This paper describes the implementation of a prediction model for real-time assessment of
weather related outages in the electric transmission system. The network data and historical …

Determining fault location in power systems using the available measurements and models
is an important task since it allows the maintenance crews to inspect the site where the fault …

Implementation on rapid and accurate bandgap prediction has great practical implications
for a range of applications. While quantum mechanical computations are enormously …

Machine learning (ML) models for analyzing medical data are critical for both accelerating
development of novel diagnostic and treatment strategies and improving the accuracy of …

We present an approach for data-driven prediction of high-dimensional chaotic time series
generated by spatially-extended systems. The algorithm employs a convolutional …

Decision trees and logistic regression are one of the most popular and well-known machine
learning algorithms, frequently used to solve a variety of real-world problems. Stability of …

In this paper, we introduce the notion of “learning capacity” for algorithms that learn from
data, which is analogous to the Shannon channel capacity for communication systems. We …

Stacking is a general approach for combining multiple models toward greater predictive
accuracy. It has found various application across different domains, ensuing from its meta …

The primary goal of Machine learning (ML) models in the prediction of medical conditions is
to accurately predict (classify) the occurrence of a disease, or therapy. Many ML models …

Predictive modeling is an ever-increasingly important part of decision making. The advances
in Machine Learning predictive modeling have spread across many domains bringing …