BACKGROUND It remains unclear whether it is possible to develop a spatiotemporal epidemic prediction model for cryptosporidiosis disease. This paper examined the impact of social economic and weather factors on cryptosporidiosis and explored the possibility of developing such a model using social economic and weather data in Queensland, Australia. METHODS Data on weather variables, notified c...

Certified public accounts’ (CPAs) audit opinions of going concern are the important basis for evaluating whether enterprises can achieve normal operations and sustainable development. This study aims to construct prediction models help CPAs auditors make more effective/correct judgments on opinion decisions by deep learning algorithms, using following methods: neural networks (DNN), recurrent n...

Classification is the process of identifying and grouping an object into same group or category can be used to a large-sized dataset, some commonly classification methods are CART (Classification And Regression Tree) Naïve Bayes. This study discusses comparison Bayes by measuring accuracy, precision, recall, f1-score values with 3 scenarios training testing dataset distribution. Accuracy, measu...

Recently, corpus-based text-to-speech (CB-TTS) has been actively studied through the world. Statistical training methods are generally applied for prosodic rules in CB-TTS, and classification and regression tree (CART) is one of the mostly used methods. In this paper, we present an efficient CART training approach of zscore based phonetic normalization. The idea of ours comes from the fact that...

Background: Fertility is one of the important subjects in public health and demographic studies which affects population growth. The main objective of this paper was to introduce and apply a tree model to classify the ideal number of children and children ever born in the study of "Marriage and Fertility Attitudes of Married 15-49 Years Old Women in Semnan Province in Iran, 2012". Methods: Cla...

BACKGROUND Risk stratification for mortality in intracerebral haemorrhage (ICH) helps guide care, but existing clinical prediction rules are too cumbersome for clinical practice because of their complexity. AIM To develop a simple decision tree model of in-hospital mortality risk stratification for ICH patients. METHODS We collected information on spontaneous ICH patients hospitalized in a ...

We aim to provide a tool for independent system operators detect the collusion and identify colluding firms by using day-ahead data. In this paper, an approach based on supervised machine learning is presented detection in electricity markets. The possible scenarios of among generation are firstly identified. Then, each scenario load demand, market equilibrium computed. Market points under diff...

A personal credit evaluation algorithm is proposed by the design of a decision tree with boosting algorithm, and classification carried out. By comparison conventional it shown that acts to speed up processing time. The Classification Regression Tree (CART) showed 90.95% accuracy, slightly higher than without boosting, 90.31%. To avoid overfitting model on training set due unreasonable data div...