In recent years, there has been an exponential growth in the number of complex documents
and texts that require a deeper understanding of machine learning methods to be able to …

In this paper, sixty-eight research articles published between 2000 and 2017 as well as
textbooks which employed four classification algorithms: K-Nearest-Neighbor (KNN) …

In order to uncover the meanings of 'book of life', 155 different biological language models
(BLMs) for DNA, RNA and protein sequence analysis are discussed in this study, which are …

Bayesian optimization (BayesOpt) is a gold standard for query-efficient continuous
optimization. However, its adoption for drug design has been hindered by the discrete, high …

The extraction of useful insights from text with various types of statistical algorithms is
referred to as text mining, text analytics, or machine learning from text. The choice of …

This is a book on data analysis with a specific focus on the practice of predictive modeling.
The term predictive modeling may stir associations such as machine learning, pattern …

As more firms adopt big data analytics to better understand their customers and differentiate
their offerings from competitors, it becomes increasingly difficult to generate strategic value …

Hate speech detection in social media texts is an important Natural language Processing
task, which has several crucial applications like sentiment analysis, investigating …

The metric learning problem is concerned with learning a distance function tuned to a
particular task, and has been shown to be useful when used in conjunction with nearest …

The quality of the interpretation of the sentiment in the online buzz in the social media and
the online news can determine the predictability of financial markets and cause huge gains …