Neutrosophic Fuzzy Hybrid Method Using Metaheuristic Algorithms for IMRT Treatment
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-67192-0_81
Reference21 articles.
1. Aslam, M., Alamri, F.S.: Algorithm for generating neutrosophic data using the accept-reject method. J. Big Data 10(1) (2023). https://doi.org/10.1186/s40537-023-00855-9
2. Chakraborty, A., Mondal, S.P., Alam, S., Mahata, A.: Cylindrical neutrosophic single-valued number and its application in networking problem, multi-criterion group decision-making problem and graph theory. CAAI Trans. Intell. Technol. 5(2), 68–77 (2020). https://doi.org/10.1049/trit.2019.0083
3. Chander, S., Padmanabha, V., Mani, J.: Jaya spider monkey optimization-driven deep convolutional LSTM for the prediction of COVID’19. Bio-Algo. Med-Syst. 16(4) (2020). https://doi.org/10.1515/bams-2020-0030
4. Dias, J., Rocha, H., Ventura, T., Ferreira, B., do Carmo Lopes, M.: A heuristic based on fuzzy inference systems for multiobjective IMRT treatment planning. In: Nicosia, G., Pardalos, P., Giuffrida, G., Umeton, R. (eds.) Machine Learning, Optimization, and Big Data, MOD 2017. Lecture Notes in Computer Science, vol. 10710. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-72926-8_22
5. Dokur, E., Yuzgec, U., Kurban, M.: Performance comparison of hybrid neuro-fuzzy models using meta-heuristic algorithms for short-term wind speed forecasting. Electrica 21(3), 305–321 (2021). https://doi.org/10.5152/electrica.2021.21042
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