N.S. Reddy

2.9k total citations
129 papers, 2.2k citations indexed

About

N.S. Reddy is a scholar working on Mechanical Engineering, Materials Chemistry and Electrical and Electronic Engineering. According to data from OpenAlex, N.S. Reddy has authored 129 papers receiving a total of 2.2k indexed citations (citations by other indexed papers that have themselves been cited), including 65 papers in Mechanical Engineering, 53 papers in Materials Chemistry and 35 papers in Electrical and Electronic Engineering. Recurrent topics in N.S. Reddy's work include Metallurgy and Material Forming (24 papers), Microstructure and Mechanical Properties of Steels (21 papers) and Titanium Alloys Microstructure and Properties (19 papers). N.S. Reddy is often cited by papers focused on Metallurgy and Material Forming (24 papers), Microstructure and Mechanical Properties of Steels (21 papers) and Titanium Alloys Microstructure and Properties (19 papers). N.S. Reddy collaborates with scholars based in South Korea, India and United States. N.S. Reddy's co-authors include Jong‐Taek Yeom, Uma Maheshwera Reddy Paturi, P.L. Narayana, Jae‐Keun Hong, Chan Hee Park, Chong Soo Lee, Jeoung Han Kim, B.S. Reddy, Bharat B. Panigrahi and Hyo‐Jun Ahn and has published in prestigious journals such as SHILAP Revista de lepidopterología, The Science of The Total Environment and Carbon.

In The Last Decade

N.S. Reddy

116 papers receiving 2.1k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
N.S. Reddy South Korea 29 1.2k 946 577 416 251 129 2.2k
Chao Liu China 31 1.7k 1.4× 728 0.8× 452 0.8× 294 0.7× 520 2.1× 168 2.8k
Abbas Bahrami Iran 29 1.3k 1.1× 994 1.1× 442 0.8× 224 0.5× 259 1.0× 109 2.3k
Lin Li China 27 873 0.7× 490 0.5× 459 0.8× 367 0.9× 400 1.6× 144 2.1k
Yiyu Wang China 24 1.0k 0.8× 696 0.7× 210 0.4× 888 2.1× 100 0.4× 118 2.2k
Maosheng Zheng China 23 613 0.5× 818 0.9× 232 0.4× 349 0.8× 227 0.9× 111 1.9k
Yufeng Zhang China 38 3.6k 2.9× 717 0.8× 235 0.4× 421 1.0× 390 1.6× 196 4.3k
Swadesh Kumar Singh India 31 2.3k 1.9× 1.6k 1.7× 2.0k 3.4× 159 0.4× 266 1.1× 202 3.3k

Countries citing papers authored by N.S. Reddy

Since Specialization
Citations

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

Fields of papers citing papers by N.S. Reddy

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by N.S. Reddy. 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 N.S. Reddy. The network helps show where N.S. Reddy may publish in the future.

