Subraya Krishna Bhat

507 total citations · 1 hit paper
49 papers, 232 citations indexed

About

Subraya Krishna Bhat is a scholar working on Mechanical Engineering, Biomedical Engineering and Electrical and Electronic Engineering. According to data from OpenAlex, Subraya Krishna Bhat has authored 49 papers receiving a total of 232 indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Mechanical Engineering, 13 papers in Biomedical Engineering and 9 papers in Electrical and Electronic Engineering. Recurrent topics in Subraya Krishna Bhat's work include Advanced Machining and Optimization Techniques (7 papers), Advanced machining processes and optimization (7 papers) and Natural Fiber Reinforced Composites (4 papers). Subraya Krishna Bhat is often cited by papers focused on Advanced Machining and Optimization Techniques (7 papers), Advanced machining processes and optimization (7 papers) and Natural Fiber Reinforced Composites (4 papers). Subraya Krishna Bhat collaborates with scholars based in India, United States and Japan. Subraya Krishna Bhat's co-authors include Deepak Doreswamy, Deepak Dharrao, Anupkumar M. Bongale, Hiroshi Yamada, Nidambur Vasudev Ballal, Noriyuki Sakata, Raviraja Adhikari, T. Jagadeesha, Peter J. Beck and Shyamasunder N Bhat and has published in prestigious journals such as SHILAP Revista de lepidopterología, Scientific Reports and Expert Systems with Applications.

In The Last Decade

Subraya Krishna Bhat

36 papers receiving 219 citations

Hit Papers

Forecasting Stock Market Prices Using Machine Learning an... 2023 2026 2024 2025 2023 25 50 75

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Subraya Krishna Bhat India 8 62 50 45 43 26 49 232
Liyang Xiao China 7 38 0.6× 80 1.6× 39 0.9× 56 1.3× 3 0.1× 11 370
Francisco-Javier Gimeno-Blanes Spain 10 30 0.5× 18 0.4× 13 0.3× 46 1.1× 9 0.3× 42 322
Mengran Zhu United States 10 7 0.1× 10 0.2× 19 0.4× 104 2.4× 11 0.4× 16 286
Yasser A. Hosni United States 8 39 0.6× 4 0.1× 51 1.1× 76 1.8× 6 0.2× 25 333
Xiangyang Ren China 10 4 0.1× 101 2.0× 113 2.5× 123 2.9× 16 0.6× 33 311
Jin-Hyuk Lee South Korea 12 11 0.2× 19 0.4× 95 2.1× 128 3.0× 4 0.2× 47 366
Mohammad Khoshnevisan United States 8 11 0.2× 100 2.0× 106 2.4× 77 1.8× 5 0.2× 23 342
Ayşe Merve Acılar Türkiye 8 12 0.2× 12 0.2× 19 0.4× 56 1.3× 17 0.7× 12 285
Yuqi Wang China 9 5 0.1× 10 0.2× 18 0.4× 22 0.5× 85 3.3× 32 254

Countries citing papers authored by Subraya Krishna Bhat

Since Specialization
Citations

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

Fields of papers citing papers by Subraya Krishna Bhat

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Subraya Krishna Bhat. 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 Subraya Krishna Bhat. The network helps show where Subraya Krishna Bhat may publish in the future.

