Pradipta Maji

TL;DR: This study assesses the state-of-the-art machine learning methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018, and investigates the challenge of identifying the best ML algorithms for each of these tasks.

TL;DR: The RFPCM comprises a judicious integration of the principles of rough and fuzzy sets that incorporates both probabilistic and possibilistic memberships simultaneously to avoid the problems of noise sensitivity of fuzzy C-means and the coincident clusters of PCM.

TL;DR: A hybrid unsupervised learning algorithm, termed as rough-fuzzy c-means, is proposed, which comprises a judicious integration of the principles of rough sets and fuzzy sets and which enables efficient handling of overlapping partitions.

TL;DR: A new feature selection algorithm is presented based on rough set theory that selects a set of genes from microarray data by maximizing the relevance and significance of the selected genes.

TL;DR: Extensive experimental results demonstrate better performance of the proposed scheme over popular classification algorithms in respect of memory overhead and retrieval time with comparable classification accuracy.