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220822 ||| eng |
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|a 9783036511610
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|a 9783036511603
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|a books978-3-0365-1160-3
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| 100 |
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|a Lytras, Miltiadis
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| 245 |
0 |
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|a Artificial Intelligence and Cognitive Computing
|h Elektronische Ressource
|b Methods, Technologies, Systems, Applications and Policy Making
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| 260 |
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|a Basel, Switzerland
|b MDPI - Multidisciplinary Digital Publishing Institute
|c 2021
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| 300 |
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|a 1 electronic resource (278 p.)
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| 653 |
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|a static Young's modulus
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| 653 |
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|a machine learning
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|a data mining
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|a rough set
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|a trajectories
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|a mixed integer linear programming
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| 653 |
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|a research hotspot
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| 653 |
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|a decision-making system
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| 653 |
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|a policy keyword
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| 653 |
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|a air quality
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| 653 |
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|a rheological properties
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| 653 |
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|a well productivity
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| 653 |
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|a location prediction
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| 653 |
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|a n/a
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| 653 |
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|a performance
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| 653 |
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|a Multi-hop routing
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|a speech output correction
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| 653 |
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|a deep learning
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|a Internet of things
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|a system
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|a Poisson's ratio
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|a sandstone
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|a neural networks
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| 653 |
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|a self-adaptive differential evolution
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|a most-matching
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|a spoofing
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| 653 |
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|a data envelopment analysis
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| 653 |
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|a Network lifetime
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|a knowledge map visualization
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|a LSTM
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|a empirical correlations
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| 653 |
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|a devonian shale
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|a Artificial intelligence
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|a smart tourism
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|a policy documents quantification
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|a barnett shale
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|a international research
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|a smart cities
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|a artificial neural network
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|a new models
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|a histogram of oriented gradients
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|a Information technology industries / bicssc
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|a spatiotemporal
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|a minimum miscibility pressure (MMP)
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| 653 |
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|a fishbone multilateral wells
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|a predictive models
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| 653 |
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|a real-time
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|a clustering
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|a Wireless nodes
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|a security
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|a diesel engine quality
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| 653 |
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|a speech recognition
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| 653 |
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|a elastic parameters
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| 653 |
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|a Long Short Term Memory (LSTM)
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| 653 |
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|a water-based drill-in fluid
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|a anomalies
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| 653 |
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|a artificial intelligence
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| 653 |
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|a visual analytics
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|a assembly clearance
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| 653 |
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|a face recognition
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| 653 |
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|a dimension reduction
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| 653 |
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|a Hybrid clustering
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| 653 |
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|a CO2 flooding
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| 653 |
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|a sandstone formations
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| 653 |
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|a multivariate
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| 653 |
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|a total organic carbon
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| 653 |
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|a decision making
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| 653 |
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|a regular patterns
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| 700 |
1 |
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|a Visvizi, Anna
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| 700 |
1 |
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|a Lytras, Miltiadis
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| 700 |
1 |
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|a Visvizi, Anna
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| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b DOAB
|a Directory of Open Access Books
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| 500 |
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|a Creative Commons (cc), https://creativecommons.org/licenses/by/4.0/
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| 028 |
5 |
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|a 10.3390/books978-3-0365-1160-3
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| 856 |
4 |
2 |
|u https://directory.doabooks.org/handle/20.500.12854/76299
|z DOAB: description of the publication
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| 856 |
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|u https://www.mdpi.com/books/pdfview/book/3715
|7 0
|x Verlag
|3 Volltext
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|a 000
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|a 576
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|a 700
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|a Artificial intelligence (AI) is a subject garnering increasing attention in both academia and the industry today. The understanding is that AI-enhanced methods and techniques create a variety of opportunities related to improving basic and advanced business functions, including production processes, logistics, financial management and others. As this collection demonstrates, AI-enhanced tools and methods tend to offer more precise results in the fields of engineering, financial accounting, tourism, air-pollution management and many more. The objective of this collection is to bring these topics together to offer the reader a useful primer on how AI-enhanced tools and applications can be of use in today's world. In the context of the frequently fearful, skeptical and emotion-laden debates on AI and its value added, this volume promotes a positive perspective on AI and its impact on society. AI is a part of a broader ecosystem of sophisticated tools, techniques and technologies, and therefore, it is not immune to developments in that ecosystem. It is thus imperative that inter- and multidisciplinary research on AI and its ecosystem is encouraged. This collection contributes to that.
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