dr. M.R. Machado (Marcos)

Business & Economics

# Financial Industry
# Learning Algorithm
# Lending
# Machine Learning
# Revenue

Engineering & Materials Science

# Industry
# Learning Algorithms
# Machine Learning

Faculty of Behavioural, Management and Social Sciences (BMS), Industrial Engineering & Business Information Systems (IEBIS)

Medeiros, E. P. , Machado, M. R., de Freitas, E. D. G., da Silva, D. S., & de Souza, R. W. R. (2024). Applications of machine learning algorithms to support COVID-19 diagnosis using X-rays data information. Expert systems with applications, 238(Part B), Article 122029. https://doi.org/10.1016/j.eswa.2023.122029

Moreira, J. , Ugulino, W. , Machado, M. R. , & Pires, L. F. (2023). Challenge-Based Learning and Constructive Alignment: A Challenge for Information Systems' Educators. In G. Reilly, M. Murphy, B. V. Nagy, & H.-M. Jarvinen (Eds.), SEFI 2023 - 51st Annual Conference of the European Society for Engineering Education: Engineering Education for Sustainability, Proceedings (pp. 2517-2526). Societe Europeenne pour la Formation des Ingenieurs (SEFI). https://doi.org/10.21427/V8GC-C794

Osterrieder, J., Hadji Misheva, B. , & Machado, M. (2023). Digital Finance: Reaching New Frontiers. Open Research Europe, 3, Article 38. https://doi.org/10.12688/openreseurope.15386.1

Machado, M. R., & Karray, S. (2022). Assessing credit risk of commercial customers using hybrid machine learning algorithms. Expert systems with applications, 200, Article 116889. https://doi.org/10.1016/j.eswa.2022.116889

Machado, M. R., & Karray, S. (2022). Applying hybrid machine learning algorithms to assess customer risk-adjusted revenue in the financial industry. Electronic commerce research and applications, 56, Article 101202. https://doi.org/10.1016/j.elerap.2022.101202

Machado, M. R., & Karray, S. (2022). Integrating Customer Portfolio Theory and the Multiple Sources of Risk Approaches to Model Risk-Adjusted Revenue. IFAC-papersonline, 55(16), 356-363. https://doi.org/10.1016/j.ifacol.2022.09.050

Machado, M. R., Karray, S., & De Sousa, I. T. (2019). LightGBM: An effective decision tree gradient boosting method to predict customer loyalty in the finance industry. In 14th International Conference on Computer Science and Education, ICCSE 2019 (pp. 1111-1116). Article 8845529 IEEE. https://doi.org/10.1109/ICCSE.2019.8845529

MacHado, M. R., Kenichi Fujii, M., De Oliveira Ribeiro, C., & Rego, E. E. (2019). An Agent-Based Model Applied to Brazilian Wind Energy Auctions. IEEE Latin America Transactions, 17(5), 865-874. Article 8891956. https://doi.org/10.1109/TLA.2019.8891956

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Naar mijn profiel

Bachelor

Master

Vakken in het huidig collegejaar worden toegevoegd op het moment dat zij definitief zijn in het Osiris systeem. Daarom kan het zijn dat de lijst nog niet compleet is voor het gehele collegejaar.