Aneesh Krishna

1.4k total citations
119 papers, 791 citations indexed

About

Aneesh Krishna is a scholar working on Information Systems, Artificial Intelligence and Management Information Systems. According to data from OpenAlex, Aneesh Krishna has authored 119 papers receiving a total of 791 indexed citations (citations by other indexed papers that have themselves been cited), including 50 papers in Information Systems, 48 papers in Artificial Intelligence and 23 papers in Management Information Systems. Recurrent topics in Aneesh Krishna's work include Advanced Software Engineering Methodologies (29 papers), Service-Oriented Architecture and Web Services (23 papers) and Business Process Modeling and Analysis (22 papers). Aneesh Krishna is often cited by papers focused on Advanced Software Engineering Methodologies (29 papers), Service-Oriented Architecture and Web Services (23 papers) and Business Process Modeling and Analysis (22 papers). Aneesh Krishna collaborates with scholars based in Australia, India and Norway. Aneesh Krishna's co-authors include Wanquan Liu, Ajmal Mian, Raj P. Gopalan, Duc-Son Pham, Aditya Ghose, Sajib Mistry, Arshinder Kaur, Mahbuba Afrin, Lov Kumar and Senjian An and has published in prestigious journals such as SHILAP Revista de lepidopterología, Scientific Reports and Pattern Recognition.

In The Last Decade

Aneesh Krishna

105 papers receiving 722 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Aneesh Krishna Australia 13 257 205 202 93 66 119 791
Shailendra Narayan Singh India 19 209 0.8× 370 1.8× 150 0.7× 175 1.9× 50 0.8× 115 1.1k
Andrea Corradini Italy 17 187 0.7× 594 2.9× 170 0.8× 30 0.3× 42 0.6× 108 1.1k
Alberto Faro Italy 13 183 0.7× 127 0.6× 164 0.8× 67 0.7× 59 0.9× 80 556
Ravie Chandren Muniyandi Malaysia 16 81 0.3× 210 1.0× 106 0.5× 148 1.6× 46 0.7× 80 951
Zhongmin Liu China 14 164 0.6× 123 0.6× 33 0.2× 78 0.8× 33 0.5× 33 562
K. G. Srinivasa India 15 114 0.4× 356 1.7× 228 1.1× 18 0.2× 78 1.2× 129 821
Joe Tekli Lebanon 17 171 0.7× 364 1.8× 202 1.0× 12 0.1× 119 1.8× 51 750
Shakeel Ahmed Saudi Arabia 14 64 0.2× 150 0.7× 86 0.4× 26 0.3× 36 0.5× 60 605
M. P. S. Bhatia India 19 165 0.6× 503 2.5× 332 1.6× 24 0.3× 170 2.6× 126 1.2k

Countries citing papers authored by Aneesh Krishna

Since Specialization
Citations

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

Fields of papers citing papers by Aneesh Krishna

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Aneesh Krishna

This figure shows the co-authorship network connecting the top 25 collaborators of Aneesh Krishna. A scholar is included among the top collaborators of Aneesh Krishna 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 Aneesh Krishna. Aneesh Krishna 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.
Li, Jianxin, Liang Qu, Taotao Cai, et al.. (2025). AI-generated content in cross-domain applications: Research trends, challenges and propositions. Knowledge-Based Systems. 330. 114634–114634.
2.
Hasan, Md Rakibul, Md Zakir Hossain, Shreya Ghosh, Aneesh Krishna, & Tom Gedeon. (2025). Empathy Detection From Text, Audiovisual, Audio or Physiological Signals: A Systematic Review of Task Formulations and Machine Learning Methods. IEEE Transactions on Affective Computing. 16(4). 2507–2525.
3.
Wang, Lei, Md Zakir Hossain, Syed Mohammed Shamsul Islam, et al.. (2025). TIME 2025: 1st International Workshop on Transformative Insights in Multi-faceted Evaluation. 1848–1851. 1 indexed citations
4.
Mahmud, Redowan, Jiong Jin, Jonathan Kua, et al.. (2025). Trusted Microservice Orchestration for Secure Edge Computing in Industrial Cyber-Physical Systems. IEEE Network. 39(4). 70–78. 1 indexed citations
5.
Afrin, Mahbuba, et al.. (2024). MURE: Multi-layer real-time livestock management architecture with unmanned aerial vehicles using deep reinforcement learning. Future Generation Computer Systems. 161. 454–466. 5 indexed citations
6.
Pham, Duc-Son, et al.. (2023). Improved Short-term Dense Bottleneck network for efficient scene analysis. Computer Vision and Image Understanding. 235. 103795–103795. 3 indexed citations
7.
Pham, Duc-Son, et al.. (2023). A real-time semantic segmentation model using iteratively shared features in multiple sub-encoders. Pattern Recognition. 140. 109557–109557. 30 indexed citations
8.
Krishna, Aneesh, et al.. (2019). Mixed-strategic Reasoning of the i* Goal Model. Journal of the Association for Information Systems. 116. 1 indexed citations
9.
Krishna, Aneesh, et al.. (2018). Optimal Reasoning of Opposing Non-functional Requirements based on Game Theory. Journal of the Association for Information Systems. 1 indexed citations
10.
Krishna, Aneesh, et al.. (2018). Gamifying Information System Testing-Qualitative Validation through Focus Group Discussion.. 1 indexed citations
11.
Krishna, Aneesh, et al.. (2018). CEA Based Reasoning with the i∗ Framework. Pacific Asia Conference on Information Systems. 174. 1 indexed citations
12.
Krishna, Aneesh, et al.. (2016). TOWARDS DEEP LEARNING IN GENOME-WIDE ASSOCIATION INTERACTION STUDIES. eSpace (Curtin University). 20. 7 indexed citations
13.
Krishna, Aneesh, et al.. (2016). OPTIMAL GOAL PROGRAMMING OF SOFTGOALS IN GOAL- ORIENTED REQUIREMENTS ENGINEERING. eSpace (Curtin University). 202. 3 indexed citations
14.
Krishna, Aneesh, et al.. (2015). Reasoning about Goal Satisfaction for Early Requirements Engineering in the i* Framework using Inter-Actor Dependency. Journal of the Association for Information Systems. 89. 4 indexed citations
15.
Krishna, Aneesh, et al.. (2014). Detecting SNP Interactions in Balanced and Imbalanced Datasets using Associative Classification. eSpace (Curtin University). 14(1). 7–18. 2 indexed citations
16.
Krishna, Aneesh, et al.. (2013). Optimal selection of operationalizations for non-functional requirements. eSpace (Curtin University). 69–78. 11 indexed citations
17.
Krishna, Aneesh, et al.. (2012). Supporting quantitative reasoning of non-functional requirements: a process-oriented approach. 88–92. 12 indexed citations
18.
Krishna, Aneesh. (2011). A Process Oriented Approach to Model Non-Functional Requirements Proposition Extending UML.. eSpace (Curtin University). 736–739.
19.
Krishna, Aneesh, et al.. (2010). Business modeling with the support of multiple notations in requirements engineering. Journal of the Association for Information Systems. 1766. 2 indexed citations
20.
Krishna, Aneesh, et al.. (2003). Agent-assisted Distributed Requirements Elicitation and Management.. Software Engineering and Knowledge Engineering. 526–533. 1 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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