Debahuti Mishra

2.3k total citations
139 papers, 1.4k citations indexed

About

Debahuti Mishra is a scholar working on Artificial Intelligence, Molecular Biology and Computer Vision and Pattern Recognition. According to data from OpenAlex, Debahuti Mishra has authored 139 papers receiving a total of 1.4k indexed citations (citations by other indexed papers that have themselves been cited), including 48 papers in Artificial Intelligence, 41 papers in Molecular Biology and 22 papers in Computer Vision and Pattern Recognition. Recurrent topics in Debahuti Mishra's work include Gene expression and cancer classification (33 papers), Data Mining Algorithms and Applications (16 papers) and Evolutionary Algorithms and Applications (14 papers). Debahuti Mishra is often cited by papers focused on Gene expression and cancer classification (33 papers), Data Mining Algorithms and Applications (16 papers) and Evolutionary Algorithms and Applications (14 papers). Debahuti Mishra collaborates with scholars based in India, Singapore and South Korea. Debahuti Mishra's co-authors include Amiya Kumar Rath, Sandeep Kumar Satapathy, Pradeep Kumar Mallick, Kailash Shaw, Sashikala Mishra, Rudra Kalyan Nayak, Prayag Tiwari, Gia Nhu Nguyen, Shikha Misra and M. S. Sodha and has published in prestigious journals such as SHILAP Revista de lepidopterología, Journal of Applied Physics and Science Advances.

In The Last Decade

Debahuti Mishra

120 papers receiving 1.3k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Debahuti Mishra India 18 482 259 182 170 136 139 1.4k
Weimin Li China 25 749 1.6× 445 1.7× 80 0.4× 74 0.4× 59 0.4× 176 2.1k
Tsang Ing Ren Brazil 21 518 1.1× 588 2.3× 84 0.5× 37 0.2× 64 0.5× 122 1.5k
Alberto Guillén Spain 20 425 0.9× 105 0.4× 50 0.3× 45 0.3× 187 1.4× 66 1.6k
Monica Bianchini Italy 20 762 1.6× 441 1.7× 170 0.9× 23 0.1× 46 0.3× 84 1.9k
Cornelio Yáñez-Márquéz Mexico 21 567 1.2× 144 0.6× 45 0.2× 28 0.2× 65 0.5× 142 1.2k
Min Zhang China 29 1.5k 3.1× 476 1.8× 129 0.7× 26 0.2× 50 0.4× 249 2.9k
D. Dutta Majumder India 19 360 0.7× 537 2.1× 66 0.4× 27 0.2× 126 0.9× 125 1.4k
Yuguo Chen United States 22 453 0.9× 69 0.3× 102 0.6× 15 0.1× 99 0.7× 91 1.5k
Miguel García-Torres Spain 17 362 0.8× 219 0.8× 144 0.8× 17 0.1× 152 1.1× 70 1.2k
Tomás F. Pena Spain 13 645 1.3× 216 0.8× 267 1.5× 11 0.1× 41 0.3× 103 2.0k

