Laxman Singh

771 total citations
52 papers, 481 citations indexed

About

Laxman Singh is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Laxman Singh has authored 52 papers receiving a total of 481 indexed citations (citations by other indexed papers that have themselves been cited), including 22 papers in Artificial Intelligence, 20 papers in Computer Vision and Pattern Recognition and 13 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Laxman Singh's work include AI in cancer detection (12 papers), Medical Image Segmentation Techniques (5 papers) and Brain Tumor Detection and Classification (5 papers). Laxman Singh is often cited by papers focused on AI in cancer detection (12 papers), Medical Image Segmentation Techniques (5 papers) and Brain Tumor Detection and Classification (5 papers). Laxman Singh collaborates with scholars based in India, United States and Saudi Arabia. Laxman Singh's co-authors include Zainul Abdin Jaffery, Rajeev Kumar, Matthew Chin Heng Chua, Quang H. Nguyen, Binh P. Nguyen, Sudhir Kumar Sharma, K. Vijayakumar, Devendra Kumar, Mrinal Pandey and Jay Yadav and has published in prestigious journals such as Computers & Electrical Engineering, Environmental Technology & Innovation and Journal of Ambient Intelligence and Humanized Computing.

In The Last Decade

Laxman Singh

42 papers receiving 434 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Laxman Singh India 13 186 154 129 53 51 52 481
Hameedur Rahman Pakistan 13 231 1.2× 195 1.3× 115 0.9× 18 0.3× 69 1.4× 62 596
S. Phani Praveen India 14 134 0.7× 203 1.3× 61 0.5× 80 1.5× 108 2.1× 53 685
Yousef Alhwaiti Saudi Arabia 10 100 0.5× 141 0.9× 87 0.7× 19 0.4× 68 1.3× 22 385
Nouf Abdullah Almujally Saudi Arabia 14 237 1.3× 166 1.1× 80 0.6× 10 0.2× 69 1.4× 65 599
Abeer Saber Egypt 13 156 0.8× 408 2.6× 222 1.7× 33 0.6× 33 0.6× 24 694
Yaghoub Pourasad Iran 12 239 1.3× 164 1.1× 95 0.7× 26 0.5× 36 0.7× 30 542
C. Karthikeyan India 9 69 0.4× 135 0.9× 73 0.6× 16 0.3× 40 0.8× 51 359
T. Saravanan India 14 104 0.6× 102 0.7× 29 0.2× 29 0.5× 95 1.9× 57 488
R. Joshua Samuel Raj India 8 100 0.5× 132 0.9× 56 0.4× 44 0.8× 111 2.2× 28 513
Mehrez Marzougui Saudi Arabia 13 180 1.0× 136 0.9× 58 0.4× 18 0.3× 13 0.3× 50 505

Countries citing papers authored by Laxman Singh

Since Specialization
Citations

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

Fields of papers citing papers by Laxman Singh

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Laxman Singh

This figure shows the co-authorship network connecting the top 25 collaborators of Laxman Singh. A scholar is included among the top collaborators of Laxman Singh 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 Laxman Singh. Laxman Singh 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.
Singh, Laxman, et al.. (2025). Enhanced hyper tuning using bioinspired-based deep learning model for accurate lung cancer detection and classification. The International Journal of Artificial Organs. 48(10). 774–793.
2.
Singh, Laxman, et al.. (2025). Hybrid ensemble framework for multimodal biometric system using binary patterns and deep learning. International Journal of Information Technology.
3.
Kumar, Devendra, et al.. (2024). A novel scheme of classification for non-functional requirements using CNN with LSTM and GRU new hidden layer. International Journal of Grid and Utility Computing. 1(1).
4.
Kumar, Devendra, Anil Rai, & Laxman Singh. (2024). A novel scheme of classification for non-functional requirements using CNN with LSTM and GRU new hidden layer. International Journal of Grid and Utility Computing. 15(5). 484–497.
5.
Yadav, Jay, Zainul Abdin Jaffery, & Laxman Singh. (2024). Automatic Face Recognition Using Legendre Moments and Auto-Associative Memory. 1625–1629.
6.
Singh, Laxman, et al.. (2023). Design of an Efficient Integrated Feature Engineering based Deep Learning Model Using CNN for Customer’s Review Helpfulness Prediction. Wireless Personal Communications. 133(4). 2125–2161. 3 indexed citations
7.
Singh, Laxman, et al.. (2023). Prediction of customer review’s helpfulness based on sentences encoding using CNN-BiGRU model. Journal of Autonomous Intelligence. 6(3). 699–699. 1 indexed citations
8.
Singh, Laxman, et al.. (2023). Integrated feature engineering based deep learning model for predicting customer’s review helpfulness. Journal of Intelligent & Fuzzy Systems. 44(6). 8851–8868. 3 indexed citations
9.
Singh, Laxman, et al.. (2023). Malicious Node Detection in Vehicular Ad-hoc Network (VANET) using Enhanced Beacon Trust Management with Clustering Protocol (EBTM-CP). Wireless Personal Communications. 130(1). 321–346. 12 indexed citations
10.
11.
Singh, Laxman, et al.. (2022). A Systematic Review on the Detection and Classification of Plant Diseases Using Machine Learning. International Journal of Software Innovation. 11(1). 1–25. 12 indexed citations
12.
Singh, Laxman, et al.. (2022). Automated Detection of Lung Cancer using Transfer Learning based Deep Learning. 500–504. 9 indexed citations
13.
Kumar, Rajeev, et al.. (2021). Path planning for the autonomous robots using modified grey wolf optimization approach. Journal of Intelligent & Fuzzy Systems. 40(5). 9453–9470. 39 indexed citations
14.
Mishra, Anju, Laxman Singh, & Mrinal Pandey. (2021). Short Survey on machine learning techniques used for diabetic retinopathy detection. 4 indexed citations
15.
Nguyen, Quang H., et al.. (2020). Diabetic Retinopathy Detection using Deep Learning. 103–107. 70 indexed citations
16.
Singh, Laxman, Sunil Kumar Chaudhary, Yogesh Kumar Verma, Jay Yadav, & Rajeev Kumar. (2019). Smart Volume Controller for Mobile Phones. International Journal of Engineering and Advanced Technology. 9(2). 256–259. 3 indexed citations
17.
Singh, Laxman & Zainul Abdin Jaffery. (2017). Hybrid technique for the detection of suspicious lesions in digital mammograms. International Journal of Biomedical Engineering and Technology. 24(2). 184–184. 3 indexed citations
18.
Singh, Laxman, et al.. (2017). Comparative Analysis of Recurrent Networks for Pattern Storage and Recalling of Static Images. International Journal of Computer Applications. 170(10). 15–19. 4 indexed citations
19.
Jaffery, Zainul Abdin & Laxman Singh. (2014). Computerised segmentation of suspicious lesions in the digital mammograms. Computer Methods in Biomechanics and Biomedical Engineering Imaging & Visualization. 5(2). 77–86. 6 indexed citations
20.
Singh, Laxman, et al.. (2009). Segmentation and Characterization of Brain Tumor from MR Images. 5. 815–819. 24 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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