Gopal Krishna Nayak

1.0k total citations
21 papers, 538 citations indexed

About

Gopal Krishna Nayak is a scholar working on Computer Vision and Pattern Recognition, Neurology and Artificial Intelligence. According to data from OpenAlex, Gopal Krishna Nayak has authored 21 papers receiving a total of 538 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Computer Vision and Pattern Recognition, 9 papers in Neurology and 8 papers in Artificial Intelligence. Recurrent topics in Gopal Krishna Nayak's work include Brain Tumor Detection and Classification (9 papers), Advanced Neural Network Applications (6 papers) and Radiomics and Machine Learning in Medical Imaging (3 papers). Gopal Krishna Nayak is often cited by papers focused on Brain Tumor Detection and Classification (9 papers), Advanced Neural Network Applications (6 papers) and Radiomics and Machine Learning in Medical Imaging (3 papers). Gopal Krishna Nayak collaborates with scholars based in India, Italy and United States. Gopal Krishna Nayak's co-authors include Sanjay Saxena, Biswajit Jena, Jasjit S. Suri, Luca Saba, Rakesh Kumar Lenka, Suchismita Das, Neeraj Sharma, Mannudeep K. Kalra, Luca Saba and Debasish Jena and has published in prestigious journals such as Cancers, Computers in Biology and Medicine and Multimedia Tools and Applications.

In The Last Decade

Gopal Krishna Nayak

19 papers receiving 521 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Gopal Krishna Nayak India 10 217 162 161 143 37 21 538
Changhee Han Japan 10 325 1.5× 249 1.5× 113 0.7× 316 2.2× 65 1.8× 27 762
Melike Şah Cyprus 12 436 2.0× 141 0.9× 380 2.4× 314 2.2× 64 1.7× 39 861
Muhammad Waqas Nadeem Pakistan 10 150 0.7× 176 1.1× 115 0.7× 211 1.5× 36 1.0× 18 674
Salman Qadri Pakistan 17 182 0.8× 176 1.1× 116 0.7× 198 1.4× 93 2.5× 45 845
Catherine Garbay France 14 378 1.7× 91 0.6× 92 0.6× 206 1.4× 39 1.1× 50 691
Shokofeh Anari Ireland 7 324 1.5× 201 1.2× 273 1.7× 213 1.5× 72 1.9× 12 640
Faheem Akhtar Pakistan 16 138 0.6× 253 1.6× 71 0.4× 333 2.3× 23 0.6× 58 795
Zhipeng Jia United States 8 209 1.0× 228 1.4× 66 0.4× 359 2.5× 33 0.9× 16 644
O. G. Kakde India 9 274 1.3× 158 1.0× 309 1.9× 209 1.5× 60 1.6× 33 661
Boran Şekeroğlu Cyprus 16 171 0.8× 262 1.6× 69 0.4× 309 2.2× 56 1.5× 51 923

Countries citing papers authored by Gopal Krishna Nayak

Since Specialization
Citations

This map shows the geographic impact of Gopal Krishna Nayak's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Gopal Krishna Nayak with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Gopal Krishna Nayak more than expected).

Fields of papers citing papers by Gopal Krishna Nayak

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Gopal Krishna Nayak. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Gopal Krishna Nayak. The network helps show where Gopal Krishna Nayak may publish in the future.

