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020			_a9789813369191
024	7		_a10.1007/978-981-33-6919-1 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQA76 _b2021 EB
245	1	0	_aAdvances in Applications of Data-Driven Computing _cedited by Jagdish Chand Bansal, Lance C. C. Fung, Milan Simic, Ankush Ghosh.
250			_aFirst edition 2021
264		1	_aSingapore _bSpringer International Pulishing _c2021
300			_a1 recurso en línea (XII, 182 páginas) _b92 ilustraciones, 60 ilustraciones a color
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_aarchivo de texto _bPDF
490	0		_aAdvances in Intelligent Systems and Computing _x2194-5365 _v1319
490	0		_aIntelligent Technologies and Robotics (SpringerNature-42732)
490	0		_aIntelligent Technologies and Robotics (R0) (SpringerNature-43728)
505	0		_aGenetic Algorithm based Two Tiered Load Balancing Scheme for Cloud Data Centers -- KNN-DK: A Modified k-nn Classifier With Dynamic k-Nearest Neighbors -- Identification of Emotions from Sentences using Natural Language Processing For Small Dataset -- Comparison and Analysis of RNN-LSTMs and CNNs for Social Reviews Classification -- Blockchain Based Model for Expanding IoT Device Data Security -- Linear Dynamical Model as Market Indicator of the National Stock Exchange of India -- E- Focused Crawler and Hierarchical Agglomerative Clustering approach for Automated Categorization of Feature Level Health Care sentiments on Social Media -- Error Detection Algorithm for Cloud Outsourced Big Data -- Framing Fire Detection System of higher efficacy Using Supervised Machine Learning Techniques -- Twitter Data Sentiment Analysis using Naive Bayes Classifier and Generation of Heat Map for Analyzing Intensity Geographically -- Computing Mortality for ICU Patients using Cloud based Data -- Early Detection of Poisonous Gas Leakage in Pipe-lines in An Industrial Environment UsingGas Sensor, Automated with IoT(Internet of Things). .
520	3		_aThis book aims to foster machine and deep learning approaches to data-driven applications, in which data governs the behaviour of applications. Applications of Artificial intelligence (AI)-based systems play a significant role in today's software industry. The sensors data from hardware-based systems making a mammoth database, increasing day by day. Recent advances in big data generation and management have created an avenue for decision-makers to utilize these huge volumes of data for different purposes and analyses. AI-based application developers have long utilized conventional machine learning techniques to design better user interfaces and vulnerability predictions. However, with the advancement of deep learning-based and neural-based networks and algorithms, researchers are able to explore and learn more about data and their exposed relationships or hidden features. This new trend of developing data-driven application systems seeks the adaptation of computational neural network algorithms and techniques in many application domains, including software systems, cyber security, human activity recognition, and behavioural modelling. As such, computational neural networks algorithms can be refined to address problems in data-driven applications. Original research and review works with model and build data-driven applications using computational algorithm are included as chapters in this book.
988			_aSpringer_Robotics_2021
650		7	_2embne _aInformática _9139268
700	1		_aBansal, Jagdish Chand _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt _9100803
700	1		_aFung, Lance Chun Che, _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt _9682013 _d1955-
700	1		_aSimic, Milan _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt _9682014
700	1		_aGhosh, Ankush _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt _9681984
776	0	8	_iPrinted edition: _z9789813369184
776	0	8	_iPrinted edition: _z9789813369207
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-981-33-6919-1 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE