SLIIT Conference and Symposium Proceedings

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All SLIIT faculties annually conduct international conferences and symposiums. Publications from these events are included in this collection.

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Subject: search.filters.subject.Principal Component Analysis

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    PublicationOpen Access
    Support Vector Machine Based an Efficient and Accurate Seasonal Weather Forecasting Approach with Minimal Data Quantities
    (SLIIT, 2022-02-11) Chandrasekara, S; Tennekoon, S; Abhayasinghe, N; Seneviratne, L
    Climate change makes a big impact in our daily activities. Therefore, forecasting climate changes prior to its actual occurrences is important. Even though highly accurate weather prediction systems throughout the world are available, they require mass amounts of data exceeding thousands of data points to obtain a significant accuracy. This study was aimed at proposing a Support Vector Machine based approach to carryout seasonal weather predictions up to thirty-minute intervals, the results of which would be considerably effective with respect to predictions carried out with models trained with annual datasets. The model was trained utilizing a dataset corresponding to the district of Kandy which consisted of 136 samples, 20 features, and 5 labels. By means of carrying out numerous data preprocessing steps, the model was trained, and the relevant hyperparameters were optimized considering the grid search algorithm to yield a maximum accuracy of 86%, once tested via the k-fold cross validation. The performance of the Support Vector Machine was also then compared for the same dataset with that of the K-Nearest Neighbor algorithm which consumed relatively fewer computing resources. An optimal accuracy of 61% was observed for this model for a K-value of 27. This approach supported the concept of a Support Vector Machine’s ability to perceive time series forecasts to a relatively higher degree and its ability to perform effectively in higher dimensional datasets with smaller number of samples. As per the future work, the Receiver Operating Characteristic analysis is proposed to be carried out to evaluate the performance of the model and the dataset size is proposed to be further enhanced to a maximum of a thousand samples to yield the best performance results.
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    PublicationEmbargo
    Assessment of Drought Tolerance Ability of Selected Finger Millet Varieties in Sri Lanka
    (Faculty of Humanities and Sciences,SLIIT, 2021-09-25) Thakshila, G. P. G. I; Gimhani, D. R; Koodalugodaarachchi, V
    Finger millet (Eleusine coracana (L.) Gaertnis is a highly nutritious cereal crop widely cultivated in Sri Lanka. Drought is a major abiotic stress which leads to limit plant’s growth and productivity. Two cultivated finger millet varieties (Rawana and Oshada) and three promising accessions (ACC: 7090, ACC: 7088, and ACC: 12415) were screened in a poly-house for drought tolerance using morpho-physiological traits and assessed using 5 selected Simple Sequence Repeat (SSR) markers (UGEP3, UGEP10, UGEP24, UGEP60, and UGEP78). Drought response was assessed using 9 morpho-physiological parameters. Through parallel analysis with control plants, it indicated that the variety Oshada performed effectively under drought stress compared to the other genotypes while Rawana indicated more sensitiveness to water withholding. DNA from each finger millet genotypes was amplified using selected SSR markers separately. Even though all five selected SSR markers exhibited comparatively higher polymorphism among the finger millet genotypes in previous studies, none of the markers showed the presence of polymorphism in the narrow genetic variation among the studied five genotypes. Interrelationships among the different agronomic traits measured were studied using Principal Component Analysis (PCA). PCA revealed that shoot dry weight (SDW), shoot fresh weight (SFW), shoot length (SL) and root dry weight (RDW) have contributed to the two principal components collectively. Hence, these traits can be effectively used in breeding programmes to generate variability. Furthermore, studies should be conducted with a greater number of SSR markers in order to have an in-depth assessment of genetic variability in the cultivated finger millet genotypes.