Chandrashekhar Azad

763 total citations · 1 hit paper
30 papers, 461 citations indexed

About

Chandrashekhar Azad is a scholar working on Artificial Intelligence, Information Systems and Materials Chemistry. According to data from OpenAlex, Chandrashekhar Azad has authored 30 papers receiving a total of 461 indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Artificial Intelligence, 5 papers in Information Systems and 5 papers in Materials Chemistry. Recurrent topics in Chandrashekhar Azad's work include Text and Document Classification Technologies (6 papers), Machine Learning in Materials Science (5 papers) and Artificial Intelligence in Healthcare (4 papers). Chandrashekhar Azad is often cited by papers focused on Text and Document Classification Technologies (6 papers), Machine Learning in Materials Science (5 papers) and Artificial Intelligence in Healthcare (4 papers). Chandrashekhar Azad collaborates with scholars based in India. Chandrashekhar Azad's co-authors include Pradip Dhal, Uday Kumar, Ravi Ranjan Kumar, Puja Kumari, Vikas Yadav, S. K. Nandy, Praveen K. Tyagi and Ashok Mehta and has published in prestigious journals such as SHILAP Revista de lepidopterología, International Journal of Hydrogen Energy and Applied Soft Computing.

In The Last Decade

Chandrashekhar Azad

26 papers receiving 451 citations

Hit Papers

A comprehensive survey on feature selection in the variou... 2021 2026 2022 2024 2021 50 100 150 200 250
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.

  1. A comprehensive survey on feature selection in the various fields of machine learning
    2021 273 citations Pradip Dhal, Chandrashekhar Azad Applied Intelligence profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Chandrashekhar Azad India 8 206 76 51 49 38 30 461
Shen Li-ming China 5 231 1.1× 78 1.0× 36 0.7× 40 0.8× 49 1.3× 8 493
Zhe Yu China 2 224 1.1× 52 0.7× 33 0.6× 40 0.8× 49 1.3× 3 451
Pradip Dhal India 7 194 0.9× 75 1.0× 51 1.0× 39 0.8× 22 0.6× 13 386
Piotr S. Szczepaniak Poland 12 217 1.1× 84 1.1× 45 0.9× 46 0.9× 17 0.4× 58 477
Azah Kamilah Muda Malaysia 13 157 0.8× 91 1.2× 54 1.1× 33 0.7× 18 0.5× 71 483
Enas Elgeldawi Egypt 7 153 0.7× 49 0.6× 42 0.8× 32 0.7× 21 0.6× 13 505
Jesús Maillo Spain 8 325 1.6× 81 1.1× 100 2.0× 51 1.0× 47 1.2× 10 561
Kannan Krithivasan India 13 169 0.8× 75 1.0× 73 1.4× 84 1.7× 22 0.6× 39 448
Yangguang Liu China 11 220 1.1× 96 1.3× 52 1.0× 47 1.0× 20 0.5× 52 468
La The Vinh South Korea 11 147 0.7× 235 3.1× 50 1.0× 57 1.2× 25 0.7× 18 491

Countries citing papers authored by Chandrashekhar Azad

Since Specialization
Citations

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

Fields of papers citing papers by Chandrashekhar Azad

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Chandrashekhar Azad

This figure shows the co-authorship network connecting the top 25 collaborators of Chandrashekhar Azad. A scholar is included among the top collaborators of Chandrashekhar Azad 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 Chandrashekhar Azad. Chandrashekhar Azad 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.
Azad, Chandrashekhar, et al.. (2025). Predicting Förster critical distance using machine learning techniques. AIP conference proceedings. 3266. 20071–20071.
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Dhal, Pradip & Chandrashekhar Azad. (2024). Zone Oriented Binary Multi‐Objective Charged System Search Based Feature Selection Approach for Multi‐Label Classification. Expert Systems. 42(2). 3 indexed citations
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Azad, Chandrashekhar, et al.. (2024). Study and prediction of photocurrent density with external validation using machine learning models. International Journal of Hydrogen Energy. 92. 1335–1355. 1 indexed citations
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Azad, Chandrashekhar, et al.. (2024). Comprehensive overview of Alzheimer's disease utilizing Machine Learning approaches. Multimedia Tools and Applications. 83(37). 85277–85329. 2 indexed citations
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Azad, Chandrashekhar, et al.. (2023). Stokes shift prediction of fluorescent organic dyes using machine learning based hybrid cascade models. Dyes and Pigments. 222. 111918–111918. 12 indexed citations
11.
Dhal, Pradip & Chandrashekhar Azad. (2023). Hybrid momentum accelerated bat algorithm with GWO based optimization approach for spam classification. Multimedia Tools and Applications. 83(9). 26929–26969. 7 indexed citations
12.
Kumari, Puja, et al.. (2023). Defense Inducer Compounds Up-regulated the Peroxidase, Polyphenol Oxidase, and Total Phenol Activities against Spot Blotch Disease of Wheat. The Plant Pathology Journal. 39(2). 159–170. 4 indexed citations
13.
Dhal, Pradip & Chandrashekhar Azad. (2023). A fine-tuning deep learning with multi-objective-based feature selection approach for the classification of text. Neural Computing and Applications. 36(7). 3525–3553. 12 indexed citations
14.
Azad, Chandrashekhar, et al.. (2022). Predicting Happiness Score During Covid-19 Using Machine Learning. 1–6. 8 indexed citations
15.
Yadav, Vikas, et al.. (2022). A filter-based feature selection approach for the prediction of Alzheimer's diseases through audio classification. 2022 2nd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE). 1890–1894. 4 indexed citations
16.
Dhal, Pradip & Chandrashekhar Azad. (2022). A multi-objective evolutionary feature selection approach for the classification of multi-label data. 2022 2nd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE). 1986–1989. 5 indexed citations
17.
Dhal, Pradip & Chandrashekhar Azad. (2022). A lightweight filter based feature selection approach for multi-label text classification. Journal of Ambient Intelligence and Humanized Computing. 14(9). 12345–12357. 14 indexed citations
18.
Dhal, Pradip & Chandrashekhar Azad. (2021). A comprehensive survey on feature selection in the various fields of machine learning. Applied Intelligence. 52(4). 4543–4581. 273 indexed citations breakdown →
19.
Dhal, Pradip & Chandrashekhar Azad. (2021). A multi-objective feature selection method using Newton’s law based PSO with GWO. Applied Soft Computing. 107. 107394–107394. 52 indexed citations
20.
Azad, Chandrashekhar, et al.. (2018). Design Space Exploration of an Execution-Driven Functional Simulation Methodology. 295–300. 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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