Youcef Djenouri

Youcef Djenouri

Førsteamanuensis
Institutt for mikrosystemer
Fakultet for teknologi, naturvitenskap og maritime fag
Campus Vestfold
Dr. Youcef Djenouri is an Associate Professor at University of South-Eastern Norway, and a senior researcher at NORCE (Norwegian Research Center) from 2023. He was a research scientist at SINTEF, and a postdoc researcher at NTNU, and SDU. His research interests include AI, smart city applications, security and privacy. Dr. Youcef Djenouri published more than 150 research papers in top conferences and journals such as ICDM, ICDE, ACM KDD, IEEE TIST, IEEE TII, IEEE TCYB, and others. He is also in the list of 2% most outstanding researchers according to Stanford statistics. Dr. Youcef Djenouri is an Associate Editor in IEEE Transactions on Computational Social Systems, Neural Processing Letters, Discover AI journal, and Editorial Board in Applied Intelligence. He also organized workshops in top conferences such as ICDM, KDD, DSAA, and PAKDD.

Ansvarsområder

Associate Editor 

IEEE Transactions on Computational Social Systems

Neural Processing Letters

Discover Artificial Intelligence

International Journal of Applied Intelligence

Program Committee Member

AAAI, IEEE Big Data Congress, DCAI, KDD Workshops, ICDM Workshops, IEEE SMC, IJCNN, IEEE Big Data Congress, PAKDD Workshops (Program Chair), FLAIRS Conference....

Invited Reviewer for Journals

ACM Transactions on Knowledge Discovery from Data, IEEE Transactions on Neural Network, and Learning Systems, IEEE Transactions on Big Data. IEEE Transactions on Emerging Topics in Computational Intelligence, IEEE Access, Information Processing Letters, Information Sciences, Journal of Parallel and Distributed    Computing, Journal of Supercomputing, Cluster Computing, Expert Systems with Applications, Engineering Application of Artificial Intelligence, Artificial Intelligence in Medicine, Applied Soft Computing, Knowledge-based Systems , Concurrency Computation Practice and Experience, Mathematical and Computational Applications, IET Intelligent Transportation Systems, Applied Sciences, Algorithms, Sensors, Journal of Visual Computer...

Invited Reviewer for Conferences

KDD Workshops, PAKDD Workshops, ADMA, BDA, IEEE GLOBECOM...

Kompetanse

Experience: 

2023--Now: Associate Professor at University of South-Eastern Norway 

2023--Now: Senior Researcher at Norwegian Research Center

2020--2023: Research Scientist at SINTEF

2018--2020: Postdoctoral Researcher at NTNU

2017--2018: Postdoctoral Researcher at Southern Denmark University

2014--2016: Assistant Professor at University of Saad Dahleb

2014--2015: Junior Researcher at CERIST

2011--2014: Research Assistant at USTHB

Publikasjoner

[1] Asma Belhadi et al. “BIoMT-ISeg: Blockchain internet of medical things for intelligent segmentation”. In: Frontiers in Physiology 13 (2023), p. 2744.

[2] Asma Belhadi et al. “Fast and Accurate Framework for Ontology Matching in Web of Things”. In: ACM Transactions on Asian and Low-Resource Language Information Processing (2023).

[3] Youcef Djenouri et al. “A Secure Parallel Pattern Mining System for Medical Internet of Things”. In: IEEE/ACM Transactions on Computational Biology and Bioinformatics (2023).

[4] Youcef Djenouri et al. “Advanced Pattern-Mining System for Fake News Analysis”. In: IEEE Transactions on Computational Social Systems (2023).

[5] Youcef Djenouri et al. “Hybrid graph convolution neural network and branchand-bound optimization for traffic flow forecasting”. In: Future Generation Computer Systems 139 (2023), pp. 100–108.

[6] Usman Ahmed et al. “Knowledge graph based trajectory outlier detection in sustainable smart cities”. In: Sustainable Cities and Society 78 (2022), p. 103580. '

[7] Asma Belhadi et al. “Group intrusion detection in the Internet of Things using a hybrid recurrent neural network”. In: Cluster Computing (2022), pp. 1–12.

