Principles and Applications of Data Science

Data science is an emerging multidisciplinary field which lies at the intersection of computer science, statistics, and mathematics, with different applications and related to data mining, deep learning, and big data. This Special Issue on “Principles and Applications of Data Science” focuses on the latest developments in the theories, techniques, and applications of data science. The topics include data cleansing, data mining, machine learning, deep learning, and the applications of medical and healthcare, as well as social media

Climate Change and Environmental Sustainability-Volume 2

Our world is facing many challenges, such as poverty, hunger, resource shortage, environmental degradation, climate change, and increased inequalities and conflicts. To address such challenges, the United Nations proposed the Sustainable Development Goals (SDG), consisting of 17 interlinked global goals, as the strategic blueprint of world sustainable development. Nevertheless, the implementation of the SDG framework has been very challenging and the COVID-19 pandemic has further impeded the SDG implementation progress. Accelerated efforts are needed to enable all stakeholders, ranging from national and local governments, civil society, private sector, academia and youth, to contribute to addressing this dilemma. This volume of the Climate Change and Environmental Sustainability book series aims to offer inspiration and creativity on approaches to sustainable development. Among other things, it covers topics of COVID-19 and sustainability, environmental pollution, food production, clean energy, low-carbon transport promotion, and strategic governance for sustainable initiatives. This book can reveal facts about the challenges we are facing on the one hand and provide a better understanding of drivers, barriers, and motivations to achieve a better and more sustainable future for all on the other. Research presented in this volume can provide different stakeholders, including planners and policy makers, with better solutions for the implementation of SDGs. Prof. Bao-Jie He acknowledges the Project NO. 2021CDJQY-004 supported by the Fundamental Research Funds for the Central Universities. We appreciate the assistance from Mr. Lifeng Xiong, Mr. Wei Wang, Ms. Xueke Chen and Ms. Anxian Chen at School of Architecture and Urban Planning, Chongqing University, China

Energy Use Efficiency

Energy is one of the most important factors of production. Its efficient use is crucial for ensuring production and environmental quality. Unlike normal goods with supply management, energy is demand managed. Efficient energy use—or energy efficiency—aims to reduce the amount of energy required to provide products and services. Energy use efficiency can be achieved in situations such as housing, offices, industrial production, transport and agriculture as well as in public lighting and services. The use of energy can be reduced by using technology that is energy saving. This Special Issue is a collection of research on energy use efficiency

Modelado de las concentraciones locales de ozono en la Zona Centro del Area Metropolitana de la Ciudad de México

La contaminación del aire constituye el problema medioambiental de principal atención en las áreas urbanas debido a que afecta la salud de la población, en especial a la de los niños. Es por ello, que la construcción de modelos de ozono que capturen tan preciso como sea posible el comportamiento de este gas en la atmósfera resulta ser el principal interés no sólo del área científica sino de las agencias gubernamentales. En esta investigación se identifican modelos de concentraciones de ozono para la Región Oriental Austriaca por medio de una metodología de Soft Computing denominada Razonamiento Inductivo Difuso (FIR), la cual es una herramienta muy útil para modelar y simular aquellos sistemas de los cuales no hay conocimiento previo disponible o éste es muy escaso. Es sabido que las variaciones en las funciones de pertenencia tienen un efecto en la eficiencia de los sistemas basados en reglas difusas. La metodología FIR no es una excepción. La eficiencia de los procesos de identificación del modelado cualitativo y de predicción de FIR está muy influenciada por los parámetros de discretización de las variables del sistema, es decir, del número de clases de cada variable y de las funciones de pertenencia que definen su semántica. Es por ello que en este trabajo se presenta una metodología híbrida, unos nuevos Sistemas Genéticos Difusos (SGDs) en el contexto de la metodología FIR que sugieren de manera automática parámetros de discretización adecuados. En este trabajo se describen en detalle los componentes principales de los métodos que utilizamos.Air pollution is the main environmental problem of care in urban areas because it affects people's health, particularly that of children. For that reason, the construction of models of ozone catching as precise as possible the behaviour of this gas in the atmosphere appears to be the main area of interest is not only scientific but government agencies. In this research identifies models of ozone concentrations for the Eastern Region of Austria through a Soft Computing methodology called Fuzzy Inductive Reasoning (FIR), which is a very useful tool for modeling and simulating those systems which no knowledge prior available or it is very low. It is known that variations in the membership functions have an effect on the efficiency of systems based on fuzzy rules. The methodology FIR is no exception. The efficiency of the qualitative modeling and prediction process of FIR is strongly influenced by the discretization parameters of the system variables, ie the number of classes of each variable and membership functions that define its semantics. In this work presents a hybrid methodology, new Genetic Fuzzy Systems (GFSs) in the context of the FIR methodology that suggests by automatic way the discretization parameters suitable. This paper describes in detail the main components of these methods.Postprint (published version

