Malaysian Journal of Computer Science

A STUDY ON NORMALISATION METHODS IN CITATION ANALYSIS (2016-2025)

2025-12-28T23:57:26+08:00

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.

COMPARATIVE ANALYSIS OF MISSING DATA IMPUTATION METHODS FOR FLOOD FEATURES FROM LANGAT RIVER IN SELANGOR, MALAYSIA

2025-06-23T11:21:33+08:00

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%).

ENHANCING SMART FARMING WITH CONTAINERIZED DEEP LEARNING AND KUBERNETES: UTILIZING HIPPOPOTAMUS OPTIMIZED ATTENTION MODEL FOR PREDICTIVE AGRICULTURE

2025-05-29T15:34:42+08:00

The integration of deep learning technologies into agriculture has the potential to revolutionize smart farming by enhancing efficiency, sustainability, and productivity. This study focuses on leveraging the Hippopotamus Optimized Attention Hierarchically Gated Recurrent Algorithm (HOA-HGRA) within a containerized environment to analyze and predict critical agricultural variables such as weather patterns, crop yield, and soil moisture. The proposed methodology involves containerizing deep learning models like HOA-HGRA and orchestrating them with Kubernetes on HPC clusters. This enables precise monitoring and management of crop growth, soil conditions, and livestock health, ensuring optimal resource utilization and enhanced productivity. The hyperparameters tuning and the performance optimization are performed by applying the Oppositional Hippopotamus optimization with opposition learning-based strategy. The overall performance of the AHGR-OH model is validated by utilizing the France-CGIAR BRIDGE, Smart Agriculture, Smart precision agriculture, Smart Farming Irrigation Systems, and IoT in Smart Farming Market Report datasets. Moreover, key metrics such as latency, precision, F1-score, recall, scalability, accuracy, MSE, and ROC are utilized to estimate the effectiveness of the AHGR-OH method. By comparing, the developed method grants 2s latency, 0.5 MSE, higher scalability, precision, F1-score, accuracy, and recall of 98.5%, 97.9%, 97.4%, 99.1%, and 97.9% respectively. This paper demonstrates the potential of the AHGR-OH Algorithm to revolutionize smart farming practices.

BYOD SECURITY POLICY COMPLIANCE IN MALAYSIAN PUBLIC UNIVERSITIES: AN INTEGRATED PMT –TPB – GDT MODEL WITH ISA MODERATION

2025-12-29T01:09:50+08:00

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.