Namal Rathnayake, PhD.

Namal Rathnayake, PhD.

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Pages

Posts

Future Blog Post

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Blog Post number 1

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portfolio

publications

A Comparison of Fuzzy Logic Controller and PID Controller for Differential Drive Wall-Following Mobile Robot

Published in 14th Conference on Industrial and Information Systems (ICIIS), 2019

Download paper here

Recommended citation: Ratnayake, R. M. N. B., T. S. De Silva, and C. J. Rodrigo. "A comparison of fuzzy logic controller and pid controller for differential drive wall-following mobile robot." 2019 14th Conference on Industrial and Information Systems (ICIIS). IEEE, 2019. https://ieeexplore.ieee.org/document/9063333/

Performance Comparison of the ANFIS based Quad-Copter Controller Algorithms

Published in IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2021

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Recommended citation: N. Rathnayake, T. L. Dang and Y. Hoshino, "Performance Comparison of the ANFIS based Quad-Copter Controller Algorithms," 2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Luxembourg, Luxembourg, 2021, pp. 1-8, doi: 10.1109/FUZZ45933.2021.9494344. https://ieeexplore.ieee.org/abstract/document/9494344/

Classification of Human Emotions using Ensemble Classifier by Analysing EEG Signals

Published in IEEE Third International Conference on Cognitive Machine Intelligence (CogMI), 2022

Abstract

Recommended citation: L. I. Mampitiya, R. Nalmi and N. Rathnayake, "Classification of Human Emotions using Ensemble Classifier by Analysing EEG Signals," 2021 IEEE Third International Conference on Cognitive Machine Intelligence (CogMI), Atlanta, GA, USA, 2021, pp. 71-77, doi: 10.1109/CogMI52975.2021.00018. https://ieeexplore.ieee.org/abstract/document/9750281

An Efficient Automatic Fruit-360 Image Identification and Recognition Using a Novel Modified Cascaded-ANFIS Algorithm.

Published in Sensors, 2022

Abstract

Automated fruit identification is always challenging due to its complex nature. Usually, the fruit types and sub-types are location-dependent; thus, manual fruit categorization is also still a challenging problem. Literature showcases several recent studies incorporating the Convolutional Neural Network-based algorithms (VGG16, Inception V3, MobileNet, and ResNet18) to classify the Fruit-360 dataset. However, none of them are comprehensive and have not been utilized for the total 131 fruit classes. In addition, the computational efficiency was not the best in these models. A novel, robust but comprehensive study is presented here in identifying and predicting the whole Fruit-360 dataset, including 131 fruit classes with 90,483 sample images. An algorithm based on the Cascaded Adaptive Network-based Fuzzy Inference System (Cascaded-ANFIS) was effectively utilized to achieve the research gap. Color Structure, Region Shape, Edge Histogram, Column Layout, Gray-Level Co-Occurrence Matrix, Scale-Invariant Feature Transform, Speeded Up Robust Features, Histogram of Oriented Gradients, and Oriented FAST and rotated BRIEF features are used in this study as the features descriptors in identifying fruit images. The algorithm was validated using two methods: iterations and confusion matrix. The results showcase that the proposed method gives a relative accuracy of 98.36%. The Fruit-360 dataset is unbalanced; therefore, the weighted precision, recall, and FScore were calculated as 0.9843, 0.9841, and 0.9840, respectively. In addition, the developed system was tested and compared against the literature-found state-of-the-art algorithms for the purpose. Comparison studies present the acceptability of the newly developed algorithm handling the whole Fruit-360 dataset and achieving high computational efficiency.

Recommended citation: Rathnayake, Namal, et al. "An Efficient Automatic Fruit-360 Image Identification and Recognition Using a Novel Modified Cascaded-ANFIS Algorithm." Sensors 22.12 (2022): 4401. https://www.mdpi.com/1671982

An Efficient Ocular Disease Recognition System Implementation using GLCM and LBP based Multilayer Perception Algorithm

Published in , 2022

Abstract

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.

