Decision-making Improvement in Dynamic Environments Using Machine Learning
Abstract
Doi: 10.28991/HEF-2022-03-01-04
Full Text: PDF
Keywords
References
Singh, S. (2021). Applications in Ubiquitous Computing. In R. Kumar & S. Paiva (Eds.), EAI/Springer Innovations in Communication and Computing. Springer, Cham, Switzerland. doi:10.1007/978-3-030-35280-6.
Castro Garrido, P., Luque Ruiz, I., & Gómez-Nieto, M. Á. (2014). OBCAS: An agent-based system and ontology for mobile context aware interactions. Journal of Intelligent Information Systems, 43(1), 33–57. doi:10.1007/s10844-014-0305-8.
Zhao, T. (2016). The Generation and Evolution of Adaptation Rules in Requirements Driven Self-Adaptive Systems. Proceedings - 2016 IEEE 24th International Requirements Engineering Conference, RE 2016, 456–461. doi:10.1109/RE.2016.18.
Liu, Y., Zhang, W., & Jiao, W. (2016). A generative genetic algorithm for evolving adaptation rules of software systems. ACM International Conference Proceeding Series, 18-September-2016, 103–107. doi:10.1145/2993717.2993731.
Goldberg, D. (1989). Genetic algorithms in search. In Optimization and machine learning. Addison-Wesley Longman Publishing Co., Boston, MA, United States.
Asuncion, A., & Newman, D. (2007). UCI Machine Learning Repository. Irvine University of California, Irvine, CA, United States.
Paiva, L., Costa, R., Figueiras, P., & Lima, C. (2014). Discovering semantic relations from unstructured data for ontology enrichment: Asssociation rules based approach. Iberian Conference on Information Systems and Technologies, CISTI, 1 6. doi:10.1109/CISTI.2014.6877008.
Han, J., Pei, J., & Yin, Y. (2000). Mining frequent patterns without candidate generation. ACM SIGMOD Record, 29(2), 1–12. doi:10.1145/335191.335372
Idoudi, R., Ettabaa, K. S., Solaiman, B., & Mnif, N. Association rules-based ontology enrichment. International Journal of Web Applications, 8(1), 16–25.
Agrawal, R., & Srikant, R. (1994). Fast Algorithms for Mining Association Rules in Large Databases. Proc. of the 20th International Conference on Very Large Data Bases (VLDB’94), 1215, 487–499.
Chang, M., D’Aniello, G., Gaeta, M., Orciuoli, F., Sampson, D., & Simonelli, C. (2020). Building Ontology-Driven Tutoring Models for Intelligent Tutoring Systems Using Data Mining. IEEE Access, 8, 48151–48162. doi:10.1109/ACCESS.2020.2979281.
Gabroveanu, M., & Diaconescu, I. M. (2008). Extracting semantic annotations from moodle data. CEUR Workshop Proceedings, 428, 1–5.
Kaliappan, J., Sai, S. M., & Shaily Preetham, K. (2019). Weblog and retail industries analysis using a robust modified Apriori algorithm. In International Journal of Innovative Technology and Exploring Engineering 8, (6), 1727–1733.
Davagdorj, K., & Ryu, K. H. (2018). Association Rule Mining on Head and Neck Squamous Cell Carcinoma Cancer using FP Growth algorithm. Proceedings of the International Conference on Information, System and Convergence Applications, 31 Jan – 2 Feb 2018, Bangkok, Thailand.
Asadianfam, S., Kolivand, H., & Asadianfam, S. (2020). A new approach for web usage mining using case based reasoning. SN Applied Sciences, 2(7), 1–11. doi:10.1007/s42452-020-3046-z.
Miswan, N. H., Sulaiman, I. M., Chan, C. S., & Ng, C. G. (2021). Association rules mining for hospital readmission: A case study. Mathematics, 9(21), 2706. doi:10.3390/math9212706.
Islam, M. R., Liu, S., Biddle, R., Razzak, I., Wang, X., Tilocca, P., & Xu, G. (2021). Discovering dynamic adverse behavior of policyholders in the life insurance industry. Technological Forecasting and Social Change, 163, 120486. doi:10.1016/j.techfore.2020.120486.
Sánchez-de-Madariaga, R., Martinez-Romo, J., Escribano, J. M. C., & Araujo, L. (2022). Semi-supervised incremental learning with few examples for discovering medical association rules. BMC Medical Informatics and Decision Making, 22(1), 1–11. doi:10.1186/s12911-022-01755-3.
Zulkernain, S., Madiraju, P., & Ahamed, S. I. (2010). A mobile intelligent interruption management system. Journal of Universal Computer Science 16(15), 2060-2080.
Sarker, I. H. (2019). A machine learning based robust prediction model for real-life mobile phone data. Internet of Things (Netherlands), 5, 180–193. doi:10.1016/j.iot.2019.01.007.
Basha, M.S. (2021). Early Prediction of Cardio Vascular Disease by Performing Associative Classification on Medical Datasets and Using Genetic Algorithm. In Lecture Notes in Networks and Systems 248, 393–402. doi:10.1007/978-981-16-3153-5_42.
Mahmood, A., Shi, K., Khatoon, S., & Xiao, M. (2013). Data mining techniques for wireless sensor networks: A survey. International Journal of Distributed Sensor Networks, 2013(7), 406316. doi:10.1155/2013/406316.
Jabla, R., Khemaja, M., Buendia, F., & Faiz, S. (2021). Automatic ontology-based model evolution for learning changes in dynamic environments. Applied Sciences (Switzerland), 11(22), 10770. doi:10.3390/app112210770.
Quinlan, J. R. (1986). Induction of Decision Trees. Machine Learning, 1(1), 81–106. doi:10.1023/A:1022643204877.
Witten, I. H., Frank, E., & Geller, J. (2002). Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. SIGMOD Record, 31(1), 76–77. doi:10.1145/507338.507355.
Suresh, A., Udendhran, R., & Balamurgan, M. (2020). Hybridized neural network and decision tree based classifier for prognostic decision making in breast cancers. Soft Computing, 24(11), 7947–7953. doi:10.1007/s00500-019-04066-4.
Fielding, R. T., & Taylor, R. N. (2002). Principled Design of the Modern Web Architecture. ACM Transactions on Internet Technology, 2(2), 115–150. doi:10.1145/514183.514185.
DOI: 10.28991/HEF-2022-03-01-04
Refbacks
- There are currently no refbacks.
Copyright (c) 2022 Roua Jabla, Maha Khemaja, Sami Faiz