Governments and businesses increasingly rely on data analytics and machine learning (ML) for improving their competitive edge in areas such as consumer satisfaction, threat intelligence, decision making, and product efficiency. However, by cleverly corrupting a subset of data used as input to a target’s ML algorithms, an adversary can perturb outcomes and compromise the effectiveness of ML tech...

Artificial Intelligence (AI) and machine learning (ML) have now spawned a new field within health care science research. These predictive analytics tools are starting to change various facets of our clinical domains including the practice laboratory medicine. Many these ML studies also populate literature landscape as we know it but unfamiliarity average reader basic knowledge critical concepts...

Social media allows people interact to express their thoughts or feelings about different subjects. However, some of users may write offensive twits to other via social media which known as cyber bullying. Successful prevention depends on automatically detecting malicious messages. Automatic detection of bullying in the text of social media by analyzing the text "twits" via one of the machine l...

ABSTRACT The existing open-cluster membership determination algorithms are either prior dependent on some known parameters of clusters or not automatable to large samples clusters. In this paper, we present ml-moc, a new machine-learning-based approach identify likely members open using the Gaia DR2 data and no priori information about cluster parameters. We use k-nearest neighbour (kNN) algori...

Machine learning is an established and frequently used technique in industry academia, but a standard process model to improve success efficiency of machine applications still missing. Project organizations practitioners face manifold challenges risks when developing have need for guidance meet business expectations. This paper therefore proposes the development applications, covering six phase...

BACKGROUND To improve health outcomes and cut health care costs, we often need to conduct prediction/classification using large clinical datasets (aka, clinical big data), for example, to identify high-risk patients for preventive interventions. Machine learning has been proposed as a key technology for doing this. Machine learning has won most data science competitions and could support many c...