[8] Asma Belhadi et al. “Hybrid intelligent framework for automated medical learning”. In: Expert Systems 39.6 (2022), e12737.

[9] Djamel Djenour, Roufaida Laidi, and Youcef Djenouri. “Deep Learning for Estimating Sleeping Sensorfffdfffdfffds Values in Sustainable IoT Applications”. In: 2022 International Balkan Conference on Communications and Networking (BalkanCom). IEEE. 2022, pp. 147–151.

[10] Youcef Djenouri, Asma Belhadi, and Jerry Chun-Wei Lin. “Recurrent neural network with density-based clustering for group pattern detection in energy systems”. In: Sustainable Energy Technologies and Assessments 52 (2022), p. 102308.

[11] Youcef Djenouri et al. “An edge-driven multi-agent optimization model for infectious disease detection”. In: Applied Intelligence (2022), pp. 1–12.

[12] Youcef Djenouri et al. “An Intelligent Collaborative Image-Sensing System for Disease Detection”. In: IEEE Sensors Journal (2022).

[13] Youcef Djenouri et al. “An ontology matching approach for semantic modeling: A case study in smart cities”. In: Computational Intelligence 38.3 (2022), pp. 876–902.

[14] Youcef Djenouri et al. “Artificial intelligence of medical things for disease detection using ensemble deep learning and attention mechanism”. In: Expert Systems (2022), e13093.

[15] Youcef Djenouri et al. “Deep learning based decomposition for visual navigation in industrial platforms”. In: Applied Intelligence 52.7 (2022), pp. 8101– 8117.

[16] Youcef Djenouri et al. “Deep learning based hashtag recommendation system for multimedia data”. In: Information Sciences 609 (2022), pp. 1506–1517.

[17] Youcef Djenouri et al. “How Image Retrieval and Matching Can Improve Object Localisation on Offshore Platforms”. In: Intelligent Data Engineering and Automated Learning–IDEAL 2022: 23rd International Conference, IDEAL 2022, Manchester, UK, November 24–26, 2022, Proceedings. Springer International Publishing Cham. 2022, pp. 262–270.

[18] Youcef Djenouri et al. “Hybrid RESNET and Regional Convolution Neural Network Framework for Accident Estimation in Smart Roads”. In: IEEE Transactions on Intelligent Transportation Systems (2022).

[19] Youcef Djenouri et al. “Intelligent Deep Fusion Network for Anomaly Identification in Maritime Transportation Systems”. In: IEEE Transactions on Intelligent Transportation Systems (2022).

[20] Youcef Djenouri et al. “Intelligent deep fusion network for urban traffic flow anomaly identification”. In: Computer Communications 189 (2022), pp. 175– 181.

[21] Youcef Djenouri et al. “Intelligent Graph Convolutional Neural Network for Road Crack Detection”. In: IEEE Transactions on Intelligent Transportation Systems (2022).

[22] Youcef Djenouri et al. “Sensor data fusion for the industrial artificial intelligence of things”. In: Expert Systems 39.5 (2022), e12875.

[23] Youcef Djenouri et al. “Toward a Cognitive-Inspired Hashtag Recommendation for Twitter Data Analysis”. In: IEEE Transactions on Computational Social Systems (2022).

[24] Youcef Djenouri et al. “Vehicle detection using improved region convolution neural network for accident prevention in smart roads”. In: Pattern Recognition Letters 158 (2022), pp. 42–47.

[25] Youcef Djenouri et al. “When explainable AI meets IoT applications for supervised learning”. In: Cluster Computing (2022), pp. 1–11.

[26] Jerry Chun-Wei Lin et al. “Efficient evolutionary computation model of closed high-utility itemset mining”. In: Applied Intelligence (2022), pp. 1– 13.

[27] Tinhinane Mezair et al. “A sustainable deep learning framework for fault detection in 6G Industry 4.0 heterogeneous data environments”. In: Computer Communications 187 (2022), pp. 164–171.

