Algorithms and software for data mining and machine learning: a critical comparative view from a systematic review of the literature

2021 ◽  
Author(s):  
Gilda Taranto-Vera ◽  
Purificación Galindo-Villardón ◽  
Javier Merchán-Sánchez-Jara ◽  
Julio Salazar-Pozo ◽  
Alex Moreno-Salazar ◽  
...  
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Data Mining ◽  
Review Of The Literature

scholarly journals A systematic review of data mining and machine learning for air pollution epidemiology

2017 ◽  
Vol 17 (1) ◽  
Author(s):  
Colin Bellinger ◽  
Mohomed Shazan Mohomed Jabbar ◽  
Osmar Zaïane ◽  
Alvaro Osornio-Vargas
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Data Mining ◽  
Air Pollution ◽  
Air Pollution Epidemiology

scholarly journals Machine Learning to Assist in Pneumonia Decision Making: A Systematic Review of the Literature

2020 ◽  
Author(s):  
Victor Silva ◽  
Amanda Days Ramos Novo ◽  
Damires Souza ◽  
Alex Rêgo
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Decision Making ◽  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Research Area ◽  
Review Of The Literature

Clinical decision support systems is a research area in which Machine Learning (ML) techniques can be applied. Nevertheless, specifically in assisting pneumonia decision making, the use of ML has not been so expressive. To help matters, this work aims to contribute to the evolution of the intersection of such areas by presenting a Systematic Review of the Literature. It provides results which may help to identify, interpret and evaluate how ML techniques have been applied and some research enhancements yet to be done.

Machine learning in burn care and research: A systematic review of the literature

Burns ◽  
2015 ◽  
Vol 41 (8) ◽  
pp. 1636-1641 ◽  
Author(s):  
Nehemiah T. Liu ◽  
Jose Salinas
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Review Of The Literature ◽  
Burn Care

Computerized decision support and machine learning applications for the prevention and treatment of childhood obesity: A systematic review of the literature

2020 ◽  
Vol 104 ◽  
pp. 101844 ◽  
Author(s):  
Andreas Triantafyllidis ◽  
Eleftheria Polychronidou ◽  
Anastasios Alexiadis ◽  
Cleilton Lima Rocha ◽  
Douglas Nogueira Oliveira ◽  
...  
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Decision Support ◽  
Childhood Obesity ◽  
Review Of The Literature ◽  
Computerized Decision Support ◽  
Prevention And Treatment ◽  
Machine Learning Applications

scholarly journals A Review of Verification and Validation for Space Autonomous Systems

2021 ◽  
Author(s):  
Rafael C. Cardoso ◽  
Georgios Kourtis ◽  
Louise A. Dennis ◽  
Clare Dixon ◽  
Marie Farrell ◽  
...  
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Autonomous Systems ◽  
Verification And Validation ◽  
Compositional Verification ◽  
Review Of The Literature ◽  
Static Verification ◽  
Modular Systems ◽  
Potential Value ◽  
Verification Model

Abstract Purpose of Review The deployment of hardware (e.g., robots, satellites, etc.) to space is a costly and complex endeavor. It is of extreme importance that on-board systems are verified and validated through a variety of verification and validation techniques, especially in the case of autonomous systems. In this paper, we discuss a number of approaches from the literature that are relevant or directly applied to the verification and validation of systems in space, with an emphasis on autonomy. Recent Findings Despite advances in individual verification and validation techniques, there is still a lack of approaches that aim to combine different forms of verification in order to obtain system-wide verification of modular autonomous systems. Summary This systematic review of the literature includes the current advances in the latest approaches using formal methods for static verification (model checking and theorem proving) and runtime verification, the progress achieved so far in the verification of machine learning, an overview of the landscape in software testing, and the importance of performing compositional verification in modular systems. In particular, we focus on reporting the use of these techniques for the verification and validation of systems in space with an emphasis on autonomy, as well as more general techniques (such as in the aeronautical domain) that have been shown to have potential value in the verification and validation of autonomous systems in space.

scholarly journals Data Mining and Machine Learning to Promote Smart Cities: A Systematic Review from 2000 to 2018

2019 ◽  
Vol 11 (4) ◽  
pp. 1077 ◽  
Author(s):  
Jovani Souza ◽  
Antonio Francisco ◽  
Cassiano Piekarski ◽  
Guilherme Prado
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Data Mining ◽  
Predictive Analytics ◽  
Smart Cities ◽  
The Sustainable Development ◽  
Governmental Agencies ◽  
Common Technique ◽  
Development Goals ◽  
Sustainable Services

Smart cities (SC) promote economic development, improve the welfare of their citizens, and help in the ability of people to use technologies to build sustainable services. However, computational methods are necessary to assist in the process of creating smart cities because they are fundamental to the decision-making process, assist in policy making, and offer improved services to citizens. As such, the aim of this research is to present a systematic review regarding data mining (DM) and machine learning (ML) approaches adopted in the promotion of smart cities. The Methodi Ordinatio was used to find relevant articles and the VOSviewer software was performed for a network analysis. Thirty-nine significant articles were identified for analysis from the Web of Science and Scopus databases, in which we analyzed the DM and ML techniques used, as well as the areas that are most engaged in promoting smart cities. Predictive analytics was the most common technique and the studies focused primarily on the areas of smart mobility and smart environment. This study seeks to encourage approaches that can be used by governmental agencies and companies to develop smart cities, being essential to assist in the Sustainable Development Goals.

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