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Applications of Computational Intelligence to Power Systems

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ISBN: 9783039217601 / 9783039217618 Year: Pages: 116 DOI: 10.3390/books978-3-03921-761-8 Language: eng
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General) --- General and Civil Engineering
Added to DOAB on : 2019-11-08 11:31:56
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Electric power systems around the world are changing in terms of structure, operation, management and ownership due to technical, financial, and ideological reasons. Power systems keep on expanding in terms of geographical areas, asset additions, and the penetration of new technologies in generation, transmission, and distribution. The conventional methods for solving the power system design, planning, operation, and control problems have been extensively used for different applications, but these methods suffer from several difficulties, thus providing suboptimal solutions. Computationally intelligent methods can offer better solutions for several conditions and are being widely applied in electrical engineering applications. This Special Issue represents a thorough treatment of computational intelligence from an electrical power system engineer’s perspective. Thorough, well-organised, and up-to-date, it examines in detail some of the important aspects of this very exciting and rapidly emerging technology, including machine learning, particle swarm optimization, genetic algorithms, and deep learning systems. Written in a concise and flowing manner by experts in the area of electrical power systems who have experience in the application of computational intelligence for solving many complex and difficult power system problems, this Special Issue is ideal for professional engineers and postgraduate students entering this exciting field.

Selected Papers from IEEE ICKII 2018

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ISBN: 9783039212736 / 9783039212743 Year: Pages: 82 DOI: 10.3390/books978-3-03921-274-3 Language: eng
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General) --- General and Civil Engineering
Added to DOAB on : 2019-12-09 11:49:15
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Electronic engineering and design innovation are both academic and practical engineering fields that involve systematic technological materialization through scientific principles and engineering designs. Technological innovation via electronic engineering includes electrical circuits and devices, computer science and engineering, communications and information processing, and electrical engineering communications. The Special Issue selected excellent papers presented at the International Conference on Knowledge Innovation and Invention 2018 (IEEE ICKII 2018) on the topic of electronics and their applications. This conference was held on Jeju Island, South Korea, 23–27 July 2018, and it provided a unified communication platform for researchers from all over the world. The main goal of this Special Issue titled “Selected papers from IEEE ICKII 2018” is to discover new scientific knowledge relevant to the topic of electronics and their applications.

Flood Forecasting Using Machine Learning Methods

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ISBN: 9783038975489 Year: Pages: 376 DOI: 10.3390/books978-3-03897-549-6 Language: eng
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General) --- General and Civil Engineering --- Environmental Engineering
Added to DOAB on : 2019-03-08 11:42:05
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This book is a printed edition of the Special Issue Flood Forecasting Using Machine Learning Methods that was published in Water

Keywords

data scarce basins --- runoff series --- data forward prediction --- ensemble empirical mode decomposition (EEMD) --- stopping criteria --- method of tracking energy differences (MTED) --- deep learning --- convolutional neural networks --- superpixel --- urban water bodies --- high-resolution remote-sensing images --- monthly streamflow forecasting --- artificial neural network --- ensemble technique --- phase space reconstruction --- empirical wavelet transform --- hybrid neural network --- flood forecasting --- self-organizing map --- bat algorithm --- particle swarm optimization --- flood routing --- Muskingum model --- machine learning methods --- St. Venant equations --- rating curve method --- nonlinear Muskingum model --- hydrograph predictions --- flood routing --- Muskingum model --- hydrologic models --- improved bat algorithm --- Wilson flood --- Karahan flood --- flood susceptibility modeling --- ANFIS --- cultural algorithm --- bees algorithm --- invasive weed optimization --- Haraz watershed --- ANN-based models --- flood inundation map --- self-organizing map (SOM) --- recurrent nonlinear autoregressive with exogenous inputs (RNARX) --- ensemble technique --- artificial neural networks --- uncertainty --- streamflow predictions --- sensitivity --- flood forecasting --- extreme learning machine (ELM) --- backtracking search optimization algorithm (BSA) --- the upper Yangtze River --- deep learning --- LSTM network --- water level forecast --- the Three Gorges Dam --- Dongting Lake --- Muskingum model --- wolf pack algorithm --- parameters --- optimization --- flood routing --- flash-flood --- precipitation-runoff --- forecasting --- lag analysis --- random forest --- machine learning --- flood prediction --- flood forecasting --- hydrologic model --- rainfall–runoff, hybrid & --- ensemble machine learning --- artificial neural network --- support vector machine --- natural hazards & --- disasters --- adaptive neuro-fuzzy inference system (ANFIS) --- decision tree --- survey --- classification and regression trees (CART), data science --- big data --- artificial intelligence --- soft computing --- extreme event management --- time series prediction --- LSTM --- rainfall-runoff --- flood events --- flood forecasting --- data assimilation --- particle filter algorithm --- micro-model --- Lower Yellow River --- ANN --- hydrometeorology --- flood forecasting --- real-time --- postprocessing --- machine learning --- early flood warning systems --- hydroinformatics --- database --- flood forecast --- Google Maps

