Sujata Dash

Sujata Dash received her Ph.D. degree in Computational Modelling from Berhampur University, Orissa, India in 1995. She is an Associate Professor in P.G. Department of Computer Science & Application, North Orissa University, at Baripada, India. She has published more than 80 technical papers in international journals, conferences and book chapters of reputed publications. She has guided many scholars for their Ph.D degrees in computer science. She is associated with many professional bodies like the CSI, the ISTE, the OITS, the OMS, the IACSIT, the IMS and the IAENG. She is on the editorial board of several international journals and also a reviewer of many international journals. Her current research interests include Machine Learning, Distributed Data Mining, Bioinformatics, Intelligent Agent, Web Data Mining, Image Processing and Cloud Computing.

Publications

Automatic Test Data Generation Using Bio-Inspired Algorithms: A Travelogue
Automatic Test Data Generation Using Bio-Inspired Algorithms: A Travelogue
Madhumita Panda, Sujata Dash. © 2021. 19 pages.
This chapter presents an overview of some widely accepted bio-inspired metaheuristic algorithms which would be helpful in solving the problems of software testing. Testing is an...
Genetic Diagnosis of Cancer by Evolutionary Fuzzy-Rough based Neural-Network Ensemble
Genetic Diagnosis of Cancer by Evolutionary Fuzzy-Rough based Neural-Network Ensemble
Sujata Dash, Bichitrananda Patra. © 2020. 18 pages.
High dimension and small sample size is an inherent problem of gene expression datasets which makes the analysis process more complex. The present study has developed a novel...
Hybrid Ensemble Learning Methods for Classification of Microarray Data: RotBagg Ensemble Based Classification
Hybrid Ensemble Learning Methods for Classification of Microarray Data: RotBagg Ensemble Based Classification
Sujata Dash. © 2020. 19 pages.
Efficient classification and feature extraction techniques pave an effective way for diagnosing cancers from microarray datasets. It has been observed that the conventional...
Handbook of Research on Modeling, Analysis, and Application of Nature-Inspired Metaheuristic Algorithms
Handbook of Research on Modeling, Analysis, and Application of Nature-Inspired Metaheuristic Algorithms
Sujata Dash, B.K. Tripathy, Atta ur Rahman. © 2018. 538 pages.
The digital age is ripe with emerging advances and applications in technological innovations. Mimicking the structure of complex systems in nature can provide new ideas on how to...
Metaheuristic-Based Hybrid Feature Selection Models
Metaheuristic-Based Hybrid Feature Selection Models
Sujata Dash. © 2018. 22 pages.
This chapter focuses on key applications of metaheuristic techniques in the field of gene selection and classification of microarray data. The metaheuristic techniques are...
Automatic Test Data Generation Using Bio-Inspired Algorithms: A Travelogue
Automatic Test Data Generation Using Bio-Inspired Algorithms: A Travelogue
Madhumita Panda, Sujata Dash. © 2018. 20 pages.
This chapter presents an overview of some widely accepted bio-inspired metaheuristic algorithms which would be helpful in solving the problems of software testing. Testing is an...
Defect Detection of Fabrics by Grey-Level Co-Occurrence Matrix and Artificial Neural Network
Defect Detection of Fabrics by Grey-Level Co-Occurrence Matrix and Artificial Neural Network
Dilip k. Choudhury, Sujata Dash. © 2018. 13 pages.
The class of Textiles produced from terephthalic acid and ethylene glycol by condensation polymerization has many end-uses for example these are used as filter fabric in railway...
Handbook of Research on Computational Intelligence Applications in Bioinformatics
Handbook of Research on Computational Intelligence Applications in Bioinformatics
Sujata Dash, Bidyadhar Subudhi. © 2016. 514 pages.
Developments in the areas of biology and bioinformatics are continuously evolving and creating a plethora of data that needs to be analyzed and decrypted. Since it can be...
Hybrid Ensemble Learning Methods for Classification of Microarray Data: RotBagg Ensemble Based Classification
Hybrid Ensemble Learning Methods for Classification of Microarray Data: RotBagg Ensemble Based Classification
Sujata Dash. © 2016. 20 pages.
Efficient classification and feature extraction techniques pave an effective way for diagnosing cancers from microarray datasets. It has been observed that the conventional...