关注
Ratnakar Dash
Ratnakar Dash
Associate professor, CSE Department , NIT Rourkela
在 nitrkl.ac.in 的电子邮件经过验证
标题
引用次数
引用次数
年份
Brain MR image classification using two-dimensional discrete wavelet transform and AdaBoost with random forests
DR Nayak, R Dash, B Majhi
Neurocomputing 177, 188-197, 2016
2882016
Mammogram classification using two dimensional discrete wavelet transform and gray-level co-occurrence matrix for detection of breast cancer
S Beura, B Majhi, R Dash
Neurocomputing 154, 1-14, 2015
2542015
An early detection of low rate DDoS attack to SDN based data center networks using information distance metrics
KS Sahoo, D Puthal, M Tiwary, JJPC Rodrigues, B Sahoo, R Dash
Future Generation Computer Systems 89, 685-697, 2018
1862018
Automated breast cancer detection in digital mammograms: A moth flame optimization based ELM approach
D Muduli, R Dash, B Majhi
Biomedical Signal Processing and Control 59, 101912, 2020
1202020
Motion blur parameters estimation for image restoration
R Dash, B Majhi
Optik 125 (5), 1634-1640, 2014
1002014
Automated diagnosis of breast cancer using multi-modal datasets: A deep convolution neural network based approach
D Muduli, R Dash, B Majhi
Biomedical Signal Processing and Control 71, 102825, 2022
992022
Underwater fish species recognition using deep learning techniques
BV Deep, R Dash
2019 6th International Conference on Signal Processing and Integrated …, 2019
902019
Toward secure software-defined networks against distributed denial of service attack
KS Sahoo, SK Panda, S Sahoo, B Sahoo, R Dash
The Journal of Supercomputing 75, 4829-4874, 2019
742019
Discrete ripplet-II transform and modified PSO based improved evolutionary extreme learning machine for pathological brain detection
DR Nayak, R Dash, B Majhi
Neurocomputing 282, 232-247, 2018
682018
Automated diagnosis of multi-class brain abnormalities using MRI images: a deep convolutional neural network based method
DR Nayak, R Dash, B Majhi
Pattern Recognition Letters 138, 385-391, 2020
642020
Combining extreme learning machine with modified sine cosine algorithm for detection of pathological brain
DR Nayak, R Dash, B Majhi, S Wang
Computers & Electrical Engineering 68, 366-380, 2018
572018
Automated pathological brain detection system: A fast discrete curvelet transform and probabilistic neural network based approach
DR Nayak, R Dash, B Majhi, V Prasad
Expert Systems with Applications 88, 152-164, 2017
572017
A deep stacked random vector functional link network autoencoder for diagnosis of brain abnormalities and breast cancer
DR Nayak, R Dash, B Majhi, RB Pachori, Y Zhang
Biomedical Signal Processing and Control 58, 101860, 2020
542020
Hyperspectral image classification: A k-means clustering based approach
S Ranjan, DR Nayak, KS Kumar, R Dash, B Majhi
2017 4th International Conference on Advanced Computing and Communication …, 2017
542017
Deep extreme learning machine with leaky rectified linear unit for multiclass classification of pathological brain images
DR Nayak, D Das, R Dash, S Majhi, B Majhi
Multimedia Tools and Applications 79, 15381-15396, 2020
492020
An empirical evaluation of extreme learning machine: application to handwritten character recognition
D Das, DR Nayak, R Dash, B Majhi
Multimedia Tools and Applications 78, 19495-19523, 2019
452019
Stationary wavelet transform and AdaBoost with SVM based pathological brain detection in MRI scanning
D Ranjan Nayak, R Dash, B Majhi
CNS & Neurological Disorders-Drug Targets (Formerly Current Drug Targets-CNS …, 2017
452017
On the placement of controllers for designing a wide area software defined networks
KS Sahoo, S Sahoo, A Sarkar, B Sahoo, R Dash
TENCON 2017-2017 IEEE Region 10 Conference, 3123-3128, 2017
442017
Pathological brain detection using curvelet features and least squares SVM
DR Nayak, R Dash, B Majhi
Multimedia Tools and Applications 77, 3833-3856, 2018
432018
Fast discrete curvelet transform and modified PSO based improved evolutionary extreme learning machine for breast cancer detection
D Muduli, R Dash, B Majhi
Biomedical Signal Processing and Control 70, 102919, 2021
422021
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