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47 results on '"Mukherjee, Somenath"'

3. Seismic Bearing Capacity of Shallow Footing in Layered Soil

Author

Tung, Smita, Mukherjee, Sibapriya, Roy, Narayon, Mukherjee, Somenath, di Prisco, Marco, Series Editor, Chen, Sheng-Hong, Series Editor, Vayas, Ioannis, Series Editor, Kumar Shukla, Sanjay, Series Editor, Sharma, Anuj, Series Editor, Kumar, Nagesh, Series Editor, Wang, Chien Ming, Series Editor, Patel, Satyajit, editor, Solanki, C. H., editor, Reddy, Krishna R., editor, and Shukla, Sanjay Kumar, editor

Published
2021
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4. Pseudo-static Stability Analysis of Multilayered Slopes Using Sarma’s Method of Non-vertical Slices

Author

Mukherjee, Somenath, Metya, Subhadeep, Bhattacharya, Gautam, di Prisco, Marco, Series Editor, Chen, Sheng-Hong, Series Editor, Vayas, Ioannis, Series Editor, Kumar Shukla, Sanjay, Series Editor, Sharma, Anuj, Series Editor, Kumar, Nagesh, Series Editor, Wang, Chien Ming, Series Editor, Patel, Satyajit, editor, Solanki, C. H., editor, Reddy, Krishna R., editor, and Shukla, Sanjay Kumar, editor

Published
2021
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6. A conservation rule for constructing bibliometric network matrices

Author

Prathap, Gangan and Mukherjee, Somenath

Subjects
Computer Science - Digital Libraries
Abstract

The social network analysis of bibliometric data needs matrices to be recast in a network framework. In this paper we argue that a simple conservation rule requires that this should be done only using fractional counting so that conservation at the paper level will be faithfully reproduced at higher levels ofaggregation (i.e. author, institute, country, journal etc.) of the complex network., Comment: 8 pages, 2 tables

Published
2016

8. Investigation of Fractality and Stationarity Behaviour on Earthquake

Author

Sadhukhan, Bikash, Mukherjee, Somenath, Agarwal, Sugam, Barbosa, Simone Diniz Junqueira, Series Editor, Filipe, Joaquim, Series Editor, Kotenko, Igor, Series Editor, Sivalingam, Krishna M., Series Editor, Washio, Takashi, Series Editor, Yuan, Junsong, Series Editor, Zhou, Lizhu, Series Editor, Ghosh, Ashish, Series Editor, Thampi, Sabu M., editor, Marques, Oge, editor, Krishnan, Sri, editor, Li, Kuan-Ching, editor, Ciuonzo, Domenico, editor, and Kolekar, Maheshkumar H., editor

Published
2019
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25. Stock market prediction using deep learning algorithms.

Author

Mukherjee, Somenath, Sadhukhan, Bikash, Sarkar, Nairita, Roy, Debajyoti, and De, Soumil

Abstract

The Stock Market is one of the most active research areas, and predicting its nature is an epic necessity nowadays. Predicting the Stock Market is quite challenging, and it requires intensive study of the pattern of data. Specific statistical models and artificially intelligent algorithms are needed to meet this challenge and arrive at an appropriate solution. Various machine learning and deep learning algorithms can make a firm prediction with minimised error possibilities. The Artificial Neural Network (ANN) or Deep Feed‐forward Neural Network and the Convolutional Neural Network (CNN) are the two network models that have been used extensively to predict the stock market prices. The models have been used to predict upcoming days' data values from the last few days' data values. This process keeps on repeating recursively as long as the dataset is valid. An endeavour has been taken to optimise this prediction using deep learning, and it has given substantial results. The ANN model achieved an accuracy of 97.66%, whereas the CNN model achieved an accuracy of 98.92%. The CNN model used 2‐D histograms generated out of the quantised dataset within a particular time frame, and prediction is made on that data. This approach has not been implemented earlier for the analysis of such datasets. As a case study, the model has been tested on the recent COVID‐19 pandemic, which caused a sudden downfall of the stock market. The results obtained from this study was decent enough as it produced an accuracy of 91%. [ABSTRACT FROM AUTHOR]

Published
2023
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