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DIDS : a Deep Neural Network based real-time Intrusion detection system for IoT

Vishwakarma, Monika; Kesswani, Nishtha - In: Decision analytics journal 5 (2022), pp. 1-9

The number of people using the Internet of Things (IoT) devices has exploded in recent years. The instantaneous … the constrained devices, current solutions are insufficient to protect the complete safety scope of IoT networks. An … Learning (DL) techniques, skilled in embedding intellect in IoT devices and networks, can address various security issues. In …

Persistent link: https://www.econbiz.de/10014337042

News media versus FRED-MD for macroeconomic forecasting

Ellingsen, Jon; Larsen, Vegard Høghaug; Thorsrud, Leif … - In: Journal of applied econometrics 37 (2022) 1, pp. 63-81

Persistent link: https://www.econbiz.de/10013165174

News media vs. FRED-MD for macroeconomic forecasting

Ellingsen, Jon; Larsen, Vegard Høghaug; Thorsrud, Leif … - 2020

Using a unique dataset of 22.5 million news articles from the Dow Jones Newswires Archive, we perform an in depth real-time out-of-sample forecasting comparison study with one of the most widely used data sets in the newer forecasting literature, namely the FRED-MD dataset. Focusing on U.S. GDP,...

Persistent link: https://www.econbiz.de/10012417502

News media vs. FRED-MD for macroeconomic forecasting

Ellingsen, Jon; Larsen, Vegard Høghaug; Thorsrud, Leif … - 2020

Persistent link: https://www.econbiz.de/10012304069

The quality management ecosystem for predictive maintenance in the Industry 4.0 era

Lee, Sang M.; Lee, Donhee; Kim, Youn Sung - In: International journal of quality innovation 5 (2019) 4, pp. 1-11

The Industry 4.0 era requires new quality management systems due to the ever increasing complexity of the global business environment and the advent of advanced digital technologies. This study presents new ideas for predictive quality management based on an extensive review of the literature on...

Persistent link: https://www.econbiz.de/10012210732

Nowcasting business cycle turning points with stock networks and machine learning

Azqueta-Gavaldon, Andres; Hirschbühl, Dominik; … - 2020

We propose a granular framework that makes use of advanced statistical methods to approximate developments in economy-wide expected corporate earnings. In particular, we evaluate the dynamic network structure of stock returns in the United States as a proxy for the transmission of shocks through...

Persistent link: https://www.econbiz.de/10012316961

News media vs. FRED-MD for macroeconomic forecasting

Ellingsen, Jon; Larsen, Vegard Høghaug; Thorsrud, Leif … - 2020

Persistent link: https://www.econbiz.de/10012437560

SLA-aware operational efficiency in AI-enabled service chains : challenges ahead

Engel, Robert; Fernandez, Pablo; Ruiz-Cortes, Antonio; … - In: Information systems and e-business management : ISeB 20 (2022) 1, pp. 199-221

Persistent link: https://www.econbiz.de/10013186531

Virtual Sensors

Martin, Dominik; Kühl, Niklas; Satzger, Gerhard - In: Business & Information Systems Engineering 63 (2021) 3, pp. 315-323

Persistent link: https://www.econbiz.de/10014501515

Artificial intelligent technologies for the construction industry : how are they perceived and utilized in Australia?

Regona, Massimo; Yigitcanlar, Tan; Xia, Bo; Li, Rita Yi Man - In: Journal of open innovation : technology, market, and … 8 (2022) 1, pp. 1-23

Artificial intelligence (AI) is a powerful technology that can be utilized throughout a construction project lifecycle. Transition to incorporate AI technologies in the construction industry has been delayed due to the lack of know-how and research. There is also a knowledge gap regarding how...

Persistent link: https://www.econbiz.de/10012801636