WAVELET TRANSFORM APPLIED IN ECG SIGNAL PROCESSING

In this paper, we have introduced the compression of electrocardiogram (ECG) using a wavelet transformation. We will treat ECG as a signal and will implement a matched filter; and more precisely, the Wiener filter which is proportional to the signal itself. Also, we will use this filter to detect the positions of the heart beats. In this application, we will be using the white noise. However, the matched filter will not be proportional to the signal itself. Other results we have computed for this ECG signal are the mean difference of the heart beats and the heart rate. All these results are well–established diagnostic tools for cardiac diseases

WAVELET TRANSFORM APPLIED IN ECG SIGNAL PROCESSING

In this paper, we have introduced the compression of electrocardiogram (ECG) using a wavelet transformation. We will treat ECG as a signal and will implement a matched filter; and more precisely, the Wiener filter which is proportional to the signal itself. Also, we will use this filter to detect the positions of the heart beats. In this application, we will be using the white noise. However, the matched filter will not be proportional to the signal itself. Other results we have computed for this ECG signal are the mean difference of the heart beats and the heart rate. All these results are well–established diagnostic tools for cardiac diseases

New Root-Finding Methods for Nonlinear Equations

In this paper we present three new methods of order four using an accelerating generator that generates root-finding methods of arbitrary order of convergence, based on existing third-order multiple root-finding methods free from the third derivative. The first method requires two-function and three-derivative evaluation per step, and two other methods require one-function and two-derivative evaluation per step. Numerical examples suggest that these methods are competitive to other fourth-order methods for multiple roots and have a higher informational efficiency than the known methods of the same order.&nbsp

A toolbox of machine learning software to support microbiome analysis

The human microbiome has become an area of intense research due to its potential impact on human health. However, the analysis and interpretation of this data have proven to be challenging due to its complexity and high dimensionality. Machine learning (ML) algorithms can process vast amounts of data to uncover informative patterns and relationships within the data, even with limited prior knowledge. Therefore, there has been a rapid growth in the development of software specifically designed for the analysis and interpretation of microbiome data using ML techniques. These software incorporate a wide range of ML algorithms for clustering, classification, regression, or feature selection, to identify microbial patterns and relationships within the data and generate predictive models. This rapid development with a constant need for new developments and integration of new features require efforts into compile, catalog and classify these tools to create infrastructures and services with easy, transparent, and trustable standards. Here we review the state-of-the-art for ML tools applied in human microbiome studies, performed as part of the COST Action ML4Microbiome activities. This scoping review focuses on ML based software and framework resources currently available for the analysis of microbiome data in humans. The aim is to support microbiologists and biomedical scientists to go deeper into specialized resources that integrate ML techniques and facilitate future benchmarking to create standards for the analysis of microbiome data. The software resources are organized based on the type of analysis they were developed for and the ML techniques they implement. A description of each software with examples of usage is provided including comments about pitfalls and lacks in the usage of software based on ML methods in relation to microbiome data that need to be considered by developers and users. This review represents an extensive compilation to date, offering valuable insights and guidance for researchers interested in leveraging ML approaches for microbiome analysis

A toolbox of machine learning software to support microbiome analysis

Peer reviewed: TrueAcknowledgements: This article is based upon work from COST Action ML4Microbiome “Statistical and machine learning techniques in human microbiome studies,” CA18131, supported by COST (European Cooperation in Science and Technology), www.cost.eu.The human microbiome has become an area of intense research due to its potential impact on human health. However, the analysis and interpretation of this data have proven to be challenging due to its complexity and high dimensionality. Machine learning (ML) algorithms can process vast amounts of data to uncover informative patterns and relationships within the data, even with limited prior knowledge. Therefore, there has been a rapid growth in the development of software specifically designed for the analysis and interpretation of microbiome data using ML techniques. These software incorporate a wide range of ML algorithms for clustering, classification, regression, or feature selection, to identify microbial patterns and relationships within the data and generate predictive models. This rapid development with a constant need for new developments and integration of new features require efforts into compile, catalog and classify these tools to create infrastructures and services with easy, transparent, and trustable standards. Here we review the state-of-the-art for ML tools applied in human microbiome studies, performed as part of the COST Action ML4Microbiome activities. This scoping review focuses on ML based software and framework resources currently available for the analysis of microbiome data in humans. The aim is to support microbiologists and biomedical scientists to go deeper into specialized resources that integrate ML techniques and facilitate future benchmarking to create standards for the analysis of microbiome data. The software resources are organized based on the type of analysis they were developed for and the ML techniques they implement. A description of each software with examples of usage is provided including comments about pitfalls and lacks in the usage of software based on ML methods in relation to microbiome data that need to be considered by developers and users. This review represents an extensive compilation to date, offering valuable insights and guidance for researchers interested in leveraging ML approaches for microbiome analysis.</jats:p

A toolbox of machine learning software to support microbiome analysis

The human microbiome has become an area of intense research due to its potential impact on human health. However, the analysis and interpretation of this data have proven to be challenging due to its complexity and high dimensionality. Machine learning (ML) algorithms can process vast amounts of data to uncover informative patterns and relationships within the data, even with limited prior knowledge. Therefore, there has been a rapid growth in the development of software specifically designed for the analysis and interpretation of microbiome data using ML techniques. These software incorporate a wide range of ML algorithms for clustering, classification, regression, or feature selection, to identify microbial patterns and relationships within the data and generate predictive models. This rapid development with a constant need for new developments and integration of new features require efforts into compile, catalog and classify these tools to create infrastructures and services with easy, transparent, and trustable standards. Here we review the state-of-the-art for ML tools applied in human microbiome studies, performed as part of the COST Action ML4Microbiome activities. This scoping review focuses on ML based software and framework resources currently available for the analysis of microbiome data in humans. The aim is to support microbiologists and biomedical scientists to go deeper into specialized resources that integrate ML techniques and facilitate future benchmarking to create standards for the analysis of microbiome data. The software resources are organized based on the type of analysis they were developed for and the ML techniques they implement. A description of each software with examples of usage is provided including comments about pitfalls and lacks in the usage of software based on ML methods in relation to microbiome data that need to be considered by developers and users. This review represents an extensive compilation to date, offering valuable insights and guidance for researchers interested in leveraging ML approaches for microbiome analysis.Peer reviewe