Subhendu Kumar Pani

2.0k total citations
65 papers, 1.1k citations indexed

About

Subhendu Kumar Pani is a scholar working on Artificial Intelligence, Information Systems and Computer Networks and Communications. According to data from OpenAlex, Subhendu Kumar Pani has authored 65 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 22 papers in Artificial Intelligence, 16 papers in Information Systems and 15 papers in Computer Networks and Communications. Recurrent topics in Subhendu Kumar Pani's work include IoT and Edge/Fog Computing (9 papers), Data Mining Algorithms and Applications (6 papers) and Blockchain Technology Applications and Security (6 papers). Subhendu Kumar Pani is often cited by papers focused on IoT and Edge/Fog Computing (9 papers), Data Mining Algorithms and Applications (6 papers) and Blockchain Technology Applications and Security (6 papers). Subhendu Kumar Pani collaborates with scholars based in India, Saudi Arabia and United States. Subhendu Kumar Pani's co-authors include Chinmay Chakraborty, Agbotiname Lucky Imoize, Hemanta Kumar Bhuyan, Nitin Goyal, Satyabrata Dash, S. Dash, Cheng‐Chi Lee, Arun Kumar, Arun Kumar Rana and Umesh Kumar Lilhore and has published in prestigious journals such as Scientific Reports, IEEE Access and Sensors.

In The Last Decade

Subhendu Kumar Pani

55 papers receiving 1.0k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Subhendu Kumar Pani India 19 295 269 209 126 105 65 1.1k
Muhammad Saleem Pakistan 15 208 0.7× 245 0.9× 217 1.0× 135 1.1× 117 1.1× 36 896
BalaAnand Muthu China 20 301 1.0× 334 1.2× 264 1.3× 177 1.4× 165 1.6× 55 1.3k
Areej Fatima Pakistan 17 188 0.6× 278 1.0× 215 1.0× 150 1.2× 97 0.9× 50 1.0k
Surjeet Dalal India 20 189 0.6× 340 1.3× 244 1.2× 118 0.9× 65 0.6× 84 995
Sarita Simaiya India 18 208 0.7× 333 1.2× 347 1.7× 105 0.8× 125 1.2× 88 1.2k
Feras Al‐Obeidat United Arab Emirates 20 326 1.1× 526 2.0× 275 1.3× 117 0.9× 100 1.0× 98 1.3k
Muhammad Muzammal Pakistan 16 426 1.4× 246 0.9× 387 1.9× 140 1.1× 174 1.7× 36 1.1k
Abdullah Alghamdi Saudi Arabia 18 336 1.1× 307 1.1× 225 1.1× 102 0.8× 94 0.9× 127 1.1k
C. B. Sivaparthipan China 14 188 0.6× 224 0.8× 187 0.9× 116 0.9× 109 1.0× 39 1.0k
Sabah Mohammed Canada 16 200 0.7× 376 1.4× 265 1.3× 124 1.0× 160 1.5× 157 1.2k

Countries citing papers authored by Subhendu Kumar Pani

Since Specialization
Citations

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

Fields of papers citing papers by Subhendu Kumar Pani

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Subhendu Kumar Pani. 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 Subhendu Kumar Pani. The network helps show where Subhendu Kumar Pani may publish in the future.

