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ORCIDRajashree Dash
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2020 – today
- 2024
[j24]Smita Mohanty, Rajashree Dash
:
RVFLN-CDFPA: a random vector functional link neural network optimized using a chaotic differential flower pollination algorithm for day ahead Net Asset Value prediction. Evol. Syst. 15(3): 731-757 (2024)- 2023
- [j23]Smita Mohanty, Rajashree Dash:
A flower pollination algorithm based Chebyshev polynomial neural network for net asset value prediction. Evol. Intell. 16(1): 115-131 (2023) - 2022
- [j22]Santosh Kumar Behera, Rajashree Dash:
A novel feature selection technique for enhancing performance of unbalanced text classification problem. Intell. Decis. Technol. 16(1): 51-69 (2022) - [j21]Sidharth Samal, Rajashree Dash:
A novel MCDM ensemble approach of designing an ELM based predictor for stock index price forecasting. Intell. Decis. Technol. 16(2): 387-406 (2022) - [j20]Santosh Kumar Behera, Rajashree Dash:
Performance Enhancement of the Unbalanced Text Classification Problem Through a Modified Chi Square-Based Feature Selection Technique: A Mod-Chi based FS technique. Int. J. Intell. Inf. Technol. 18(1): 1-23 (2022) - [j19]Rasmita Dash, Rajashree Dash, Rasmita Rautray:
An evolutionary framework based microarray gene selection and classification approach using binary shuffled frog leaping algorithm. J. King Saud Univ. Comput. Inf. Sci. 34(3): 880-891 (2022) - [j18]Rasmiranjan Mohakud, Rajashree Dash:
Designing a grey wolf optimization based hyper-parameter optimized convolutional neural network classifier for skin cancer detection. J. King Saud Univ. Comput. Inf. Sci. 34(8 Part B): 6280-6291 (2022) - [j17]Rasmiranjan Mohakud, Rajashree Dash:
Skin cancer image segmentation utilizing a novel EN-GWO based hyper-parameter optimized FCEDN. J. King Saud Univ. Comput. Inf. Sci. 34(10 Part B): 9889-9904 (2022) - [j16]Smita Mohanty, Rajashree Dash:
A novel chaotic flower pollination algorithm for modelling an optimized low-complexity neural network-based NAV predictor model. Prog. Artif. Intell. 11(4): 349-366 (2022) - [j15]Smita Mohanty, Rajashree Dash:
Evaluating chaotic functions with flower pollination algorithm for modelling an optimized low complexity neural network based NAV predictor model. Soft Comput. 26(18): 9395-9417 (2022) - 2021
- [j14]Sidharth Samal, Rajashree Dash:
A TOPSIS-ELM framework for stock index price movement prediction. Intell. Decis. Technol. 15(2): 201-220 (2021) - [j13]Rajashree Dash, Anuradha Routray, Rasmita Dash, Rasmita Rautray:
Designing an efficient predictor model using PSNN and crow search based optimization technique for gold price prediction. Intell. Decis. Technol. 15(2): 281-289 (2021) - 2020
- [j12]Rajashree Dash:
Performance analysis of an evolutionary recurrent Legendre Polynomial Neural Network in application to FOREX prediction. J. King Saud Univ. Comput. Inf. Sci. 32(9): 1000-1011 (2020)
2010 – 2019
- 2019
- [j11]Rajashree Dash, Sidharth Samal, Rasmita Dash, Rasmita Rautray:
An integrated TOPSIS crow search based classifier ensemble: In application to stock index price movement prediction. Appl. Soft Comput. 85 (2019) - [j10]Rasmita Rautray, Rakesh Chandra Balabantaray, Rasmita Dash, Rajashree Dash:
CSMDSE-Cuckoo Search Based Multi Document Summary Extractor: Cuckoo Search Based Summary Extractor. Int. J. Cogn. Informatics Nat. Intell. 13(4): 56-70 (2019) - [j9]Rasmita Rautray, Rasmita Dash, Rajashree Dash:
Performance analysis of Modified Shuffled Frog leaping Algorithm for Multi-document Summarization Problem. Informatica (Slovenia) 43(3) (2019) - 2018
- [j8]Rajashree Dash:
Performance analysis of a higher order neural network with an improved shuffled frog leaping algorithm for currency exchange rate prediction. Appl. Soft Comput. 67: 215-231 (2018) - [j7]Rajashree Dash:
DECPNN: A hybrid stock predictor model using Differential Evolution and Chebyshev Polynomial neural network. Intell. Decis. Technol. 12(1): 93-104 (2018) - 2017
- [j6]Ajit Kumar Rout, P. K. Dash, Rajashree Dash, Ranjeeta Bisoi:
Forecasting financial time series using a low complexity recurrent neural network and evolutionary learning approach. J. King Saud Univ. Comput. Inf. Sci. 29(4): 536-552 (2017) - 2016
- [j5]Rajashree Dash, P. K. Dash:
An evolutionary hybrid Fuzzy Computationally Efficient EGARCH model for volatility prediction. Appl. Soft Comput. 45: 40-60 (2016) - [j4]Rajashree Dash, Pradipta Kishore Dash:
Efficient stock price prediction using a Self Evolving Recurrent Neuro-Fuzzy Inference System optimized through a Modified technique. Expert Syst. Appl. 52: 75-90 (2016) - [j3]Rajashree Dash, Pradipta Kishore Dash:
Prediction of Financial Time Series Data using Hybrid Evolutionary Legendre Neural Network: Evolutionary LENN. Int. J. Appl. Evol. Comput. 7(1): 16-32 (2016) - 2015
- [j2]Rajashree Dash, Pradipta Kishore Dash, Ranjeeta Bisoi:
A differential harmony search based hybrid interval type2 fuzzy EGARCH model for stock market volatility prediction. Int. J. Approx. Reason. 59: 81-104 (2015) - 2014
- [j1]Rajashree Dash, Pradipta Kishore Dash, Ranjeeta Bisoi:
A self adaptive differential harmony search based optimized extreme learning machine for financial time series prediction. Swarm Evol. Comput. 19: 25-42 (2014) - [c1]Mukesh Kumar Mishra, Rajashree Dash:
A Comparative Study of Chebyshev Functional Link Artificial Neural Network, Multi-layer Perceptron and Decision Tree for Credit Card Fraud Detection. ICIT 2014: 228-233
Coauthor Index
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