Co-authorship network of co-authors of Gopal Krishna Nayak

This figure shows the co-authorship network connecting the top 25 collaborators of Gopal Krishna Nayak. A scholar is included among the top collaborators of Gopal Krishna Nayak based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Gopal Krishna Nayak. Gopal Krishna Nayak is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Das, Suchismita, Gopal Krishna Nayak, Luca Saba, et al.. (2022). An artificial intelligence framework and its bias for brain tumor segmentation: A narrative review. Computers in Biology and Medicine. 143. 105273–105273. 86 indexed citations
2.
Jena, Biswajit, Sanjay Saxena, Gopal Krishna Nayak, et al.. (2022). Brain Tumor Characterization Using Radiogenomics in Artificial Intelligence Framework. Cancers. 14(16). 4052–4052. 44 indexed citations
3.
Jena, Biswajit, Gopal Krishna Nayak, Sudip Paul, & Sanjay Saxena. (2022). An Exhaustive Analytical Study of U-Net Architecture on Two Diverse Biomedical Imaging Datasets of Electron Microscopy Drosophila ssTEM and Brain MRI BraTS-2021 for Segmentation. SN Computer Science. 3(5). 9 indexed citations
4.
Das, Suchismita, et al.. (2022). Deep learning-based ensemble model for brain tumor segmentation using multi-parametric MR scans. Open Computer Science. 12(1). 211–226. 22 indexed citations
5.
Panigrahi, Tapas Kumar, et al.. (2022). A novel modified random walk grey wolf optimisation approach for non-smooth and non-convex economic load dispatch. International Journal of Innovative Computing and Applications. 13(2). 59–59. 2 indexed citations
6.
Jena, Biswajit, Sarthak Jain, Gopal Krishna Nayak, & Sanjay Saxena. (2022). Analysis of depth variation of U-NET architecture for brain tumor segmentation. Multimedia Tools and Applications. 82(7). 10723–10743. 26 indexed citations
7.
8.
Jena, Biswajit, Sanjay Saxena, Gopal Krishna Nayak, et al.. (2021). Artificial intelligence-based hybrid deep learning models for image classification: The first narrative review. Computers in Biology and Medicine. 137. 104803–104803. 113 indexed citations
9.
Jena, Biswajit, Gopal Krishna Nayak, & Sanjay Saxena. (2021). An empirical study of different machine learning techniques for brain tumor classification and subsequent segmentation using hybrid texture feature. Machine Vision and Applications. 33(1). 47 indexed citations
10.
Jena, Biswajit, Gopal Krishna Nayak, & Sanjay Saxena. (2021). Convolutional neural network and its pretrained models for image classification and object detection: A survey. Concurrency and Computation Practice and Experience. 34(6). 37 indexed citations
11.
Nayak, Gopal Krishna, et al.. (2021). Effect of learning parameters on the performance of U-Net Model in segmentation of Brain tumor. Multimedia Tools and Applications. 81(24). 34717–34735. 25 indexed citations
12.
Nayak, Gopal Krishna, et al.. (2020). Process Model Discovery from Unlabeled Event Logs by Using Non-Overlapping Sequential Distinct Event Patterns. International Journal of Engineering Research and Technology. 13(10). 3055–3055.
13.
Jena, Biswajit, Gopal Krishna Nayak, & Sanjay Saxena. (2020). Comprehensive Review of Abdominal Image Segmentation using Soft and Hard Computing Approaches. 1–5. 4 indexed citations
14.
Nayak, Gopal Krishna, et al.. (2020). Social recognition and employee engagement: The effect of social media in organizations. International Journal of Engineering Business Management. 12. 2639219814–2639219814. 24 indexed citations
15.
Jena, Biswajit, Gopal Krishna Nayak, & Sanjay Saxena. (2019). Maximum Payload for Digital Image Steganography Obtained by Mixed Edge Detection Mechanism. 4 indexed citations
16.
Lenka, Rakesh Kumar, et al.. (2018). An Architecture to Support Interoperability in IoT Devices. 705–710. 7 indexed citations
17.
Nayak, Gopal Krishna, et al.. (2018). A task-level parallelism approach for process discovery. International Journal of Engineering & Technology. 7(4). 2446–2446. 1 indexed citations
18.
Nayak, Gopal Krishna, et al.. (2016). Big data visualization: Tools and challenges. 656–660. 76 indexed citations
19.
20.
Nayak, Gopal Krishna, et al.. (2008). Information Systems Management in Public Sector Organizations. 289–294. 2 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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