Manoj Kumar Naik

965 total citations
32 papers, 707 citations indexed

About

Manoj Kumar Naik is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Media Technology. According to data from OpenAlex, Manoj Kumar Naik has authored 32 papers receiving a total of 707 indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Computer Vision and Pattern Recognition, 13 papers in Artificial Intelligence and 6 papers in Media Technology. Recurrent topics in Manoj Kumar Naik's work include Metaheuristic Optimization Algorithms Research (12 papers), Medical Image Segmentation Techniques (8 papers) and Advanced Image and Video Retrieval Techniques (8 papers). Manoj Kumar Naik is often cited by papers focused on Metaheuristic Optimization Algorithms Research (12 papers), Medical Image Segmentation Techniques (8 papers) and Advanced Image and Video Retrieval Techniques (8 papers). Manoj Kumar Naik collaborates with scholars based in India, United States and Russia. Manoj Kumar Naik's co-authors include Rutuparna Panda, Ajith Abraham, Bijaya Ketan Panigrahi, Monorama Swain, Sanjay Agrawal, Shilpa Pathak, Ajit R. Kulkarni, Sumit Agrawal, Atul Joshi and Mihir Narayan Mohanty and has published in prestigious journals such as SHILAP Revista de lepidopterología, IEEE Access and Biochemical Pharmacology.

In The Last Decade

Manoj Kumar Naik

30 papers receiving 687 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Manoj Kumar Naik India 15 371 245 123 95 94 32 707
Salvador Hinojosa Mexico 16 377 1.0× 281 1.1× 137 1.1× 116 1.2× 31 0.3× 36 825
Xiaoxu Peng China 12 555 1.5× 297 1.2× 221 1.8× 101 1.1× 54 0.6× 16 941
Bahaa El-din Helmy Egypt 8 299 0.8× 135 0.6× 75 0.6× 129 1.4× 32 0.3× 9 551
Xinming Zhang China 12 409 1.1× 119 0.5× 169 1.4× 71 0.7× 49 0.5× 33 686
Jaffar Atwan Jordan 5 405 1.1× 104 0.4× 139 1.1× 106 1.1× 32 0.3× 11 664
Zhikai Xing China 18 328 0.9× 285 1.2× 72 0.6× 236 2.5× 37 0.4× 44 881
Omar Ávalos Mexico 12 250 0.7× 123 0.5× 112 0.9× 104 1.1× 22 0.2× 33 560
Junzhi Li China 11 344 0.9× 128 0.5× 137 1.1× 71 0.7× 57 0.6× 23 577
Swarnajit Ray India 16 388 1.0× 380 1.6× 78 0.6× 43 0.5× 35 0.4× 27 752
Shaoqiu Zheng China 14 516 1.4× 162 0.7× 220 1.8× 125 1.3× 89 0.9× 20 899

