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Applied Artificial Neural Networks

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ISBN: 9783038422709 9783038422716 Year: Pages: XIV, 244 DOI: 10.3390/books978-3-03842-271-6 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Computer Science
Added to DOAB on : 2016-11-11 19:33:54
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Since their re-popularisation in the mid-1980s, artificial neural networks have seen an explosion of research across a diverse spectrum of areas. While an immense amount of research has been undertaken in artificial neural networks themselves—in terms of training, topologies, types, etc.—a similar amount of work has examined their application to a whole host of real-world problems. Such problems are usually difficult to define and hard to solve using conventional techniques. Examples include computer vision, speech recognition, financial applications, medicine, meteorology, robotics, hydrology, etc.This Special Issue focuses on the second of these two research themes, that of the application of neural networks to a diverse range of fields and problems. It collates contributions concerning neural network applications in areas such as engineering, hydrology and medicine.

Intelligentes Gesamtmaschinenmanagement für elektrische Antriebssysteme

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Book Series: Karlsruher Schriftenreihe Fahrzeugsystemtechnik / Institut für Fahrzeugsystemtechnik ISSN: 18696058 ISBN: 9783731507741 Year: Volume: 60 Pages: XVI, 150 p. DOI: 10.5445/KSP/1000081063 Language: GERMAN
Publisher: KIT Scientific Publishing
Subject: Technology (General)
Added to DOAB on : 2019-07-28 18:37:01
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Innovative electric propulsion systems are increasingly applied to off-highway machines, to gain efficiency optimization of the work process. This book focuses on the aspect of work process optimization and achieves forward-looking results through the use of intelligent, adaptive overall machine management.

Artificial Neural Networks as Models of Neural Information Processing

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Book Series: Frontiers Research Topics ISSN: 16648714 ISBN: 9782889454013 Year: Pages: 220 DOI: 10.3389/978-2-88945-401-3 Language: English
Publisher: Frontiers Media SA
Subject: Science (General) --- Neurology
Added to DOAB on : 2018-11-16 17:17:57
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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.

Machine Learning With Radiation Oncology Big Data

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Book Series: Frontiers Research Topics ISSN: 16648714 ISBN: 9782889457304 Year: Pages: 146 DOI: 10.3389/978-2-88945-730-4 Language: English
Publisher: Frontiers Media SA
Subject: Medicine (General) --- Oncology
Added to DOAB on : 2019-01-23 14:53:43
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Radiation oncology is uniquely positioned to harness the power of big data as vast amounts of data are generated at an unprecedented pace for individual patients in imaging studies and radiation treatments worldwide. The big data encountered in the radiotherapy clinic may include patient demographics stored in the electronic medical record (EMR) systems, plan settings and dose volumetric information of the tumors and normal tissues generated by treatment planning systems (TPS), anatomical and functional information from diagnostic and therapeutic imaging modalities (e.g., CT, PET, MRI and kVCBCT) stored in picture archiving and communication systems (PACS), as well as the genomics, proteomics and metabolomics information derived from blood and tissue specimens. Yet, the great potential of big data in radiation oncology has not been fully exploited for the benefits of cancer patients due to a variety of technical hurdles and hardware limitations.With recent development in computer technology, there have been increasing and promising applications of machine learning algorithms involving the big data in radiation oncology. This research topic is intended to present novel technological breakthroughs and state-of-the-art developments in machine learning and data mining in radiation oncology in recent years.

Quantum Foundations. 90 Years of Uncertainty

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ISBN: 9783038977544 Year: Pages: 188 DOI: 10.3390/books978-3-03897-755-1 Language: eng
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Physics (General) --- Science (General)
Added to DOAB on : 2019-04-05 10:34:31
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The name of Joseph Fourier is also inseparable from the study of the mathematics of heat. Modern research on heat equations explores the extension of the classical diffusion equation on Riemannian, sub-Riemannian manifolds, and Lie groups. In parallel, in geometric mechanics, Jean-Marie Souriau interpreted the temperature vector of Planck as a space-time vector, obtaining, in this way, a phenomenological model of continuous media, which presents some interesting properties.

Molecular Modeling in Drug Design

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ISBN: 9783038976141 Year: Pages: 220 DOI: 10.3390/books978-3-03897-615-8 Language: eng
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Chemistry (General) --- Science (General)
Added to DOAB on : 2019-04-05 10:34:31
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This book is a printed edition of the Special Issue Molecular Modeling in Drug Design that was published in Molecules

Keywords

hyperlipidemia --- squalene synthase (SQS) --- molecular modeling --- drug discovery --- Traditional Chinese Medicine --- molecular dynamics simulation --- biophenols --- natural compounds --- amyloid fibrils --- Alzheimer’s disease --- ligand–protofiber interactions --- adhesion --- FimH --- rational drug design --- molecular dynamics --- molecular docking --- ligand binding --- EphA2-ephrin A1 --- PPI inhibition --- interaction energy --- in silico screening --- adenosine --- boron cluster --- adenosine receptors --- AR ligands --- aggregation --- promiscuous mechanism --- human ecto-5?-nucleotidase --- virtual screening --- enzymatic assays --- turbidimetry --- dynamic light scattering --- docking --- solvent effect --- binding affinity --- scoring function --- molecular dynamics --- target-focused pharmacophore modeling --- density-based clustering --- structure-based drug design --- AutoGrid --- grid maps --- probe energies --- method development --- steered molecular dynamics --- all-atom molecular dynamics simulation --- resultant dipole moment --- mechanical stability --- protein-peptide interactions --- molecular dynamics --- proteins --- molecular recognition --- protein protein interactions --- artificial intelligence --- deep learning --- neural networks --- property prediction --- quantitative structure-activity relationship (QSAR) --- quantitative structure-property prediction (QSPR) --- de novo design --- adenosine receptor --- metadynamics --- extracellular loops --- allosterism --- molecular dynamics --- cosolvent molecular dynamics --- drug design --- fragment screening --- docking

