Domestic violence (DV) is a global social and public health issue that is highly gendered. Being able to accurately predict DV recidivism, i.e., re-offending of a previously convicted offender, can speed up and improve risk assessment procedures for police and front-line agencies, better protect victims of DV, and potentially prevent future reoccurrences of DV. Previous work in DV recidivism ha...

Today, kidney stone detection is done manually on medical images. This process time-consuming and subjective as it depends the physician. study aims to classify healthy or patient persons according status of stones from images using various machine learning methods Convolutional Neural Networks (CNNs). We evaluated such Decision Trees (DT), Random Forest (RF), Support Vector Machines (SVC), Mul...

Multimedia on demand (MOD) is an interactive system that provides a number of value-added services in addition to traditional TV services, such as video on demand and interactive online learning. This opens a new marketing and managerial problem for the telecommunication industry to retain valuable MOD customers. Data mining techniques have been widely applied to develop customer churn predicti...

In this paper we describe two parallel experiments on the integration of machine learning (ML) methods into the Spanish and Japanese rule-based sentence realization modules developed at Microsoft Research. The paper explores the use of decision trees (DT) for the lexical selection of the copula in Spanish and the insertion of a locative postposition in Japanese. We show that it is possible to m...

This paper demonstrates the use of Data Mining (DM) techniques in exploratory research. A robust model for identifying the factors that explain the success of Open Source Software (OSS) projects is created, validated and tested. The predictive modeling techniques of Logistic Regression (LR), Decision Trees (DT) and Neural Networks (NN) are used together in this analysis. Using Text Mining resul...

Predicting the quality of software modules prior to testing or system operations allows a focused software quality improvement endeavor. Decision trees are very attractive for classification problems, because of their comprehensibility and white box modeling features. However, optimizing the classification accuracy and the tree size is a difficult problem, and to our knowledge very few studies ...

Let G be a graph and Ds Dt two dominating sets of size k. Does there exist sequence 〈D0=Ds,D1,…,Dℓ−1,Dℓ=Dt〉 such that Di+1 can obtained from Di by replacing one vertex with its neighbors? In this paper, we investigate the complexity decision problem. We first prove problem is PSPACE-complete, even when restricted to split, bipartite or bounded treewidth graphs. On other hand, it solved in polyn...

This paper presents an alternative way of making predictions on the effectiveness and efficacy Renewable Energy (RE) policies using Decision Trees (DT). As a data-driven process for decision-making, analysis uses target achievement, predicting whether or not RE will likely be achieved (efficacy) to what degree (effectiveness), depending different criteria, including geographical context, charac...

The management of information flow for production improvement has always been a target in the research. In this paper, focus is on analysis model characteristics shop floor operations based influence that dimension (support or medium), direction and quality have value using machine learning classification algorithms. obtained results algorithms used to analyze are Decision Trees (DT) Random For...