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1
InceptionTime: Finding AlexNet for time series classification
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InceptionTime: Finding AlexNet for time series classification

Ismail Fawaz, Hassan ; Lucas, Benjamin ; Forestier, Germain ; Pelletier, Charlotte ; Schmidt, Daniel F. ; Weber, Jonathan ; Webb, Geoffrey I. ; Idoumghar, Lhassane ; Muller, Pierre-Alain ; Petitjean, François

Data mining and knowledge discovery, 2020-11, Vol.34 (6), p.1936-1962 [Periódico revisado por pares]

New York: Springer US

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2
Deep learning for time series classification: a review
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Deep learning for time series classification: a review

Ismail Fawaz, Hassan ; Forestier, Germain ; Weber, Jonathan ; Idoumghar, Lhassane ; Muller, Pierre-Alain

Data mining and knowledge discovery, 2019-07, Vol.33 (4), p.917-963 [Periódico revisado por pares]

New York: Springer US

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3
ROCKET: exceptionally fast and accurate time series classification using random convolutional kernels
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ROCKET: exceptionally fast and accurate time series classification using random convolutional kernels

Dempster, Angus ; Petitjean, François ; Webb, Geoffrey I.

Data mining and knowledge discovery, 2020-09, Vol.34 (5), p.1454-1495 [Periódico revisado por pares]

New York: Springer US

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4
Re-examining the Unified Theory of Acceptance and Use of Technology (UTAUT): Towards a Revised Theoretical Model
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Re-examining the Unified Theory of Acceptance and Use of Technology (UTAUT): Towards a Revised Theoretical Model

Dwivedi, Yogesh K. ; Rana, Nripendra P. ; Jeyaraj, Anand ; Clement, Marc ; Williams, Michael D.

Information systems frontiers, 2019-06, Vol.21 (3), p.719-734 [Periódico revisado por pares]

New York: Springer US

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5
The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances
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The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances

Bagnall, Anthony ; Lines, Jason ; Bostrom, Aaron ; Large, James ; Keogh, Eamonn

Data mining and knowledge discovery, 2017-05, Vol.31 (3), p.606-660 [Periódico revisado por pares]

New York: Springer US

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6
On the evaluation of unsupervised outlier detection: measures, datasets, and an empirical study
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On the evaluation of unsupervised outlier detection: measures, datasets, and an empirical study

Campos, Guilherme O. ; Zimek, Arthur ; Sander, Jörg ; Campello, Ricardo J. G. B. ; Micenková, Barbora ; Schubert, Erich ; Assent, Ira ; Houle, Michael E.

Data mining and knowledge discovery, 2016-07, Vol.30 (4), p.891-927 [Periódico revisado por pares]

New York: Springer US

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7
Selective Search for Object Recognition
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Selective Search for Object Recognition

Uijlings, J. R. R. ; van de Sande, K. E. A. ; Gevers, T. ; Smeulders, A. W. M.

International journal of computer vision, 2013-09, Vol.104 (2), p.154-171 [Periódico revisado por pares]

Boston: Springer US

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8
Exploring the influential factors of continuance intention to use mobile Apps: Extending the expectation confirmation model
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Exploring the influential factors of continuance intention to use mobile Apps: Extending the expectation confirmation model

Tam, Carlos ; Santos, Diogo ; Oliveira, Tiago

Information systems frontiers, 2020-02, Vol.22 (1), p.243-257 [Periódico revisado por pares]

New York: Springer US

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9
Security, Privacy and Risks Within Smart Cities: Literature Review and Development of a Smart City Interaction Framework
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Security, Privacy and Risks Within Smart Cities: Literature Review and Development of a Smart City Interaction Framework

Ismagilova, Elvira ; Hughes, Laurie ; Rana, Nripendra P. ; Dwivedi, Yogesh K.

Information systems frontiers, 2022-04, Vol.24 (2), p.393-414 [Periódico revisado por pares]

New York: Springer US

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10
Counterfactual explanations and how to find them: literature review and benchmarking
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Counterfactual explanations and how to find them: literature review and benchmarking

Guidotti, Riccardo

Data mining and knowledge discovery, 2022-04 [Periódico revisado por pares]

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