Apeksha Koul

1.7k total citations · 1 hit paper
20 papers, 436 citations indexed

About

Apeksha Koul is a scholar working on Artificial Intelligence, Radiology, Nuclear Medicine and Imaging and Pulmonary and Respiratory Medicine. According to data from OpenAlex, Apeksha Koul has authored 20 papers receiving a total of 436 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Artificial Intelligence, 7 papers in Radiology, Nuclear Medicine and Imaging and 5 papers in Pulmonary and Respiratory Medicine. Recurrent topics in Apeksha Koul's work include COVID-19 diagnosis using AI (7 papers), Advanced Chemical Sensor Technologies (2 papers) and Brain Tumor Detection and Classification (2 papers). Apeksha Koul is often cited by papers focused on COVID-19 diagnosis using AI (7 papers), Advanced Chemical Sensor Technologies (2 papers) and Brain Tumor Detection and Classification (2 papers). Apeksha Koul collaborates with scholars based in India, Saudi Arabia and Australia. Apeksha Koul's co-authors include Yogesh Kumar, Sukhpreet Kaur, Anish Gupta, Jana Shafi, Pushpendra Singh Sisodia, Kavita Kavita, Mehdi Gheisari, Surender Singh, Wathiq Mansoor and Sonali Dash and has published in prestigious journals such as Scientific Reports, Frontiers in Oncology and Multimedia Tools and Applications.

In The Last Decade

Apeksha Koul

19 papers receiving 425 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 Systematic Review on Metaheuristic Optimization Techniques for Feature Selections in Disease Diagnosis: Open Issues and Challenges

2022 96 citations Sukhpreet Kaur, Yogesh Kumar et al. Archives of Computational Methods in Engineering profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Apeksha Koul India	11	189	83	78	76	49	20	436
Mohemmed Sha Saudi Arabia	9	137 0.7×	67 0.8×	58 0.7×	63 0.8×	56 1.1×	45	319
Worku Gachena Negera Ethiopia	6	112 0.6×	59 0.7×	55 0.7×	58 0.8×	54 1.1×	9	316
Happy Nkanta Monday China	14	252 1.3×	70 0.8×	124 1.6×	279 3.7×	43 0.9×	49	606
S. Punitha India	11	321 1.7×	42 0.5×	103 1.3×	146 1.9×	72 1.5×	42	494
Rekha Singh India	14	170 0.9×	62 0.7×	116 1.5×	182 2.4×	47 1.0×	20	418
Shankar Thawkar India	14	228 1.2×	45 0.5×	142 1.8×	218 2.9×	53 1.1×	23	511
Abhijith Reddy Beeravolu Australia	5	214 1.1×	229 2.8×	35 0.4×	115 1.5×	34 0.7×	6	473
Jyothisha J. Nair India	11	131 0.7×	34 0.4×	72 0.9×	59 0.8×	23 0.5×	49	304
Abdulkader Helwan Cyprus	10	132 0.7×	33 0.4×	56 0.7×	94 1.2×	44 0.9×	22	330
Jiangang Ma Australia	11	168 0.9×	43 0.5×	55 0.7×	65 0.9×	18 0.4×	29	468

Countries citing papers authored by Apeksha Koul

Since Specialization

Citations

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

Fields of papers citing papers by Apeksha Koul

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Apeksha Koul

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

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Shafi, Jana, et al.. (2025). Data-driven water quality prediction using hybrid machine learning approaches for sustainable development goal 6. Environment Development and Sustainability. 1 indexed citations

Johri, Prashant, et al.. (2025). Enhancing lymphoma cancer detection using deep transfer learning on histopathological images. Scientific Reports. 15(1). 38042–38042.

