In this paper, we develop topological data analysis methods for classification tasks on univariate time series. As an application, perform binary and ternary two public datasets that consist of physiological signals collected under stress non-stress conditions. We accomplish our goal by using persistent homology to engineer stable features after use a delay embedding the subwindowing instead wi...

The nonparametric estimation(NE) of kernel polynomial regression (KPR) model is a powerful tool to visually depict the effect of covariates on response variable, when there exist unstructured and heterogeneous data. In this paper we introduce KPR model that is the mixture of nonparametric regression models with bootstrap algorithm, which is considered in a heterogeneous and unstructured framewo...

A clustering algorithm partitions a set of data points into smaller sets (clusters) such that each subset is more tightly packed than the whole. Many approaches to clustering translate the vector data into a graph with edges reflecting a distance or similarity metric on the points, then look for highly connected subgraphs. We introduce such an algorithm based on ideas borrowed from the topologi...

Topological data analysis is a new approach to processing digital data, focusing on the fact that topological properties are quite important for efficient data comparison. In particular, persistent topology and homology are relevant mathematical tools in TDA, and their study is attracting more and more researchers. As a matter of fact, in many applications data can be represented by continuous ...

Influenzanet is a system to monitor the activity of influenza-like-illness [ILI] with the aid of internet volunteers. Topological data analysis [TDA] examines the structure of data and contributes to the development of medicine, studying properties of a continuous space by the analysis of a discrete sample of it. Using TDA we analyze the topology of Influenzanet data identifying noise and disti...

By executing different fingerprint-image matching algorithms on large data sets, it reveals that the match and non-match similarity scores have no specific underlying distribution function. Thus, it requires a nonparametric analysis for fingerprint-image matching algorithms on large data sets without any assumption about such irregularly discrete distribution functions. A precise receiver opera...