Co-authorship network of co-authors of N.S. Reddy

This figure shows the co-authorship network connecting the top 25 collaborators of N.S. Reddy. A scholar is included among the top collaborators of N.S. Reddy 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 N.S. Reddy. N.S. Reddy 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.
Paturi, Uma Maheshwera Reddy, et al.. (2025). Evaluating Machine Learning Models for Predicting Hardness of AlCoCrCuFeNi High-Entropy Alloys. Crystals. 15(5). 404–404. 2 indexed citations
2.
Yeom, Jong‐Taek, Jae Ho Kim, Junha Yang, et al.. (2025). Optimized Process Design for Uniform Microstructure and High-Strength Ti-6Al-4 V Alloy Fasteners in Aerospace Applications. Metals and Materials International.
3.
Jayasubramaniyan, S., Jueun Kim, Donghwi Kim, et al.. (2024). Surface fluorinated graphite suppressing the lithium dendrite formation for fast chargeable lithium ion batteries. Carbon. 219. 118808–118808. 19 indexed citations
4.
Jayasubramaniyan, S., Donghwi Kim, N.S. Reddy, et al.. (2024). Achieving high volumetric energy density in graphite anodes through polymer coating with improved electrolyte impregnation. Journal of Materials Chemistry A. 12(33). 22201–22209.
5.
6.
Reddy, B.S., P.L. Narayana, Uma Maheshwera Reddy Paturi, et al.. (2023). Modeling capacitance of carbon-based supercapacitors by artificial neural networks. Journal of Energy Storage. 72. 108537–108537. 13 indexed citations
7.
Paturi, Uma Maheshwera Reddy, et al.. (2023). Estimation of surface roughness of direct metal laser sintered AlSi10Mg using artificial neural networks and response surface methodology. Materials and Manufacturing Processes. 38(14). 1798–1808. 9 indexed citations
8.
Reddy, B.S., Uma Maheshwera Reddy Paturi, Jaekyung Sung, et al.. (2023). Neural Network Models for Estimating the Impact of Physicochemical and Operational Parameters on the Specific Capacity of Activated-Carbon-Based Supercapacitors. Energy & Fuels. 37(19). 15084–15094.
9.
Paturi, Uma Maheshwera Reddy, et al.. (2022). Artificial neural networks modelling for power coefficient of Archimedes screw turbine for hydropower applications. Journal of the Brazilian Society of Mechanical Sciences and Engineering. 44(10). 8 indexed citations
10.
Paturi, Uma Maheshwera Reddy, et al.. (2022). Estimation of machinability performance in wire-EDM on titanium alloy using neural networks. Materials and Manufacturing Processes. 37(9). 1073–1084. 26 indexed citations
11.
Paturi, Uma Maheshwera Reddy, et al.. (2022). The Role of Machine Learning in Tribology: A Systematic Review. Archives of Computational Methods in Engineering. 30(2). 1345–1397. 71 indexed citations
12.
Reddy, N.S., et al.. (2021). Electro spark coating of AlCoCrFeNi high entropy alloy on AISI410 stainless steel. Materials Letters. 304. 130580–130580. 16 indexed citations
13.
Yeom, Jong‐Taek, et al.. (2021). Development of artificial neural networks software for arsenic adsorption from an aqueous environment. Environmental Research. 203. 111846–111846. 16 indexed citations
14.
Narayana, P.L., Xiaosong Wang, Abeer Ali Alnuaim, et al.. (2021). Artificial neural networks modeling for lead removal from aqueous solutions using iron oxide nanocomposites from bio-waste mass. Environmental Research. 199. 111370–111370. 32 indexed citations
15.
Paturi, Uma Maheshwera Reddy, et al.. (2021). Progress of machinability on the machining of Inconel 718: A comprehensive review on the perception of cleaner machining. Cleaner Engineering and Technology. 5. 100323–100323. 30 indexed citations
16.
Narayana, P.L., Jae H. Kim, Sangwon Lee, et al.. (2021). Novel eutectoid Ti-5Ni alloy fabricated via direct energy deposition. Scripta Materialia. 200. 113918–113918. 28 indexed citations
17.
Reddy, B.S., P.L. Narayana, S. K. Khadheer Pasha, et al.. (2021). Knowledge extraction of sonophotocatalytic treatment for acid blue 113 dye removal by artificial neural networks. Environmental Research. 204(Pt D). 112359–112359. 13 indexed citations
18.
Reddy, B.S., et al.. (2021). Egg white derived carbon materials as an efficient sulfur host for high-performance lithium-sulfur batteries and its electrochemical properties. Materials Research Bulletin. 140. 111310–111310. 13 indexed citations
19.
Reddy, N.S., et al.. (2020). Blocking of Zeolite Pore by Loading Ni-Pt Nanoparticles for Maximization of Isomerization Selectivity. Korean Journal of Chemical Engineering. 58(4). 658–664.
20.
Alnaqi, Abdulwahab A., et al.. (2016). A Neural Network Predictive Model for Welded Marine Pipeline Internal Corrosion. Research Journal of Applied Sciences Engineering and Technology. 13(7). 585–592. 1 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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