The key aspect in coal production is realizing safe and efficient mining to maximize the
utilization of the resources. A requirement for sustainable economic development is realizing …

Functional data analysis (FDA) is a statistical framework that allows for the analysis of
curves, images, or functions on higher dimensional domains. The goals of FDA, such as …

A fundamental problem in functional data analysis is to classify a functional observation
based on training data. The application of functional data classification has gained immense …

In recent years, feature selection has become a challenging problem in several machine
learning fields, such as classification problems. Support Vector Machine (SVM) is a well …

Many real-life applications consider nominal categorical predictor variables that have a
hierarchical structure, eg economic activity data in Official Statistics. In this paper, we focus …

Interval-valued functional clustering is a novel approach for functional data analysis where
each observation is represented by an interval. Existing interval-valued functional clustering …

This paper introduces a novel methodology for Feature Selection for Functional
Classification, FSFC, that addresses the challenge of jointly performing feature selection and …

Classification (supervised-learning) of multivariate functional data is considered when the
elements of the random functional vector of interest are defined on different domains. In this …

Multivariate functional data are prevalent in various fields such as biology, climatology, and
finance. Motivated by the World Health Data applications, in this study, we propose and …

FunctionalData Analysis (FDA) has become a very important field in recent years due to its
wide range of applications. However, there are several real-life applications in which hybrid …