Browsing by Author "Mampitiya, L. I"

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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.
Embargo
An Efficient Ocular Disease Recognition System Implementation using GLCM and LBP based Multilayer Perception Algorithm
(IEEE, 2022-08-03) Rathnayake, N; Mampitiya, L. I
This research study is focused on the classification of ocular diseases by referring to a well-known dataset. The data is divided into seven classes: diabetes, glaucoma, cataract, normal, hypertension, age-related macular degeneration, pathological myopia, and other diseases/abnormalities. A Neural Network is used for the classification of diseases. In addition, the GLCM and LBP feature extracting methods have been used to carry out the feature extraction for the fundus images. This study compares five different ocular disease recognizing techniques. Moreover, the proposed model was evaluated regarding precision, recall, and accuracy. The proposed solution outperformed existing state-of-the-art algorithms, achieving 99.58% accuracy.