• J. P. Pramod
• Kanneboina Vaishnavi Yadav
• Bhagavatula Veda Bharati

Researchers around the globe are working on improving the machine learning algorithms for modeling prediction and analytics problems. No single best machine learning algorithm is present which is applicable for all the possible cases of problems. Corporate distress signals are important for both institutions and banks when evaluating firms’ performances. This has led to the rise of a paradigm shift in machine learning called federated learning (FL) that allows for decentralized model training over distributed data sources. Cervical cancer is a serious public health issue worldwide, and early identification is crucial for better patient outcomes. Recent study has investigated how ML and DL approaches may be used to increase the accuracy of vagina tests. Stocks in the Chinese stock market can be divided into ST stocks and normal stocks, so to prevent investors from buying potential ST stocks, this paper first performs SMOTEENN oversampling data preprocessing for the ST stock category, and selects 139 financial indicators and technical factor as predictive features. Then, it combines the Boruta algorithm and Copula entropy method for feature selection, effectively improving the machine learning model’s performance in ST stock classification, with the AUC values of the two models reaching 98% on the test set.

Machine Learning, modeling prediction, Machine Learning Techniques, Federated Learning, analytics problems.

"A REVIEW OF RECENT ADVANCES AND ENSEMBLE METHODS FOR MACHINE LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 11, page no.c604-c610, November-2024, Available :http://www.jetir.org/papers/JETIR2411279.pdf

"A REVIEW OF RECENT ADVANCES AND ENSEMBLE METHODS FOR MACHINE LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 11, page no. ppc604-c610, November-2024, Available at : http://www.jetir.org/papers/JETIR2411279.pdf