J. Hemalatha

456 citations
19 papers · 302 indexed · 1 hit paper · h-index 6
Topics
Advanced Steganography and Watermarking Techniques (12 papers)Digital Media Forensic Detection (11 papers)Chaos-based Image/Signal Encryption (7 papers)
Journals
SHILAP Revista de lepidopterologíaPLoS ONEMultimedia Tools and Applications

In The Last Decade

J. Hemalatha

15 papers receiving 287 citations

Hit Papers

An Efficient DenseNet-Based Deep Learning Model for Malwa...2021202620222024202150100150

Peers

J. Hemalatha
Comparison fields: 5 of 61
  • Signal Processing 143
  • Computer Networks and Communications 129
  • Computer Vision and Pattern Recognition 116
  • Artificial Intelligence 112
  • Information Systems 49
Replace Pooja Yadav with:
Pooja Yadav India
Manaar Alam India
Ya-Qin Zhang China
S. Abijah Roseline India
Ke Shen United States
Martin Vuagnoux Switzerland
Laura Gheorghe Romania
Xiaochen Lian China
Songjie Wei China
Mário Almeida United Kingdom
J. Hemalatha relative to Pooja Yadav India Pooja Yadav's profile →
Citations per field
00.5×4.1×
Pooja Yadav · 1×
Citations per year

Countries citing papers authored by J. Hemalatha

Since Specialization
Citations

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

Fields of papers citing papers by J. Hemalatha

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of J. Hemalatha

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

All Works

19 of 19 papers shown
#WorkIndexed citations
1 14
2 0
3 0
4 0
5 39
6 13
7 3
8 1
9 7
10 2
11 44
12 1
13
An Efficient DenseNet-Based Deep Learning Model for Malware Detectionbreakdown →
171
14 1
15 1
16 0
17 3
18 1
19 1

About J. Hemalatha

J. Hemalatha is a scholar working on Computer Vision and Pattern Recognition, Human-Computer Interaction and Media Technology, having authored 19 papers that have together received 302 indexed citations. Recurring topics across this work include Advanced Steganography and Watermarking Techniques (12 papers), Digital Media Forensic Detection (11 papers) and Chaos-based Image/Signal Encryption (7 papers). The work is most often cited by research in Signal Processing (143 citations), Computer Vision and Pattern Recognition (116 citations) and Computer Networks and Communications (129 citations). J. Hemalatha has collaborated with scholars based in India, Saudi Arabia and United States. Frequent co-authors include S. Geetha, S. Abijah Roseline, Robertas Damaševičius, Seifedine Kadry, Adıtya Kumar Sahu, Monalisa Sahu, Gandharba Swain, M. Sekar, Chandan Kumar and Adnan Gutub. Their work appears in journals such as SHILAP Revista de lepidopterología, PLoS ONE and Multimedia Tools and Applications.

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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