Deep Knowledge Representation based on Compositional Semantics for Chinese Geography

Proceedings of the 9th International Conference on Agents and Artificial Intelligence ◽

10.5220/0006108900170023 ◽

2017 ◽

Cited By ~ 1

Author(s):

Shengwu Xiong ◽

Xiaodong Wang ◽

Pengfei Duan ◽

Zhe Yu ◽

Abdelghani Dahou

Keyword(s):

Knowledge Representation ◽

Compositional Semantics ◽

Deep Knowledge

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An initial evaluation of the detection and diagnosis of power plant faults using a deep knowledge representation of physical behaviour

Expert Systems ◽

10.1111/j.1468-0394.1987.tb00132.x ◽

1987 ◽

Vol 4 (2) ◽

pp. 90-99 ◽

Cited By ~ 26

Author(s):

M.R. HERBERT ◽

G.H. WILLIAMS

Keyword(s):

Power Plant ◽

Knowledge Representation ◽

Initial Evaluation ◽

Physical Behaviour ◽

Deep Knowledge ◽

Detection And Diagnosis

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Study of Fault Diagnosis Approach Based on Rules of Deep Knowledge Representation of Signed Directed Graph

2005 IEEE International Conference on Industrial Technology ◽

10.1109/icit.2005.1600741 ◽

2006 ◽

Author(s):

Wen-liang Cao ◽

Bing-shu Wang ◽

Liang-yu Ma ◽

Qin Yan ◽

Wei Hao ◽

...

Keyword(s):

Fault Diagnosis ◽

Knowledge Representation ◽

Directed Graph ◽

Deep Knowledge ◽

Signed Directed Graph ◽

Diagnosis Approach

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Deep Learning and Deep Knowledge Representation in the Human Brain

Springer Series on Bio- and Neurosystems - Time-Space, Spiking Neural Networks and Brain-Inspired Artificial Intelligence ◽

10.1007/978-3-662-57715-8_3 ◽

2018 ◽

pp. 87-123

Author(s):

Nikola K. Kasabov

Keyword(s):

Deep Learning ◽

Knowledge Representation ◽

Human Brain ◽

Deep Knowledge

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Evolving Processes in Time-Space. Deep Learning and Deep Knowledge Representation in Time-Space. Brain-Inspired AI

Springer Series on Bio- and Neurosystems - Time-Space, Spiking Neural Networks and Brain-Inspired Artificial Intelligence ◽

10.1007/978-3-662-57715-8_1 ◽

2018 ◽

pp. 3-37

Author(s):

Nikola K. Kasabov

Keyword(s):

Deep Learning ◽

Knowledge Representation ◽

Deep Knowledge ◽

Time Space

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Deep Learning and Deep Knowledge Representation of fMRI Data

Springer Series on Bio- and Neurosystems - Time-Space, Spiking Neural Networks and Brain-Inspired Artificial Intelligence ◽

10.1007/978-3-662-57715-8_10 ◽

2018 ◽

pp. 361-395

Author(s):

Nikola K. Kasabov

Keyword(s):

Deep Learning ◽

Knowledge Representation ◽

Fmri Data ◽

Deep Knowledge

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Brain-Inspired SNN for Deep Learning in Time-Space and Deep Knowledge Representation. NeuCube

Springer Series on Bio- and Neurosystems - Time-Space, Spiking Neural Networks and Brain-Inspired Artificial Intelligence ◽

10.1007/978-3-662-57715-8_6 ◽

2018 ◽

pp. 201-243

Author(s):

Nikola K. Kasabov

Keyword(s):

Deep Learning ◽

Knowledge Representation ◽

Deep Knowledge ◽

Time Space

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Deep learning and deep knowledge representation in Spiking Neural Networks for Brain-Computer Interfaces

Neural Networks ◽

10.1016/j.neunet.2019.08.029 ◽

2020 ◽

Vol 121 ◽

pp. 169-185 ◽

Cited By ~ 4

Author(s):

Kaushalya Kumarasinghe ◽

Nikola Kasabov ◽

Denise Taylor

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Knowledge Representation ◽

Spiking Neural Networks ◽

Brain Computer Interfaces ◽

Deep Knowledge ◽

Computer Interfaces

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Deep Learning and Deep Knowledge Representation of EEG Data

Springer Series on Bio- and Neurosystems - Time-Space, Spiking Neural Networks and Brain-Inspired Artificial Intelligence ◽

10.1007/978-3-662-57715-8_8 ◽

2018 ◽

pp. 291-337

Author(s):

Nikola K. Kasabov

Keyword(s):

Deep Learning ◽

Knowledge Representation ◽

Deep Knowledge ◽

Eeg Data

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From 'deep knowledge' to 'the light of reason': sources for philosophy of education in Ethiopia

Comparative Education ◽

10.1080/0305006042000284529 ◽

2004 ◽

Vol 40 (4) ◽

pp. 531-544 ◽

Cited By ~ 6

Author(s):

David Bridges ◽

Amare Asgedom ◽

Setargew Kenaw

Keyword(s):

Philosophy Of Education ◽

Deep Knowledge

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Reductionism and Knowledge Representation : Can They Be Integrated?

Contemporary Psychology ◽

10.1037/004806 ◽

2004 ◽

Vol 49 (5) ◽

pp. 580-581

Author(s):

Arthur B. Markman

Keyword(s):

Knowledge Representation

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