60461946-932c-424b-b4fb-790dc8682cb4 beie director@blueeyesintelligence.org 10.35940/ijrte.F6871.0310622 10.1109/TKDE.2013.39M. Zhang and Z. Zhou, "A Review on Multi-Label Learning Algorithms" in IEEE Transactions on Knowledge & Data Engineering, vol. 26, no. 08, pp. 1819-1837, 2014. doi: 10.1109/TKDE.2013.39Trends in extreme learning machines: a review, by Huang, G., Huang, G., Song, S., & You, K. (2015). Neural Networks, (HIC: 0 , CV: 0)10.1016/j.inffus.2013.04.006A survey of multiple classifier systems as hybrid systems , by Corchado, E., Graña, M., & Wozniak, M. (2014). Information Fusion, 16, 3-17. ( HIC: 1 , CV: 22)solo-learn: A Library of Self-supervised Methods for Visual Representation Learning Victor Guilherme Turrisi da Costa, Enrico Fini, Moin Nabi, Nicu Sebe, Elisa Ricci, 2022.DoubleML - An Object-Oriented Implementation of Double Machine Learning in Python Philipp Bach, Victor Chernozhukov, Malte S. Kurz, Martin Spindler; 23(53):1−6, 2022htps://www.sciencedirect.com/topics/engineering/machine-learning-algorithmhttps://machinelearningmastery.com/a-tour-of-machine-learning-algorithms/https://en.wikipedia.org/wiki/Machine_learningApproximate Information State for Approximate Planning and Reinforcement Learning in Partially Observed Systems Jayakumar Subramanian, Amit Sinha, Raihan Seraj, Aditya Mahajan, 2022http://places.csail.mit.edu/places_NIPS14.pdf10.1109/ICECA.2018.8474918K. Shailaja, B. Seetharamulu and M. A. Jabbar, "Machine Learning in Healthcare: A Review," 2018 Second International Conference on Electronics, Communication and Aerospace Technology (ICECA), 2018, pp.910-914, doi:10.1109/ICECA.2018.847491810.1007/s13534-018-0058-3Park, C., Took, C.C. & Seong, JK. Machine learning in biomedical engineering. Biomed. Eng. Lett. 8, 1-3 (2018). https://doi.org/10.1007/s13534-018-0058-362(6)I. Zacharaki1609200910.1002/mrm.22147. Classification of brain tumor type and grade using MRI texture and shape in a machine learning schemeEvangelia I. Zacharaki; Sumei Wang; Sanjeev Chawla; Dong Soo Yoo; Ronald Wolf; Elias R. Melhem; Christos Davatzikos (2009). Classification of brain tumor type and grade using MRI texture and shape in a machine learning scheme. 62(6), 1609-161810.1002/cnm.2481Sachdeva, Jainy, et al. "A dual neural network ensemble approach for multiclass brain tumor classification." International journal for numerical methods in biomedical engineering 28.11 (2012): 1107-112010.1038/s41598-020-59115-yXu, Y., Ju, L., Tong, J. et al. Machine Learning Algorithms for Predicting the Recurrence of Stage IV Colorectal Cancer After Tumor Resection. Sci Rep 10, 2519 (2020). https://doi.org/10.1038/s41598-020-59115-y10.1038/s41573-019-0024-5Vamathevan, J., Clark, D., Czodrowski, P. et al. Applications of machine learning in drug discovery and development. Nat Rev Drug Discov 18, 463-477 (2019). https://doi.org/10.1038/s41573-019-0024-510.1016/j.drudis.2020.12.003Moe Elbadawi, Simon Gaisford, Abdul W. Basit, Advanced machine-learning techniques in drug discovery, Drug Discovery Today, Volume 26, Issue 3, 2021, Pages 769-777,ISSN1359-6446, https://doi.org/10.1016/j.drudis.2020.12.00310.1016/S2589-7500(20)30102-3Lee, Cecilia S., and Aaron Y. Lee. "Clinical applications of continual learning machine learning." The Lancet Digital Health 2.6 (2020): e279-e28110.1002/cpt.1907Bica, Ioana, et al. "From real‐world patient data to individualized treatment effects using machine learning: current and future methods to address underlying challenges." Clinical Pharmacology & Therapeutics 109.1 (2021): 87-100