Recommended citation: L. I. Mampitiya and N. Rathnayake, "An Efficient Ocular Disease Recognition System Implementation using GLCM and LBP based Multilayer Perception Algorithm," 2022 IEEE 21st Mediterranean Electrotechnical Conference (MELECON), Palermo, Italy, 2022, pp. 978-983, doi: 10.1109/MELECON53508.2022.9843023. https://ieeexplore.ieee.org/abstract/document/9843023

Performance Comparison of Sea Fish Species Classification using Hybrid and Supervised Machine Learning Algorithms

Published in Moratuwa Engineering Research Conference (MERCon), 2022

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Recommended citation: L. I. Mampitiya, R. Nalmi and N. Rathnayake, "Performance Comparison of Sea Fish Species Classification using Hybrid and Supervised Machine Learning Algorithms," 2022 Moratuwa Engineering Research Conference (MERCon), Moratuwa, Sri Lanka, 2022, pp. 1-6, doi: 10.1109/MERCon55799.2022.9906206. https://ieeexplore.ieee.org/abstract/document/9906206

Efficient and Low-Cost Skin Cancer Detection System Implementation with a Comparative Study Between Traditional and CNN-Based Models

Published in Journal of Computational and Cognitive Engineering., 2022

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Recommended citation: Mampitiya, L., Rathnayake, N., & De Silva, S. (2022). Efficient and Low-Cost Skin Cancer Detection System Implementation with a Comparative Study Between Traditional and CNN-Based Models. Journal of Computational and Cognitive Engineering. https://doi.org/10.47852/bonviewJCCE2202482 https://ojs.bonviewpress.com/index.php/JCCE/article/view/482

A comparative study for COVID-19 cases forecasting with loss function as AIC and MSE in RNN family and ARIMA

Published in Joint 12th International Conference on Soft Computing and Intelligent Systems and 23rd International Symposium on Advanced Intelligent Systems (SCIS&ISIS), 2023

Abstract

Recommended citation: N. Dohi, N. Rathnayake and Y. Hoshino, "A comparative study for COVID-19 cases forecasting with loss function as AIC and MSE in RNN family and ARIMA," 2022 Joint 12th International Conference on Soft Computing and Intelligent Systems and 23rd International Symposium on Advanced Intelligent Systems (SCIS&ISIS), Ise, Japan, 2022, pp. 1-5, doi: 10.1109/SCISISIS55246.2022.10001870. https://ieeexplore.ieee.org/abstract/document/10001870

Projected Water Levels and Identified Future Floods: A Comparative Analysis for Mahaweli River, Sri Lanka

Published in IEEE Acces, 2023

Abstract

The Rainfall-Runoff (R-R) relationship is essential to the hydrological cycle. Sophisticated hydrological models can accurately investigate R-R relationships; however, they require many data. Therefore, machine learning and soft computing techniques have taken the attention in the environment of limited hydrological, meteorological, and geological data. The accuracy of such models depends on the various parameters, including the quality of inputs and outputs and the used algorithms. However, identifying a perfect algorithm is still challenging. This study develops a fuzzy logic-based algorithm called Cascaded-ANFIS to accurately predict runoff based on rainfall. The model was compared against three regression algorithms: Long Short-Term Memory, Grated Recurrent Unit, and Recurrent Neural Networks. These algorithms have been selected due to their outstanding performances in similar studies. The models were tested on the Mahaweli River, the longest in Sri Lanka. The results showcase that the Cascaded-ANFIS-based model outperforms the other algorithms. The correlation coefficient of each algorithm’s predictions was 0.9330, 0.9120, 0.9133, 0.8915, 0.6811, 0.6811, and 0.6734 for the Cascaded-ANFIS, LSTM, GRU, RNN, Linear, Ridge, and Lasso regression models respectively. Hence, this study concludes that the proposed algorithm is 21% more accurate than the second-best LSTM algorithm. In addition, Shared Socio-economic Pathways (SSP2-4.5 and SSP5-8.5 scenarios) were used to generate future rainfalls, forecast the near-future and mid-future water levels, and identify potential flood events. The future forecasting results indicate a decrease in flood events and magnitudes in both SSP2-4.5 and SSP5-8.5 scenarios. Furthermore, the SSP5-8.5 scenario shows drought weather from May to August yearly. The results of this study can effectively be used to manage and control water resources and mitigate flood damages.

Recommended citation: N. Rathnayake, U. Rathnayake, I. Chathuranika, T. L. Dang and Y. Hoshino, "Projected Water Levels and Identified Future Floods: A Comparative Analysis for Mahaweli River, Sri Lanka," in IEEE Access, vol. 11, pp. 8920-8937, 2023, doi: 10.1109/ACCESS.2023.3238717. https://ieeexplore.ieee.org/abstract/document/10024291

talks

teaching

Teaching experience 1

Undergraduate course, University 1, Department, 2014

This is a description of a teaching experience. You can use markdown like any other post.

Teaching experience 2

Workshop, University 1, Department, 2015

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