[28] Tinhinane Mezair et al. “Towards an Advanced Deep Learning for the Internet of Behaviors: Application to Connected Vehicles”. In: ACM Transactions on Sensor Networks 19.2 (2022), pp. 1–18.

[29] Usman Ahmed et al. “A deep Q-learning sanitization approach for privacy preserving data mining”. In: Adjunct Proceedings of the 2021 International Conference on Distributed Computing and Networking. 2021, pp. 43–48.

[30] Usman Ahmed et al. “A nutrient recommendation system for soil fertilization based on evolutionary computation”. In: Computers and Electronics in Agriculture 189 (2021), p. 106407.

[31] Usman Ahmed et al. “Detection of Trajectory Outliers in Intelligent Transportation Systems”. In: 2021 IEEE International Conference on Big Data (Big Data). IEEE. 2021, pp. 5484–5490.

[32] Usman Ahmed et al. “Deviation point curriculum learning for trajectory outlier detection in cooperative intelligent transport systems”. In: IEEE Transactions on Intelligent Transportation Systems (2021).

[33] Asma Belhadi et al. “Deep learning for pedestrian collective behavior analysis in smart cities: A model of group trajectory outlier detection”. In: Information Fusion 65 (2021), pp. 13–20.

[34] Asma Belhadi et al. “Hybrid group anomaly detection for sequence data: application to trajectory data analytics”. In: IEEE Transactions on Intelligent Transportation Systems (2021).

[35] Asma Belhadi et al. “Machine learning for identifying group trajectory outliers”. In: ACM Transactions on Management Information Systems (TMIS) 12.2 (2021), pp. 1–25.

[36] Asma Belhadi et al. “Privacy reinforcement learning for faults detection in the smart grid”. In: Ad Hoc Networks 119 (2021), p. 102541.

[37] Asma Belhadi et al. “Reinforcement learning multi-agent system for faults diagnosis of mircoservices in industrial settings”. In: Computer Communications 177 (2021), pp. 213–219.

[38] Asma Belhadi et al. “SS-ITS: Secure scalable intelligent transportation systems”. In: The Journal of Supercomputing 77.7 (2021), pp. 7253–7269.

[39] Youcef Djenouri, Djamel Djenouri, and Jerry Chun-Wei Lin. “Trajectory outlier detection: New problems and solutions for smart cities”. In: ACM Transactions on Knowledge Discovery from Data (TKDD) 15.2 (2021), pp. 1–28.

[40] Youcef Djenouri and Jon Hjelmervik. “Hybrid decomposition convolution neural network and vocabulary forest for image retrieval”. In: 2020 25th International Conference on Pattern Recognition (ICPR). IEEE. 2021, pp. 3064– 3070.

[41] Youcef Djenouri et al. “An Efficient and Accurate GPU-based Deep Learning Model for Multimedia Recommendation”. In: ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM) (2021).

[42] Youcef Djenouri et al. “Cluster-based information retrieval using pattern mining”. In: Applied Intelligence 51.4 (2021), pp. 1888–1903.

[43] Youcef Djenouri et al. “Emergent deep learning for anomaly detection in internet of everything”. In: IEEE Internet of Things Journal (2021).

[44] Youcef Djenouri et al. “Exploring decomposition for solving pattern mining problems”. In: ACM Transactions on Management Information Systems (TMIS) 12.2 (2021), pp. 1–36.

[45] Youcef Djenouri et al. “Fast and accurate deep learning framework for secure fault diagnosis in the industrial internet of things”. In: IEEE Internet of Things Journal (2021).

[46] Youcef Djenouri et al. “Intelligent blockchain management for distributed knowledge graphs in IoT 5G environments”. In: Transactions on Emerging Telecommunications Technologies (2021), e4332.

[47] Youcef Djenouri et al. “Secure collaborative augmented reality framework for biomedical informatics”. In: IEEE Journal of Biomedical and Health Informatics (2021).

[48] Essam H Houssein et al. “An improved opposition-based marine predators algorithm for global optimization and multilevel thresholding image segmentation”. In: Knowledge-Based Systems 229 (2021), p. 107348.

