CrossRef Text and Data Mining
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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
Plant Pathol J. 2022;38(4):395-402.   Published online August 1, 2022

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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   Crossref logo
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Optimized developed artificial neural network-based models to predict the blast-induced ground vibration
Innovative Infrastructure Solutions. 2018;3(1):   Crossref logo
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Using Artificial Neural Network to Predict Blast-Induced Ground Vibration
Applied Mechanics and Materials. 2012;170-173:1013-1016   Crossref logo

Artificial Neural Network Model for Predict of Silicon Content in Hot Metal Blast Furnace
Materials Science Forum. 2016;869:572-577   Crossref logo

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   Crossref logo
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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   Crossref logo
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Dynamic Factor Model and Artificial Neural Network Models: To Combine Forecasts or Combine Models?
Advanced Applications for Artificial Neural Networks. 2018;   Crossref logo

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   Crossref logo
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Multifunctional Models, Including an Artificial Neural Network, to Predict the Compressive Strength of Self-Compacting Concrete
Applied Sciences. 2022;12(16):8161   Crossref logo

Artificial Neural Network-Based Residential Energy Consumption Prediction Models Considering Residential Building Information and User Features in South Korea
Sustainability. 2019;12(1):109   Crossref logo