Conference Proceedings

M. Utami, Y. Hoshino, and N. Rathnayake, “Accuracy validation of cardiac disease dataset with Cascaded ANFIS,” in 2024 Joint 13th International Conference on Soft Computing and Intelligent Systems and 25th International Symposium on Advanced Intelligent Systems (SCIS&ISIS), IEEE, 2024, pp. 1–4.

Y. Hoshino, N. Rathnayake, T. L. Dang, and U. Rathnayake, “Empirical research on 3D analysis and flow prediction of upstream rivers using drones,” in 2024 Joint 13th International Conference on Soft Computing and Intelligent Systems and 25th International Symposium on Advanced Intelligent Systems (SCIS&ISIS), IEEE, 2024, pp. 1–6.

N. Rathnayake and M. Minamide, “Enhancing data assimilation with machine learning: A study using the Lorenz-96 model and Ensemble Kalman Filter,” in EUMETSAT Meteorological Satellite Conference 2024, 2024.

N. Rathnayake, U. Rathnayake, and Y. Hoshino, “Unveiling key factors influencing student grades through explainable AI,” in ATU DigitalEd Conference 2024, 2024.

N. Rathnayake, U. Rathnayake, and Y. Hoshino, “Complexity of optimal control of combined sewer systems and potential solutions from artificial intelligence,” in SUDSnet 2024 - 20th Anniversary Conference, 2024.

N. Rathnayake, Y. Hoshino, B. Ðurin, U. Rathnayake, and A. Šantatić, “Advancing hydrological forecasting: Machine learning approaches for enhanced water flow prediction in the Bednja River, Croatia,” in 18th International Symposium on Water Management and Hydraulic Engineering (WMHE 2024), 2024, pp. 116–116.

N. Rathnayake, U. Rathnayake, and Y. Hoshino, “Leveraging traditional AI and ML for enhanced navigation and exploration capabilities of autonomous underwater vehicles,” in The 8th International Symposium on Frontier Technology (ISFT), 2024.

N. Rathnayake and M. Minamide, “Improving data assimilation using machine learning: Insights from the Lorenz-96 model and Ensemble Kalman Filter,” in Japan Geoscience Union 2024, 2024.

N. Rathnayake, T. L. Dang, A. Miyazaki, and Y. Hoshino, “An efficient approach for age-wise rice seeds classification using SURF-BOF with modified Cascaded-ANFIS algorithm,” in 15th International Conference on Machine Vision (ICMV 2022), SPIE, vol. 12701, 2023, pp. 471–479.

M. Utami, Y. Hoshino, and N. Rathnayake, “Design and implementation of ANFIS on FPGA and verification with a class classification problem,” in International Workshop on Advanced Computational Intelligence and Intelligent Informatics (IWACIII 2023), vol. 1931, 2023, pp. 241–252.

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,” in 2022 Joint 12th International Conference on Soft Computing and Intelligent Systems and 23rd International Symposium on Advanced Intelligent Systems (SCIS&ISIS), IEEE, 2022, pp. 1–5.

L. I. Mampitiya, R. Nalmi, and N. Rathnayake, “Performance comparison of sea fish species classification using hybrid and supervised machine learning algorithms,” in 2022 Moratuwa Engineering Research Conference (MERCon), IEEE, 2022, pp. 1–6.

L. I. Mampitiya and N. Rathnayake, “An efficient ocular disease recognition system implementation using GLCM and LBP-based multilayer perceptron algorithm,” in 2022 IEEE 21st Mediterranean Electrotechnical Conference (MELECON), IEEE, 2022, pp. 978–983.

N. Rathnayake, T. Linh Dang, and Y. Hoshino, “Designing and implementation of novel ensemble model based on ANFIS and gradient boosting methods for hand gestures classification,” in Proceedings of the 11th International Symposium on Information and Communication Technology (SoICT), 2022, pp. 283–289.

N. Rathnayake, U. Rathnayake, T. L. Dang, and Y. Hoshino, “Streamflow prediction using Cascaded-ANFIS algorithm in Kelani River, Sri Lanka,” in 10th International Symposium on Computational Intelligence and Industrial Applications (ISCIIA 2022), 2022.

L. I. Mampitiya, R. Nalmi, and N. Rathnayake, “Classification of human emotions using ensemble classifier by analyzing EEG signals,” in 2021 IEEE Third International Conference on Cognitive Machine Intelligence (CogMI), IEEE, 2021, pp. 71–77.

N. Rathnayake, T. Dang, and Y. Hoshino, “Performance comparison of the ANFIS-based quad-copter controller algorithms,” in 2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), IEEE, 2021, pp. 1–8.

R. Rathnayake, T. De Silva, and C. Rodrigo, “A comparison of the fuzzy logic controller and PID controller for differential drive wall-following mobile robot,” in 2019 14th Conference on Industrial and Information Systems (ICIIS), IEEE, 2019, pp. 523–528.

R. Rathnayake and L. Seneviratne, “An efficient approach towards image stitching in aerial images,” in 2018 3rd International Conference on Information Technology Research (ICITR), IEEE, 2018, pp. 1–6.

R. N. Bandara and S. Gaspe, “Fuzzy logic controller design for an unmanned aerial vehicle,” in 2016 IEEE International Conference on Information and Automation for Sustainability (ICIAfS), IEEE, 2016, pp. 1–5.