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River, which supply 90% of the readily accessible water, are key elements of universal water source system. Terengganu River situated in Terengganu, Malaysia is a modern busy city known for tourism, fishing, and industry. Due to that, it has increased risk of water pollution exposure. Therefore, this paper proposes unsupervised ML include Autoencoder and Self-Organizing Map (SOM) for clustering water pollution area along the Terengganu River. Then, uses Silhouette analysis to assess the total of optimum clusters in a dataset. Next, applies Adjusted Rank Index (ARI) to discover the finest comparing within original data with Autoencoder and SOM. Lastly, applies Elbow method to double verify the most excellent clusters for each clustering algorithm. Lastly, lists of polluted area in each cluster are retrieved from 14 main sampling stations with 24 water quality parameters, including 405 water samples. Result shows different cluster with different water samples. Thus, offer different strategies to manage polluted area for Terengganu River. [ABSTRACT FROM AUTHOR]

Accurate assessments of epidemiological associations between health outcomes and routinely observed proximal and distal determinants of health are fundamental for the execution of effective public health interventions and policies. Methods to couple big public health data with modern statistical techniques offer greater granularity for describing and understanding data quality, disease distributions, and potential predictive connections between population-level indicators with areal-based health outcomes. This study applied clustering techniques to explore patterns of diabetes burden correlated with local socio-economic inequalities in Malaysia, with a goal of better understanding the factors influencing the collation of these clusters. Through multi-modal secondary data sources, district-wise diabetes crude rates from 271,553 individuals with diabetes sampled from 914 primary care clinics throughout Malaysia were computed. Unsupervised machine learning methods using hierarchical clustering to a set of 144 administrative districts was applied. Differences in characteristics of the areas were evaluated using multivariate non-parametric test statistics. Five statistically significant clusters were identified, each reflecting different levels of diabetes burden at the local level, each with contrasting patterns observed under the influence of population-level characteristics. The hierarchical clustering analysis that grouped local diabetes areas with varying socio-economic, demographic, and geographic characteristics offer opportunities to local public health to implement targeted interventions in an attempt to control the local diabetes burden., (© 2024. The Author(s).)

Transferring a local manufacturing company to a national-wide supply chain network with wholesalers and retailers is a significant problem in manufacturing systems. In this research, a hybrid PCA-K-means is used to transfer a local chocolate manufacturing firm near Kuala Lumpur into a national-wide supply chain. For this purpose, the appropriate locations of the wholesaler's center points were found according to the geographical and population features of the markets in Malaysia. To this end, four wholesalers on the left island of Malaysia are recognized, which were located in the north area, right area, middle area, and south area. Similarly, two wholesalers were identified on the right island, which were in Sarawak and WP Labuan. In order to evaluate the performance of the proposed method, its outcomes are compared with other unsupervised-learning methods such as the WARD and CLINK methods. The outcomes indicated that K-means could successfully determine the best locations for the wholesalers in the supply chain network with a higher score (0.812). [ABSTRACT FROM AUTHOR]