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  • Search: subject:"Convolutional Neural Networks (CNN)"
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Year of publication
Subject
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Convolutional neural networks (CNN) 3 Candlestick 2 Convolutional Neural Networks (CNN) 2 Financial Vision 2 Flaw detection 2 Gramian Angular Field (GAF) 2 Laser Powder Bed Fusion (PBF-LB/M, L-PBF) 2 Machine learning 2 Neural networks 2 Neuronale Netze 2 Online monitoring 2 Patterns Classification 2 SWIR thermography 2 Selective Laser Melting (SLM) 2 Time-Series 2 Bewertung 1 Classification 1 Consumer behaviour 1 Customer satisfaction 1 Data Mining 1 Data mining 1 Deep learning 1 Emotion 1 Endogeneity 1 Evaluation 1 Financial analysis 1 Finanzanalyse 1 Internet marketing 1 Klassifikation 1 Konsumentenverhalten 1 Kundenzufriedenheit 1 Language 1 Lexicons 1 Long-short term memory (LSTM) Networks 1 Natural language processing (NLP) 1 Online ratings 1 Online retailing 1 Online reviews 1 Online-Handel 1 Online-Marketing 1
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Online availability
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Free 5 CC license 1
Type of publication
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Article 4 Book / Working Paper 1
Type of publication (narrower categories)
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Article 3 Arbeitspapier 1 Article in journal 1 Aufsatz in Zeitschrift 1 Graue Literatur 1 Non-commercial literature 1 Working Paper 1
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Language
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English 5
Author
All
Altenburg, Simon J. 2 Breese, Philipp P. 2 Chen, Jun-Hao 2 Mohr, Gunther 2 Oster, Simon 2 Tsai, Yun-Cheng 2 Ulbricht, Alexander 2 Chakraborty, Ishita 1 Kim, Minkyung 1 Sudhir, K. 1
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Published in...
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Journal of Intelligent Manufacturing 2 Cowles Foundation discussion paper 1 Financial Innovation 1 Financial innovation : FIN 1
Source
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EconStor 3 ECONIS (ZBW) 2
Showing 1 - 5 of 5
Cover Image
A deep learning framework for defect prediction based on thermographic in-situ monitoring in laser powder bed fusion
Oster, Simon; Breese, Philipp P.; Ulbricht, Alexander; … - In: Journal of Intelligent Manufacturing 35 (2023) 4, pp. 1687-1706
The prediction of porosity is a crucial task for metal based additive manufacturing techniques such as laser powder bed fusion. Short wave infrared thermography as an in-situ monitoring tool enables the measurement of the surface radiosity during the laser exposure. Based on the thermogram data,...
Persistent link: https://www.econbiz.de/10015179593
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Cover Image
A deep learning framework for defect prediction based on thermographic in-situ monitoring in laser powder bed fusion
Oster, Simon; Breese, Philipp P.; Ulbricht, Alexander; … - In: Journal of Intelligent Manufacturing 35 (2023) 4, pp. 1687-1706
The prediction of porosity is a crucial task for metal based additive manufacturing techniques such as laser powder bed fusion. Short wave infrared thermography as an in-situ monitoring tool enables the measurement of the surface radiosity during the laser exposure. Based on the thermogram data,...
Persistent link: https://www.econbiz.de/10015403246
Saved in:
Cover Image
Encoding candlesticks as images for pattern classification using convolutional neural networks
Chen, Jun-Hao; Tsai, Yun-Cheng - In: Financial Innovation 6 (2020) 1, pp. 1-19
Candlestick charts display the high, low, opening, and closing prices in a specific period. Candlestick patterns emerge because human actions and reactions are patterned and continuously replicate. These patterns capture information on the candles. According to Thomas Bulkowski's Encyclopedia of...
Persistent link: https://www.econbiz.de/10012602860
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Cover Image
Encoding candlesticks as images for pattern classification using convolutional neural networks
Chen, Jun-Hao; Tsai, Yun-Cheng - In: Financial innovation : FIN 6 (2020) 26, pp. 1-19
Candlestick charts display the high, low, opening, and closing prices in a specific period. Candlestick patterns emerge because human actions and reactions are patterned and continuously replicate. These patterns capture information on the candles. According to Thomas Bulkowski’s Encyclopedia...
Persistent link: https://www.econbiz.de/10012268935
Saved in:
Cover Image
Attribute sentiment scoring with online text reviews : accounting for language structure and attribute self-selection
Chakraborty, Ishita; Kim, Minkyung; Sudhir, K. - 2019
Persistent link: https://www.econbiz.de/10012053024
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