Machine-learning models for analyzing TSOM images of nanostructures

Machine Learning Models for Housing Prices Forecasting using Registration Data

This article has been compiled to identify the best model of housing price forecasting using machine learning methods with maximum accuracy and minimum error. Five important machine learning algorithms are used to predict housing prices, including Nearest Neighbor Regression Algorithm (KNNR), Support Vector Regression Algorithm (SVR), Random Forest Regression Algorithm (RFR), Extreme Gradient B...

Comparison of Machine Learning Algorithms for Broad Leaf Species Classification Using UAV-RGB Images

Abstract: Knowing the tree species combination of forests provides valuable information for studying the forest’s economic value, fire risk assessment, biodiversity monitoring, and wildlife habitat improvement. Fieldwork is often time-consuming and labor-required, free satellite data are available in coarse resolution and the use of manned aircraft is relatively costly. Recently, unmanned aeria...

Machine Learning for Analyzing Human Brain Function

Dust source mapping using satellite imagery and machine learning models

Predicting dust sources area and determining the affecting factors is necessary in order to prioritize management and practice deal with desertification due to wind erosion in arid areas. Therefore, this study aimed to evaluate the application of three machine learning models (including generalized linear model, artificial neural network, random forest) to predict the vulnerability of dust cent...

Using financial risk measures for analyzing generalization performance of machine learning models

We propose a unified machine learning model (UMLM) for two-class classification, regression and outlier (or novelty) detection via a robust optimization approach. The model embraces various machine learning models such as support vector machine-based and minimax probability machine-based classification and regression models. The unified framework makes it possible to compare and contrast existi...