ラスナヤケ ナマル

論文

• Effectiveness of Rolling-Sum Preprocessing in River Mouth Water Depth Prediction Using Machine Learning

  Yukinobu Hoshino, Namal Rathnayake, Tuan Linh Dang, Upaka Rathnayake

  2027年
• Beyond wind speed: Integrating oceanic indices and time-lagged features for superior wind energy prediction

  Namal Rathnayake, Mahesh Yadev, Jeevani Jayasinghe, Upaka Rathnayake, Masashi Minamide, Yukinobu Hoshino

  PLOS One 2026年3月19日
• Machine learning-based Direct Normal Irradiance (DNI) forecasting using satellite data for Concentrated Solar Power (CSP) plants with Thermal Energy Storage (TES)

  Namal Rathnayake, Samantha Wijewardane

  SCIENTIFIC REPORTS 16(1) 2026年2月27日
• Neural Signatures of Speed and Regular Reading: A Machine Learning and Explainable AI (XAI) Study of Sinhalese and Japanese

  Thishuli Walpola, Namal Rathnayake, Hoang Ngoc Thanh, Niluka Dilhani, Atsushi Senoo

  Information 2026年1月21日
• AI-Powered Soil Temperature Modeling for Sustainable Agriculture in Arid Regions: A Case Study of Bustan, Uzbekistan

  Lakindu Mampitiya, Namal Rathnayake, Kenjabek Rozumbetov, Valery Erkudov, Mirzohid Koriyev, Komali Kantamaneni, Upaka Rathnayake

  Journal of Data Science and Intelligent Systems 2026年1月14日
• Temporal Morphological Analysis of NK Cells

  Takuho Myojin, Rathnayake Namal, Shinpei Yamamoto, Yukinobu Hoshino

  2026年
• UFR-GAN: A lightweight multi-degradation image restoration model

  Binh An Nguyen, Minh Bao Kha, Duc Manh Dao, Huu Kien Nguyen, My Duyen Nguyen, The Vu Nguyen, Namal Rathnayake, Yukinobu Hoshino, Tuan Linh Dang

  Pattern Recognition Letters 2025年11月
• Machine Learning Approaches for Simulating Temporal Changes in Bed Profiles Around Cylindrical Bridge Pier: A Comparative Analysis

  Ahad Molavi, Fariborz Ahmadzadeh Kaleybar, Namal Rathnayake, Upaka Rathnayake, Mehdi Fuladipanah, Hazi Mohammad Azamathulla

  Hydrology 2025年9月15日
• Controlling Heterogeneous Multi-Agent Systems Under Uncertainty Using Fuzzy Inference and Evolutionary Search

  Yukinobu Hoshino, Keigo Yoshimi, Tuan Linh Dang, Namal Rathnayake

  Information 2025年8月25日
• Explainable artificial intelligence to estimate the Sri Lankan (Ceylon) Tea crop yield

  Lakindu Mampitiya, Harindu S. Sumanasekara, Namal Rathnayake, Yukinobu Hoshino, Upaka Rathnayake

  Smart Agricultural Technology 2025年8月
• A novel application with explainable machine learning (SHAP and LIME) to predict soil N, P, and K nutrient content in cabbage cultivation

  Thilina Abekoon, Hirushan Sajindra, Namal Rathnayake, Imesh U. Ekanayake, Anuradha Jayakody, Upaka Rathnayake

  SMART AGRICULTURAL TECHNOLOGY 11 2025年8月
• Efficient Hotspot Detection in Solar Panels via Computer Vision and Machine Learning

  Nayomi Fernando, Lasantha Seneviratne, Nisal Weerasinghe, Namal Rathnayake, Yukinobu Hoshino

  Information 2025年7月15日
• AI-Driven Forecasting of PM₁₀ Concentrations in the Republic of Ireland: Integrating Machine Learning for Urban Air Quality Prediction

  Lakindu Mampitiya, Namal Rathnayake, Ramen Ghosh, Yukinobu Hoshino, Upaka Rathnayake

  AEROSOL SCIENCE AND ENGINEERING 2025年5月26日
• Leveraging level data for accurate downstream flow prediction in the Narmada River Basin with advanced machine learning models

  Vijendra Kumar, Namal Rathnayake, S. Hariprasad Reddy, Naresh Kedam, Upaka Rathnayake, Yukinobu Hoshino

  Journal of Hydroinformatics 27(2) 141-158 2025年2月1日
• Hybridizing Fuzzy Clustering and Ensemble Learning for Accurate River Water Level Prediction

  Namal Rathnayake, Yukinobu Hoshino

  2025 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS, FUZZ 2025年
• Flow Velocity Analysis of Rivers Using Farneback Optical Flow and STIV Techniques With Drone Data

  Yukinobu Hoshino, Namal Rathnayake, Tuan Linh Dang, Upaka Rathnayake

  2025年
• Enhancing Water Level Prediction Using Ensemble Machine Learning Models: A Comparative Analysis

  Saleh Alsulamy, Vijendra Kumar, Ozgur Kisi, Naresh Kedam, Namal Rathnayake

  Water Resources Management 2025年
• Analyzing the impact of socioeconomic indicators on gender inequality in Sri Lanka: A machine learning-based approach

  Sherin Kularathne, Amanda Perera, Namal Rathnayake, Upaka Rathnayake, Yukinobu Hoshino

  PLOS ONE 19(12) 2024年12月26日
• Advancing water quality assessment and prediction using machine learning models, coupled with explainable artificial intelligence (XAI) techniques like shapley additive explanations (SHAP) for interpreting the black-box nature

  Randika K. Makumbura, Lakindu Mampitiya, Namal Rathnayake, D. P. P. Meddage, Shagufta Henna, Tuan Linh Dang, Yukinobu Hoshino, Upaka Rathnayake

  RESULTS IN ENGINEERING 23 2024年9月
• AI-driven predictions of geophysical river flows with vegetation

  Sanjit Kumar, Mayank Agarwal, Vishal Deshpande, James R. Cooper, Khabat Khosravi, Namal Rathnayake, Yukinobu Hoshino, Komali Kantamaneni, Upaka Rathnayake

  SCIENTIFIC REPORTS 14(1) 2024年7月16日