@article{ejlali_arian_taghiyeh_chambers_sadeghi_taghiye_cakdi_handfield_2024, title={Developing hybrid machine learning models to assign health score to railcar fleets for optimal decision making}, volume={250}, ISSN={["1873-6793"]}, url={https://doi.org/10.1016/j.eswa.2024.123931}, DOI={10.1016/j.eswa.2024.123931}, abstractNote={A large amount of data is generated during the operation of a railcar fleet, which can easily lead to dimensional disaster and reduce the resiliency of the railcar network. To solve these issues and offer predictive maintenance, this research introduces a hybrid fault diagnosis expert system method that combines density-based spatial clustering of applications with noise (DBSCAN) and principal component analysis (PCA). Firstly, the DBSCAN method is used to cluster categorical data that are similar to one another within the same group. Secondly, PCA algorithm is applied to reduce the dimensionality of the data and eliminate redundancy in order to improve the accuracy of fault diagnosis. Finally, we explain the engineered features and evaluate the selected models by using the Gain Chart and Area Under Curve (AUC) metrics. We use the hybrid expert system model to enhance maintenance planning decisions by assigning a health score to the railcar system of the North American Railcar Owner (NARO). The model is based on data from one specific railcar type. We utilize the results of data-driven models to assign a health rate to each railcar. Finally, we use data from NARO to evaluate and verify our proposed framework.}, journal={EXPERT SYSTEMS WITH APPLICATIONS}, author={Ejlali, Mahyar and Arian, Ebrahim and Taghiyeh, Sajjad and Chambers, Kristina and Sadeghi, Amir Hossein and Taghiye, Emad and Cakdi, Demet and Handfield, Robert B.}, year={2024}, month={Sep} }
 @article{taghiyeh_lengacher_handfield_2021, title={Loss rate forecasting framework based on macroeconomic changes: Application to US credit card industry}, volume={165}, ISSN={["1873-6793"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85090568347&partnerID=MN8TOARS}, DOI={10.1016/j.eswa.2020.113954}, abstractNote={A major part of the balance sheets of the largest U.S. banks consists of credit card portfolios. Hence, managing the charge-off rates is a vital task for the profitability of the credit card industry. Different macroeconomic conditions affect individuals' behavior in paying down their debts. In this paper, we propose an expert system for loss forecasting in the credit card industry using macroeconomic indicators. We select the indicators based on a thorough review of the literature and experts' opinions covering all aspects of the economy, consumer, business, and government sectors. The state of the art machine learning models are used to develop the proposed expert system framework. We develop two versions of the forecasting expert system, which utilize different approaches to select between the lags added to each indicator. Among 19 macroeconomic indicators that were used as the input, six were used in the model with optimal lags, and seven indicators were selected by the model using all lags. The features that were selected by each of these models covered all three sectors of the economy. Using the charge-off data for the top 100 US banks ranked by assets from the first quarter of 1985 to the second quarter of 2019, we achieve mean squared error values of 1.15E−03 and 1.04E−03 using the model with optimal lags and the model with all lags, respectively. The proposed expert system gives a holistic view of the economy to the practitioners in the credit card industry and helps them to see the impact of different macroeconomic conditions on their future loss.}, journal={EXPERT SYSTEMS WITH APPLICATIONS}, author={Taghiyeh, Sajjad and Lengacher, David C. and Handfield, Robert B.}, year={2021}, month={Mar} }