Finding Contents or People

Using visible and near infrared spectroscopy and machine learning for estimating total petroleum hydrocarbons in contaminated soils
Using visible and near infrared spectroscopy and machine learning for estimating total petroleum hydrocarbons in contaminated soils
Using visible and near infrared spectroscopy and machine learning for estimating total petroleum hydrocarbons in contaminated soils
CWBCM method to determine the importance of classification performance evaluation criteria in machine learning: Case studies of COVID-19, Diabetes, and Thyroid Disease
A deep learning-genetic algorithm approach for aerodynamic inverse design via optimization of pressure distribution
Risk factors and population attributable fractions for displaced abomasum in Iranian dairy cattle: a retrospective analysis of field data
Risk factors and population attributable fractions for displaced abomasum in Iranian dairy cattle: a retrospective analysis of field data
Three-dimensional wake transition of rectangular cylinders and temporal prediction of flow patterns based on a machine learning algorithm
Developing an intelligent and sustainable model to improve E-learning satisfaction based on the learner's personality type: data mining approach in high education systems
Amending clayey and sandy soils with nano - bio phosphorous for regulating tomato growth, biochemical, and physiological characteristics
Improving indoor localization using mobile UWB sensor and deep reinforcement learning
Combining game theory and evolutionary algorithms for optimizing hybrid RDWC-pervaporation-vapor permeation process equipped with solar heating system
Use of the time series and multi-temporal features of Sentinel-1/2 satellite imagery to predict soil inorganic and organic carbon in a low-relief area with a semi-arid environment
Use of the time series and multi-temporal features of Sentinel-1/2 satellite imagery to predict soil inorganic and organic carbon in a low-relief area with a semi-arid environment
A Reliable and Ensemble Forecasting model for Slow-moving and Repairable Spare Parts: Data Mining Approach