracy. This limitation is more critical in modern ransomware attacks that rely heavily on encrypted command‐and‐control (C2) communication channels. This study proposes an improved method of detecting ransomware through Suricata as evidence to address these concerns. This study focuses on improving detection accuracy, reducing false positive rates and enhancing encrypted traffic analysis. This was accomplished using Suricata, an open‐source intrusion detection and prevention system. The structured methodology was implemented in three phase, which consist of design, development and testing and validation. In the design phase, the data set is prepared and IDS is configured. In the development phase, Suricata rule sets were optimized with JA3 TLS fingerprints to analyze encrypted ransomware traffic. The system was validated using real‐world ransomware datasets to ensure realistic evaluation conditions. As a result, the IDS can detect known and emerging ransomware communication patterns more effectively. Additionally, the system was optimized to achieve maximum throughput and reduce resource consumption while performing real‐time analysis. Performance evaluation was conducted using standard metrics, including detection accuracy, false positive and processing time. The proposed method achieved 96.8% detection accuracy, 2.4% false positive rate and reduced processing time from 240 to 30 s, representing a 10 times improvement compared to baseline Suricata. In addition, system performance remained acceptable while improving ransomware detection. These findings show that JA3 fingerprinting and optimized Suricata rules can improve real‐time ransomware detection in a cost‐effective and scalable way.

The purpose of this study is to examine the travel‐related perceptions and behaviours of the residents of the Klang Valley and categorise them into different groups. This research employs the analytic technique of hierarchical clustering analysis (HCA) and is focused on the area of environmental concern, economic viability, the existing infrastructure and the awareness of state‐sponsored subsidies. HCA was applied to the collected data of respondents from the Klang Valley, where data dimensionality reduction from 16 to 5 was applied, and the data reliability testing was conducted. The identified clusters were subjected to attitude and motivation analysis in order to determine the statistically significant differences. The study discovered that there are travel‐related customers groups who possess distinctly different travel‐related parameters. Some of them possess a high focus on travel costs, while others concern on the environment or have a greater awareness of the available technologies. The insights from this segmentation would be critical for policy formulation and strengthen electric vehicles (EV) companies, local authorities and policy makers in their marketing strategies. It adds to the body of literature by a localised context of how EV could become a tool toward sustainable transportation development in Malaysia with regards to EV adoption.

The work presented develops an autonomous system that identifies shifts in market instability through a synergistic integration of agentic artificial intelligence with statistical and deep learning techniques. The system operates through a multi‐agent architecture that streams real‐time market data, detects changes in volatility and produces corresponding trade position recommendations. Its analytical engine applies statistical methods such as principal component analysis and clustering together with deep learning models that use attention‐based recurrent neural networks. Using performance data from 2000 to 2023, the system achieved an accuracy of 87 percent in classification and generated average yearly risk‐adjusted returns that exceeded benchmark portfolio strategies by 2.3 percent. These outcomes highlight the growing capabilities of agentic artificial intelligence in the financial sector and demonstrate its adaptive predictive strength in enhancing clarity and supporting informed decision‐making.

This study examines the nurse rostering problem in the maternity ward of a military hospital to improve operational efficiency while maintaining nurse well‐being. In the initial phase, a preemptive 0–1 goal programming model (GP) is introduced to generate feasible nurse duty rosters. This model guarantees fair rotation within specified working‐day limits, avoids overlapping assignments for all nurses across all shift types and prevents nurses from working 7 consecutive days. Additionally, three soft constraints are considered, where the model prevents irregular rest patterns, such as isolated work or off days, and ensures equitable workload distribution. Although the 0–1 GP model generates satisfactory schedules, the soft constraint of avoiding isolated working days is not fulfilled. Therefore, the model is further refined by using binary integer programming (BIP), which incorporates nurse categories (senior, community, and health assistants' nurses) as key components in the maternity ward. The soft constraints addressed in an initial model are now treated as hard constraints. Apart from that, this improved model also considers additional hard constraints, including night shift sequencing and recovery, daily‐on‐call duty, and adequate rest days constraints to improve nurse well‐being. Actual duty rosters collected from the head nurse of a maternity ward are used to evaluate the performance of both models. Computational results show that the improved BIP model produces optimal schedules that satisfy operational requirements outlined by Lumut Armed Forces Hospital. For a 14‐day scheduling cycle, the model, however, requires a minimum of 15 nurses, with each category comprising at least the average number of nurses to ensure model feasibility. Overall, the findings demonstrate that extending the initial formulation, from a 0–1 GP model to an improved BIP model results in a comprehensive model and improves solution quality for complex healthcare rostering problems.