Lalit Mohan Goyal
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- Medical Image Segmentation Techniques 2
- Neurology top 5%
- Media Technology top 5%
- Advanced Image Fusion Techniques 2
- Artificial Intelligence top 5%
- Imbalanced Data Classification Techniques 3
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- Data Mining Algorithms and Applications 6
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- Rough Sets and Fuzzy Logic 3
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- Data Management and Algorithms 3
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- COVID-19 Pandemic Impacts 2
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- COVID-19 and Mental Health 2
- Co-authors
- Mamta MittalD. Jude HemanthAmit VermaIqbaldeep KaurSudipta RoyMuhammad Attique KhanJasleen Kaur SethiBhavneet Kaur
- Journals
- SHILAP Revista de lepidopterología (2 papers)IEEE Access (1 paper)Sensors (1 paper)
- Partner nations
- IndiaUnited StatesItaly
In The Last Decade
Lalit Mohan Goyal
39 papers receiving 1.1k citations
Peers
Comparison fields: 5 of 141
- Computer Vision and Pattern Recognition 346
- Neurology 140
- Media Technology 91
- Artificial Intelligence 320
- Health Informatics 11
Countries citing papers authored by Lalit Mohan Goyal
This map shows the geographic impact of Lalit Mohan Goyal'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 Lalit Mohan Goyal with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Lalit Mohan Goyal more than expected).
Fields of papers citing papers by Lalit Mohan Goyal
This network shows the impact of papers produced by Lalit Mohan Goyal. 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 Lalit Mohan Goyal. The network helps show where Lalit Mohan Goyal may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Lalit Mohan Goyal, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2022 | 2 | |
| 2 | 2021 | 31 | |
| 3 | 2021 | 2 | |
| 4 | 2020 | 20 | |
| 5 | 2020 | 22 | |
| 6 | 2020 | 35 | |
| 7 | 2020 | 5 | |
| 8 | 2020 | 2 | |
| 9 | 2020 | 23 | |
| 10 | 2020 | 13 | |
| 11 | 2019 | 53 | |
| 12 | 2019 | 30 | |
| 13 | 2019 | 123 | |
| 14 | 2019 | 179 | |
| 15 | 2018 | 4 | |
| 16 | 2018 | 16 | |
| 17 | 2018 | 30 | |
| 18 | 2018 | 25 | |
| 19 | 2018 | 45 | |
| 20 | 2014 | 1 |
About Lalit Mohan Goyal
Lalit Mohan Goyal is a scholar working on Information Systems, Computer Vision and Pattern Recognition and Signal Processing, having authored 41 papers that have together received 1.2k indexed citations. Recurring topics across this work include Data Mining Algorithms and Applications (6 papers), Imbalanced Data Classification Techniques (3 papers), Rough Sets and Fuzzy Logic (3 papers), Data Management and Algorithms (3 papers), Advanced Image Fusion Techniques (2 papers), COVID-19 Pandemic Impacts (2 papers), Medical Image Segmentation Techniques (2 papers) and COVID-19 and Mental Health (2 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (346 citations), Neurology (140 citations) and Media Technology (91 citations). Lalit Mohan Goyal has collaborated with scholars based in India, United States and Italy. Frequent co-authors include Mamta Mittal, D. Jude Hemanth, Amit Verma, Iqbaldeep Kaur, Sudipta Roy, Muhammad Attique Khan, Jasleen Kaur Sethi, Bhavneet Kaur, Suresh Chandra Satapathy and Tai-hoon Kim. Their work appears in journals such as SHILAP Revista de lepidopterología, IEEE Access and Sensors.
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.