[49] Jerry Chun-Wei Lin, Youcef Djenouri, and Gautam Srivastava. “Efficient closed high-utility pattern fusion model in large-scale databases”. In: Information Fusion 76 (2021), pp. 122–132.

[50] Jerry Chun-Wei Lin et al. “A predictive GA-based model for closed highutility itemset mining”. In: Applied Soft Computing 108 (2021), p. 107422.

[51] Jerry Chun-Wei Lin et al. “ASRNN: A recurrent neural network with an attention model for sequence labeling”. In: Knowledge-Based Systems 212 (2021), p. 106548.

[52] Jerry Chun-Wei Lin et al. “Large-Scale Closed High-Utility Itemset Mining”. In: 2021 International Conference on Data Mining Workshops (ICDMW). IEEE. 2021, pp. 591–598.

[53] Jerry Chun-Wei Lin et al. “Linguistic frequent pattern mining using a compressed structure”. In: Applied Intelligence 51.7 (2021), pp. 4806–4823.

[54] Jerry Chun-Wei Lin et al. “Mining profitable and concise patterns in largescale Internet of Things environments”. In: Wireless Communications and Mobile Computing 2021 (2021).

[55] Jerry Chun-Wei Lin et al. “Scalable mining of high-utility sequential patterns with three-tier MapReduce model”. In: ACM Transactions on Knowledge Discovery from Data (TKDD) 16.3 (2021), pp. 1–26.

[56] Khiati Mustapha et al. “LSTM for Periodic Broadcasting in Green IoT Applications over Energy Harvesting Enabled Wireless Networks: Case Study on ADAPCAST”. In: 2021 17th International Conference on Mobility, Sensing and Networking (MSN). IEEE. 2021, pp. 694–699.

[57] Martha Roseberry et al. “Self-adjusting k nearest neighbors for continual learning from multi-label drifting data streams”. In: Neurocomputing 442 (2021), pp. 10–25.

[58] Gautam Srivastava et al. “Security protocol of sensitive high utility itemset hiding in shared IoT environments”. In: Digital Communications and Networks (2021).

[59] Jimmy Ming-Tai Wu et al. “A Graphic CNN-LSTM Model for Stock Price Predication”. In: International Conference on Artificial Intelligence and Soft Computing. Springer, Cham. 2021, pp. 258–268.

[60] Jimmy Ming-Tai Wu et al. “Mining of High-Utility Patterns in Big IoT Databases”. In: International Conference on Artificial Intelligence and Soft Computing. Springer, Cham. 2021, pp. 205–216.

[61] Jimmy Ming-Tai Wu et al. “Mining of high-utility patterns in big IoT-based databases”. In: Mobile Networks and Applications 26.1 (2021), pp. 216–233.

[62] Usman Ahmed et al. “An evolutionary model to mine high expected utility patterns from uncertain databases”. In: IEEE transactions on emerging topics in computational intelligence 5.1 (2020), pp. 19–28.

[63] Usman Ahmed et al. “Efficient mining of Pareto-front high expected utility patterns”. In: International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems. Springer, Cham. 2020, pp. 872– 883.

[64] Asma Belhadi et al. “A data-driven approach for Twitter hashtag recommendation”. In: IEEE Access 8 (2020), pp. 79182–79191.

[65] Asma Belhadi et al. “A general-purpose distributed pattern mining system”. In: Applied Intelligence 50.9 (2020), pp. 2647–2662.

[66] Asma Belhadi et al. “A recurrent neural network for urban long-term traffic flow forecasting”. In: Applied Intelligence 50.10 (2020), pp. 3252–3265.

[67] Asma Belhadi et al. “A two-phase anomaly detection model for secure intelligent transportation ride-hailing trajectories”. In: IEEE Transactions on Intelligent Transportation Systems 22.7 (2020), pp. 4496–4506.

[68] Asma Belhadi et al. “Deep learning versus traditional solutions for group trajectory outliers”. In: IEEE Transactions on Cybernetics (2020).

