• A Sanitization Approach to Secure Shared Data in an IoT Environment 

      Lin, Chun Wei; Wu, Jimmy Ming-Tai; Fournier-Viger, Philippe; Djenouri, Youcef; Chen, Chun-Hao; Zhang, Yuyu (Journal article; Peer reviewed, 2019)
      Internet of Things (IoT) supports high flexibility and convenience in several applications because the IoT devices continuously transfer, share, and exchange data without human intervention. During shared or exchanged ...
    • A Survey on Urban Traffic Anomalies Detection Algorithms 

      Djenouri, Youcef; Belhadi, Asma; Lin, Chun Wei; Djenouri, Djamel; Cano, Alberto (Journal article; Peer reviewed, 2019)
      This paper reviews the use of outlier detection approaches in urban traffic analysis. We divide existing solutions into two main categories: flow outlier detection and trajectory outlier detection. The first category groups ...
    • Adapted k-Nearest Neighbors for Detecting Anomalies on Spatio-Temporal Traffic Flow 

      Djenouri, Youcef; Belhadi, Asma; Lin, Chun Wei; Djenouri, Djamel; Cano, Alberto (Journal article; Peer reviewed, 2019)
      Outlier detection is an extensive research area, which has been intensively studied in several domains such as biological sciences, medical diagnosis, surveillance, and traffic anomaly detection. This paper explores advances ...
    • 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 ...
    • A data-driven approach for twitter hashtag recommendation 

      Belhadi, Asma; Djenouri, Youcef; Lin, Jerry Chun-Wei; Cano, Alberto (Journal article; Peer reviewed, 2020)
      This paper addresses the hashtag recommendation problem using high average-utility pattern mining. We introduce a novel framework called PM-HRec (Pattern Mining for Hashtag Recommendation). It consists of two main stages. ...
    • Deep learning for pedestrian collective behavior analysis in smart cities: A model of group trajectory outlier detection 

      Belhadi, Asma; Djenouri, Youcef; Srivastava, Gautam; Djenouri, Djamel; Lin, Jerry Chun-Wei; Fortino, Giancarlo (Peer reviewed; Journal article, 2021)
      This paper introduces a new model to identify collective abnormal human behaviors from large pedestrian data in smart cities. To accurately solve the problem, several algorithms have been proposed in this paper. These can ...
    • Efficient Chain Structure for High-Utility Sequential Pattern Mining 

      Lin, Jerry Chun-Wei; Li, Yuanfa; Fournier-Viger, Philippe; Djenouri, Youcef; Zhang, Ji (Peer reviewed; Journal article, 2020)
      High-utility sequential pattern mining (HUSPM) is an emerging topic in data mining, which considers both utility and sequence factors to derive the set of high-utility sequential patterns (HUSPs) from the quantitative ...
    • Emergent Deep Learning for Anomaly Detection in Internet of Everything 

      Djenouri, Youcef; Djenouri, Djamel; Belhadi, Asma; Srivastava, Gautam; Lin, Jerry Chun-Wei (Peer reviewed; Journal article, 2021)
      This research presents a new generic deep learning framework for anomaly detection in the Internet of Everything (IoE). It combines decomposition methods, deep neural networks, and evolutionary computation to better detect ...
    • Exploring Decomposition for Solving Pattern Mining Problems 

      Djenouri, Youcef; Lin, Jerry Chun-Wei; Nørvåg, Kjetil; Ramampiaro, Heri; Yu, Philip S. (Peer reviewed; Journal article, 2021)
      This article introduces a highly efficient pattern mining technique called Clustering-based Pattern Mining (CBPM). This technique discovers relevant patterns by studying the correlation between transactions in the transaction ...
    • Exploring Pattern Mining Algorithms for Hashtag Retrieval Problem 

