Feature selection of high-dimensional labeled data with limited observations is critical for making powerful predictive modeling accessible, scalable, and interpretable domain experts. Spectroscopy data, which records the interaction between matter electromagnetic radiation, particularly holds a lot information in single sample. Since acquiring such complex task, it crucial to exploit best anal...

This research essay highlights the need to integrate predictive analytics into information systems research and shows several concrete ways in which this goal can be accomplished. Predictive analytics include empirical methods (statistical and other) that generate data predictions as well as methods for assessing predictive power. Predictive analytics not only assist in creating practically use...

Machine learning methods that have been used in data-driven predictive modeling of mineral prospectivity (e.g., artificial neural networks) invariably require large number of training prospect/locations and are unable to handle missing values in certain evidential data. The Random Forests (RF) algorithm, which is a machine learning method, has recently been applied to data-driven predictive map...

PURPOSE Bronchiolitis is the most common cause of illness leading to hospitalization in young children. At present, many bronchiolitis management decisions are made subjectively, leading to significant practice variation among hospitals and physicians caring for children with bronchiolitis. To standardize care for bronchiolitis, researchers have proposed various models to predict the disease co...

The objective of this study was to investigate the growth rate of Chlorella vulgaris for CO2 biofixation and biomass production. Six mathematical growth models (Logistic, Gompertz, modified Gompertz, Baranyi, Morgan and Richards) were used to evaluate the biomass productivity in continuous processes and to predict the following parameters of cell growth: lag phase duration (λ), maximum specific...

the aim of this study was providing plant species predictive habitat models by using logisticregression method. for this purpose, study area conducted in north east rangelands of semnanmodeling vegetation data in addition to site condition in formation including topography, and soil wasprepared. sampling was done within each unit of sampling parallel transects and 1 vertical transectwith 750m l...

Predictive modeling, often called “predictive analytics” in a commercial context, encompasses a variety of statistical techniques that analyze historical and present facts to make predictions about unknown events. Often the unknown events are in the future, but prediction can be applied to any type of unknown whether it be in the past or future. In our case, we present some experiments applying...