Rajashree Dash

27 papers Journal 26Unranked 1
YearRankTypeTitle / Venue / Authors
2025 J jnl
A Hybrid Model for Classification of Skin Cancer Images After Segmentation.
Int. J. Image Graph.
Rasmiranjan Mohakud, Rajashree Dash
2025 J jnl
Developing a two stage optimized random vector functional link neural network based predictor model utilizing a swift crow search algorithm.
Clust. Comput.
Sidharth Samal, Rajashree Dash
2024 J jnl
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.
Smita Mohanty, Rajashree Dash
2023 J jnl
A flower pollination algorithm based Chebyshev polynomial neural network for net asset value prediction.
Evol. Intell.
Smita Mohanty, Rajashree Dash
2022 J jnl
A novel MCDM ensemble approach of designing an ELM based predictor for stock index price forecasting.
Intell. Decis. Technol.
Sidharth Samal, Rajashree Dash
2022 J jnl
A novel chaotic flower pollination algorithm for modelling an optimized low-complexity neural network-based NAV predictor model.
Prog. Artif. Intell.
Smita Mohanty, Rajashree Dash
2022 J jnl
A novel feature selection technique for enhancing performance of unbalanced text classification problem.
Intell. Decis. Technol.
Santosh Kumar Behera, Rajashree Dash
2022 J jnl
An evolutionary framework based microarray gene selection and classification approach using binary shuffled frog leaping algorithm.
J. King Saud Univ. Comput. Inf. Sci.
Rasmita Dash, Rajashree Dash, Rasmita Rautray
2022 J jnl
Designing a grey wolf optimization based hyper-parameter optimized convolutional neural network classifier for skin cancer detection.
J. King Saud Univ. Comput. Inf. Sci.
Rasmiranjan Mohakud, Rajashree Dash
2022 J jnl
Evaluating chaotic functions with flower pollination algorithm for modelling an optimized low complexity neural network based NAV predictor model.
Soft Comput.
Smita Mohanty, Rajashree Dash
2022 J jnl
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.
Santosh Kumar Behera, Rajashree Dash
2022 J jnl
Skin cancer image segmentation utilizing a novel EN-GWO based hyper-parameter optimized FCEDN.
J. King Saud Univ. Comput. Inf. Sci.
Rasmiranjan Mohakud, Rajashree Dash
2021 J jnl
A TOPSIS-ELM framework for stock index price movement prediction.
Intell. Decis. Technol.
Sidharth Samal, Rajashree Dash
2021 J jnl
Designing an efficient predictor model using PSNN and crow search based optimization technique for gold price prediction.
Intell. Decis. Technol.
Rajashree Dash, Anuradha Routray, Rasmita Dash, Rasmita Rautray
2020 J jnl
Performance analysis of an evolutionary recurrent Legendre Polynomial Neural Network in application to FOREX prediction.
J. King Saud Univ. Comput. Inf. Sci.
Rajashree Dash
2019 J jnl
An integrated TOPSIS crow search based classifier ensemble: In application to stock index price movement prediction.
Appl. Soft Comput.
Rajashree Dash, Sidharth Samal, Rasmita Dash, Rasmita Rautray
2019 J jnl
CSMDSE-Cuckoo Search Based Multi Document Summary Extractor: Cuckoo Search Based Summary Extractor.
Int. J. Cogn. Informatics Nat. Intell.
Rasmita Rautray, Rakesh Chandra Balabantaray, Rasmita Dash, Rajashree Dash
2019 J jnl
Performance analysis of Modified Shuffled Frog leaping Algorithm for Multi-document Summarization Problem.
Informatica (Slovenia)
Rasmita Rautray, Rasmita Dash, Rajashree Dash
2018 J jnl
DECPNN: A hybrid stock predictor model using Differential Evolution and Chebyshev Polynomial neural network.
Intell. Decis. Technol.
Rajashree Dash
2018 J jnl
Performance analysis of a higher order neural network with an improved shuffled frog leaping algorithm for currency exchange rate prediction.
Appl. Soft Comput.
Rajashree Dash
2017 J jnl
Forecasting financial time series using a low complexity recurrent neural network and evolutionary learning approach.
J. King Saud Univ. Comput. Inf. Sci.
Ajit Kumar Rout, P. K. Dash, Rajashree Dash, Ranjeeta Bisoi
2016 J jnl
An evolutionary hybrid Fuzzy Computationally Efficient EGARCH model for volatility prediction.
Appl. Soft Comput.
Rajashree Dash, P. K. Dash
2016 J jnl
Efficient stock price prediction using a Self Evolving Recurrent Neuro-Fuzzy Inference System optimized through a Modified technique.
Expert Syst. Appl.
Rajashree Dash, Pradipta Kishore Dash
2016 J jnl
Prediction of Financial Time Series Data using Hybrid Evolutionary Legendre Neural Network: Evolutionary LENN.
Int. J. Appl. Evol. Comput.
Rajashree Dash, Pradipta Kishore Dash
2015 J jnl
A differential harmony search based hybrid interval type2 fuzzy EGARCH model for stock market volatility prediction.
Int. J. Approx. Reason.
Rajashree Dash, Pradipta Kishore Dash, Ranjeeta Bisoi
2014 — conf
A Comparative Study of Chebyshev Functional Link Artificial Neural Network, Multi-layer Perceptron and Decision Tree for Credit Card Fraud Detection.
ICIT
Mukesh Kumar Mishra, Rajashree Dash
2014 J jnl
A self adaptive differential harmony search based optimized extreme learning machine for financial time series prediction.
Swarm Evol. Comput.
Rajashree Dash, Pradipta Kishore Dash, Ranjeeta Bisoi
edge-cases.md
← Index edge-cases.md markdown 
# Edge Cases Samples
This is a list of samples that represent some edge cases where the extractors fail (completely or in part) to parse information we know to be there

