Shibaprasad Sen

917 total citations
28 papers, 512 citations indexed

About

Shibaprasad Sen is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Media Technology. According to data from OpenAlex, Shibaprasad Sen has authored 28 papers receiving a total of 512 indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Computer Vision and Pattern Recognition, 12 papers in Artificial Intelligence and 9 papers in Media Technology. Recurrent topics in Shibaprasad Sen's work include Handwritten Text Recognition Techniques (11 papers), Vehicle License Plate Recognition (8 papers) and Natural Language Processing Techniques (4 papers). Shibaprasad Sen is often cited by papers focused on Handwritten Text Recognition Techniques (11 papers), Vehicle License Plate Recognition (8 papers) and Natural Language Processing Techniques (4 papers). Shibaprasad Sen collaborates with scholars based in India, United States and Germany. Shibaprasad Sen's co-authors include Ram Sarkar, Somnath Chatterjee, Rishav Pramanik, Kaushik Roy, Seyedali Mirjalili, Aleksandr Sinitca, Dmitrii Kaplun, João Paulo Papa, Luis A. de Souza and Friedhelm Schwenker and has published in prestigious journals such as Scientific Reports, Expert Systems with Applications and Pattern Recognition Letters.

In The Last Decade

Shibaprasad Sen

26 papers receiving 500 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Shibaprasad Sen India 14 258 179 167 63 45 28 512
Edmar Rezende Brazil 7 130 0.5× 209 1.2× 138 0.8× 46 0.7× 67 1.5× 8 497
Zeyu Ren United Kingdom 8 228 0.9× 213 1.2× 186 1.1× 45 0.7× 13 0.3× 14 566
Wenjin Lu China 12 286 1.1× 307 1.7× 110 0.7× 18 0.3× 65 1.4× 34 566
Qi Zhu China 11 155 0.6× 245 1.4× 53 0.3× 58 0.9× 18 0.4× 48 440
Awais Mahmood Saudi Arabia 17 398 1.5× 185 1.0× 109 0.7× 41 0.7× 22 0.5× 45 711
V. Seethalakshmi India 8 165 0.6× 118 0.7× 142 0.9× 19 0.3× 60 1.3× 22 472
Jiebo Song China 3 337 1.3× 343 1.9× 35 0.2× 46 0.7× 18 0.4× 3 594
Karim Mokrani Algeria 9 210 0.8× 117 0.7× 51 0.3× 21 0.3× 16 0.4× 21 445
Sang‐Woong Lee South Korea 10 232 0.9× 212 1.2× 116 0.7× 28 0.4× 13 0.3× 19 519
L. Jani Anbarasi India 13 165 0.6× 208 1.2× 161 1.0× 17 0.3× 23 0.5× 87 493

Countries citing papers authored by Shibaprasad Sen

Since Specialization
Citations

This map shows the geographic impact of Shibaprasad Sen'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 Shibaprasad Sen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Shibaprasad Sen more than expected).

Fields of papers citing papers by Shibaprasad Sen

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Shibaprasad Sen. 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 Shibaprasad Sen. The network helps show where Shibaprasad Sen may publish in the future.

Co-authorship network of co-authors of Shibaprasad Sen

This figure shows the co-authorship network connecting the top 25 collaborators of Shibaprasad Sen. A scholar is included among the top collaborators of Shibaprasad Sen 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 Shibaprasad Sen. Shibaprasad Sen 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.
2.
Chatterjee, Somnath, Himadri Mukherjee, Shibaprasad Sen, Sk Md Obaidullah, & Kaushik Roy. (2023). City name recognition for Indian postal automation: Exploring script dependent and independent approach. Multimedia Tools and Applications. 83(8). 22371–22394. 3 indexed citations
3.
Mukherjee, Himadri, et al.. (2023). A bi-stage approach to North Indian raga distinction. Multimedia Tools and Applications. 83(15). 45163–45183. 1 indexed citations
4.
Chowdhury, Ratul, et al.. (2023). An implementation of bi-phase network intrusion detection system by using real-time traffic analysis. Expert Systems with Applications. 224. 119831–119831. 19 indexed citations
5.
Sen, Shibaprasad, et al.. (2023). Optimization of microscopy image compression using convolutional neural networks and removal of artifacts by deep generative adversarial networks. Multimedia Tools and Applications. 83(20). 58961–58980. 1 indexed citations
6.
Pramanik, Rishav, et al.. (2023). Microstructural segmentation using a union of attention guided U-Net models with different color transformed images. Scientific Reports. 13(1). 5737–5737. 20 indexed citations
7.
Chatterjee, Somnath, et al.. (2023). Moth-flame optimization based deep feature selection for facial expression recognition using thermal images. Multimedia Tools and Applications. 83(4). 11299–11322. 5 indexed citations
8.
Chatterjee, Somnath, et al.. (2022). Comparative study on the performance of the state-of-the-art CNN models for handwritten Bangla character recognition. Multimedia Tools and Applications. 82(11). 16929–16950. 2 indexed citations
9.
Sen, Shibaprasad, et al.. (2022). An Ensemble of CNN Models for Parkinson’s Disease Detection Using DaTscan Images. Diagnostics. 12(5). 1173–1173. 34 indexed citations
10.
Chowdhury, Ratul, et al.. (2022). An optimal feature based network intrusion detection system using bagging ensemble method for real-time traffic analysis. Multimedia Tools and Applications. 81(28). 41225–41247. 13 indexed citations
11.
Pramanik, Rishav, et al.. (2022). A fuzzy distance-based ensemble of deep models for cervical cancer detection. Computer Methods and Programs in Biomedicine. 219. 106776–106776. 85 indexed citations
12.
Sen, Shibaprasad, et al.. (2022). A Two-Stage Deep Feature Selection Method for Online Handwritten Bangla and Devanagari Basic Character Recognition. SN Computer Science. 3(4). 4 indexed citations
13.
Sen, Shibaprasad, et al.. (2022). Advances in online handwritten recognition in the last decades. Computer Science Review. 46. 100515–100515. 21 indexed citations
14.
Sen, Shibaprasad, et al.. (2021). A bi-stage feature selection approach for COVID-19 prediction using chest CT images. Applied Intelligence. 51(12). 8985–9000. 74 indexed citations
15.
Chatterjee, Somnath, et al.. (2021). Prediction of COVID-19 from Chest CT Images Using an Ensemble of Deep Learning Models. Applied Sciences. 11(15). 7004–7004. 40 indexed citations
16.
Chatterjee, Somnath, et al.. (2021). A deep learning model for classifying human facial expressions from infrared thermal images. Scientific Reports. 11(1). 20696–20696. 27 indexed citations
17.
Chatterjee, Somnath, et al.. (2021). Breast cancer detection from thermal images using a Grunwald-Letnikov-aided Dragonfly algorithm-based deep feature selection method. Computers in Biology and Medicine. 141. 105027–105027. 53 indexed citations
18.
Sen, Shibaprasad, et al.. (2020). CTRL –CapTuRedLight: a novel feature descriptor for online Assamese numeral recognition. Multimedia Tools and Applications. 80(20). 30033–30056. 1 indexed citations
19.
Sen, Shibaprasad, et al.. (2019). Feature Selection for Recognition of Online Handwritten Bangla Characters. Neural Processing Letters. 50(3). 2281–2304. 10 indexed citations
20.
Sen, Shibaprasad, et al.. (2019). Online Bangla handwritten word recognition using HMM and language model. Neural Computing and Applications. 32(14). 9939–9951. 5 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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