Koul, Apeksha, Surbhi Gupta, & Azra Shah. (2024). Optimized-ANN for Predicting Cancer Using Gene Expression RNA-Seq Data. 1–8. 1 indexed citations

Kumar, Yogesh, et al.. (2024). Automated detection and recognition system for chewable food items using advanced deep learning models. Scientific Reports. 14(1). 6589–6589. 5 indexed citations

Koul, Apeksha, et al.. (2024). Enhancing the detection of airway disease by applying deep learning and explainable artificial intelligence. Multimedia Tools and Applications. 83(31). 76773–76805. 8 indexed citations

Kim, SeongKi, et al.. (2024). Advanced CNN models in gastric cancer diagnosis: enhancing endoscopic image analysis with deep transfer learning. Frontiers in Oncology. 14. 1431912–1431912. 1 indexed citations

Kumar, Yogesh, et al.. (2023). Automated System to Diagnose and Detect the Allergy, Common Cold, Flu, and Covid using Machine Learning Approaches. 1–6. 1 indexed citations

Koul, Apeksha, et al.. (2023). An Analysis of Deep Transfer Learning-Based Approaches for Prediction and Prognosis of Multiple Respiratory Diseases Using Pulmonary Images. Archives of Computational Methods in Engineering. 31(2). 1023–1049. 19 indexed citations

Kumar, Yogesh, et al.. (2023). Machine Learning-based Approaches for Crop Recommendations and Prediction. 370–376. 1 indexed citations

10.

Koul, Apeksha, et al.. (2022). Artificial Intelligence Techniques to Predict the Airway Disorders Illness: A Systematic Review. Archives of Computational Methods in Engineering. 30(2). 831–864. 47 indexed citations

11.

Gupta, Anish, Apeksha Koul, & Yogesh Kumar. (2022). Pancreatic Cancer Detection using Machine and Deep Learning Techniques. 151–155. 32 indexed citations

12.

Kaur, Sukhpreet, et al.. (2022). A Systematic Review on Metaheuristic Optimization Techniques for Feature Selections in Disease Diagnosis: Open Issues and Challenges. Archives of Computational Methods in Engineering. 30(3). 1863–1895. 96 indexed citations breakdown →

13.

Kumar, Yogesh, Apeksha Koul, Sukhpreet Kaur, & Yu‐Chen Hu. (2022). Machine Learning and Deep Learning Based Time Series Prediction and Forecasting of Ten Nations’ COVID-19 Pandemic. SN Computer Science. 4(1). 91–91. 21 indexed citations

14.

Koul, Apeksha, Yogesh Kumar, & Anish Gupta. (2022). A Study on Bladder Cancer Detection using AI-based Learning Techniques. 19.1. 600–604. 11 indexed citations

15.

Kumar, Yogesh, et al.. (2022). A deep learning approaches and fastai text classification to predict 25 medical diseases from medical speech utterances, transcription and intent. Soft Computing. 26(17). 8253–8272. 27 indexed citations

16.

Kumar, Yogesh, et al.. (2022). A deep learning approaches in text-to-speech system: a systematic review and recent research perspective. Multimedia Tools and Applications. 82(10). 15171–15197. 51 indexed citations

17.

Singh, Surender, Wathiq Mansoor, Yogesh Kumar, et al.. (2022). Computational Intelligence and Metaheuristic Techniques for Brain Tumor Detection through IoMT‐Enabled MRI Devices. Wireless Communications and Mobile Computing. 2022(1). 32 indexed citations

18.

Kumar, Yogesh, et al.. (2022). Early Prediction of Neonatal Jaundice using Artificial Intelligence Techniques. 222–226. 18 indexed citations

19.

Kumar, Yogesh, Apeksha Koul, Pushpendra Singh Sisodia, et al.. (2021). Heart Failure Detection Using Quantum‐Enhanced Machine Learning and Traditional Machine Learning Techniques for Internet of Artificially Intelligent Medical Things. Wireless Communications and Mobile Computing. 2021(1). 60 indexed citations

20.

Koul, Apeksha, et al.. (2020). Semantic Segmentation and Contextual Information Based Image Scene Interpretation: A Review. 148–153. 4 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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