Artificial Intelligence and Cognitive Computing

Artificial intelligence (AI) is a subject garnering increasing attention in both academia and the industry today. The understanding is that AI-enhanced methods and techniques create a variety of opportunities related to improving basic and advanced business functions, including production processes, logistics, financial management and others. As this collection demonstrates, AI-enhanced tools and methods tend to offer more precise results in the fields of engineering, financial accounting, tourism, air-pollution management and many more. The objective of this collection is to bring these topics together to offer the reader a useful primer on how AI-enhanced tools and applications can be of use in today’s world. In the context of the frequently fearful, skeptical and emotion-laden debates on AI and its value added, this volume promotes a positive perspective on AI and its impact on society. AI is a part of a broader ecosystem of sophisticated tools, techniques and technologies, and therefore, it is not immune to developments in that ecosystem. It is thus imperative that inter- and multidisciplinary research on AI and its ecosystem is encouraged. This collection contributes to that

Assessment of Socio-Economic Sustainability and Resilience after COVID-19

The pandemic period has caused severe socio-economic damage, but it is accompanied by environmental deterioration that can also affect economic opportunities and social equity. In the face of this double risk, future generations are ready to be resilient and make their contribution not only on the consumption side, but also through their inclusion in all companies by bringing green and circular principles with them. Policy makers can also favor this choice

Healthy Housing 2016: Proceedings of the 7th International Conference on Energy and Environment of Residential Buildings, 20-24 November, 2016

This conference series was initiated in Sendai, Japan in 2000 and has played a significant role in the development of Energy and Environment of Residential Buildings. It is well attended by researchers and academics from Asia, Europe and North America. In 2016 the conference moves to Brisbane, Australia, hosted by Queensland University of Technology and has adopted the overarching theme of Healthy Housing. The conference offers an exciting program that will not only facilitate research exchange between academics, but will supports and facilitatse dialogue with industry (housing providers) and government (housing regulators). These procedings collect the papers for the 2016 conference covering the topics: – Sustainable design and construction – Indoor environment – Neighbourhood planning – Modelling, simulation and post-occupancy evaluation – Building information and management – Valuation, marketing and financ