[69] Asma Belhadi et al. “Exploring pattern mining algorithms for hashtag retrieval problem”. In: IEEE Access 8 (2020), pp. 10569–10583.

[70] Asma Belhadi et al. “Space–time series clustering: Algorithms, taxonomy, and case study on urban smart cities”. In: Engineering Applications of Artificial Intelligence 95 (2020), p. 103857.

[71] Asma Belhadi et al. “Trajectory outlier detection: Algorithms, taxonomies, evaluation, and open challenges”. In: ACM Transactions on Management Information Systems (TMIS) 11.3 (2020), pp. 1–29.

[72] Hiba Belhadi et al. “Data mining-based approach for ontology matching problem”. In: Applied Intelligence 50.4 (2020), pp. 1204–1221.

[73] Youcef Djenouri, Gautam Srivastava, and Jerry Chun-Wei Lin. “Fast and accurate convolution neural network for detecting manufacturing data”. In: IEEE Transactions on Industrial Informatics 17.4 (2020), pp. 2947–2955.

[74] Youcef Djenouri et al. “Fast and accurate group outlier detection for trajectory data”. In: European Conference on Advances in Databases and Information Systems. Springer, Cham. 2020, pp. 60–70.

[75] Youcef Djenouri et al. “When the Decomposition Meets the Constraint Satisfaction Problem”. In: IEEE Access 8 (2020), pp. 207034–207043.

[76] Jerry Chun-Wei Lin et al. “Efficient chain structure for high-utility sequential pattern mining”. In: IEEE Access 8 (2020), pp. 40714–40722.

[77] Jerry Chun-Wei Lin et al. “Incrementally updating the high average-utility patterns with pre-large concept”. In: Applied Intelligence 50.11 (2020), pp. 3788– 3807.

[78] Jerry Chun-Wei Lin et al. “Mining multiple fuzzy frequent patterns with compressed list structures”. In: 2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE. 2020, pp. 1–8.

[79] Jerry Chun-Wei Lin et al. “Privacy-preserving multiobjective sanitization model in 6G IoT environments”. In: IEEE Internet of Things Journal 8.7 (2020), pp. 5340–5349.

[80] Gautam Srivastava et al. “Uncertain-driven analytics of sequence data in IoCV environments”. In: IEEE Transactions on Intelligent Transportation Systems 22.8 (2020), pp. 5403–5414.

[81] Asma Belhadi, Youcef Djenouri, and Jerry Chun-Wei Lin. “Comparative study on trajectory outlier detection algorithms”. In: 2019 International Conference on Data Mining Workshops (ICDMW). IEEE. 2019, pp. 415–423.

[82] Hiba Belhadi et al. “Exploring pattern mining for solving the ontology matching problem”. In: European Conference on Advances in Databases and Information Systems. Springer, Cham. 2019, pp. 85–93.

[83] Hiba Belhadi et al. “GFSOM: genetic feature selection for ontology matching”. In: Genetic and Evolutionary Computing: Proceedings of the Twelfth International Conference on Genetic and Evolutionary Computing, December 14-17, Changzhou, Jiangsu, China 12. Springer Singapore. 2019, pp. 655– 660.

[84] Djamel Djenouri et al. “Machine learning for smart building applications: Review and taxonomy”. In: ACM Computing Surveys (CSUR) 52.2 (2019), pp. 1–36.

[85] Youcef Djenouri et al. “A novel parallel framework for metaheuristic-based frequent itemset mining”. In: 2019 IEEE Congress on Evolutionary Computation (CEC). IEEE. 2019, pp. 1439–1445.

[86] Youcef Djenouri et al. “A survey on urban traffic anomalies detection algorithms”. In: IEEE Access 7 (2019), pp. 12192–12205.

[87] Youcef Djenouri et al. “Adapted k-nearest neighbors for detecting anomalies on spatio–temporal traffic flow”. In: IEEE Access 7 (2019), pp. 10015–10027.

[88] Youcef Djenouri et al. “Bee swarm optimization for solving the MAXSAT problem using prior knowledge”. In: Soft Computing 23.9 (2019), pp. 3095– 3112.