**Sample:**

**VT:**

**Description:**

**Other:**


## PE Samples
### DotNET
**Sample:** `"/Users/p4c0/_samples/test-unzipped/98eca73a0e33a41a72ac55c4bd0a75e5c83fdc3de382d031346045105cdf10e7"`

**VT:** https://www.virustotal.com/gui/file/98eca73a0e33a41a72ac55c4bd0a75e5c83fdc3de382d031346045105cdf10e7/details

**Description:**
It is recognized as dotnet but it fails to extract any information, returning an empty dictionary:
```
{
    "sha256": "98eca73a0e33a41a72ac55c4bd0a75e5c83fdc3de382d031346045105cdf10e7",
    "md5": "a32eca5321baaf3d9b62ed129458a814",
    "sha1": "4524e99a3849c815a48579fb916e53cded739f46",
    "dotnet": {
        "Event" : [],
        "ExternalUnmanagedMethods" : [],
        "ExternalUnmanagedModules" : [],
        "ManifestResource" : [],
        "Resources" : []
    },
    "analysis_date": "2025-01-22 13:16:20.897"
}
```

with the following errors:
```
2025-01-22 14:16:20,896 - 98eca73a0e33a41a72ac55c4bd0a75e5c83fdc3de382d031346045105cdf10e7 - ERROR - Format error dotnet file 98eca73a0e33a41a72ac55c4bd0a75e5c83fdc3de382d031346045105cdf10e7 Full error : 'CLR header of file is most likely corrupt.'
98eca73a0e33a41a72ac55c4bd0a75e5c83fdc3de382d031346045105cdf10e7 - ERROR - Error getting metadata tables for 98eca73a0e33a41a72ac55c4bd0a75e5c83fdc3de382d031346045105cdf10e7: 'NoneType' object has no attribute 'existent_metadata_tables'
2025-01-22 14:16:20,896 - 98eca73a0e33a41a72ac55c4bd0a75e5c83fdc3de382d031346045105cdf10e7 - ERROR - Error extracting GUID for 98eca73a0e33a41a72ac55c4bd0a75e5c83fdc3de382d031346045105cdf10e7: 'NoneType' object has no attribute 'guid_stream_guids'
2025-01-22 14:16:20,896 - 98eca73a0e33a41a72ac55c4bd0a75e5c83fdc3de382d031346045105cdf10e7 - ERROR - Error getting CLR version for 98eca73a0e33a41a72ac55c4bd0a75e5c83fdc3de382d031346045105cdf10e7: 'NoneType' object has no attribute 'get_runtime_target_version'
2025-01-22 14:16:20,896 - 98eca73a0e33a41a72ac55c4bd0a75e5c83fdc3de382d031346045105cdf10e7 - ERROR - Error getting module info for 98eca73a0e33a41a72ac55c4bd0a75e5c83fdc3de382d031346045105cdf10e7: 'NoneType' object has no attribute 'metadata_table_exists'
2025-01-22 14:16:20,896 - 98eca73a0e33a41a72ac55c4bd0a75e5c83fdc3de382d031346045105cdf10e7 - ERROR - Error getting basic properties for 98eca73a0e33a41a72ac55c4bd0a75e5c83fdc3de382d031346045105cdf10e7: 'info'
2025-01-22 14:16:20,897 - 98eca73a0e33a41a72ac55c4bd0a75e5c83fdc3de382d031346045105cdf10e7 - ERROR - Error getting assembly flags for 98eca73a0e33a41a72ac55c4bd0a75e5c83fdc3de382d031346045105cdf10e7: 'NoneType' object has no attribute 'clr_header'
2025-01-22 14:16:20,897 - 98eca73a0e33a41a72ac55c4bd0a75e5c83fdc3de382d031346045105cdf10e7 - ERROR - Error getting more metadata/info clr header for 98eca73a0e33a41a72ac55c4bd0a75e5c83fdc3de382d031346045105cdf10e7: 'NoneType' object has no attribute 'get_number_of_streams'
2025-01-22 14:16:20,897 - 98eca73a0e33a41a72ac55c4bd0a75e5c83fdc3de382d031346045105cdf10e7 - ERROR - Error getting streams for 98eca73a0e33a41a72ac55c4bd0a75e5c83fdc3de382d031346045105cdf10e7: 'NoneType' object has no attribute 'get_stream_names'
2025-01-22 14:16:20,897 - 98eca73a0e33a41a72ac55c4bd0a75e5c83fdc3de382d031346045105cdf10e7 - ERROR - Error getting resources for 98eca73a0e33a41a72ac55c4bd0a75e5c83fdc3de382d031346045105cdf10e7: 'NoneType' object has no attribute 'has_resources'
```
**Other:**