Neha Bharill

1.6k total citations · 1 hit paper
24 papers, 1.0k citations indexed

About

Neha Bharill is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Molecular Biology. According to data from OpenAlex, Neha Bharill has authored 24 papers receiving a total of 1.0k indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Artificial Intelligence, 11 papers in Computer Vision and Pattern Recognition and 8 papers in Molecular Biology. Recurrent topics in Neha Bharill's work include Advanced Clustering Algorithms Research (12 papers), Face and Expression Recognition (11 papers) and Machine Learning in Bioinformatics (7 papers). Neha Bharill is often cited by papers focused on Advanced Clustering Algorithms Research (12 papers), Face and Expression Recognition (11 papers) and Machine Learning in Bioinformatics (7 papers). Neha Bharill collaborates with scholars based in India, Australia and China. Neha Bharill's co-authors include Aruna Tiwari, Om Prakash Patel, Mukesh Prasad, Amit Saxena, Weiping Ding, Akshansh Gupta, Chin‐Teng Lin, Meng Joo Er, Milind B. Ratnaparkhe and Omprakash Kaiwartya and has published in prestigious journals such as IEEE Access, Neurocomputing and Engineering Applications of Artificial Intelligence.

In The Last Decade

Neha Bharill

21 papers receiving 1.0k citations

Hit Papers

A review of clustering te... 2017 2026 2020 2023 2017 250 500 750
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 review of clustering techniques and developments
    2017 850 citations Neha Bharill, Om Prakash Patel et al. profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Neha Bharill India 9 447 240 109 100 89 24 1.0k
Renato Cordeiro de Amorim United Kingdom 14 416 0.9× 222 0.9× 102 0.9× 109 1.1× 61 0.7× 37 888
Abiodun M. Ikotun South Africa 9 570 1.3× 239 1.0× 157 1.4× 108 1.1× 171 1.9× 20 1.6k
Xuedong Gao China 9 513 1.1× 194 0.8× 186 1.7× 185 1.9× 72 0.8× 72 1.1k
Chaoqun Ma China 4 517 1.2× 235 1.0× 181 1.7× 173 1.7× 142 1.6× 5 1.3k
Iñigo Perona Spain 7 501 1.1× 152 0.6× 125 1.1× 179 1.8× 77 0.9× 14 1.0k
Jianfei Chen China 15 514 1.1× 282 1.2× 90 0.8× 53 0.5× 141 1.6× 77 1.3k
Basabi Chakraborty Japan 17 522 1.2× 241 1.0× 99 0.9× 147 1.5× 48 0.5× 131 1.0k
Ibai Gurrutxaga Spain 12 614 1.4× 164 0.7× 162 1.5× 202 2.0× 88 1.0× 31 1.3k
Olatz Arbelaitz Spain 14 636 1.4× 189 0.8× 185 1.7× 221 2.2× 104 1.2× 43 1.4k
Hae-Sang Park South Korea 4 520 1.2× 244 1.0× 171 1.6× 199 2.0× 140 1.6× 5 1.4k

Countries citing papers authored by Neha Bharill

Since Specialization
Citations

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

Fields of papers citing papers by Neha Bharill

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Neha Bharill

This figure shows the co-authorship network connecting the top 25 collaborators of Neha Bharill. A scholar is included among the top collaborators of Neha Bharill 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 Neha Bharill. Neha Bharill 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.
Bharill, Neha, et al.. (2025). Active learning with Gaussian Process Regression for solving non-linear time-dependent partial differential equations. Engineering Applications of Artificial Intelligence. 160. 111879–111879. 1 indexed citations
2.
Tiwari, Aruna, et al.. (2024). A taxonomy of unsupervised feature selection methods including their pros, cons, and challenges. The Journal of Supercomputing. 80(16). 24212–24240. 3 indexed citations
3.
4.
Tiwari, Aruna, et al.. (2023). A novel feature extraction technique for the clustering of SNP data. Journal of Oilseeds Research. 40(Specialissue).
5.
Tiwari, Aruna, et al.. (2023). Scalable Kernelized Deep Fuzzy Clustering Algorithms for Big Data. 1 indexed citations
6.
7.
Tiwari, Aruna, et al.. (2023). An incremental clustering method based on multiple objectives for dynamic data analysis. Multimedia Tools and Applications. 83(13). 38145–38165. 4 indexed citations
8.
Tiwari, Aruna, et al.. (2023). A Novel Clustering-Based Hybrid Feature Selection Approach Using Ant Colony Optimization. Arabian Journal for Science and Engineering. 48(8). 10727–10744. 10 indexed citations
9.
Tiwari, Aruna, et al.. (2023). Apache Spark-based scalable feature extraction approaches for protein sequence and their clustering performance analysis. International Journal of Data Science and Analytics. 15(4). 359–378.
10.
Patel, Om Prakash, et al.. (2022). Advanced Quantum Inspired Evolutionary Optimization Algorithm. 57–64.
11.
Tiwari, Aruna, et al.. (2021). Apache Spark based kernelized fuzzy clustering framework for single nucleotide polymorphism sequence analysis. Computational Biology and Chemistry. 92. 107454–107454. 21 indexed citations
12.
Tiwari, Aruna, et al.. (2020). A Novel Scalable Kernelized Fuzzy Clustering Algorithms Based on In-Memory Computation for Handling Big Data. IEEE Transactions on Emerging Topics in Computational Intelligence. 5(6). 908–919. 14 indexed citations
13.
Bharill, Neha, Om Prakash Patel, & Aruna Tiwari. (2017). Quantum-inspired evolutionary approach for selection of optimal parameters of fuzzy clustering. International Journal of Systems Assurance Engineering and Management. 9(4). 875–887. 6 indexed citations
14.
Bharill, Neha, et al.. (2016). Fuzzy Based Clustering Algorithms to Handle Big Data with Implementation on Apache Spark. 95–104. 16 indexed citations
15.
Bharill, Neha, et al.. (2016). Fuzzy Based Scalable Clustering Algorithms for Handling Big Data Using Apache Spark. IEEE Transactions on Big Data. 2(4). 339–352. 48 indexed citations
16.
Bharill, Neha, Om Prakash Patel, & Aruna Tiwari. (2015). An Enhanced Quantum-Inspired Evolutionary Fuzzy Clustering. 772–779. 7 indexed citations
17.
Bharill, Neha, et al.. (2015). A Novel Technique of Feature Extraction with Dual Similarity Measures for Protein Sequence Classification. Procedia Computer Science. 48. 795–801. 5 indexed citations
18.
Patel, Om Prakash, Neha Bharill, & Aruna Tiwari. (2015). A Quantum-Inspired Fuzzy based Evolutionary algorithm for data clustering. 1–8. 12 indexed citations
19.
20.
Bharill, Neha & Aruna Tiwari. (2014). Enhanced cluster validity index for the evaluation of optimal number of clusters for Fuzzy C-Means algorithm. 1526–1533. 15 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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