Blar i Kristiania Open Archive på emneord "deep learning"

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    • BIoMT-ISeg: Blockchain internet of medical things for intelligent segmentation 

      Belhadi, Asma; Holland, Jon-Olav; Yazidi, Anis; Srivastava, Gautam; Lin, Jerry Chun-Wei; Djenouri, Youcef (Peer reviewed; Journal article, 2023)
      In the quest of training complicated medical data for Internet of Medical Things (IoMT) scenarios, this study develops an end-to-end intelligent framework that incorporates ensemble learning, genetic algorithms, blockchain ...

    • Deep learning based hashtag recommendation system for multimedia data 

      Djenouri, Youcef; Belhadi, Asma; Srivastava, Gautam; Lin, Jerry Chun-Wei (Peer reviewed; Journal article, 2022)
      This work aims to provide a novel hybrid architecture to suggest appropriate hashtags to a collection of orpheline tweets. The methodology starts with defining the collection of batches used in the convolutional neural ...

    • Interpretable intrusion detection for next generation of Internet of Things 

      Djenouri, Youcef; Belhadi, Asma; Srivastava, Gautam; Lin, Jerry Chun-Wei; Yazidi, Anis (Peer reviewed; Journal article, 2023)
      This paper presents a new framework for intrusion detection in the next-generation Internet of Things. MinMax normalization strategy is used to collect and preprocess data. The Marine Predator algorithm is then used to ...

    • Toward a Cognitive-Inspired Hashtag Recommendation for Twitter Data Analysis 

      Djenouri, Youcef; Belhadi, Asma; Srivastava, Gautam; Lin, Jerry Chun-Wei (Peer reviewed; Journal article, 2022)
      Abstract: This research investigates hashtag suggestions in a heterogeneous and huge social network, as well as a cognitive-based deep learning solution based on distributed knowledge graphs. Community detection is first ...

    • When explainable AI meets IoT applications for supervised learning 

      Djenouri, Youcef; Belhadi, Asma; Srivastava, Gautam; Lin, Jerry Chun-Wei (Peer reviewed; Journal article, 2022)
      This paper introduces a novel and complete framework for solving different Internet of Things (IoT) applications, which explores eXplainable AI (XAI), deep learning, and evolutionary computation. The IoT data coming from ...