Data management and machine learning are two important tasks in data science. However, they have been independently studied so far. We argue that they should be complementary to each other. On the one hand, machine learning requires data management techniques to extract, integrate, clean the data, to support scalable and usable machine learning, making it user-friendly and easily deployable. On...

While research has been conducted in machine learning algorithms and in privacy preserving in data mining (PPDM), a gap in the literature exists which combines the aforementioned areas to determine how PPDM affects common machine learning algorithms. The aim of this research is to narrow this literature gap by investigating how a common PPDM algorithm, K-Anonymity, affects common machine learni...

Deep learning is one of the subsets of machine learning that is widely used in Artificial Intelligence (AI) field such as natural language processing and machine vision. The learning algorithms require optimization in multiple aspects. Generally, model-based inferences need to solve an optimized problem. In deep learning, the most important problem that can be solved by optimization is neural n...

Cumulative burden assessment (CuBA) has the potential to inform planning and decision-making on health disparities related to multiple environmental burdens. However, scholars have raised concerns about the social complexity to be dealt with while conducting CuBA, suggesting that it should be addressed in an adaptive, participatory and transdisciplinary (APT) approach. APT calls for deliberatio...

This paper is about the construction of various specific machine learning paradigms using a primitive-based generic machine learning model. The generic model identifies five functional components involved in a machine learning process, including an input, a transformation, a control, an output, and a knowledge base. It also identifies a set of basic machine learning mechanisms for each componen...