      Belhadi, Asma; Djenouri, Youcef; Lin, Jerry Chun-Wei; Zhang, Chongsheng; Cano, Alberto (Journal article; Peer reviewed, 2020)
      Hashtag is an iconic feature to retrieve the hot topics of discussion on Twitter or other social networks. This paper incorporates the pattern mining approaches to improve the accuracy of retrieving the relevant information ...
    • Fast and Accurate Deep Learning Framework for Secure Fault Diagnosis in the Industrial Internet of Things 

      Djenouri, Youcef; Belhadi, Asma; Srivastava, Gautam; Ghosh, Uttam; Chatterjee, Pushpita; Lin, Jerry Chun-Wei (Peer reviewed; Journal article, 2021)
      This paper introduced a new deep learning framework for fault diagnosis in electrical power systems. The framework integrates the convolution neural network and different regression models to visually identify which faults ...
    • Fast and accurate group outlier detection for trajectory data 

      Djenouri, Youcef; Nørvåg, Kjetil; Ramampiaro, Heri; Lin, Jerry Chun-Wei (Peer reviewed; Journal article, 2020)
      Previous approaches to solve the trajectory outlier detection problem exclusively examine single outliers. However, anomalies in trajectory data may often occur in groups. This paper introduces a new problem, group trajectory ...
    • A general-purpose distributed pattern mining system 

      Belhadi, Asma; Djenouri, Youcef; Lin, Jerry Chun-Wei; Cano, Alberto (Journal article; Peer reviewed, 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 ...
    • GFSOM: Genetic Feature Selection for Ontology Matching 

      Belhadi, Hiba; Akli-Astouati, Karima; Djenouri, Youcef; Lin, Chun Wei; Wu, Jimmy Ming-Tai (Journal article; Peer reviewed, 2019)
      This paper studies the ontology matching problem and proposes a genetic feature selection approach for ontology matching (GFSOM), which exploits the feature selection using the genetic approach to select the most appropriate ...
    • Hybrid intelligent framework for automated medical learning 

      Belhadi, Asma; Djenouri, Youcef; Diaz, Vicente Garcia; Houssein, Essam H.; Lin, Jerry Chun-Wei (Peer reviewed; Journal article, 2021)
      This paper investigates the automated medical learning and proposes hybrid intelligent framework, called Hybrid Automated Medical Learning (HAML). The goal is the efficient combination of several intelligent components in ...
    • 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. This ...
    • Intelligent blockchain management for distributed knowledge graphs in IoT 5G environments 

      Djenouri, Youcef; Srivastava, Gautam; Belhadi, Asma; Lin, Jerry Chun-Wei (Peer reviewed; Journal article, 2021)
      This article introduces a new problem of distributed knowledge graph, in IoT 5G setting. We developed an end-to-end solution for solving such problem by exploring the blockchain management and intelligent method for producing ...
    • Linguistic frequent pattern mining using a compressed structure 

      Lin, Jerry Chun-Wei; Ahmed, Usman; Srivastava, Gautam; Wu, Jimmy Ming-Tai; Hong, Tzung-Pei; Djenouri, Youcef (Peer reviewed; Journal article, 2021)
      Traditional association-rule mining (ARM) considers only the frequency of items in a binary database, which provides insufficient knowledge for making efficient decisions and strategies. The mining of useful information ...
    • Maintenance of discovered high average-utility itemsets in dynamic databases 

      Zhang, Binbin; Lin, Jerry Chun-Wei; Shao, Yinan; Fournier-Viger, Philippe; Djenouri, Youcef (Journal article; Peer reviewed, 2018)
      High-utility itemset mining (HUIM) is an extension of traditional frequent itemset mining, which considers both quantities and unit profits of items in a database to reveal highly profitable itemsets regardless of their ...
    • Mining Profitable and Concise Patterns in Large-Scale Internet of Things Environments 

      Lin, Jerry Chun-Wei; Djenouri, Youcef; Srivastava, Gautam; Fournier-Viger, Philippe (Peer reviewed; Journal article, 2021)
      In recent years, HUIM (or a.k.a. high-utility itemset mining) can be seen as investigated in an extensive manner and studied in many applications especially in basket-market analysis and its relevant applications. Since ...