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A modified random forest incremental interpretation method for explaining artificial and deep neural networks in cycle time prediction
Chen, Toly; Wang, Yu-Cheng - In: Decision analytics journal 7 (2023), pp. 1-11
intelligence (XAI) techniques use simpler decision rules to approximate the prediction process and/or outcomes of ANNs (or DNNs …). However, these decision rules may not conform to domain knowledge, which limits their usefulness. This study proposes a …-output relationship of an ANN (or DNN) is fitted by decision rules using a random forest approach. Then, domain knowledge is extracted …
Persistent link: https://www.econbiz.de/10014436432
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Development of software and algorithms of parallel learning of artificial neural networks using CUDA technologies
Sokolovskyy, Yaroslav; Manokhin, Denys; Kaplunsky, Yaroslav - In: Technology audit and production reserves 5 (2021) 2/61, pp. 21-25
Persistent link: https://www.econbiz.de/10013167345
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A machine learning approach to portfolio pricing and risk management for high-dimensional problems
Fernandez-Arjona, Lucio; Filipović, Damir - 2020
We present a general framework for portfolio risk management in discrete time, based on a replicating martingale. This martingale is learned from a finite sample in a supervised setting. The model learns the features necessary for an effective low-dimensional representation, overcoming the curse...
Persistent link: https://www.econbiz.de/10012219260
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A machine learning approach to portfolio pricing and risk management for high-dimensional problems
Fernandez-Arjona, Lucio; Filipović, Damir - In: Mathematical finance : an international journal of … 32 (2022) 4, pp. 982-1019
Persistent link: https://www.econbiz.de/10013463383
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Supporting management decisions by using artificial neural networks for exchange rate prediction
Badea Stroie, Laura Maria - In: Journal of accounting & management information systems … 12 (2013) 4, pp. 578-594
Management Information Systems are meant to create methods for data management, leading to better decision making. By …
Persistent link: https://www.econbiz.de/10010339364
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Intelligent stock prediction : a neural network approach
Shahrour, Mohamad Hassan; Dekmak, Mostafa - In: International journal of financial engineering 10 (2023) 1, pp. 1-14
Persistent link: https://www.econbiz.de/10014251199
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A real-time autonomous machine learning system for face recognition using pre-trained convolutional neural networks
Maj, Michał; Korzeniak, Jacek; Stokłosa, Józef; … - In: European research studies 27 (2024), pp. 3-13
Persistent link: https://www.econbiz.de/10014543361
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Is artificial intelligence really more accurate in predicting bankruptcy?
Letkovský, Stanislav; Jeňcová, Sylvia; … - In: International Journal of Financial Studies : open … 12 (2024) 1, pp. 1-19
(ANNs), support vector machines (SVMs), and decision trees (DTs). The analysis aims to determine which of the employed …
Persistent link: https://www.econbiz.de/10014502270
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Governance of artificial intelligence applications in a business audit via a fusion fuzzy multiple rule-based decision-making model
Hu, Kuang-Hua; Chen, Fu-Hsiang; Hsu, Ming-Fu; Tzeng, … - In: Financial innovation : FIN 9 (2023) 1, pp. 1-31
introduce an innovative fuzzy multiple rule-based decision making framework that integrates soft computing, fuzzy set theory … because it provides decision-makers with comprehensive and intuitive messages about their operations and assists them in …, and a multi-attribute decision making algorithm. The results display that the order of priority in improvement-(A) AI …
Persistent link: https://www.econbiz.de/10014364135
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Machine learning and deep learning
Janiesch, Christian; Zschech, Patrick; Heinrich, Kai - In: Electronic markets : EM ; the international journal of … 31 (2021) 3, pp. 685-695
Persistent link: https://www.econbiz.de/10012666765