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000933225 019__ $$a1155884244$$a1157604679$$a1158387466
000933225 020__ $$a9783030487911$$q(electronic book)
000933225 020__ $$a3030487911$$q(electronic book)
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000933225 0248_ $$a10.1007/978-3-030-48
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000933225 1112_ $$aEANN (Conference)$$n(21st :$$d2020 :$$cOnline)
000933225 24510 $$aProceedings of the 21st EANN (Engineering Applications of Neural Networks) 2020 Conference :$$bProceedings of the EANN 2020 /$$cLazaros Iliadis, Plamen Parvanov Angelov, Chrisina Jayne, Elias Pimenidis, editors.
000933225 2463_ $$aEANN 2020
000933225 260__ $$aCham :$$bSpringer,$$c2020.
000933225 300__ $$a1 online resource (630 pages).
000933225 336__ $$atext$$btxt$$2rdacontent
000933225 337__ $$acomputer$$bc$$2rdamedia
000933225 338__ $$aonline resource$$bcr$$2rdacarrier
000933225 4901_ $$aProceedings of the International Neural Networks Society ;$$vv. 2
000933225 500__ $$aInternational conference proceedings.
000933225 5050_ $$aA compact sequence encoding scheme for online human activity recognition in HRI applications -- Classification of Coseismic Landslides using Fuzzy and Machine Learning Techniques -- Evaluating the Transferability of Personalised Exercise Recognition Models -- Deep Learning-Based Computer Vision Application with Multiple Built-In Data Science-Oriented Capabilities -- Visual Movement Prediction for Stable Grasp Point Detection -- Accomplished level of reliability for seismic structural damage prediction using artificial neural networks -- Efficient Implementation of a Self-Sufficient Solar-Powered Real-Time Deep Learning-Based System -- Leveraging Radar Features to Improve Point Clouds Segmentation with Neural Networks -- LSTM Neural Network for Fine-Granularity Estimation on Baseline Load of Fast Demand Response -- Predicting Permeability Based On Core Analysis.
000933225 506__ $$aAccess limited to authorized users.
000933225 520__ $$aThis book gathers the proceedings of the 21st Engineering Applications of Neural Networks Conference, which is supported by the International Neural Networks Society (INNS). Artificial Intelligence (AI) has been following a unique course, characterized by alternating growth spurts and "AI winters." Today, AI is an essential component of the fourth industrial revolution and enjoying its heyday. Further, in specific areas, AI is catching up with or even outperforming human beings. This book offers a comprehensive guide to AI in a variety of areas, concentrating on new or hybrid AI algorithmic approaches with robust applications in diverse sectors. One of the advantages of this book is that it includes robust algorithmic approaches and applications in a broad spectrum of scientific fields, namely the use of convolutional neural networks (CNNs), deep learning and LSTM in robotics/machine vision/engineering/image processing/medical systems/the environment; machine learning and meta learning applied to neurobiological modeling/optimization; state-of-the-art hybrid systems; and the algorithmic foundations of artificial neural networks.
000933225 588__ $$aDescription based on print version record.
000933225 650_0 $$aNeural networks (Computer science)$$vCongresses.
000933225 7001_ $$aIliadis, Lazaros.
000933225 7001_ $$aAngelov, Plamen Parvanov.
000933225 7001_ $$aJayne, Chrisina.
000933225 7001_ $$aPimenidis, Elias.
000933225 77608 $$iPrint version:$$aIliadis, Lazaros$$tProceedings of the 21st EANN (Engineering Applications of Neural Networks) 2020 Conference : Proceedings of the EANN 2020$$dCham : Springer,c2020$$z9783030487904
000933225 830_0 $$aProceedings of the International Neural Networks Society ;$$vv. 2.
000933225 852__ $$bebk
000933225 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-3-030-48791-1$$zOnline Access$$91397441.1
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000933225 980__ $$aEBOOK
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