1. Home
  2. Vol 3, No 1 (2022)
  3. Jabla

Decision-making Improvement in Dynamic Environments Using Machine Learning

Roua Jabla, Maha Khemaja, Sami Faiz

Abstract


The proliferation of ubiquitous computing with smartphones makes context models and information extremely rich and dynamic on account of highly dynamic environments. However, defining rules at design time may impair their efficiency and the decision-making process at runtime. Therefore, it is important to address decision-making problems leveraged by dynamic environments and context-model evolution. In this sense, a solution that could emerge is the continuous rule knowledge base evolution at runtime. In this paper, we propose a decision adaptation component that relies on generating new rules due to changes occurring around users at runtime. This component aims to support decision-making in dynamic environments and to alleviate the human effort needed to infer new rules. A case study was conducted to illustrate the implementation of the proposed component for the rule knowledge database enrichment and the decision-making improvement. Moreover, an experimental evaluation is provided to assess the effectiveness of the proposed component. The results show that this component exhibits better effectiveness than other well-known algorithms and state-of-the-art approaches.

 

Doi: 10.28991/HEF-2022-03-01-04

Full Text: PDF


Keywords


Ubiquitous Computing; Context-Awareness; Rule Generation; Decision-Making; Human–computer Interaction.

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.


Full Text: PDF

DOI: 10.28991/HEF-2022-03-01-04

Refbacks

  • There are currently no refbacks.


Copyright (c) 2022 Roua Jabla, Maha Khemaja, Sami Faiz