Gopalakrishnan E. A

757 total citations · 1 hit paper
6 papers, 467 citations indexed

About

Gopalakrishnan E. A is a scholar working on Cognitive Neuroscience, Artificial Intelligence and Neurology. According to data from OpenAlex, Gopalakrishnan E. A has authored 6 papers receiving a total of 467 indexed citations (citations by other indexed papers that have themselves been cited), including 2 papers in Cognitive Neuroscience, 2 papers in Artificial Intelligence and 2 papers in Neurology. Recurrent topics in Gopalakrishnan E. A's work include Machine Learning in Healthcare (2 papers), Brain Tumor Detection and Classification (2 papers) and ECG Monitoring and Analysis (1 paper). Gopalakrishnan E. A is often cited by papers focused on Machine Learning in Healthcare (2 papers), Brain Tumor Detection and Classification (2 papers) and ECG Monitoring and Analysis (1 paper). Gopalakrishnan E. A collaborates with scholars based in India, Saudi Arabia and United States. Gopalakrishnan E. A's co-authors include Soman K.P., Vijay Menon, V. Sowmya, Vinayakumar Ravi, Chinmay Chakraborty, V. V. Sajith Variyar and Riju Ramachandran Menon and has published in prestigious journals such as Computer Methods and Programs in Biomedicine, IEEE Transactions on Engineering Management and Multimedia Tools and Applications.

In The Last Decade

Gopalakrishnan E. A

5 papers receiving 431 citations

Hit Papers

align trajectories

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.

NSE Stock Market Prediction Using Deep-Learning Models

2018 364 citations Gopalakrishnan E. A, Vijay Menon et al. Procedia Computer Science profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Gopalakrishnan E. A India	4	315	153	99	94	81	6	467
Ji Ding China	6	149 0.5×	66 0.4×	30 0.3×	48 0.5×	66 0.8×	12	255
Sifan Wu China	6	129 0.4×	59 0.4×	68 0.7×	33 0.4×	40 0.5×	19	284
Haoyu Zhang China	4	256 0.8×	136 0.9×	66 0.7×	100 1.1×	77 1.0×	7	388
Qianggang Ding China	6	121 0.4×	58 0.4×	69 0.7×	33 0.4×	40 0.5×	7	234
Ozan Kocadağlı Türkiye	9	88 0.3×	20 0.1×	91 0.9×	20 0.2×	28 0.3×	21	309
Xiang Ma China	9	159 0.5×	93 0.6×	55 0.6×	34 0.4×	58 0.7×	21	255
Sudarsan Padhy India	10	120 0.4×	76 0.5×	85 0.9×	60 0.6×	46 0.6×	18	287
Zehong Yang China	5	138 0.4×	102 0.7×	159 1.6×	38 0.4×	54 0.7×	11	321
Piyush Kumar India	10	39 0.1×	27 0.2×	28 0.3×	18 0.2×	12 0.1×	31	258
Haolei Chen China	4	120 0.4×	64 0.4×	80 0.8×	64 0.7×	44 0.5×	13	273

Countries citing papers authored by Gopalakrishnan E. A

Since Specialization

Citations

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

Fields of papers citing papers by Gopalakrishnan E. A

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Gopalakrishnan E. A. 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 Gopalakrishnan E. A. The network helps show where Gopalakrishnan E. A may publish in the future.

Co-authorship network of co-authors of Gopalakrishnan E. A

This figure shows the co-authorship network connecting the top 25 collaborators of Gopalakrishnan E. A. A scholar is included among the top collaborators of Gopalakrishnan E. A 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 Gopalakrishnan E. A. Gopalakrishnan E. A is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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6 of 6 papers shown

1.

A, Gopalakrishnan E., et al.. (2025). An Indian database for grading wound healing and cross-corpus classification using perturbation-based Explainable AI models. Computer Methods and Programs in Biomedicine. 271. 108981–108981.

2.

Sowmya, V., et al.. (2023). Deep learning-based approach for multi-stage diagnosis of Alzheimer’s disease. Multimedia Tools and Applications. 83(6). 16799–16822. 26 indexed citations

3.

Ravi, Vinayakumar, et al.. (2022). Identification of intracranial haemorrhage (ICH) using ResNet with data augmentation using CycleGAN and ICH segmentation using SegAN. Multimedia Tools and Applications. 81(25). 36257–36273. 20 indexed citations

4.

Ravi, Vinayakumar, et al.. (2021). Explainable Deep Learning-Based Approach for Multilabel Classification of Electrocardiogram. IEEE Transactions on Engineering Management. 70(8). 2787–2799. 54 indexed citations

5.

Variyar, V. V. Sajith, Gopalakrishnan E. A, V. Sowmya, & Soman K.P.. (2019). A Complex Network Approach for Plant Growth Analysis using Images. 249–253. 3 indexed citations

6.

A, Gopalakrishnan E., et al.. (2018). NSE Stock Market Prediction Using Deep-Learning Models. Procedia Computer Science. 132. 1351–1362. 364 indexed citations breakdown →

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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