Selected Papers from the 2018 41st International Conference on Telecommunications and Signal Processing (TSP)

Authors: --- ---
ISBN: 9783039210404 / 9783039210411 Year: Pages: 194 DOI: 10.3390/books978-3-03921-041-1 Language: eng
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General) --- General and Civil Engineering
Added to DOAB on : 2019-08-28 11:21:27
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This Special Issue contains a series of excellent research works on telecommunications and signal processing, selected from the 2018 41st International Conference on Telecommunications and Signal Processing (TSP) which was held on July 4–6, 2018, in Athens, Greece. The conference was organized in cooperation with the IEEE Region 8 (Europe, Middle East, and Africa), IEEE Greece Section, IEEE Czechoslovakia Section, and IEEE Czechoslovakia Section SP/CAS/COM Joint Chapter by seventeen universities from the Czech Republic, Hungary, Turkey, Taiwan, Japan, Slovak Republic, Spain, Bulgaria, France, Slovenia, Croatia, and Poland, for academics, researchers, and developers, and serves as a premier international forum for the annual exchange and promotion of the latest advances in telecommunication technology and signal processing. The aim of the conference is to bring together both novice and experienced scientists, developers, and specialists, to meet new colleagues, collect new ideas, and establish new cooperation between research groups from universities, research centers, and private sectors worldwide. This collection of 10 papers is highly recommended for researchers, and believed to be interesting, inspiring, and motivating for readers in their further research.

Flood Forecasting Using Machine Learning Methods

Authors: --- ---
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

MEMS Accelerometers

Authors: --- ---
ISBN: 9783038974147 / 9783038974154 Year: Pages: 252 DOI: 10.3390/books978-3-03897-415-4 Language: eng
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General) --- General and Civil Engineering
Added to DOAB on : 2019-06-26 08:44:06
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Micro-electro-mechanical system (MEMS) devices are widely used for inertia, pressure, and ultrasound sensing applications. Research on integrated MEMS technology has undergone extensive development driven by the requirements of a compact footprint, low cost, and increased functionality. Accelerometers are among the most widely used sensors implemented in MEMS technology. MEMS accelerometers are showing a growing presence in almost all industries ranging from automotive to medical. A traditional MEMS accelerometer employs a proof mass suspended to springs, which displaces in response to an external acceleration. A single proof mass can be used for one- or multi-axis sensing. A variety of transduction mechanisms have been used to detect the displacement. They include capacitive, piezoelectric, thermal, tunneling, and optical mechanisms. Capacitive accelerometers are widely used due to their DC measurement interface, thermal stability, reliability, and low cost. However, they are sensitive to electromagnetic field interferences and have poor performance for high-end applications (e.g., precise attitude control for the satellite). Over the past three decades, steady progress has been made in the area of optical accelerometers for high-performance and high-sensitivity applications but several challenges are still to be tackled by researchers and engineers to fully realize opto-mechanical accelerometers, such as chip-scale integration, scaling, low bandwidth, etc.

Keywords

low-temperature co-fired ceramic (LTCC) --- capacitive accelerometer --- wireless --- process optimization --- performance characterization --- MEMS accelerometer --- mismatch of parasitic capacitance --- electrostatic stiffness --- high acceleration sensor --- piezoresistive effect --- MEMS --- micro machining --- turbulent kinetic energy dissipation rate --- probe --- microelectromechanical systems (MEMS) piezoresistive sensor chip --- Taguchi method --- marine environmental monitoring --- accelerometer --- frequency --- acceleration --- heat convection --- motion analysis --- auto-encoder --- dance classification --- deep learning --- self-coaching --- wavelet packet --- classification of horse gaits --- MEMS sensors --- gait analysis --- rehabilitation assessment --- body sensor network --- MEMS accelerometer --- electromechanical delta-sigma --- built-in self-test --- in situ self-testing --- digital resonator --- accelerometer --- activity monitoring --- regularity of activity --- sleep time duration detection --- indoor positioning --- WiFi-RSSI radio map --- MEMS-IMU accelerometer --- zero-velocity update --- step detection --- stride length estimation --- field emission --- hybrid integrated --- vacuum microelectronic --- cathode tips array --- interface ASIC --- micro-electro-mechanical systems (MEMS) --- delaying mechanism --- safety and arming system --- accelerometer --- multi-axis sensing --- capacitive transduction --- inertial sensors --- three-axis accelerometer --- micromachining --- miniaturization --- stereo visual-inertial odometry --- fault tolerant --- hostile environment --- MEMS-IMU --- mode splitting --- Kerr noise --- angular-rate sensing --- whispering-gallery-mode --- optical microresonator --- three-axis acceleration sensor --- MEMS technology --- sensitivity --- L-shaped beam --- n/a

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