Browsing SINTEF Digital by Journals "Applied intelligence (Boston)"

Now showing items 1-6 of 6

    • Cluster-based information retrieval using pattern mining 

      Djenouri, Youcef; Belhadi, Asma; Djenouri, Djamel; Lin, Jerry Chun-Wei (Peer reviewed; Journal article, 2020)
      This paper addresses the problem of responding to user queries by fetching the most relevant object from a clustered set of objects. It addresses the common drawbacks of cluster-based approaches and targets fast, high-quality ...

    • Deep learning based decomposition for visual navigation in industrial platforms 

      Djenouri, Youcef; Hatleskog, Johan; Hjelmervik, Jon M.; Bjorne, Elias; Utstumo, Trygve; Mobarhan, Milad (Peer reviewed; Journal article, 2021)
      In the heavy asset industry, such as oil & gas, offshore personnel need to locate various equipment on the installation on a daily basis for inspection and maintenance purposes. However, locating equipment in such GPS ...

    • An edge-driven multi-agent optimization model for infectious disease detection 

      Djenouri, Youcef; Srivastava, Gautam; Yazidi, Anis; Lin, Jerry Chun-Wei (Peer reviewed; Journal article, 2022)
      This research work introduces a new intelligent framework for infectious disease detection by exploring various emerging and intelligent paradigms. We propose new deep learning architectures such as entity embedding networks, ...

    • A general-purpose distributed pattern mining system 

      Belhadi, Asma; Djenouri, Youcef; Lin, Jerry Chun-Wei; Cano, Alberto (Peer reviewed; Journal article, 2020)
      This paper explores five pattern mining problems and proposes a new distributed framework called DT-DPM: Decomposition Transaction for Distributed Pattern Mining. DT-DPM addresses the limitations of the existing pattern ...

    • Incrementally updating the high average-utility patterns with pre-large concept 

      Lin, Jerry Chun-Wei; Pirouz, Matin; Djenouri, Youcef; Cheng, Chien-Fu; Ahmed, Usman (Peer reviewed; Journal article, 2020)
      High-utility itemset mining (HUIM) is considered as an emerging approach to detect the high-utility patterns from databases. Most existing algorithms of HUIM only consider the itemset utility regardless of the length. ...

    • A recurrent neural network for urban long-term traffic flow forecasting 

      Belhadi, Asma; Djenouri, Youcef; Djenouri, Djamel; Lin, Jerry Chun-Wei (Peer reviewed; Journal article, 2020)
      This paper investigates the use of recurrent neural network to predict urban long-term traffic flows. A representation of the long-term flows with related weather and contextual information is first introduced. A recurrent ...