A comparative analysis of classical machine learning and deep learning techniques for predicting lung cancer survivability
Author:
Publisher
Springer Science and Business Media LLC
Subject
Computer Networks and Communications,Hardware and Architecture,Media Technology,Software
Link
https://link.springer.com/content/pdf/10.1007/s11042-023-16349-y.pdf
Reference47 articles.
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3. Ajagbe SA, Oki OA, Oladipupo MA, Nwanakwaugwum A (2022) Investigating the efficiency of deep learning models in bioinspired object detection. International conference on electrical, computer, and energy technologies, ICECET 2022 (pp. 1–6). https://doi.org/10.1109/ICECET55527.2022.9872568
4. Al-Emran M, Al-Kabi MN, Marques G (2021) A survey of using machine learning algorithms during the COVID-19 pandemic. In: Emerging technologies during the era of COVID-19 pandemic. Springer Science and Business Media Deutschland GmbH, pp 1–8. https://doi.org/10.1007/978-3-030-67716-9_1
5. Alshurideh M, Al Kurdi B, Salloum SA, Arpaci I, Al-Emran M (2023) Predicting the actual use of m-learning systems: a comparative approach using PLS-SEM and machine learning algorithms. Interact Learn Environ 31(3):1214–1228. https://doi.org/10.1080/10494820.2020.1826982
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