Modeling a second-generation glucose oxidase biosensor with statistical machine learning methods

2014 ◽  
pp. 163-183
Author(s):  
Livier Renteria-Gutierrez ◽  
Lluis A. Belanche-Muñoz ◽  
Felix F. Gonzalez-Navarro ◽  
Margarita Stoytcheva
Keyword(s):  
Machine Learning ◽  
Glucose Oxidase ◽  
Second Generation ◽  
Statistical Machine Learning ◽  
Learning Methods ◽  
Machine Learning Methods

scholarly journals Glucose Oxidase Biosensor Modeling and Predictors Optimization by Machine Learning Methods

Sensors ◽  
2016 ◽  
Vol 16 (11) ◽  
pp. 1483 ◽  
Author(s):  
Felix Gonzalez-Navarro ◽  
Margarita Stilianova-Stoytcheva ◽  
Livier Renteria-Gutierrez ◽  
Lluís Belanche-Muñoz ◽  
Brenda Flores-Rios ◽  
...  
Keyword(s):  
Machine Learning ◽  
Glucose Oxidase ◽  
Learning Methods ◽  
Machine Learning Methods

scholarly journals Statistical Machine Learning Methods and Remote Sensing for Sustainable Development Goals: A Review

2018 ◽  
Vol 10 (9) ◽  
pp. 1365 ◽  
Author(s):  
Jacinta Holloway ◽  
Kerrie Mengersen
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Sustainable Development ◽  
Sustainable Development Goals ◽  
Remote Sensing Data ◽  
Statistical Machine Learning ◽  
Learning Methods ◽  
Sensing Data ◽  
Machine Learning Methods ◽  
Development Goals

Interest in statistical analysis of remote sensing data to produce measurements of environment, agriculture, and sustainable development is established and continues to increase, and this is leading to a growing interaction between the earth science and statistical domains. With this in mind, we reviewed the literature on statistical machine learning methods commonly applied to remote sensing data. We focus particularly on applications related to the United Nations World Bank Sustainable Development Goals, including agriculture (food security), forests (life on land), and water (water quality). We provide a review of useful statistical machine learning methods, how they work in a remote sensing context, and examples of their application to these types of data in the literature. Rather than prescribing particular methods for specific applications, we provide guidance, examples, and case studies from the literature for the remote sensing practitioner and applied statistician. In the supplementary material, we also describe the necessary steps pre and post analysis for remote sensing data; the pre-processing and evaluation steps.

scholarly journals Automatic Anomaly Detection on In-Production Manufacturing Machines Using Statistical Learning Methods

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2344 ◽  
Author(s):  
Federico Pittino ◽  
Michael Puggl ◽  
Thomas Moldaschl ◽  
Christina Hirschl
Keyword(s):  
Machine Learning ◽  
Anomaly Detection ◽  
Statistical Learning ◽  
Control Charts ◽  
Industry 4.0 ◽  
Production Environment ◽  
Statistical Machine Learning ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
And Control

Anomaly detection is becoming increasingly important to enhance reliability and resiliency in the Industry 4.0 framework. In this work, we investigate different methods for anomaly detection on in-production manufacturing machines taking into account their variability, both in operation and in wear conditions. We demonstrate how the nature of the available data, featuring any anomaly or not, is of importance for the algorithmic choice, discussing both statistical machine learning methods and control charts. We finally develop methods for automatic anomaly detection, which obtain a recall close to one on our data. Our developed methods are designed not to rely on a continuous recalibration and hand-tuning by the machine user, thereby allowing their deployment in an in-production environment robustly and efficiently.

scholarly journals Applying Statistical Machine Learning Methods to Analysis Differences in the Severity Level of COVID-19 among Countries

2021 ◽  
pp. 219-234
Author(s):  
Wen Yin ◽  
◽  
Chenchen Pan ◽  
Nanyi Deng ◽  
Dong Ji
Keyword(s):  
Machine Learning ◽  
Life Expectancy ◽  
Mean Squared Error ◽  
Negative Impact ◽  
Influential Factors ◽  
Machine Learning Techniques ◽  
Statistical Machine Learning ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Squared Error

The COVID-19 pandemic has caused a significant negative impact on countries around the world, and there appears to be an observable difference in severity among nations. This study aims to provide an insight into the roles many social and economic factors played in contributing to this variation. By investigating potential patterns through exploratory data analysis, followed by constructing models using several popular machine learning techniques, we examine the validity of the underlying assumptions and identifying any potential limitations. Total deaths per million population is used as dependent variable with log transformation to remove outliers. A set of factors such as life expectancy, unemployment rate and population are available in the dataset. After removing and transforming outliers, various machine learning methods with cross validation are implemented and the optimal model is determined by predefined metrics such as root-mean-squared-error (RMSE) and mean-squared-error (MAE). The results show that the Gradient Boost Machine (GBM) technique achieves the most optimal results in terms of minimum RMSE and MAE. The RMSE and MAE values indicate no over fitting issues and the GBM algorithm captures the most influential factors such as life expectancy, healthcare expense per Gross Domestic Product (GDP) and GDP per capita, which are clearly critical explanatory variables for predicting total deaths per million population.

Glucose Oxidase Biosensor Modeling by Machine Learning Methods

2014 ◽  
pp. 464-473
Author(s):  
Livier Rentería-Gutiérrez ◽  
Félix F. González-Navarro ◽  
Margarita Stilianova-Stoytcheva ◽  
Lluís A. Belanche-Muñoz ◽  
Brenda L. Flores-Ríos ◽  
...  
Keyword(s):  
Machine Learning ◽  
Glucose Oxidase ◽  
Learning Methods ◽  
Machine Learning Methods

scholarly journals Modeling the productivity of mechanized CTL harvesting with statistical machine learning methods

2020 ◽  
Vol 31 (3) ◽  
pp. 253-262
Author(s):  
Eero Liski ◽  
Pekka Jounela ◽  
Heikki Korpunen ◽  
Amanda Sosa ◽  
Ola Lindroos ◽  
...  
Keyword(s):  
Machine Learning ◽  
Statistical Machine Learning ◽  
Learning Methods ◽  
Machine Learning Methods

Adverse drug reaction analysis using statistical and machine learning methods: A systematic review (Preprint)

2021 ◽  
Author(s):  
Yu Rang Park
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Adverse Drug Reaction ◽  
Drug Reaction ◽  
Meta Analysis ◽  
Statistical Machine Learning ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Multiple Data ◽  
Current Systematic Review

UNSTRUCTURED An adverse drug reaction (ADR) is an unintended response induced by a drug. It is important to determine the association between drugs and ADRs. There are many methods to demonstrate this association. This systematic review aimed to examine the analysis tools by considering original articles that introduced statistical and machine learning methods for predicting ADRs in humans. A systematic literature review of EMBASE and PubMed was conducted based on articles published from January 2015 to March 2020. The keywords were statistical, machine learning, and deep learning methods for the detection of ADR signals in the title and abstract. We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis statement guidelines. In total, 72 articles were included in the current systematic review; of these, 51 and 21 addressed statistical and machine learning methods, respectively. This study provides a graphical overview of data-driven methods for detecting ADRs with multiple data sources for patient drug safety.

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