Co-authorship network of co-authors of Subraya Krishna Bhat

This figure shows the co-authorship network connecting the top 25 collaborators of Subraya Krishna Bhat. A scholar is included among the top collaborators of Subraya Krishna Bhat 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 Subraya Krishna Bhat. Subraya Krishna Bhat 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.
Chaudhari, Rakesh, et al.. (2025). Investigating the microstructure and mechanical properties of L-shaped structure of TM-B9 HSLA steels using WAAM process. Results in Surfaces and Interfaces. 20. 100619–100619. 1 indexed citations
2.
Bhat, Subraya Krishna, et al.. (2025). A multi-stage deep learning approach for comprehensive lung disease classification from x-ray images. Expert Systems with Applications. 277. 127220–127220. 3 indexed citations
3.
Jagadeesha, T., et al.. (2025). Investigation of Tribological Behavior of Fused Deposition Modelling Processed Parts of Polyethylene Terephthalate Glycol Polymer Material. Journal of The Institution of Engineers (India) Series D. 3 indexed citations
6.
Muralidhara, B. K., et al.. (2025). Enhanced mechanical, thermal, and wear performance of halloysite nanotube infused carbon fiber epoxy composites. Scientific Reports. 15(1). 30019–30019.
7.
Bhat, Subraya Krishna, et al.. (2025). Maximizing YOLOv2 efficiency: A study on multiclass detection of indoor objects. Results in Engineering. 26. 105405–105405. 2 indexed citations
8.
Bhat, Subraya Krishna, et al.. (2025). Optimization of deep learning-based faster R-CNN network for vehicle detection. Scientific Reports. 15(1). 38937–38937. 1 indexed citations
9.
Bhat, Subraya Krishna, et al.. (2025). Optimizing YOLOv4 Hyperparameters for Enhanced Vehicle Detection in Intelligent Transportation Systems. International Journal of Intelligent Transportation Systems Research. 23(3). 1372–1384. 1 indexed citations
10.
Jagadeesha, T., et al.. (2025). Study of optimization of process parameters on the wear behaviour of Al7075–aluminium oxide composites using Taguchi approach. Scientific Reports. 15(1). 22378–22378. 2 indexed citations
11.
Doreswamy, Deepak, et al.. (2025). AWJ machining of epoxy/crumb rubber/rice-straw powder composite: optimisation of MRR using Taguchi approach. Australian Journal of Mechanical Engineering. 1–11.
13.
Doreswamy, Deepak, et al.. (2024). Machinability of gas metal arc based 3D printed Al-Mg 5356 alloy using wire-EDM. Engineering Research Express. 6(3). 35413–35413. 3 indexed citations
14.
Bhat, Subraya Krishna, et al.. (2024). Numerical and experimental methods for the assessment of a human finger-inspired soft pneumatic actuator for gripping applications. MethodsX. 14. 103111–103111. 1 indexed citations
15.
Doreswamy, Deepak, et al.. (2024). Physico-Mechanical Characterization of Recycled Waste Tire Rubber and Rice Straw Reinforced Composite. International Journal of Engineering. 37(10). 2021–2029. 1 indexed citations
16.
Bhat, Subraya Krishna, et al.. (2023). Predictive Modelling and Optimization of Double Ring Electrode Based Cold Plasma Using Artificial Neural Network. International Journal of Engineering. 37(1). 83–93. 7 indexed citations
17.
Bongale, Anupkumar M., et al.. (2023). Research Trends in Abrasive Water Jet Machining Using Numerical SimulationTools: A Bibliometric Review. Recent Patents on Mechanical Engineering. 16(1). 19–31. 1 indexed citations
18.
Dharrao, Deepak, et al.. (2023). Forecasting Stock Market Prices Using Machine Learning and Deep Learning Models: A Systematic Review, Performance Analysis and Discussion of Implications. International Journal of Financial Studies. 11(3). 94–94. 82 indexed citations breakdown →
19.
Doreswamy, Deepak, et al.. (2023). Optimization of Surface Roughness and Surface Characterization of WED Machining of Titanium Ti-6Al-4V Alloy by Response Surface Method. Journal of Engineering Science and Technology Review. 16(1). 68–74. 4 indexed citations
20.
Kumar, Santhosh, et al.. (2018). Assessment of Pyrexia and Associated Sickness Behavior in Patients with Chronic Periodontitis. NeuroImmunoModulation. 25(3). 138–145. 3 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.

Explore authors with similar magnitude of impact

Rankless by CCL
2026