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  1. Fulltext Multistrategy self-organizing map learning for classification problems
    Hasan S, Shamsuddin SM
    Comput Intell Neurosci, 2011;2011:121787.
    PMID: 21876686 DOI: 10.1155/2011/121787
    Multistrategy Learning of Self-Organizing Map (SOM) and Particle Swarm Optimization (PSO) is commonly implemented in clustering domain due to its capabilities in handling complex data characteristics. However, some of these multistrategy learning architectures have weaknesses such as slow convergence time always being trapped in the local minima. This paper proposes multistrategy learning of SOM lattice structure with Particle Swarm Optimisation which is called ESOMPSO for solving various classification problems. The enhancement of SOM lattice structure is implemented by introducing a new hexagon formulation for better mapping quality in data classification and labeling. The weights of the enhanced SOM are optimised using PSO to obtain better output quality. The proposed method has been tested on various standard datasets with substantial comparisons with existing SOM network and various distance measurement. The results show that our proposed method yields a promising result with better average accuracy and quantisation errors compared to the other methods as well as convincing significant test.
    Matched MeSH terms: Learning/classification*
  2. Fulltext Malay sentiment analysis based on combined classification approaches and Senti-lexicon algorithm
    Al-Saffar A, Awang S, Tao H, Omar N, Al-Saiagh W, Al-Bared M
    PLoS One, 2018;13(4):e0194852.
    PMID: 29684036 DOI: 10.1371/journal.pone.0194852
    Sentiment analysis techniques are increasingly exploited to categorize the opinion text to one or more predefined sentiment classes for the creation and automated maintenance of review-aggregation websites. In this paper, a Malay sentiment analysis classification model is proposed to improve classification performances based on the semantic orientation and machine learning approaches. First, a total of 2,478 Malay sentiment-lexicon phrases and words are assigned with a synonym and stored with the help of more than one Malay native speaker, and the polarity is manually allotted with a score. In addition, the supervised machine learning approaches and lexicon knowledge method are combined for Malay sentiment classification with evaluating thirteen features. Finally, three individual classifiers and a combined classifier are used to evaluate the classification accuracy. In experimental results, a wide-range of comparative experiments is conducted on a Malay Reviews Corpus (MRC), and it demonstrates that the feature extraction improves the performance of Malay sentiment analysis based on the combined classification. However, the results depend on three factors, the features, the number of features and the classification approach.
    Matched MeSH terms: Supervised Machine Learning/classification
  3. Learning styles of Malaysian Generation 'Y' healthcare undergraduates
    Nurumal MS, Salahuddin MA, Daud A, Shahadan SZ, Abdul-Mumin K, Shorey S
    Enferm Clin, 2019 09;29 Suppl 2:471-475.
    PMID: 31350157 DOI: 10.1016/j.enfcli.2019.04.070
    OBJECTIVE: The learning styles of millennial students, also known as Generation 'Y', have been examined in the past to match the teaching style of an educator with the aim of improving behaviors, attitudes, and academic achievements. However, focus on Generation 'Y' healthcare undergraduates from a multi-cultural Asian society, is scarce and fragmented. Therefore, this research aims to identify the learning style preferences of Generation 'Y' undergraduates enrolled in varied healthcare programs at a Malaysian University.

    METHOD: A quantitative cross-sectional study design was adopted. Honey and Mumford's Learning Style Questionnaire was used to explore the learning styles.

    RESULTS: The reflector learning style was most preferred by the Malaysian healthcare undergraduates, and no significant difference was found between the learning styles of the clinical group and the semi-clinical group.

    CONCLUSIONS: Educators should engage Malaysian healthcare undergraduates in a non-threatening environment - Association between learning style and sociodemographic warrants further investigation.

    Matched MeSH terms: Learning/classification*
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