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.

scholarly journals Enabling Technologies for Urban Smart Mobility: Recent Trends, Opportunities and Challenges

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2143
Author(s):  
Sara Paiva ◽  
Mohd Abdul Ahad ◽  
Gautami Tripathi ◽  
Noushaba Feroz ◽  
Gabriella Casalino
Keyword(s):  
Traffic Congestion ◽  
Autonomous Vehicles ◽  
City Planning ◽  
Smart Cities ◽  
The Sustainable Development ◽  
Current Trends ◽  
Enabling Technologies ◽  
Entry Points ◽  
Development Goals ◽  
Smart Mobility

The increasing population across the globe makes it essential to link smart and sustainable city planning with the logistics of transporting people and goods, which will significantly contribute to how societies will face mobility in the coming years. The concept of smart mobility emerged with the popularity of smart cities and is aligned with the sustainable development goals defined by the United Nations. A reduction in traffic congestion and new route optimizations with reduced ecological footprint are some of the essential factors of smart mobility; however, other aspects must also be taken into account, such as the promotion of active mobility and inclusive mobility, encouraging the use of other types of environmentally friendly fuels and engagement with citizens. The Internet of Things (IoT), Artificial Intelligence (AI), Blockchain and Big Data technology will serve as the main entry points and fundamental pillars to promote the rise of new innovative solutions that will change the current paradigm for cities and their citizens. Mobility-as-a-service, traffic flow optimization, the optimization of logistics and autonomous vehicles are some of the services and applications that will encompass several changes in the coming years with the transition of existing cities into smart cities. This paper provides an extensive review of the current trends and solutions presented in the scope of smart mobility and enabling technologies that support it. An overview of how smart mobility fits into smart cities is provided by characterizing its main attributes and the key benefits of using smart mobility in a smart city ecosystem. Further, this paper highlights other various opportunities and challenges related to smart mobility. Lastly, the major services and applications that are expected to arise in the coming years within smart mobility are explored with the prospective future trends and scope.

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 Inequalities in the Exercise and Continuity in Building Engineering in Spain. Factor Analysis Including Gender Perspective

2021 ◽  
Vol 13 (10) ◽  
pp. 5514
Author(s):  
Irantzu Recalde-Esnoz ◽  
Daniel Ferrández ◽  
Carlos Morón ◽  
Guadalupe Dorado
Keyword(s):  
Data Mining ◽  
Factor Analysis ◽  
Gender Inequality ◽  
Sustainable Development Goals ◽  
The Sustainable Development ◽  
Agenda 2030 ◽  
Technical Architecture ◽  
Development Goals ◽  
Building Engineering ◽  
New Jobs

The building sector is one of the most relevant at world level in view of the percentage of gross domestic product (GDP) concerned, as well as the number of new jobs created. Nevertheless, it is a completely male-dominated industry. Different institutions and organisms, such as the Agenda 2030 and the Sustainable Development Goals, struggle to reduce gender inequality in different environments, including the working one. Aligned with these goals, this study provides the data exploited from the first survey regarding gender inequality within the professionals of the building engineering field in the Spanish population as a whole. This survey was developed in 2018 by the Spanish General Council of Technical Architecture and it was sent to its members. The sample involved 1353 cases. For this data mining, bivariate analyses were conducted in order to subsequently carry out a factor analysis and the socio–demographic composition of the dimensions found. Results exposed statistically meaningful differences in the eyes of women and men about those factors which facilitate practice and continuity in the profession. The most relevant conclusions drawn from the factor analysis reflect the existence of three factors: (1) work competences, (2) social capital and (3) physical appearance and being a man, dimensions in which women and men’s opinion was unevenly distributed.

