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Modern neural networks gave rise to major breakthroughs in several research areas. In neuroscience, we are witnessing a reappraisal of neural network theory and its relevance for understanding information processing in biological systems. The research presented in this book provides various perspectives on the use of artificial neural networks as models of neural information processing. We consider the biological plausibility of neural networks, performance improvements, spiking neural networks and the use of neural networks for understanding brain function.

artificial neural networks --- deep learning --- neural information processing --- brain imaging --- spiking neural networks --- backpropagation

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Recently, a need has arisen for prediction techniques that can address a variety of problems by combining methods from the rapidly developing field of machine learning with geoinformation technologies such as GIS, remote sensing, and GPS. As a result, over the last few decades, one particular machine learning technology, known as artificial neural networks, has been successfully applied to a wide range of fields in science and engineering. In addition, the development of computational and spatial technologies has led to the rapid growth of geoinformatics, which specializes in the analysis of spatial information. Thus, recently, artificial neural networks have been applied to geoinformatics and have produced valuable results in the fields of geoscience, environment, natural hazards, natural resources, and engineering. Hence, this Special Issue of the journal Applied Sciences, “Application of Artificial Neural Networks in Geoinformatics,” was successfully planned, and we here publish a collection of papers detailing novel contributions that are of relevance to these topics.

Data mining --- Machine Learning --- Artificial Neural Networks --- Spatial Database --- Geoinformatics --- Geographic Information System (GIS) --- Remote Sensing --- Global Positioning System (GPS) --- Spatial Analysis

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In last few decades, short-term load forecasting (STLF) has been one of the most important research issues for achieving higher efficiency and reliability in power system operation, to facilitate the minimization of its operation cost by providing accurate input to day-ahead scheduling, contingency analysis, load flow analysis, planning, and maintenance of power systems. There are lots of forecasting models proposed for STLF, including traditional statistical models (such as ARIMA, SARIMA, ARMAX, multi-variate regression, Kalman filter, exponential smoothing, and so on) and artificial-intelligence-based models (such as artificial neural networks (ANNs), knowledge-based expert systems, fuzzy theory and fuzzy inference systems, evolutionary computation models, support vector regression, and so on). Recently, due to the great development of evolutionary algorithms (EA) and novel computing concepts (e.g., quantum computing concepts, chaotic mapping functions, and cloud mapping process, and so on), many advanced hybrids with those artificial-intelligence-based models are also proposed to achieve satisfactory forecasting accuracy levels. In addition, combining some superior mechanisms with an existing model could empower that model to solve problems it could not deal with before; for example, the seasonal mechanism from the ARIMA model is a good component to be combined with any forecasting models to help them to deal with seasonal problems.

short term load forecasting --- statistical forecasting models --- artificial neural networks (ANNs) --- knowledge-based expert systems --- evolutionary algorithms --- meta-heuristic algorithms --- support vector regression/support vector machines --- seasonal mechanism --- novel intelligent technologies

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A topic of utmost importance in civil engineering is finding optimal solutions throughout the life cycle of buildings and infrastructural objects, including their design, manufacturing, use, and maintenance. Operational research, management science, and optimization methods provide a consistent and applicable groundwork for engineering decision-making. These topics have received the interest of researchers and, after a rigorous peer-review process, eight papers have been published in this Special Issue. The articles in this Printed Edition demonstrate how solutions in civil engineering, which bring economic, social, and environmental benefits, are obtained through a variety of methodologies and tools. Usually, decision-makers need to take into account not just a single criterion, but several different criteria and, therefore, multi-criteria decision-making (MCDM) approaches have been suggested for application in five of the published papers; the rest of the papers apply other research methods. Most approaches suggested decision models under uncertainty, proposing hybrid MCDM methods in combination with fuzzy or rough set theory, as well as D-numbers. The application areas of the proposed MCDM techniques mainly cover production/manufacturing engineering, logistics and transportation, and construction engineering and management. We hope that a summary of the Special Issue as provided here will encourage a detailed analysis of the papers included in the Printed Edition.

airplane turn time --- boarding/deboarding strategies --- seat preference --- experimental test --- internal transport --- rough Best–Worst Method (BWM) --- rough Simple Additive Weighting (SAW) --- logistics --- railway wagon --- construction --- rough number --- EDAS --- DEMATEL --- MCDM --- supply chain --- image processing --- flexible manufacturing --- tool-flank-wear monitoring --- artificial neural networks --- cost estimation --- shovel machine --- neural network --- multivariate regression --- hybrid model --- performance evaluation --- oil and gas well drilling projects --- Step-Wise Weight Assessment Ratio Analysis (SWARA) --- interval-valued fuzzy Additive Ratio Assessment --- Additive Ratio Assessment (ARAS) --- D number --- analytical network process (ANP) --- multi-attributive border approximation area comparison (MABAC) --- multi-criteria decision-making (MCDM) --- consistent fuzzy preference relation (CFPR) --- construction project risk --- risk management --- civil engineering --- architecture --- conceptual design --- parametric design --- structural analysis --- Grasshopper --- optimisation --- finite element method (FEM) --- ruled surface --- roof shell --- multi criteria decision making --- multiple-criteria decision-making (MCDM) --- hybrid MCDM --- fuzzy sets --- rough sets --- D numbers --- 3D modelling --- image processing --- experimental testing --- civil engineering --- manufacturing engineering --- transportation --- logistics

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Mineral processing deals with complex particle systems with two-, three- and more phases. The modeling and understanding of these systems are a challenge for research groups and a need for the industrial sector. This Special Issue aims to present new advances, methodologies, applications, and case studies of computer-aided analysis applied to multiphase systems in mineral processing. This includes aspects such as modeling, design, operation, optimization, uncertainty analysis, among other topics. The special issue contains a review article and eleven articles that cover different methodologies of modeling, design, optimization, and analysis in problems of adsorption, leaching, flotation, and magnetic separation, among others. Consequently, the topics covered are of interest to readers from academia and industry.

silver leaching --- thiosulfate --- mining residues --- kinetic analysis --- Pb(II)–BHA --- lead chemistry --- metal–organic collectors --- DFT calculation --- surface activation --- SEM-based image analysis --- MLA (Mineral Liberation Analyzer) --- magnetic separation --- cassiterite --- partition curve --- local regression --- froth flotation --- recovery arrangement --- circuit configuration --- process design --- K-feldspar --- tobermorite --- hydrogarnet --- hydrothermal reaction --- phase analysis --- design --- flotation circuits --- Tabu-search algorithm --- multispecies --- thickening --- water recovery --- Boycott effect --- tailings --- settling velocity --- natural convection --- fluorapatite --- density functional theory --- frontier molecular orbital --- flotation mechanism --- process optimization process --- heap leaching --- modes of operation --- discrete event simulation --- quartz --- DFT calculation --- hydroxylation --- adsorption --- flotation --- CFD --- cyclonic flow field --- flotation column --- endoscopic laser PIV --- turbulence models --- computational fluid dynamic --- molecular dynamics --- density functional theory --- discrete element simulation --- smoothed particle hydrodynamics --- flotation --- grinding --- response surface methodology --- machine learning --- artificial neural networks --- support vector machine --- hydrocyclone --- global sensitivity analysis --- uncertainty analysis --- n/a