Prasenjit Das

496 total citations · 1 hit paper
20 papers, 262 citations indexed

About

Prasenjit Das is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Computational Theory and Mathematics. According to data from OpenAlex, Prasenjit Das has authored 20 papers receiving a total of 262 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Artificial Intelligence, 4 papers in Computer Vision and Pattern Recognition and 4 papers in Computational Theory and Mathematics. Recurrent topics in Prasenjit Das's work include DNA and Biological Computing (3 papers), Groundwater and Isotope Geochemistry (3 papers) and Cellular Automata and Applications (3 papers). Prasenjit Das is often cited by papers focused on DNA and Biological Computing (3 papers), Groundwater and Isotope Geochemistry (3 papers) and Cellular Automata and Applications (3 papers). Prasenjit Das collaborates with scholars based in India, United Kingdom and South Korea. Prasenjit Das's co-authors include K. Maya, D. Padmalal, Nirmalya Kar, Chetan Sharma, Deepika Koundal, Baby Bhattacharya, K. N. Babu, Anirban Mitra, Abhishek Kumar and K. Sreelash and has published in prestigious journals such as Earth-Science Reviews, Geothermics and Journal of the Geological Society of India.

In The Last Decade

Prasenjit Das

17 papers receiving 241 citations

Hit Papers

align trajectories sort by overperformance

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Perception towards online/blended learning at the time of Covid-19 pandemic: an academic analytics in the Indian context

2021 120 citations Prasenjit Das et al. AAOU Journal profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Prasenjit Das India	7	90	50	45	37	35	20	262
Prayitno Prayitno Indonesia	7	137 1.5×	100 2.0×	7 0.2×	71 1.9×	3 0.1×	68	354
Manuel Luque Spain	9	25 0.3×	120 2.4×	31 0.7×	31 0.8×		16	276
Fatima Tuz Zahra Pakistan	9	41 0.5×	26 0.5×	25 0.6×	29 0.8×		69	313
Zhiqiang Ma United States	8	41 0.5×	73 1.5×	116 2.6×	24 0.6×	1 0.0×	23	279
Xueying Tang United States	9	14 0.2×	115 2.3×	7 0.2×	35 0.9×	4 0.1×	21	251
Tongcun Liu China	11	4 0.0×	86 1.7×	33 0.7×	85 2.3×	2 0.1×	24	281
Costin-Gabriel Chiru Romania	8	11 0.1×	68 1.4×	19 0.4×	62 1.7×	5 0.1×	34	209
Shuwen Wang China	8	24 0.3×	49 1.0×	37 0.8×	10 0.3×		40	237
Ángel Bueno Rodríguez Spain	10	14 0.2×	327 6.5×	14 0.3×	20 0.5×	9 0.3×	22	445

Countries citing papers authored by Prasenjit Das

Since Specialization

Citations

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

Fields of papers citing papers by Prasenjit Das

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Prasenjit Das

This figure shows the co-authorship network connecting the top 25 collaborators of Prasenjit Das. A scholar is included among the top collaborators of Prasenjit Das 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 Prasenjit Das. Prasenjit Das is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Das, Prasenjit, et al.. (2023). Enhancing Human-Computer Interaction: Hand Detection for Air Writing Utilizing NumPy and OpenCV. 517–521. 1 indexed citations

Vyas, Narayan, et al.. (2023). The Impact of Air Pollution on Respiratory Health Results: An Analysis of Asthma and COPD in a Population Study. 141–146. 3 indexed citations

Jain, Shaily, et al.. (2023). Machine Learning Approach for Malware Detection and Classification using Bio Inspired Algorithms. 1–6.

Koundal, Deepika, et al.. (2021). Binary Classification of COVID-19 CT Images Using CNN. International Journal of E-Health and Medical Communications. 13(2). 1–13. 26 indexed citations

Das, Prasenjit, et al.. (2021). A Comprehensive Review on Clustering in WSN: Optimization Techniques and Future Research Challenges. 3 indexed citations

Das, Prasenjit, et al.. (2021). Prediction of Diabetes Rate Using Data Mining. 132. 463–465. 2 indexed citations

Das, Prasenjit, K. Maya, & D. Padmalal. (2021). Hydrogeochemistry of the Indian thermal springs: Current status. Earth-Science Reviews. 224. 103890–103890. 15 indexed citations

Das, Prasenjit, et al.. (2021). Perception towards online/blended learning at the time of Covid-19 pandemic: an academic analytics in the Indian context. AAOU Journal. 16(1). 41–60. 120 indexed citations breakdown →

Das, Prasenjit, et al.. (2021). An Integrated Fundus Image Segmentation Algorithm for Multiple Eye Ailments. International Journal of Electrical and Electronics Research. 9(4). 125–134. 6 indexed citations

10.

Das, Prasenjit, K. Maya, & D. Padmalal. (2020). Hydrochemistry, geothermometry and origin of the low temperature thermal springs of South Konkan region, India. Geothermics. 90. 101997–101997. 30 indexed citations

11.

Maya, K., Prasenjit Das, K. Sreelash, & K. N. Babu. (2018). The Coastal Springs of Southern Kerala, SW India – Hydrology and Water Quality Assessment. Journal of the Geological Society of India. 92(5). 616–625. 6 indexed citations

12.

Roy, Sudipta, et al.. (2017). Moving Object Detection and Segmentation using Background Subtraction by Kalman Filter. Indian Journal of Science and Technology. 10(19). 1–11. 4 indexed citations

13.

Das, Prasenjit, et al.. (2017). EPIDEMIOLOGICAL STUDY OF FATAL ELECTROCUTION CASES- A MORTUARY BASED 3 YEARS RETROSPECTIVE STUDY. Journal of Evolution of Medical and Dental Sciences. 6(9). 665–667. 1 indexed citations

14.

Roy, Sudipta, et al.. (2016). Automated Numbers of Cluster Determination Using the Combination of Entropy and Histogram Peaks from Multiple Images. International Journal of Signal Processing Image Processing and Pattern Recognition. 9(7). 107–116. 3 indexed citations

15.

Das, Prasenjit, et al.. (2015). MYOCARDIAL BRIDGING AND SUDDEN DEATH: A CARE REPORT. Journal of Evolution of Medical and Dental Sciences. 4(1). 116–119. 5 indexed citations

16.

Das, Prasenjit, et al.. (2015). An Improved DNA Based Dual Cover Steganography. Procedia Computer Science. 46. 604–611. 18 indexed citations

17.

Das, Prasenjit & Nirmalya Kar. (2014). A highly secure DNA based image steganography. 1–5. 7 indexed citations

18.

Das, Prasenjit & Nirmalya Kar. (2014). A DNA based image steganography using 2D chaotic map. 1–5. 11 indexed citations

19.

Das, Prasenjit, et al.. (2009). Hyperfibrotic myelodysplastic syndrome: a report of three cases from north India.. PubMed. 26(2). 93–6. 1 indexed citations

20.

Pakhira, Malay K. & Prasenjit Das. (2008). On Development of a Generalized Visual Stochastic Optimizer. 220. 30–35.

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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