[PDF][PDF] Identification of rice varieties using machine learning algorithms
Rice, which has the highest production and consumption rates worldwide, is among the
main nutrients in terms of being economical and nutritious in our country as well. Rice goes …
main nutrients in terms of being economical and nutritious in our country as well. Rice goes …
Periodontal bone loss detection based on hybrid deep learning and machine learning models with a user-friendly application
As artificial intelligence in medical imaging is used to diagnose many diseases, it can also
be employed to diagnose whether a person has periodontal bone loss or not. Accurate and …
be employed to diagnose whether a person has periodontal bone loss or not. Accurate and …
Linear and nonlinear modeling approaches for urban air quality prediction
In this study, linear and nonlinear modeling was performed to predict the urban air quality of
the Lucknow city (India). Partial least squares regression (PLSR), multivariate polynomial …
the Lucknow city (India). Partial least squares regression (PLSR), multivariate polynomial …
[HTML][HTML] Estimation of rainfall erosivity factor in Italy and Switzerland using Bayesian optimization based machine learning models
This study aimed to evaluate the estimation accuracy of rainfall erosivity (R-factor) in Italy
and Switzerland through five Machine learning (ML) models (Decision Tree (DT), K-Nearest …
and Switzerland through five Machine learning (ML) models (Decision Tree (DT), K-Nearest …
Estimation and spatio-temporal change analysis of NPP in subtropical forests: A case study of Shaoguan, Guangdong, China
Exploring the spatial and temporal dynamic characteristics of regional forest net primary
productivity (NPP) in the context of global climate change can not only provide a theoretical …
productivity (NPP) in the context of global climate change can not only provide a theoretical …
Enhancing precipitation estimates through the fusion of weather radar, satellite retrievals, and surface parameters
Accurate and timely monitoring of precipitation remains a challenge, particularly in hyper-
arid regions such as the United Arab Emirates (UAE). The aim of this study is to improve the …
arid regions such as the United Arab Emirates (UAE). The aim of this study is to improve the …
Enhancing building energy efficiency: An integrated approach to predicting heating and cooling loads using machine learning and optimization algorithms
T Gao, X Han, J Wang, Y Geng, H Zhang… - Journal of Building …, 2024 - Elsevier
Predicting cooling and heating loads is essential for efficient building energy management
in order to maintain indoor comfort. This study employs machine learning methods …
in order to maintain indoor comfort. This study employs machine learning methods …
Capability and robustness of novel hybridized artificial intelligence technique for sediment yield modeling in Godavari River, India
Suspended sediment yield (SSY) prediction plays a crucial role in the planning of water
resource management and design. Accurate sediment prediction using conventional models …
resource management and design. Accurate sediment prediction using conventional models …
A deep learning modeling framework with uncertainty quantification for inflow-outflow predictions for cascade reservoirs
Accurate prediction of reservoir inflows and outflows and their uncertainties is essential for
managing water resources and establishing early-warning systems. However, this can be a …
managing water resources and establishing early-warning systems. However, this can be a …
An enhanced feed-forward back propagation Levenberg–Marquardt algorithm for suspended sediment yield modeling
Rivers are dynamic geological agents on the earth which transport the weathered materials
of the continent to the sea. Estimation of suspended sediment yield (SSY) is essential for …
of the continent to the sea. Estimation of suspended sediment yield (SSY) is essential for …