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| Optimizing Artificial Neural Network-Based Models to Predict Rice Blast Epidemics in Korea |
| Kyung-Tae Lee, Juhyeong Han, Kwang-Hyung Kim |
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Plant Pathol J. 2022;38(4):395-402. Published online August 1, 2022 DOI: https://doi.org/10.5423/PPJ.NT.04.2022.0062 |
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Optimizing Artificial Neural Network-Based Models to Predict Rice Blast Epidemics in Korea The Plant Pathology Journal. 2022;38(4):395-402  Optimized developed artificial neural network-based models to predict the blast-induced ground vibration Innovative Infrastructure Solutions. 2018;3(1): Using Artificial Neural Network to Predict Blast-Induced Ground Vibration Applied Mechanics and Materials. 2012;170-173:1013-1016 Artificial Neural Network Model for Predict of Silicon Content in Hot Metal Blast Furnace Materials Science Forum. 2016;869:572-577 A new combination of artificial neural network and K-nearest neighbors models to predict blast-induced ground vibration and air-overpressure Engineering with Computers. 2016;32(4):631-644 A Novel Artificial Intelligence Approach to Predict Blast-Induced Ground Vibration in Open-Pit Mines Based on the Firefly Algorithm and Artificial Neural Network Natural Resources Research. 2019;29(2):723-737 Dynamic Factor Model and Artificial Neural Network Models: To Combine Forecasts or Combine Models? Advanced Applications for Artificial Neural Networks. 2018; Mathematical and Artificial Neural Network Models to Predict the Membrane Fouling Behavior of an Intermittently-Aerated Membrane Bioreactor Under Sub-Critical Flux CLEAN - Soil, Air, Water. 2015;43(7):1002-1009 Multifunctional Models, Including an Artificial Neural Network, to Predict the Compressive Strength of Self-Compacting Concrete Applied Sciences. 2022;12(16):8161 Artificial Neural Network-Based Residential Energy Consumption Prediction Models Considering Residential Building Information and User Features in South Korea Sustainability. 2019;12(1):109 |