[89] Youcef Djenouri et al. “Exploiting GPU and cluster parallelism in single scan frequent itemset mining”. In: Information Sciences 496 (2019), pp. 363–377.

[90] Youcef Djenouri et al. “Exploiting GPU parallelism in improving bees swarm optimization for mining big transactional databases”. In: Information Sciences 496 (2019), pp. 326–342.

[91] Youcef Djenouri et al. “GBSO-RSS: GPU-based BSO for rules space summarization”. In: Big Data Analysis and Deep Learning Applications: Proceedings of the First International Conference on Big Data Analysis and Deep Learning 1st. Springer Singapore. 2019, pp. 123–129.

[92] Youcef Djenouri et al. “GPU-based swarm intelligence for Association Rule Mining in big databases”. In: Intelligent Data Analysis 23.1 (2019), pp. 57– 76.

[93] Youcef Djenouri et al. “Highly efficient pattern mining based on transaction decomposition”. In: 2019 IEEE 35th International Conference on Data Engineering (ICDE). IEEE. 2019, pp. 1646–1649.

[94] Youcef Djenouri et al. “Metaheuristics for frequent and high-utility itemset mining”. In: High-Utility Pattern Mining. Springer, Cham, 2019, pp. 261– 278.

[95] Youcef Djenouri et al. “Single scan polynomial algorithms for frequent itemset mining in big databases”. In: 2019 IEEE Congress on Evolutionary Computation (CEC). IEEE. 2019, pp. 1453–1460.

[96] Jerry Chun-Wei Lin et al. “A sanitization approach to secure shared data in an IoT environment”. In: IEEE Access 7 (2019), pp. 25359–25368.

[97] Jerry Chun-Wei Lin et al. “An efficient chain structure to mine high-utility sequential patterns”. In: 2019 International Conference on Data Mining Workshops (ICDMW). IEEE. 2019, pp. 1013–1019.

[98] Jerry Chun-Wei Lin et al. “Hiding sensitive itemsets with multiple objective optimization”. In: Soft Computing 23.23 (2019), pp. 12779–12797.

[99] Jerry Chun-Wei Lin et al. “Mining high-utility sequential patterns from big datasets”. In: 2019 IEEE International Conference on Big Data (Big Data). IEEE. 2019, pp. 2674–2680.

[100] Jimmy Ming Tai Wu et al. “A sanitization approach to secure shared data in an IoT environment”. In: Mathematical Biosciences and Engineering 16 (2019), pp. 1718–1728.

[101] Jimmy Ming-Tai Wu et al. “A swarm-based data sanitization algorithm in privacy-preserving data mining”. In: 2019 IEEE congress on evolutionary computation (CEC). IEEE. 2019, pp. 1461–1467.

[102] Jimmy Ming-Tai Wu et al. “The density-based clustering method for privacypreserving data mining”. In: (2019).

[103] Binbin Zhang et al. “A (k, p)-anonymity framework to sanitize transactional database with personalized sensitivity”. In: Journal of Internet Technology 20.3 (2019), pp. 801–808.

[104] Youcef Djenouri, Asma Belhadi, and Riadh Belkebir. “Bees swarm optimization guided by data mining techniques for document information retrieval”. In: Expert Systems with Applications 94 (2018), pp. 126–136.

[105] Youcef Djenouri, Asma Belhadi, and Philippe Fournier-Viger. “Extracting useful knowledge from event logs: a frequent itemset mining approach”. In: Knowledge-Based Systems 139 (2018), pp. 132–148.

[106] Youcef Djenouri, Djamel Djenouri, and Zineb Habbas. “Intelligent mapping between GPU and cluster computing for discovering big association rules”. In: Applied Soft Computing 65 (2018), pp. 387–399.

[107] Youcef Djenouri and Arthur Zimek. “Outlier detection in urban traffic data”. In: Proceedings of the 8th International Conference on Web Intelligence, Mining and Semantics. 2018, pp. 1–12.

