G. Madhu

631 total citations
27 papers, 361 citations indexed

About

G. Madhu is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Information Systems. According to data from OpenAlex, G. Madhu has authored 27 papers receiving a total of 361 indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Artificial Intelligence, 10 papers in Computer Vision and Pattern Recognition and 6 papers in Information Systems. Recurrent topics in G. Madhu's work include Digital Imaging for Blood Diseases (8 papers), Privacy-Preserving Technologies in Data (4 papers) and Smart Agriculture and AI (4 papers). G. Madhu is often cited by papers focused on Digital Imaging for Blood Diseases (8 papers), Privacy-Preserving Technologies in Data (4 papers) and Smart Agriculture and AI (4 papers). G. Madhu collaborates with scholars based in India, Saudi Arabia and Egypt. G. Madhu's co-authors include A. Govardhan, V. Lakshmana Gomathi Nayagam, V. R. Renjith, Sandeep Kautish, Ali Wagdy Mohamed, Manoj Kumar, Mohd Asif Shah, Irfan Ali, B. Srinivas and N. Z. Jhanjhi and has published in prestigious journals such as Journal of Hazardous Materials, Scientific Reports and Sensors.

In The Last Decade

G. Madhu

24 papers receiving 333 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
G. Madhu India	11	154	82	64	62	37	27	361
Hanna Wasyluk Poland	7	168 1.1×	79 1.0×	81 1.3×	13 0.2×	29 0.8×	11	355
Alireza Khanteymoori Iran	11	94 0.6×	19 0.2×	94 1.5×	48 0.8×	44 1.2×	36	429
Xuewen Chen United States	8	357 2.3×	65 0.8×	13 0.2×	99 1.6×	5 0.1×	19	507
Tuanfei Zhu China	8	373 2.4×	45 0.5×	9 0.1×	54 0.9×	5 0.1×	17	473
Yuanjian Zhang China	10	285 1.9×	170 2.1×	7 0.1×	89 1.4×	4 0.1×	39	566
Krung Sinapiromsaran Thailand	9	408 2.6×	35 0.4×	13 0.2×	43 0.7×	2 0.1×	34	501
Severino F. Galán Spain	11	171 1.1×	20 0.2×	46 0.7×	16 0.3×	34 0.9×	18	323
Muhammad Junaid Khan Pakistan	12	73 0.5×	40 0.5×	23 0.4×	118 1.9×	10 0.3×	43	478
Ji Wu China	10	96 0.6×	94 1.1×	20 0.3×	39 0.6×	5 0.1×	59	341

Countries citing papers authored by G. Madhu

Since Specialization

Citations

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

Fields of papers citing papers by G. Madhu

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of G. Madhu

This figure shows the co-authorship network connecting the top 25 collaborators of G. Madhu. A scholar is included among the top collaborators of G. Madhu 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 G. Madhu. G. Madhu 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

Madhu, G., et al.. (2024). Evaluating the performance of a non-uniform squash function in Capsule networks for early diabetic retinopathy detection using fundus image analysis. Results in Engineering. 23. 102820–102820.

Raj, V P Jagathy, et al.. (2024). DNN-STACK: a stacking technique based on deep neural network for detecting copy-move forgery. Neural Computing and Applications. 37(6). 4989–5004.

Madhu, G., Sandeep Kautish, Abdulaziz S. Almazyad, et al.. (2024). UCapsNet: A Two-Stage Deep Learning Model Using U-Net and Capsule Network for Breast Cancer Segmentation and Classification in Ultrasound Imaging. Cancers. 16(22). 3777–3777. 2 indexed citations

Madhu, G., Sandeep Kautish, Khalid A. Alnowibet, Hossam M. Zawbaa, & Ali Wagdy Mohamed. (2023). NIPUNA: A Novel Optimizer Activation Function for Deep Neural Networks. Axioms. 12(3). 246–246. 13 indexed citations

Madhu, G., et al.. (2023). XCovNet: An optimized xception convolutional neural network for classification of COVID-19 from point-of-care lung ultrasound images. Multimedia Tools and Applications. 83(11). 33653–33674. 17 indexed citations

Madhu, G., et al.. (2023). Secured and Privacy-Preserving Multi-Authority Access Control System for Cloud-Based Healthcare Data Sharing. Sensors. 23(5). 2617–2617. 14 indexed citations

Madhu, G., Ali Wagdy Mohamed, Sandeep Kautish, Mohd Asif Shah, & Irfan Ali. (2023). Intelligent diagnostic model for malaria parasite detection and classification using imperative inception-based capsule neural networks. Scientific Reports. 13(1). 13377–13377. 24 indexed citations

Madhu, G., et al.. (2023). Ensemble Learning Model for Gender Recognition Using the Human Voice. 1–5. 1 indexed citations

Madhu, G., et al.. (2022). New Decision-Making Technique Based on Hurwicz Criteria for燜uzzy燫anking. Computers, materials & continua/Computers, materials & continua (Print). 73(3). 4595–4609. 2 indexed citations

10.

Madhu, G., A. Govardhan, Vinayakumar Ravi, et al.. (2022). DSCN-net: a deep Siamese capsule neural network model for automatic diagnosis of malaria parasites detection. Multimedia Tools and Applications. 81(23). 34105–34127. 15 indexed citations

11.

Madhu, G., et al.. (2022). A Simple Attention Block Embedded in Standard CNN for Image Classification. 2022 International Conference on Applied Artificial Intelligence and Computing (ICAAIC). 24 3. 279–284. 1 indexed citations

12.

Madhu, G., et al.. (2021). Deep Stacked Ensemble Learning Model for COVID-19 Classification. Computers, materials & continua/Computers, materials & continua (Print). 70(3). 5467–5469. 4 indexed citations

13.

Madhu, G.. (2020). Intelligent Semantic Web Search Engines: A Brief Survey. Zenodo (CERN European Organization for Nuclear Research).

14.

Madhu, G., et al.. (2020). Capsule Networks for Malaria Parasite Classification: An Application Oriented Model. 2020 IEEE International Conference for Innovation in Technology (INOCON). 1–5. 4 indexed citations

15.

Madhu, G., et al.. (2019). A Novel Algorithm for Missing Data Imputation on Machine Learning. 173–177. 17 indexed citations

16.

Madhu, G., et al.. (2016). A New Paradigm for Development of Data Imputation Approach for Missing Value Estimation. International Journal of Electrical and Computer Engineering (IJECE). 6(6). 3222–3222. 5 indexed citations

17.

Madhu, G., et al.. (2014). Improve the Classifier Accuracy for Continuous Attributes in Biomedical Datasets Using a New Discretization Method. Procedia Computer Science. 31. 671–679. 21 indexed citations

18.

Madhu, G., et al.. (2014). A Novel Discretization Method for Continuous Attributes: A Machine Learning Approach. 4(1). 34–34. 2 indexed citations

19.

Madhu, G., et al.. (2011). Intelligent Semantic Web Search Engines: A Brief Survey. 2(1). 34–42. 47 indexed citations

20.

Renjith, V. R., et al.. (2010). Two-dimensional fuzzy fault tree analysis for chlorine release from a chlor-alkali industry using expert elicitation. Journal of Hazardous Materials. 183(1-3). 103–110. 83 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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