Countries citing papers authored by Debahuti Mishra

Since Specialization
Citations

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

Fields of papers citing papers by Debahuti Mishra

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Debahuti Mishra

This figure shows the co-authorship network connecting the top 25 collaborators of Debahuti Mishra. A scholar is included among the top collaborators of Debahuti Mishra 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 Debahuti Mishra. Debahuti Mishra 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.
Mishra, Debahuti, et al.. (2024). Enhancing Road Safety and Cybersecurity in Traffic Management Systems: Leveraging the Potential of Reinforcement Learning. IEEE Access. 12. 9963–9975. 8 indexed citations
2.
3.
Satapathy, Sandeep Kumar, et al.. (2024). Feature Extraction and Recognition of Fingerprint Using KNN-SIFT Algorithm. 247–252. 1 indexed citations
4.
Mishra, Debahuti, et al.. (2024). Predictive Monitoring in Process Mining Using Deep Learning for Better Consumer Service. IEEE Transactions on Consumer Electronics. 70(4). 7279–7290. 2 indexed citations
5.
Satapathy, Sandeep Kumar, et al.. (2023). A Comparative Analysis of Multidimensional COVID-19 Poverty Determinants: An Observational Machine Learning Approach. New Generation Computing. 41(1). 155–184. 13 indexed citations
6.
Das, Kaberi, et al.. (2023). Optimizing CNN‐LSTM hybrid classifier using HCA for biomedical image classification. Expert Systems. 40(5). 17 indexed citations
7.
Das, Abhishek, et al.. (2023). Cancerous image classification using support vector machine with optimized statistical features. AIP conference proceedings. 2921. 20010–20010. 1 indexed citations
8.
Mishra, Debahuti, et al.. (2022). A Tailored Complex Medical Decision Analysis Model for Diabetic Retinopathy Classification Based on Optimized Un-Supervised Feature Learning Approach. Arabian Journal for Science and Engineering. 48(2). 2087–2099. 5 indexed citations
9.
Mishra, Debahuti, et al.. (2022). Customer Relations and Marketing Analysis Model for Sales Enhancement. 123–128.
10.
Hoffmann, Jordan, Yohai Bar‐Sinai, Lisa M. Lee, et al.. (2019). Machine learning in a data-limited regime: Augmenting experiments with synthetic data uncovers order in crumpled sheets. Science Advances. 5(4). eaau6792–eaau6792. 45 indexed citations
11.
Ahmad, Shamshad, et al.. (2018). Hooked on Smartphones: Smartphone Usage Pattern and Related Health Risks among Medical Students in a Tertiary Centre at Kolkata -. SHILAP Revista de lepidopterología. 9(6). 402–406. 1 indexed citations
12.
Mishra, Debahuti, et al.. (2016). SNR-TR GENE RANKING METHOD: A SIGNAL-TO-NOISE RATIO BASED GENE SELECTION ALGORITHM USING TRACE RATIO FOR GENE EXPRESSION DATA. International Journal of Pharma and Bio Sciences. 1 indexed citations
13.
Mishra, Debahuti & Debahuti Mishra. (2016). AN OVERVIEW OF BIOLOGICAL NETWORKS: MECHANISMS, METHODOLOGIES AND APPLICATIONS. International Journal of Pharma and Bio Sciences. 1 indexed citations
14.
Das, Kaberi, et al.. (2014). Missing Value Imputation Using Hybrid Higher Order Neural Classifier. Indian Journal of Science and Technology. 7(12). 2007–2014. 6 indexed citations
15.
Mishra, Debahuti, et al.. (2012). A Computational Approach of Rice (Oryza Sativa) Plant miRNA Target Prediction against Tungro Virus. Procedia Engineering. 38. 1357–1361. 3 indexed citations
16.
Mishra, Debahuti, et al.. (2011). Feature Selection in Gene Expression Data Using Principal Component Analysis and Rough Set Theory. Advances in experimental medicine and biology. 696. 91–100. 20 indexed citations
17.
Mishra, Debahuti, et al.. (2009). Finding Coherent Bi-clusters from Microarray Data by Imitating the Ecosystem: An Ant Colony Algorithmic Approach.. Indian International Conference on Artificial Intelligence. 707–721. 1 indexed citations
18.
Mishra, Debahuti, Sandeep Kumar Satapathy, & Debahuti Mishra. (2009). Improved search technique using wildcards or truncation. 1–4. 8 indexed citations
19.
Mishra, Debahuti, et al.. (2008). Introducing Effects in an Image: A MATLAB Approach. INRIA a CCSD electronic archive server. 1 indexed citations
20.
Mishra, Debahuti, et al.. (2000). Prevalence of intestinal parasitic infestations among the tribal populations of Purnia district.. 12(1). 33–36.

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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