[108] Youcef Djenouri, Arthur Zimek, and Marco Chiarandini. “Outlier detection in urban traffic flow distributions”. In: 2018 IEEE international conference on data mining (ICDM). IEEE. 2018, pp. 935–940.

[109] Youcef Djenouri et al. “A new framework for metaheuristic-based frequent itemset mining”. In: Applied Intelligence 48.12 (2018), pp. 4775–4791.

[110] Youcef Djenouri et al. “An hybrid multi-core/gpu-based mimetic algorithm for big association rule mining”. In: Genetic and Evolutionary Computing: Proceedings of the Eleventh International Conference on Genetic and Evolutionary Computing, November 6-8, 2017, Kaohsiung, Taiwan 11. Springer Singapore. 2018, pp. 59–65.

[111] Youcef Djenouri et al. “Discovering strong meta association rules using bees swarm optimization”. In: Pacific-Asia Conference on Knowledge Discovery and Data Mining. Springer, Cham. 2018, pp. 195–206.

[112] Youcef Djenouri et al. “Fast and effective cluster-based information retrieval using frequent closed itemsets”. In: Information Sciences 453 (2018), pp. 154– 167.

[113] Youcef Djenouri et al. “Frequent itemset mining in big data with effective single scan algorithms”. In: Ieee Access 6 (2018), pp. 68013–68026.

[114] Youcef Djenouri et al. “How to exploit high performance computing in populationbased metaheuristics for solving association rule mining problem”. In: Distributed and Parallel Databases 36.2 (2018), pp. 369–397.

[115] Youcef Djenouri et al. “Mining diversified association rules in big datasets: A cluster/GPU/genetic approach”. In: Information Sciences 459 (2018), pp. 117– 134.

[116] Jerry Chun-Wei Lin et al. “A metaheuristic algorithm for hiding sensitive itemsets”. In: International Conference on Database and Expert Systems Applications. Springer, Cham. 2018, pp. 492–498.

[117] Jerry Chun-Wei Lin et al. “Anonymization of multiple and personalized sensitive attributes”. In: International Conference on Big Data Analytics and Knowledge Discovery. Springer, Cham. 2018, pp. 204–215.

[118] Jerry Chun-Wei Lin et al. “Maintenance algorithm for high average-utility itemsets with transaction deletion”. In: Applied Intelligence 48.10 (2018), pp. 3691–3706.

119] Jerry Chun-Wei Lin et al. “PPSF: An open-source privacy-preserving and security mining framework”. In: 2018 IEEE International Conference on Data Mining Workshops (ICDMW). IEEE. 2018, pp. 1459–1463.

[120] Binbin Zhang et al. “Maintenance of discovered high average-utility itemsets in dynamic databases”. In: Applied Sciences 8.5 (2018), p. 769.

[121] Youcef Djenouri and Marco Comuzzi. “Combining Apriori heuristic and bioinspired algorithms for solving the frequent itemsets mining problem”. In: Information Sciences 420 (2017), pp. 1–15.

[122] Youcef Djenouri and Marco Comuzzi. “GA-Apriori: Combining Apriori heuristic and genetic algorithms for solving the frequent itemsets mining problem”. In: Pacific-Asia Conference on Knowledge Discovery and Data Mining. Springer, Cham. 2017, pp. 138–148.

[123] Youcef Djenouri, Marco Comuzzi, and Djamel Djenouri. “SS-FIM: single scan for frequent itemsets mining in transactional databases”. In: Pacific-Asia Conference on Knowledge Discovery and Data Mining. Springer, Cham. 2017, pp. 644–654.

[124] Youcef Djenouri and Zineb Habbas. Fouille de R´egles d’association en GPU. Editions universitaires europ´eennes, 2017.

[125] Youcef Djenouri, Zineb Habbas, and Djamel Djenouri. “Data mining-based decomposition for solving the MAXSAT problem: toward a new approach”. In: IEEE Intelligent Systems 32.4 (2017), pp. 48–58.

[126] Youcef Djenouri et al. “Diversification heuristics in bees swarm optimization for association rules mining”. In: Pacific-Asia Conference on Knowledge Discovery and Data Mining. Springer, Cham. 2017, pp. 68–78.

