Since the launch of the first land observation satellite Landsat-1 in 1972, many machine learning algorithms have been used to classify pixels in Thematic Mapper (TM) imagery. Classification methods range from parametric supervised classification algorithms such as maximum likelihood, unsupervised algorithms such as ISODAT and k-means clustering to machine learning algorithms such as artificial...

Identification and mapping of the significant alterations are the main objectives of the exploration geochemical surveys. The field study is time-consuming and costly to produce the classified maps. Therefore, the processing of remotely sensed data, which provide timely and multi-band (multi-layer) data, can be substituted for the field study. In this study, the ASTER imagery is used for altera...

The goal of the Data Integration and Predictive Analysis System (IPAS) is to enable prediction, analysis, and response management for incidents of infectious diseases. IPAS collects and integrates comprehensive datasets of previous disease incidents and potential influencing factors to facilitate multivariate, predictive analytics of disease patterns, intensity, and timing. IPAS supports compre...

This Letter describes a procedure that incorporates textural measures in the classification of logged forests from Landsat Thematic Mapper data. The objective was to increase classification accuracy by applying recently developed algorithms in machine learning that are fast in training. Three voting classification algorithms, Arc-4x, Adaboost and bagging were also tested. Initial results using ...

Crime is hard to anticipate since it occurs at random and can occur anywhere any moment, making a difficult issue for society address. By analyzing comparing eight known prediction models: Naive Bayes, Stacking, Random Forest, Lazy:IBK, Bagging, Support Vector Machine, Convolutional Neural Network, Locally Weighted Learning – this study proposed an improved deep learning crime model using convo...

This article describes an example of using machine learning to estimate the abundance of airborne Ambrosia pollen for Tulsa, OK. Twenty-seven years of historical pollen observations were used. These pollen observations were combined with machine learning and a very complete meteorological and land surface context of 85 variables to estimate the daily Ambrosia abundance. The machine learning alg...

In this paper, we apply five machine learning models (Logistic Regression, Naive Bayes, LinearSVC, SVM with linear kernel and Random Forest) and three feature selection techniques (PCA, RFE and Heatmap) in one of the key procedures for breast cancer diagnosis. Using the biopsy cytopathology data with 30 numerical features, we achieve a high accuracy of 97.8%. We further compare performances of ...

Objectives: To propose a Bagging ensemble method to predict heart disease at early stages. The main focus of this research is increase the prediction accuracy in model. Methods: proposed system experimented with by using Cleveland datasets collected from UCI repository. dataset consists 14 attributes. In we applied different machine learning algorithms such as Decision tree, Naïve Bayes, Random...

During the last decade pathology has benefited from the rapid progress of image digitizing technologies, which led to the development of scanners, capable to produce so-called Whole Slide images (WSI) which can be explored by a pathologist on a computer screen comparable to the conventional microscope and can be used for diagnostics, research, archiving and also education and training. Digital ...

Grafted trees are trees that are constructed using two methods. The first method creates an initial tree, while the second method is used to complete the tree. In this work, the first classifier is an unpruned tree from a 10% sample of the training data. Grafting is a method for constructing ensembles of decision trees, where each tree is a grafted tree. Grafting by itself is better than Baggin...