Harsh Gupta

667 total citations · 1 hit paper
18 papers, 418 citations indexed

About

Harsh Gupta is a scholar working on Artificial Intelligence, Electrical and Electronic Engineering and Computer Networks and Communications. According to data from OpenAlex, Harsh Gupta has authored 18 papers receiving a total of 418 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Artificial Intelligence, 5 papers in Electrical and Electronic Engineering and 3 papers in Computer Networks and Communications. Recurrent topics in Harsh Gupta's work include COVID-19 diagnosis using AI (3 papers), AI in cancer detection (2 papers) and Energy Load and Power Forecasting (2 papers). Harsh Gupta is often cited by papers focused on COVID-19 diagnosis using AI (3 papers), AI in cancer detection (2 papers) and Energy Load and Power Forecasting (2 papers). Harsh Gupta collaborates with scholars based in India, United States and Taiwan. Harsh Gupta's co-authors include Anirban Chowdhury, Ram Bilas Pachori, Karl McCreadie, Pramod Gaur, Hui Wang, R. Srikant, Atilla Eryılmaz, Deeksha Chandola, Vinay Anand Tikkiwal and Ching‐Hsien Hsu and has published in prestigious journals such as IEEE Transactions on Instrumentation and Measurement, Neural Processing Letters and International Journal of Imaging Systems and Technology.

In The Last Decade

Harsh Gupta

16 papers receiving 400 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.

A Sliding Window Common Spatial Pattern for Enhancing Motor Imagery Classification in EEG-BCI

2021 186 citations Pramod Gaur, Harsh Gupta et al. IEEE Transactions on Instrumentation and Measurement profile →

Peers

Harsh Gupta

CN

EEE

AI

SP

CMN

Tsung-Yu Hsieh Taiwan

Shang-Lin Wu Taiwan

Sumit Soman India

Nicholas R. Waytowich United States

Mohammed J. Alhaddad Saudi Arabia

Esmeralda C. Djamal Indonesia

Ilham Ari Elbaith Zaeni Indonesia

Luis E. Falcón-Morales Mexico

Cédric Gouy‐Pailler France

Tsung-Yu Hsieh Taiwan

Harsh Gupta

122 ×0.7

CN

52 ×0.4

EEE

174 ×1.6

AI

44 ×0.7

SP

47 ×0.7

CMN

Citations per year, relative to Harsh Gupta Harsh Gupta (= 1×) peers Tsung-Yu Hsieh

Countries citing papers authored by Harsh Gupta

Since Specialization

Citations

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

Fields of papers citing papers by Harsh Gupta

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Harsh Gupta

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

All Works

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18 of 18 papers shown

1.

Gupta, Harsh, et al.. (2025). Longitudinal retrospective study of real-world adherence to colorectal cancer screening before and after the COVID-19 pandemic in the USA. BMJ Public Health. 3(1). e001734–e001734. 1 indexed citations

2.

Gupta, Harsh, et al.. (2024). Forecasting Commodity Prices using Machine Learning. International Journal of Scientific Research in Science and Technology. 81–89.

3.

Gupta, Harsh, et al.. (2023). Heart Disease Prediction Using Machine Learning. International Journal For Multidisciplinary Research. 5(6). 1 indexed citations

4.

Gupta, Harsh, et al.. (2022). A hybrid convolutional neural network model to detect COVID‐19 and pneumonia using chest X‐ray images. International Journal of Imaging Systems and Technology. 33(1). 39–52. 12 indexed citations

5.

Gupta, Harsh, et al.. (2022). Comparative Analysis of Machine Learning Models to Predict Depression, Anxiety and Stress. 1199–1203. 6 indexed citations

6.

Gupta, Koyel Datta, et al.. (2021). A Novel Lightweight Deep Learning-Based Histopathological Image Classification Model for IoMT. Neural Processing Letters. 55(1). 205–228. 35 indexed citations

7.

Gupta, Harsh, et al.. (2021). Automated Quadruped Robot Simulation using Internet of Things and MATLAB. Journal of Physics Conference Series. 2115(1). 12022–12022. 2 indexed citations

8.

Gaur, Pramod, Harsh Gupta, Anirban Chowdhury, et al.. (2021). A Sliding Window Common Spatial Pattern for Enhancing Motor Imagery Classification in EEG-BCI. IEEE Transactions on Instrumentation and Measurement. 70. 1–9. 186 indexed citations breakdown →

9.

Gupta, Harsh, et al.. (2021). Diagnosis of COVID-19 from chest X-ray images using Deep Learning Model.

10.

Gupta, Harsh, et al.. (2021). Position: drone camera communication meets robotic soil sensing. 18–20. 2 indexed citations

11.

Chandola, Deeksha, et al.. (2020). Multi-step ahead forecasting of global solar radiation for arid zones using deep learning. Procedia Computer Science. 167. 626–635. 56 indexed citations

12.

Gupta, Harsh, et al.. (2020). The Role of Regularization in Overparameterized Neural Networks. 4683–4688. 3 indexed citations

13.

Gupta, Harsh, R. Srikant, & Lei Ying. (2019). Finite-Time Performance Bounds and Adaptive Learning Rate Selection for Two Time-Scale Reinforcement Learning. arXiv (Cornell University). 32. 4704–4713. 13 indexed citations

14.

Gupta, Harsh, Atilla Eryılmaz, & R. Srikant. (2019). Link Rate Selection using Constrained Thompson Sampling. 24 indexed citations

15.

Gupta, Harsh, et al.. (2019). Performance Analysis of Multiple Classifiers using different Term Weighting Schemes for Sentiment Analysis. 637–641. 7 indexed citations

16.

Gupta, Harsh, et al.. (2018). A Lip Reading Model Using CNN with Batch Normalization. 1–6. 22 indexed citations

17.

Gupta, Harsh. (2018). Smartphone Based Cervical Spine Stress Prevention. Journal of Software Engineering and Applications. 11(2). 110–120. 9 indexed citations

18.

Gupta, Harsh, Atilla Eryılmaz, & R. Srikant. (2018). Low-Complexity, Low-Regret Link Rate Selection in Rapidly-Varying Wireless Channels. 540–548. 39 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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