Background: To improve health outcomes and cut healthcare costs, we often need to conduct prediction/classification using large clinical data sets, a.k.a. “clinical big data,” e.g., to identify high-risk patients for preventive interventions. Machine learning has been proposed as a key technology for doing this. Machine learning won most data science competitions and could support many clinical...

Geosciences is a field of great societal relevance that requires solutions to several urgent problems facing our humanity and the planet. As geosciences enters the era of big data, machine learning (ML)— that has been widely successful in commercial domains—offers immense potential to contribute to problems in geosciences. However, problems in geosciences have several unique challenges that are...

A new machine learning approach known as motivated learning (ML) is presented in this work. Motivated learning drives a machine to develop abstract motivations and choose its own goals. ML also provides a self-organizing system that controls a machine’s behavior based on competition between dynamically-changing pain signals. This provides an interplay of externally driven and internally generat...

The past few years have seen a tremendous growth in the popularity of smartphones. As newer features continue to be added to smartphones to increase their utility, their significance will only increase in future. Combining machine learning with mobile computing can enable smartphones to become ‘intelligent’ devices, a feature which is hitherto unseen in them. Also, the combination of machine le...

The field of machine learning (ML) is concerned with the question of how to construct algorithms that automatically improve with experience. In recent years many successful ML applications have been developed, such as datamining programs, information-filtering systems, etc. Although ML algorithms allow the detection and extraction of interesting patterns of data for several kinds of problems, m...

We introduce the concept of data-driven finite element methods. These are finite-element discretizations partial differential equations (PDEs) that resolve quantities interest with striking accuracy, regardless underlying mesh size. The methods obtained within a machine-learning framework during which parameters defining method tuned against available training data. In particular, we use stable...

During earlier months of the pandemic COVID-19 with no recommended cure or vaccine available only solution to destroy chain is self-isolation which can be maintained by physical distancing. This now understood that world require much faster accommodate and deal future spread over non-clinical methods namely data mining, augmented intelligence several Artificial Intelligence (AI) techniques. It ...

y Ohio 45433-7001 z Abstract Learning from data is the central theme of Knowledge Discovery in Databases (KDD) and the Machine Learning (ML) community. In order to handle large databases, certain assumptions are necessary to make the problem tractable. Without introducing explicit domain knowledge, a natural assumption is Occam's Razor. However, the requirement to nd solutions of low complexity...

Astrology specifies both past action as well the future prediction which act subset of astronomy that have been represented in Galaxy system. Horoscope chart is an astrological representation involves movement nine planets located twelve houses with periodic positional movements. Based on horoscope representation, some are empty and others might contain one or more planets. Specifically, this r...