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    Forecasting COVID-19 recovered cases with Artificial Neural Networks to enable designing an effective blood supply chain
    (Elsevier Ltd, 2021) Ayyildiz, E.; Erdogan, M.; Taskin, A.
    This study introduces a forecasting model to help design an effective blood supply chain mechanism for tackling the COVID-19 pandemic. In doing so, first, the number of people recovered from COVID-19 is forecasted using the Artificial Neural Networks (ANNs) to determine potential donors for convalescent (immune) plasma (CIP) treatment of COVID-19. This is performed explicitly to show the applicability of ANNs in forecasting the daily number of patients recovered from COVID-19. Second, the ANNs-based approach is further applied to the data from Italy to confirm its robustness in other geographical contexts. Finally, to evaluate its forecasting accuracy, the proposed Multi-Layer Perceptron (MLP) approach is compared with other traditional models, including Autoregressive Integrated Moving Average (ARIMA), Long Short-term Memory (LSTM), and Nonlinear Autoregressive Network with Exogenous Inputs (NARX). Compared to the ARIMA, LSTM, and NARX, the MLP-based model is found to perform better in forecasting the number of people recovered from COVID-19. Overall, the findings suggest that the proposed model is robust and can be widely applied in other parts of the world in forecasting the patients recovered from COVID-19. © 2021 Elsevier Ltd
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    Interval Type-2 Trapezoidal Fuzzy AHP and Modified Delphi Method: Evaluation of Railway Transportation for Istanbul, Turkey
    (Springer Science and Business Media Deutschland GmbH, 2023) Taskin, A.; Tumsekcali, E.
    One of the most vital forms of public transportation, particularly in large, populated areas, is rail transit. In order to improve the infrastructure of the railway public transportation systems, integration and adaptation of new technologies are of great importance. Therefore, the technological assessment of the most popular Tram, Subway, and Marmaray alternatives of Istanbul's train transit is covered in this chapter. First, expert interviews are done in the chapter once the criteria are established, and Modified Delphi approach is then used for consolidation. Subsequently, Interval Type-2 Fuzzy AHP is applied for obtaining importance weights of transport technology criteria. A sensitivity analysis is performed to show the applicability and reliability of the proposed methodology. Overall, the results show that the suggested model is reliable and can be used broadly in similar evaluation problems. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.