Project-Oriented Learning as an Optimal Methodology for the Incorporation of the SDGs in University Teaching: A Systematic Review

2021 ◽  
Author(s):  
Antonio Miñán-Espigares ◽  
Claudia-Amanda Juárez-Romero
Keyword(s):  
Systematic Review ◽  
Sustainable Development ◽  
Situated Learning ◽  
Sustainable Development Goals ◽  
University Teaching ◽  
Teaching Innovation ◽  
The Sustainable Development ◽  
Development Goals ◽  
The University ◽  
Quality Learning

The use of active methodologies in the university is a priority to achieve higher quality learning. One of these methodologies with the greatest potential for training in competencies is Project-Oriented Learning (PLA), using it in an innovative way. Associating the use of this methodology with the objectives of sustainable development, which have become even more important since the Pandemic by COVID-19, can be a good idea to achieve a more sustained and situated learning. The aim of this study is to find out to what extent research on teaching innovation with Project-Oriented Learning is associated with the Sustainable Development Goals. A systematic review was carried out as indicated by PRISMA through the following databases: WOS and Scopus. WOS found 15 articles on AoP and 6 on Project-Oriented Learning and sustainability. In Scopus 2 were found in 2019. The main results show that in the University, especially in the branches of engineering, AoP is widely used, however, it is rarely related to SDGs. Among the conclusions, we highlight the need for research on project-oriented learning and sustainable development goals.

Big Data Models and the Public Sector

Web Services ◽  
2019 ◽  
pp. 105-126
Author(s):  
N. Nawin Sona
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Big Data ◽  
Public Sector ◽  
Predictive Analytics ◽  
Data Types ◽  
The Public ◽  
Emerging Trends ◽  
Wide Range ◽  
Learning Data

This chapter aims to give an overview of the wide range of Big Data approaches and technologies today. The data features of Volume, Velocity, and Variety are examined against new database technologies. It explores the complexity of data types, methodologies of storage, access and computation, current and emerging trends of data analysis, and methods of extracting value from data. It aims to address the need for clarity regarding the future of RDBMS and the newer systems. And it highlights the methods in which Actionable Insights can be built into public sector domains, such as Machine Learning, Data Mining, Predictive Analytics and others.

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 A Comparative Analysis of Countries' Performance According to SDG Indicators based on Machine Learning

2021 ◽  
Author(s):  
Guilherme Souza ◽  
Julian Santos ◽  
Gabriel SantClair ◽  
Janaina Gomide ◽  
Luan Santos
Keyword(s):  
Climate Change ◽  
Machine Learning ◽  
Economic Growth ◽  
Principal Component Analysis ◽  
Sustainable Development ◽  
Sustainable Development Goals ◽  
Principal Component ◽  
The Sustainable Development ◽  
Development Goals ◽  
Impacts Of Climate Change

The Sustainable Development Goals (SDGs) are part of a global effort to reduce the impacts of climate change, promoting social justice and economic growth. The United Nations provides a database with hundreds of indicators to track the SDGs since 2016 for a total of 302 regions. This work aims to assess which countries are in a similar situation regarding sustainable development. Principal Component Analysis was used to reduce the dimension of the dataset and k-means algorithm was used to cluster countries according to their SDGs indicators. For the years of 2016, 2017 and 2018 were obtained 11, 13 and 11 groups, respectively. This paper also analyses clusters changes throughout the years.

Sequences, Nets, and Filters of Fuzzy Soft Multi Sets in Fuzzy Soft Multi Topological Spaces

2016 ◽  
pp. 172-190
Author(s):  
Anjan Mukherjee ◽  
Ajoy Kanti Das
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Cluster Analysis ◽  
Topological Spaces ◽  
Main Task ◽  
Filter Convergence ◽  
Common Technique ◽  
Exploratory Data ◽  
Exploratory Data Mining ◽  
Convergence Sequence

In this chapter, the authors introduce a new sequence of fuzzy soft multi sets in fuzzy soft multi topological spaces and their basic properties are studied. The concepts of subsequence, convergence sequence and cluster fuzzy soft multi sets of fuzzy soft multi sets are proposed. Actually Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar to each other than to those in other groups (clusters). It is a main task of exploratory data mining and a common technique for statistical data analysis used in many fields including machine learning, pattern recognition, image analysis, information retrieval and bioinformatics. Here the authors define the notions of net and filter and establish the correspondence between net convergence and filter convergence in fuzzy soft multi topological spaces.

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