Bikash Kanti Sarkar

Orcid: 0000-0002-3677-2649

According to our database1, Bikash Kanti Sarkar authored at least 19 papers between 2006 and 2021.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of five.

Timeline

Legend:

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PhD thesis 
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Links

On csauthors.net:

Bibliography

2021
Performance Analysis of Some Competent Learners on Medical Data: Using GA-Based Feature Selection Approach.
Int. J. Knowl. Based Organ., 2021

2020
A Systematic Review of Healthcare Big Data.
Sci. Program., 2020

Hybrid model for prediction of heart disease.
Soft Comput., 2020

A Two-Step Knowledge Extraction Framework for Improving Disease Diagnosis.
Comput. J., 2020

2018
A Hybrid Predictive Model Integrating C4.5 and Decision Table Classifiers for Medical Data Sets.
J. Inf. Technol. Res., 2018

Performance Assessment of Learning Algorithms on Multi-Domain Data Sets.
Int. J. Knowl. Discov. Bioinform., 2018

2017
Big Data and Healthcare Data: A Survey.
Int. J. Knowl. Based Organ., 2017

A case study on machine learning and classification.
Int. J. Inf. Decis. Sci., 2017

Performance analysis of GA-based iterative and non-iterative learning approaches for medical domain data sets.
Intell. Decis. Technol., 2017

A simple data discretizer.
CoRR, 2017

2016
A case study on partitioning data for classification.
Int. J. Inf. Decis. Sci., 2016

2012
A combined approach to tackle imbalanced data sets.
Int. J. Hybrid Intell. Syst., 2012

A genetic algorithm-based rule extraction system.
Appl. Soft Comput., 2012

2011
MIL: a data discretisation approach.
Int. J. Data Min. Model. Manag., 2011

Selecting informative rules with parallel genetic algorithm in classification problem.
Appl. Math. Comput., 2011

2010
Accuracy-based learning classification system.
Int. J. Inf. Decis. Sci., 2010

2009
The effect of stock, price and advertising on demand - an EOQ model.
Int. J. Model. Identif. Control., 2009

A hybrid approach to design efficient learning classifiers.
Comput. Math. Appl., 2009

2006
An efficient parallel algorithm for finding the largest and the second largest elements from a list of elements.
Proceedings of the 9th International Conference in Information Technology, 2006


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