new oversampling or hybrid sampling techniques to solve overlapping issues in Imbalnced Datasets

I want a complete research paper of the Ph.D. level in which we work on imbalanced datasets. Basically our task is to balance the real datasets. I want we introduce a new oversampling or hybrid sampling or undersampling technique or any overlapping degree or any function  through which we add synthetic samples in minority class and balance the dataset.  After balancing we will check the accuracy, f1-score, G-mean and AUC curve of our proposed method by applying any classifier like SVM, RF etc. At the last we will compare our results with already existing resampling techniques and show that our proposed method shows better results as compared to others. For more information, I am also attaching some research papers related to my field . 

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