There is a known tension between the need to analyze personal data drive business and privacy concerns. Many protection regulations, including EU General Data Protection Regulation (GDPR) California Consumer Act (CCPA), set out strict restrictions obligations on collection processing of data. Moreover, machine learning models themselves can be used derive information, as demonstrated by recent ...

Despite existing various integer programming for sequencing problems, there is not enoughinformation about practical values of the models. This paper considers the problem of minimizing maximumlateness with release dates and presents four different mixed integer programming (MIP) models to solve thisproblem. These models have been formulated for the classical single machine problem, namely sequ...

Background: Diabetes mellitus as a chronic disease is the most common disease caused by metabolic disorders and it is one of the most important health issues all around the world. Nowadays, data mining methods are applied in different fields of sciences due to data mining methods capability. Therefore, in this study, we compared the efficiency of data mining methods in predicting type 2 diabete...

there are instances in which it is desirable to determine relationships among fruit physical attributes. for example, fruits are often graded on the basis of size and projected area, but it may be more suitable and/or economical to develop a machine which grades by mass. therefore, a relationship between mass and dimensions or projected areas and/ or volume of fruits is needed. various grad...

We study machine learning of phenomenologically relevant properties string compactifications, which arise in the context heterotic line bundle models. Both supervised and unsupervised are considered. find that, for a fixed compactification manifold, relatively small neural networks capable distinguishing consistent models with correct gauge group chiral asymmetry from random without these prope...

Bitcoin as the current leader in cryptocurrencies is a new asset class receiving significant attention in the financial and investment community and presents an interesting time series prediction problem. In this paper, some forecasting models based on classical like ARIMA and machine learning approaches including Kriging, Artificial Neural Network (ANN), Bayesian method, Support Vector Machine...