Empirical Finance

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ISBN: 9783038977063 Year: Pages: 276 DOI: 10.3390/books978-3-03897-707-0 Language: eng
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Economics
Added to DOAB on : 2019-04-05 10:34:31
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There is no denying the role of empirical research in finance and the remarkable progress of empirical techniques in this research field. This Special Issue focuses on the broad topic of “Empirical Finance” and includes novel empirical research associated with financial data. One example includes the application of novel empirical techniques, such as machine learning, data mining, wavelet transform, copula analysis, and TV-VAR, to financial data. The Special Issue includes contributions on empirical finance, such as algorithmic trading, market efficiency, market microstructure, portfolio theory and asset allocation, asset pricing models, liquidity risk premium, currency crisis, return predictability, and volatility modeling.

Keywords

text similarity --- text mining --- machine learning --- SVM --- neural network --- LSTM --- credit risk --- ensemble learning --- deep learning --- bagging --- random forest --- boosting --- deep neural network --- causality-in-variance --- cross-correlation function --- housing and stock markets --- algorithmic trading --- take profit --- stop loss --- MACD --- ATR --- city banks --- dependence structure --- copula --- n/a --- market microstructure --- price discovery --- latency --- currency crisis --- random forests --- wavelet transform --- predictive accuracy --- housing price --- bank credit --- housing loans --- real estate development loans --- TVP-VAR model --- exchange rate --- volatility --- exports --- ARDL --- Vietnam --- crude oil futures prices forecasting --- convolutional neural networks --- short-term forecasting --- utility of international currency --- inertia --- liquidity risk premium --- US dollar --- Japanese yen --- cointegration --- statistical arbitrage --- natural gas --- wholesale electricity --- futures market --- spark spread --- earnings management --- earnings manipulation --- earnings quality --- initial public offering --- IPO --- asset pricing model --- data mining --- bankruptcy prediction --- financial and non-financial variables --- institutional investors’ shareholdings --- panel data model --- piecewise regression model --- global financial crisis --- gold return --- asymmetric dependence --- financial market stress --- robust regression --- quantile regression --- structural break --- flight to quality

Sustainable Directions in Tourism

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ISBN: 9783039217724 / 9783039217731 Year: Pages: 302 DOI: 10.3390/books978-3-03921-773-1 Language: eng
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Economics
Added to DOAB on : 2019-12-09 11:49:16
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Within the framework of tourism companies and tourist destinations, the question of sustainability is gaining importance. Tourists are increasingly aware of the importance of sustainability criteria, awarding greater value to sustainable destinations. Sustainability refers to a wide range of aspects related to climate change, the economic organization of tourism, social values or questions, job creation, and the necessary protection of the culture of destinations and the environment. Therefore, there is a need for studies that consider these aspects in order to achieve the sustainable development of tourist destinations. Fundamental to this is discovering to what degree tourism companies and destinations approach these questions in the strategies they use to deal with problems stemming from their attempts to be more sustainable. Conceptual papers and empirical research on the economic, social, cultural, and environmental aspects related to tourism companies and destinations are welcome. Studies that analyze how these questions and the concept of sustainability are included in tourism companies and destinations are necessary in these modern times. This book was established for these reasons, dedicated to examining sustainability in tourism. The papers included in this Special Issue can help us to determine the new directions being addressed in the research on sustainability tourism.

Keywords

learning-based tourism --- science museum --- motivation --- constraint --- museum planning --- physical environment (PhE) --- visitor behavior --- visitor satisfaction --- all-for-one tourism --- spatial analysis --- tourism evaluation --- mountain areas --- sustainability services marketing matrix --- cluster analysis --- tourism indicators --- regional disparity --- tourism --- sustainability --- climate change --- choice experiment --- islands --- sustainable tourism --- strategic planning --- A’WOT --- TOWS matrix --- agritourism --- sustainable development --- the “health” of rural settlements --- Apuseni mountains --- forecast through a logistic model --- air pollution --- inbound tourism --- China, PM10 --- regression discontinuity design --- community-based tourism --- local community --- sustainable tourism --- destination development --- sense of belonging --- pro-social/pro-environmental behavior --- discrete choice experiments --- invasive species control --- tourists’ preferences --- two stage on-site sampling --- negative externalities --- event quality --- tourist satisfaction --- place attachment --- behavioral intentions --- sport tourism --- sporting event --- ecolabel --- sustainable tourism --- certification --- hotel management --- campsites --- ecolabel adoption --- electricity --- tourism --- time series --- forecasting --- sustainability --- purchasing --- supply chain --- qualitative methodology --- hotel industry --- semantic analysis --- ICT --- essential marketing --- tourism and sustainability --- value and tourism --- internet search index --- deep learning framework --- LSTM model --- hotel accommodation demands --- forecasting performance --- tourism --- compartmentalisation --- sustainable daily practices --- young adults

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2019 (5)