[127] Youcef Djenouri et al. “GPU-based bio-inspired model for solving association rules mining problem”. In: 2017 25th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP). IEEE. 2017, pp. 262–269.

[128] Youcef Djenouri et al. “New GPU-based swarm intelligence approach for reducing big association rules space”. In: 2017 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computed, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI). IEEE. 2017, pp. 1–6.

[129] Youcef Djenouri et al. “Reducing thread divergence in GPU-based bees swarm optimization applied to association rule mining”. In: Concurrency and Computation: Practice and Experience 29.9 (2017), e3836.

[130] Youcef Djennouri, Zineb Habbas, and Aggoune-mtalaa Wassila. “Bees Swarm Optimization Metaheuristic Guided by Decomposition for Solving MAXSAT”. In: Proceedings of the 8th International Conference on Agents and Artificial Intelligence (ICAART 2016). Vol. 2. SciTePress 2016. 2016, pp. 472– 479.

[131] Youcef Djenouri et al. “Parallel BSO algorithm for association rules mining using master/worker paradigm”. In: Parallel Processing and Applied Mathematics: 11th International Conference, PPAM 2015, Krakow, Poland, September 6-9, 2015. Revised Selected Papers, Part I. Springer International Publishing Cham. 2016, pp. 258–268.

[132] Youcef Gheraibia et al. “Penguins search optimisation algorithm for association rules mining”. In: Journal of computing and information technology 24.2 (2016), pp. 165–179.

[133] Messaoud Chaa et al. “CERIST at INEX 2015: Social Book Search Track.” In: CLEF (Working Notes). 2015.

[134] Youcef Djenouri et al. “Data reordering for minimizing threads divergence in gpu-based evaluating association rules”. In: Distributed Computing and Artificial Intelligence, 12th International Conference. Springer, Cham. 2015, pp. 47–54.

[135] Youcef Djenouri et al. “GPU-based bees swarm optimization for association rules mining”. In: The Journal of Supercomputing 71.4 (2015), pp. 1318–1344.

[136] Youcef Gheraibia et al. “Penguin search optimisation algorithm for finding optimal spaced seeds”. In: International Journal of Software Science and Computational Intelligence (IJSSCI) 7.2 (2015), pp. 85–99.

[137] Amine Chemchem, Habiba Drias, and Youcef Djenouri. “Multilevel Clustering of Induction Rules: Application on Scalable Cognitive Agent”. In: International Journal of Systems and Service-Oriented Engineering (IJSSOE) 4.3 (2014), pp. 1–25.

[138] Youcef Djenouri and Habiba Drias. “Parallel bees swarm optimization for association rules mining using GPU architecture”. In: International Conference in Swarm Intelligence. Springer, Cham. 2014, pp. 50–57.

[139] Youcef Djenouri, Habiba Drias, and Ahcene Bendjoudi. “Pruning irrelevant association rules using knowledge mining”. In: International Journal of Business Intelligence and Data Mining 9.2 (2014), pp. 112–144.

[140] Youcef Djenouri, Habiba Drias, and Zineb Habbas. “Bees swarm optimisation using multiple strategies for association rule mining”. In: International Journal of Bio-Inspired Computation 6.4 (2014), pp. 239–249.

[141] Youcef Djenouri, Habiba Drias, and Zineb Habbas. “Hybrid intelligent method for association rules mining using multiple strategies”. In: International Journal of Applied Metaheuristic Computing (IJAMC) 5.1 (2014), pp. 46–64.

[142] Youcef Djenouri, Nadia Nouali-Taboudjemat, and Ahc`ene Bendjoudi. “Association rules mining using evolutionary algorithms”. In: The 9th International Conference on Bio-inspired Computing: Theories and Applications (BIC-TA 2014). LNCS. 2014.

[143] Youcef Djenouri et al. “An efficient measure for evaluating association rules”. In: 2014 6th International Conference of Soft Computing and Pattern Recognition (SoCPaR). IEEE. 2014, pp. 406–410.

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