Co-authorship network of co-authors of Subhendu Kumar Pani

This figure shows the co-authorship network connecting the top 25 collaborators of Subhendu Kumar Pani. A scholar is included among the top collaborators of Subhendu Kumar Pani 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 Subhendu Kumar Pani. Subhendu Kumar Pani 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.
Behera, Mukunda Dev, et al.. (2025). AnoLSTM-A Deep Learning Approach for Test Cases Prioritization. Procedia Computer Science. 258. 1793–1803.
2.
Chakraborty, Chinmay, et al.. (2024). An MIoT Framework of Consumer Technology for Medical Diseases Prediction. IEEE Transactions on Consumer Electronics. 70(1). 3754–3761. 15 indexed citations
3.
Dash, S., et al.. (2024). Predictive healthcare modeling for early pandemic assessment leveraging deep auto regressor neural prophet. Scientific Reports. 14(1). 5287–5287. 1 indexed citations
4.
Tripathy, Subhranshu Sekhar, Manisha Guduri, Chinmay Chakraborty, et al.. (2024). An Adaptive Explainable AI Framework for Securing Consumer Electronics-Based IoT Applications in Fog-Cloud Infrastructure. IEEE Transactions on Consumer Electronics. 71(1). 1889–1896. 6 indexed citations
5.
Pani, Subhendu Kumar, et al.. (2023). NAMSTCD: A Novel Augmented Model for Spinal Cord Segmentation and Tumor Classification Using Deep Nets. Diagnostics. 13(8). 1417–1417. 7 indexed citations
6.
Uppal, Mudita, Deepali Gupta, Nitin Goyal, et al.. (2023). A Real-Time Data Monitoring Framework for Predictive Maintenance Based on the Internet of Things. Complexity. 2023. 1–14. 17 indexed citations
7.
Pani, Subhendu Kumar, et al.. (2023). A near-optimal & load balanced resilient system design for high-performance computing platform. Cluster Computing. 26(2). 1535–1550. 2 indexed citations
8.
Pani, Subhendu Kumar, et al.. (2023). Risk Managed Cloud Adoption: An ANP Approach. International Journal of Mathematical Engineering and Management Sciences. 8(1). 78–93. 2 indexed citations
9.
Lilhore, Umesh Kumar, Agbotiname Lucky Imoize, Cheng‐Chi Lee, et al.. (2022). Enhanced Convolutional Neural Network Model for Cassava Leaf Disease Identification and Classification. Mathematics. 10(4). 580–580. 56 indexed citations
10.
Lilhore, Umesh Kumar, Agbotiname Lucky Imoize, Chun‐Ta Li, et al.. (2022). Design and Implementation of an ML and IoT Based Adaptive Traffic-Management System for Smart Cities. Sensors. 22(8). 2908–2908. 102 indexed citations
11.
Tripathy, Subhranshu Sekhar, Agbotiname Lucky Imoize, Sujit Bebortta, et al.. (2022). A Novel Edge-Computing-Based Framework for an Intelligent Smart Healthcare System in Smart Cities. Sustainability. 15(1). 735–735. 22 indexed citations
12.
Rana, Arun Kumar, Chinmay Chakraborty, Sharad Sharma, et al.. (2021). Internet of Medical Things-Based Secure and Energy-Efficient Framework for Health Care. Big Data. 10(1). 18–33. 22 indexed citations
13.
Dash, S., et al.. (2021). Intelligent computing on time-series data analysis and prediction of COVID-19 pandemics. Pattern Recognition Letters. 151. 69–75. 50 indexed citations
14.
Bhuyan, Hemanta Kumar, Chinmay Chakraborty, Subhendu Kumar Pani, & Vinayakumar Ravi. (2021). Feature and Subfeature Selection for Classification Using Correlation Coefficient and Fuzzy Model. IEEE Transactions on Engineering Management. 70(5). 1655–1669. 48 indexed citations
15.
Pani, Subhendu Kumar, et al.. (2014). Classification of Skewed Data: A Comparative Analysis of the Performance of Select Classifiers. SSRN Electronic Journal. 1 indexed citations
16.
Pani, Subhendu Kumar, et al.. (2013). Effect of missing values on data classification. Journal of Emerging Trends in Engineering and Applied Sciences. 4(2). 311–316. 2 indexed citations
17.
Pani, Subhendu Kumar, et al.. (2012). A Data Mining Approach to Identify Key Factors for Systematic Reuse. SSRN Electronic Journal. 1 indexed citations
18.
Pani, Subhendu Kumar, et al.. (2011). A Hybrid Parallel Multi-Objective Genetic Algorithm for 0/1 Knapsack Problem. Journal of Software Engineering and Applications. 4(5). 316–319.
19.
Pani, Subhendu Kumar, et al.. (2011). A Review Of Trends In Research On Web Mining. 37–41. 6 indexed citations
20.
Rajawat, A. S., M. Hanmandlu, & Subhendu Kumar Pani. (2009). Fuzzy modeling based palm print recognition system. 7. 189–192. 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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