Research Papers - Department of Electrical and Electronic Engineering

Permanent URI for this collectionhttps://rda.sliit.lk/handle/123456789/679

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Start date: 2020Subject: search.filters.subject.Machine Learning

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    Performance Comparison of Sea Fish Species Classification using Hybrid and Supervised Machine Learning Algorithms
    (IEEE, 2022-10-04) Nalmi, R; Rathnayake, N; Mampitiya, L.I
    In the domain of autonomous underwater vehicles, the classification of objects underwater is critical. The hazy effect of the medium causes this obstacle, and these effects are directed by the dissolved particles that lead to the reflecting and scattering of light during the formation process of the image. This paper mainly focuses on exploring the best possible image classifier for the underwater images of the different fish species. The classifications were carried out by different hybrid and supervised machine learning algorithms such as Support Vector Machine (SVM), Random Forest (RF), Neural Networks (NN), Logistic Regression (LR), Decision Tree (DT), and Naive Bayes (NB). This study compares the algorithms’ accuracy and time and analyzes crucial features to decide the most optimal algorithm. Furthermore, the results of this paper depict that using dimension reduction methods such as PCA and LDA increases the accuracy of some algorithms. Random Forest was able to outperforms with a higher accuracy of 99.89% with the proposed feature extraction methods.
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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.