T. Saranya
- Computer Networks and Communications top 5%
- Artificial Intelligence top 10%
- Signal Processing top 5%
- Plant Science
- Information Systems
- Co-authors
- C. DeisyTran Duc ChungS. SrideviM.K.A. Ahamed KhanKalaiarasi Sonai Muthu AnbananthenK. KousalyaA. Sheik AbdullahR. Parvathi
- Topics
- AI in cancer detection (3 papers)Digital Imaging for Blood Diseases (2 papers)Network Security and Intrusion Detection (2 papers)
In The Last Decade
T. Saranya
12 papers receiving 373 citations
Hit Papers
Peers
Comparison fields: 5 of 72
- Computer Networks and Communications 222
- Artificial Intelligence 192
- Signal Processing 126
- Plant Science 78
- Information Systems 41
Countries citing papers authored by T. Saranya
This map shows the geographic impact of T. Saranya'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 T. Saranya with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites T. Saranya more than expected).
Fields of papers citing papers by T. Saranya
This network shows the impact of papers produced by T. Saranya. 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 T. Saranya. The network helps show where T. Saranya may publish in the future.
Co-authorship network of co-authors of T. Saranya
This figure shows the co-authorship network connecting the top 25 collaborators of T. Saranya. A scholar is included among the top collaborators of T. Saranya 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 T. Saranya. T. Saranya is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 9 | |
| 3 | 3 | |
| 4 | A comparative study of deep learning and Internet of Things for precision agriculturebreakdown → | 114 |
| 5 | 2 | |
| 6 | 1 | |
| 7 | 0 | |
| 8 | 1 | |
| 9 | 8 | |
| 10 | Performance Analysis of Machine Learning Algorithms in Intrusion Detection System: A Reviewbreakdown → | 251 |
| 11 | Sentiment Analysis Of Movie Review Using Data Analytics Techniques | 2 |
| 12 | Algorithm and Design Techniques – A Survey | 0 |
| 13 | 3 | |
| 14 | Mining Features and Ranking Products From Online Customer Reviews | 4 |
| 15 | Gabor Wavelet based Detection of Architectural Distortion and Mass in Mammographic Images and Classification using Adaptive Neuro Fuzzy Inference System | 0 |
| 16 | 1 |
About T. Saranya
T. Saranya is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Computer Networks and Communications, having authored 16 papers that have together received 399 indexed citations. Recurring topics across this work include AI in cancer detection (3 papers), Digital Imaging for Blood Diseases (2 papers) and Network Security and Intrusion Detection (2 papers). The work is most often cited by research in Signal Processing (126 citations), Computer Networks and Communications (222 citations) and Artificial Intelligence (192 citations). T. Saranya has collaborated with scholars based in India, Malaysia and Vietnam. Frequent co-authors include C. Deisy, Tran Duc Chung, S. Sridevi, M.K.A. Ahamed Khan, S. Sridevi, Kalaiarasi Sonai Muthu Anbananthen, K. Kousalya, A. Sheik Abdullah, R. Parvathi and Saru Kumari. Their work appears in journals such as Engineering Applications of Artificial Intelligence, Computers in Biology and Medicine and Materials Today Proceedings.
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.