Youcef Djenouri

[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.

[144] Youcef Djenouri et al. “An improved evolutionary approach for association rules mining”. In: Bio-Inspired Computing-Theories and Applications. Springer, Berlin, Heidelberg, 2014, pp. 93–97.

[145] Habiba Drias and Youcef Djenouri. “Association Rules Mining: Application to Large-Scale Satisfiability”. In: (2014).

[146] A Chemchem, Y Djenouri, and H Drias. “Incremental induction rules clustering”. In: 2013 8th International workshop on systems, signal processing and their applications (wosspa). IEEE. 2013, pp. 492–497.

[147] Amine Chemchem, Habiba Drias, and Youcef Djenouri. “Multilevel clustering of induction rules for web meta-knowledge”. In: Advances in information systems and technologies. Springer, Berlin, Heidelberg, 2013, pp. 43–54.

[148] Youcef Djenouri, Habiba Drias, and Amine Chemchem. “A hybrid bees swarm optimization and tabu search algorithm for association rule mining”. In: 2013 World Congress on Nature and Biologically Inspired Computing. IEEE. 2013, pp. 120–125.

[149] Y Djenouri et al. “Organizing association rules with meta-rules using knowledge clustering”. In: 2013 11th International Symposium on Programming and Systems (ISPS). IEEE. 2013, pp. 109–115.

[150] Youcef Djenouri et al. “Bees swarm optimization for web association rule mining”. In: 2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology. Vol. 3. IEEE. 2012, pp. 142–146.