Countries citing papers authored by Manoj Kumar Naik

Since Specialization
Citations

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

Fields of papers citing papers by Manoj Kumar Naik

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Manoj Kumar Naik

This figure shows the co-authorship network connecting the top 25 collaborators of Manoj Kumar Naik. A scholar is included among the top collaborators of Manoj Kumar Naik 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 Manoj Kumar Naik. Manoj Kumar Naik 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.
Singh, Harshita, et al.. (2025). Calcitonin gene-related peptide (CGRP): A potential therapeutic target against sepsis and sepsis-associated multiple organ failure. Biochemical Pharmacology. 242(Pt 3). 117206–117206.
2.
Kulkarni, Ajit R., et al.. (2023). Safety and efficacy of mycophenolate in COVID-19: a nonrandomised prospective study in western India. SHILAP Revista de lepidopterología. 11. 100154–100154. 7 indexed citations
3.
4.
Naik, Manoj Kumar, et al.. (2023). ECG Signal Classification Using a CNN-LSTM Hybrid Network. 1–6. 3 indexed citations
5.
Swain, Monorama, et al.. (2023). A non-entropy-based optimal multilevel threshold selection technique for COVID-19 X-ray images using chance-based birds’ intelligence. Soft Computing. 27(24). 18991–19011. 4 indexed citations
6.
Panda, Rutuparna, Monorama Swain, Manoj Kumar Naik, Sanjay Agrawal, & Ajith Abraham. (2022). A Novel Practical Decisive Row-Class Entropy-Based Technique for Multilevel Threshold Selection Using Opposition Flow Directional Algorithm. IEEE Access. 10. 110473–110484. 5 indexed citations
7.
Naik, Manoj Kumar, Monorama Swain, Rutuparna Panda, & Ajith Abraham. (2022). An Evolutionary Dynamic Control Cuckoo Search Algorithm for Solving the Constrained Engineering Design Problems. International Journal of Swarm Intelligence Research. 13(4). 1–25. 4 indexed citations
8.
Naik, Manoj Kumar, et al.. (2022). A novel minimum generalized cross entropy-based multilevel segmentation technique for the brain MRI/dermoscopic images. Computers in Biology and Medicine. 151(Pt A). 106214–106214. 14 indexed citations
9.
Naik, Manoj Kumar, et al.. (2021). Automation in Testing with Jenkins for Software Development. Journal of University of Shanghai for Science and Technology. 23(6). 746–755. 1 indexed citations
10.
Naik, Manoj Kumar, et al.. (2021). A leader Harris hawks optimization for 2-D Masi entropy-based multilevel image thresholding. Multimedia Tools and Applications. 80(28-29). 35543–35583. 75 indexed citations
11.
Naik, Manoj Kumar, Rutuparna Panda, & Ajith Abraham. (2021). An entropy minimization based multilevel colour thresholding technique for analysis of breast thermograms using equilibrium slime mould algorithm. Applied Soft Computing. 113. 107955–107955. 42 indexed citations
12.
Naik, Manoj Kumar, et al.. (2020). A novel interdependence based multilevel thresholding technique using adaptive equilibrium optimizer. Engineering Applications of Artificial Intelligence. 94. 103836–103836. 54 indexed citations
13.
Naik, Manoj Kumar, et al.. (2020). An adaptive Harris hawks optimization technique for two dimensional grey gradient based multilevel image thresholding. Applied Soft Computing. 95. 106526–106526. 53 indexed citations
14.
Naik, Manoj Kumar, et al.. (2020). A differential evolutionary adaptive Harris hawks optimization for two dimensional practical Masi entropy-based multilevel image thresholding. Journal of King Saud University - Computer and Information Sciences. 34(6). 3011–3024. 33 indexed citations
15.
Naik, Manoj Kumar, Rutuparna Panda, & Ajith Abraham. (2020). Normalized square difference based multilevel thresholding technique for multispectral images using leader slime mould algorithm. Journal of King Saud University - Computer and Information Sciences. 34(7). 4524–4536. 52 indexed citations
16.
Naik, Manoj Kumar, et al.. (2017). CLINICOPATHOLOGICAL EVALUATION OF NECK MASSES- A DESCRIPTIVE STUDY. Journal of Evolution of Medical and Dental Sciences. 6(79). 5594–5597.
17.
Naik, Manoj Kumar, et al.. (2015). A new adaptive Cuckoo search algorithm. 1–5. 48 indexed citations
18.
Panda, Rutuparna & Manoj Kumar Naik. (2015). A novel adaptive crossover bacterial foraging optimization algorithm for linear discriminant analysis based face recognition. Applied Soft Computing. 30. 722–736. 25 indexed citations
19.
Naik, Manoj Kumar & Rutuparna Panda. (2015). A new hybrid CS-GSA algorithm for function optimization. 8. 1–6. 3 indexed citations
20.
Panda, Rutuparna, et al.. (2013). Design of two-dimensional recursive filters using bacteria foraging optimization. 188–193. 7 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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