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020			_a9789811651571
024	7		_a10.1007/978-981-16-5157-1 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQ325.5 _b2022 EB
245	0	0	_aSentimental Analysis and Deep Learning : _bProceedings of ICSADL 2021 _cedited by Subarna Shakya, Valentina Emilia Balas, Sinchai Kamolphiwong, Ke-Lin Du
250			_a1st edition 2022
264		1	_aSingapore _bSpringer International Publishing _c2022
300			_a1 recurso en línea (XXXIII, 1030 páginas) _b557 ilustraciones, 428 ilustraciones a color
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_aAdvances in Intelligent Systems and Computing _x2194-5365 _v1408
505	0		_aAnalysis of Healthcare Industry Using Machine Learning Approach: A Case Study in Bengaluru Region -- Dynamic Document Localization for Ecient Mining -- SentiSeries: A Trilogy of Customer Reviews, Sentiment Analysis and Time Series -- Video Summarization using Fully Convolutional Residual Dense Network -- An Efficient Deep Learning Approach for Detecting Pneumonia Using the Convolutional Neural Network -- QMCDS: Quantum Memory for Cloud Data Storage -- A Study towards Bangla Fake News Detection using Machine Learning and Deep Learning -- A Deep Learning Approach to Analyze the Propagation of Pandemic in America -- Graph Convolution Based Joint Learning of Rumour with Content, User Credibility, Propagation Context and Cognitive as well as Emotion Signals -- Deep Learning based Real Time Object Classification and Recognition using Supervised Learning Approach.
520			_aThis book gathers selected papers presented at the International Conference on Sentimental Analysis and Deep Learning (ICSADL 2021), jointly organized by Tribhuvan University, Nepal; Prince of Songkla University, Thailand; and Ejesra during June, 18-19, 2021. The volume discusses state-of-the-art research works on incorporating artificial intelligence models like deep learning techniques for intelligent sentiment analysis applications. Emotions and sentiments are emerging as the most important human factors to understand the prominent user-generated semantics and perceptions from the humongous volume of user-generated data. In this scenario, sentiment analysis emerges as a significant breakthrough technology, which can automatically analyze the human emotions in the data-driven applications. Sentiment analysis gains the ability to sense the existing voluminous unstructured data and delivers a real-time analysis to efficiently automate the business processes. Meanwhile, deep learning emerges as the revolutionary paradigm with its extensive data-driven representation learning architectures. This book discusses all theoretical aspects of sentimental analysis, deep learning and related topics.
988			_aSpringer_Robotics_2022
650		7	_2embne _9166090 _aAprendizaje automático _vCongresos y asambleas
776	0	8	_iPrinted edition: _z9789811651564
776	0	8	_iPrinted edition: _z9789811651588
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-981-16-5157-1 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b03/2023 _dz _eu _zSI