Aprendizaje de particiones difusas para razonamiento inductivo

Existe consenso entre los investigadores en que se pueden obtener sistemas más inteligentes por medio de la hibridación de metodologías de Soft Computing haciendo de este modo que las debilidades de unos sistemas se compensen con las bondades de otros. Los Sistemas Neurodifusos (SNDs) y los Sistemas Difusos Evolutivos (SDEs) constituyen la más notoria representatividad. Un Sistema Difuso Evolutivo es básicamente un sistema difuso robustecido por un proceso de aprendizaje basado en un Algoritmo Evolutivo (AE), en particular los Algoritmos Genéticos (AGs), los cuales están considerados actualmente como la técnica de búsqueda global más conocida y empleada. Este tipo de algoritmos presentan la capacidad de explorar y explotar espacios de búsqueda complejos, lo que les permite obtener soluciones muy próximas a la óptima. Además, la codificación genética que emplean les permite incorporar conocimiento a priori de una forma muy sencilla y aprovecharlo para guiar la búsqueda.En la presente tesis doctoral se proponen SDEs que tienen como objetivo principal el aprendizaje automático de particiones difusas para mejorar una técnica de modelado y simulación denominada Razonamiento Inductivo Difuso (FIR). Se persigue aprovechar las potencialidades de los AGs para aprender los parámetros de discretización de la metodología FIR, es decir, el número de clases por variable (granularidad) y las funciones de pertenencia (landmarks) que definen su semántica. Debido al hecho que es una metodología basada en lógica difusa, la eficiencia en el modelado y predicción de FIR está influenciada de forma directa por estos parámetros de discretización. Es así como, la determinación automática de parámetros adecuados de discretización en la metodología FIR surge como una alternativa de gran interés y utilidad al uso de valores heurísticos y/o por defecto. Más aún, automatizar la selección de los valores adecuados para estos parámetros permite el uso de la metodología FIR a usuarios no expertos en modelado de sistemas ni en lógica difusa garantizándoles el mejor rendimiento de esta metodología.Se presentan tres métodos evolutivos de aprendizaje automático de las particiones difusas: a) El aprendizaje de la granularidad con las funciones de pertenencia uniformes (AG1+EFP), b) El ajuste local de las funciones de pertenencia con un número fijo de clases para cada variable (AG1+AG2), y c) El aprendizaje en conjunto de la granularidad y de las funciones de pertenencia asociadas que definen su semántica (AG3). Dichos métodos han sido implementados en la herramienta de programación Matlab y sirven tanto para entornos Windows como para Linux.Los resultados obtenidos por los SDEs desarrollados han sido muy buenos en las cuatro aplicaciones estudiadas: sistema nervioso central humano, línea de media tensión en núcleos urbanos, estimación a corto plazo de concentraciones de ozono en Austria y estimación a largo plazo de concentraciones de ozono en México. Nuestros métodos evolutivos son los que presentan mayor eficiencia en el proceso de predicción si los comparamos con los obtenidos por otras metodologías en trabajos previos, por FIR usando valores por defecto y también, cosa no esperada, por FIR cuando los parámetros de fusificación han sido definidos por expertos en el área. En general, el AG3 y la combinación AG1+AG2, en ese orden, son los que han mostrado mejores resultados en todas las aplicaciones, seguidos por el AG1+EFP. Sin embargo el AG3 es el que presenta mayor costo computacional. Por lo tanto como conclusión general, debemos decir que los SDEs diseñados e implementados en esta tesis consiguen buenos resultados para la tarea que les ha sido encomendada en el entorno de la metodología FIR. Es pues el usuario quien debe decidir qué SDE resulta más conveniente para la aplicación que tiene entre manos, en función de las necesidades temporales y de precisión.It is commonly established that more intelligent systems can be obtained by the hybridization of Soft Computing methodologies, in order that the weaknesses of some systems be compensated with the strengths of others. Neural Fuzzy Systems (NFSs) and Evolutionary Fuzzy Systems (EFSs) are the most notorious representatives of these hybrid systems.An Evolutionary Fuzzy System is basically a fuzzy system augmented by a learning process based on an evolutionary algorithm (EA), particularly Genetic Algorithms (GAs), which are currently considered as the most well-known employed global search technique. This kind of algorithms have the ability to explore and to exploit complex search spaces, which allows the obtaining of solutions very close to the optimal ones within these spaces. Besides, the genetic codification employed allows to incorporate a priori knowledge in a very simple way and to use it to guide the search.In this PhD. thesis, we propose EFSs that improves a modeling and simulation technique the Fuzzy Inductive Reasoning (FIR). The main goal of the EFSs is to take advantage of the potentialities of GAs to learn the fuzzification parameters of FIR, i.e. the number of classes per variable (granularity) and the membership functions (landmarks) that define its semantics. Due to the fact that it is a methodology based on fuzzy logic, FIR modeling and prediction performance is directly influenced by these discretization parameters. Therefore, the automatic determination of precise fuzzification parameters in the FIR methodology is an interesting and useful alternative to the use of heuristics and/or default values. Moreover, it is expected that the automatic selection of adequate values for these parameters will open up the FIR methodology to new users, with no experience neither in systems modeling nor in fuzzy logic, guaranteeing the best performance of this methodology.Three evolutionary methods of automatic learning of fuzzy partitions are presented: a) The learning of the granularity with uniform membership functions (GA1+EFP), b) The local tuning of the membership functions with a fixed number of classes per variable (GA1+GA2), and c) The learning at the same time of the granularity and the membership functions associated that define its semantics (GA3). The evolutionary methods have been implemented in Matlab and they run in both Windows and Linux environments.The results obtained by the EFSs developed in the four applications studied, i.e. human central nervous system, maintenance costs of electrical medium line in Spanish towns, short-term estimation of ozone concentration in Austria and long-term estimation of ozone concentration in Mexico, were very good. The results obtained by our evolutionary methods have presented higher efficiency in the prediction process than those obtained by other methodologies in previous works, by FIR using default values and, even, by FIR when the fuzzification parameters have been defined by experts in the area. In general, the GA3 and the combination GA1+GA2, in that order, are the ones that have shown better results in all the applications, followed by the GA1+EFP. However, GA3 is the algorithm that presents the greatest computational cost. As general conclusion, we must say that the EFSs designed and implemented in this thesis yielded good results for the task which they were entrusted in FIR methodology. Therefore, the user should decide what EFS turns out to be more convenient for the modeling application at hand in function of time and precision needs.Postprint (published version

Urban Informatics

This open access book is the first to systematically introduce the principles of urban informatics and its application to every aspect of the city that involves its functioning, control, management, and future planning. It introduces new models and tools being developed to understand and implement these technologies that enable cities to function more efficiently – to become ‘smart’ and ‘sustainable’. The smart city has quickly emerged as computers have become ever smaller to the point where they can be embedded into the very fabric of the city, as well as being central to new ways in which the population can communicate and act. When cities are wired in this way, they have the potential to become sentient and responsive, generating massive streams of ‘big’ data in real time as well as providing immense opportunities for extracting new forms of urban data through crowdsourcing. This book offers a comprehensive review of the methods that form the core of urban informatics from various kinds of urban remote sensing to new approaches to machine learning and statistical modelling. It provides a detailed technical introduction to the wide array of tools information scientists need to develop the key urban analytics that are fundamental to learning about the smart city, and it outlines ways in which these tools can be used to inform design and policy so that cities can become more efficient with a greater concern for environment and equity