Research Publications Authored by SLIIT Staff

Permanent URI for this communityhttps://rda.sliit.lk/handle/123456789/4195

This collection includes all SLIIT staff publications presented at external conferences and published in external journals. The materials are organized by faculty to facilitate easy retrieval.

Browse

Filters

2021 - 20222

Advanced Search

Filter by

Settings

Author: search.filters.author.Rathnayake, NSubject: search.filters.subject.Classification

Search Results

Now showing 1 - 2 of 2
  • Thumbnail Image
    PublicationEmbargo
    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.
  • Thumbnail Image
    PublicationEmbargo
    Classification of Human Emotions using Ensemble Classifier by Analysing EEG Signals
    (IEEE, 2021-04-13) Mampitiya, L. I; Nalmi, R; Rathnayake, N
    This study is based on EEG brain wave classification of a well-known dataset called the EEG Brainwave Dataset. The dataset combines three classes such as positive, negative, and neutral. The classification is performed using an ensemble classifier that combines RF, KNN, DT, SVM, NB, and LR. The meta classifier is LR, while the other five algorithms work as the base classifiers. Furthermore, PCA is used as the dimension reduction method to increase the accuracy of the final output. The results are evaluated under 11 different parameters. Moreover, the accuracy of this study is compared with the seven other EEG emotion classification methods. The proposing method attained 99.25% of accuracy, outperforming the other state-of-the-art algorithms.