Synergistic feature selection and distributed classification framework for high-dimensional medical data analysis.

Feature selection and classification efficiency and accuracy are key to improving decision-making regarding medical data analysis. Since the medical datasets are large and complex, they give rise to certain problematic issues such as computational complexity, limited memory space, and a lesser number of correct classifications. In order to overcome these drawbacks, the new integrated algorithm is presented here: Synergistic Kruskal-RFE Selector and Distributed Multi-Kernel Classification Framework (SKR-DMKCF).

MapReduce based big data framework using associative Kruskal poly Kernel classifier for diabetic disease prediction.

Recent trendy applications of Artificial Intelligence are Machine Learning (ML) algorithms, which have been extensively utilized for processes like pattern recognition, object classification, effective prediction of disease etc. However, ML techniques are reasonable solutions to computation methods and modeling, especially when the data size is enormous. These facts are established due to the reason that big data field has received considerable attention from both the industrial experts and academicians.

Bioactive compounds from Holothuria atra of Indian ocean.

The sea cucumber (Holothuria atra) extracts have been evaluated for the presence of bioactive compounds and various biological activities. The methanol extracts showed anti proliferative activities against the Hela and MCF-7 cell lines. Similarly the inhibitory effects of Herpes simplex virus 1 and 2 cells were detected using the plaque reduction assay.