S
Saptarsi Goswami
Researcher at Bangabasi College
Publications - 67
Citations - 712
Saptarsi Goswami is an academic researcher from Bangabasi College. The author has contributed to research in topics: Feature selection & Cluster analysis. The author has an hindex of 11, co-authored 62 publications receiving 475 citations. Previous affiliations of Saptarsi Goswami include Information Technology University & University of Calcutta.
Papers
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Journal ArticleDOI
A feature cluster taxonomy based feature selection technique
TL;DR: The proposed feature subset selection algorithm, named as FCTFS (Feature Cluster Taxonomy based Feature Selection) has been proposed for selecting suitable feature subset from a large feature set and it can be easily used in case of supervised classification.
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Estimating gene expression from DNA methylation and copy number variation: A deep learning regression model for multi-omics integration.
TL;DR: A deep learning-based predictive model is developed using Deep Denoising Auto-encoder and Multi-layer Perceptron that can quantitatively capture how genetic and epigenetic alterations correlate with directionality of gene expression for liver hepatocellular carcinoma (LIHC).
Proceedings ArticleDOI
Automated Breast Cancer Identification by analyzing Histology Slides using Metaheuristic Supported Supervised Classification coupled with Bag-of-Features
Shouvik Chakraborty,Sankhadeep Chatterjee,Amlan Chatterjee,Kalyani Mali,Saptarsi Goswami,Soumya Sen +5 more
TL;DR: An automated computer assisted framework has been proposed to analyze and detect the type of the disease from the current condition of the breast and three models have been compared in terms of accuracy.
Journal ArticleDOI
Empirical Study on Filter based Feature Selection Methods for Text Classification
TL;DR: An empirical study comparing performance of few feature selection techniques employed with different classifiers like naive bayes, SVM, decision tree and k-NN with results of feature selection methods on various classifiers on text datasets are presented.
Journal ArticleDOI
Designing a long short-term network for short-term forecasting of global horizontal irradiance
Sourav Malakar,Saptarsi Goswami,Bhaswati Ganguli,Amlan Chakrabarti,Sugata Sen Roy,K. Boopathi,A. G. Rangaraj +6 more
TL;DR: An empirical investigation based on data from three solar stations from two climatic zones of India over two seasons finds that the number of nodes in an LSTM network, as well as batch size, is influenced by the variability of the input data.