Rajesh Vasa

1.5k total citations
86 papers, 885 citations indexed

About

Rajesh Vasa is a scholar working on Information Systems, Artificial Intelligence and Computer Science Applications. According to data from OpenAlex, Rajesh Vasa has authored 86 papers receiving a total of 885 indexed citations (citations by other indexed papers that have themselves been cited), including 32 papers in Information Systems, 31 papers in Artificial Intelligence and 14 papers in Computer Science Applications. Recurrent topics in Rajesh Vasa's work include Software Engineering Research (24 papers), Software Engineering Techniques and Practices (16 papers) and Advanced Software Engineering Methodologies (15 papers). Rajesh Vasa is often cited by papers focused on Software Engineering Research (24 papers), Software Engineering Techniques and Practices (16 papers) and Advanced Software Engineering Methodologies (15 papers). Rajesh Vasa collaborates with scholars based in Australia, Malaysia and United States. Rajesh Vasa's co-authors include John Grundy, Kon Mouzakis, Jean-Guy Schneider, Leonard Hoon, Oscar Nierstrasz, Scott Barnett, Akihiro Noguchi, Markus Lumpe, Mohamed Abdelrazek and Philip Branch and has published in prestigious journals such as SHILAP Revista de lepidopterología, Information Sciences and IEEE Transactions on Software Engineering.

In The Last Decade

Rajesh Vasa

73 papers receiving 833 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Rajesh Vasa Australia	17	463	215	146	130	126	86	885
Alf Inge Wang Norway	19	616 1.3×	181 0.8×	388 2.7×	66 0.5×	155 1.2×	93	1.8k
Rita Francese Italy	15	320 0.7×	127 0.6×	116 0.8×	87 0.7×	58 0.5×	98	913
David Lowe Australia	19	466 1.0×	151 0.7×	92 0.6×	41 0.3×	150 1.2×	82	1.4k
Andreas Jedlitschka Germany	14	509 1.1×	136 0.6×	125 0.9×	164 1.3×	112 0.9×	41	884
Stelios Xinogalos Greece	18	329 0.7×	113 0.5×	599 4.1×	67 0.5×	96 0.8×	95	1.1k
Shahriyar Amini United States	8	305 0.7×	150 0.7×	96 0.7×	29 0.2×	75 0.6×	13	755
Hugo Fukś Brazil	17	250 0.5×	167 0.8×	128 0.9×	49 0.4×	61 0.5×	112	951
Olga De Troyer Belgium	16	360 0.8×	339 1.6×	53 0.4×	44 0.3×	146 1.2×	107	987
Manuel Caeiro Rodríguez Spain	16	283 0.6×	179 0.8×	457 3.1×	26 0.2×	119 0.9×	157	1.3k
Martha E. Crosby United States	16	343 0.7×	168 0.8×	204 1.4×	76 0.6×	22 0.2×	55	890

Countries citing papers authored by Rajesh Vasa

Since Specialization

Citations

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

Fields of papers citing papers by Rajesh Vasa

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Rajesh Vasa

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

All Works

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20 of 20 papers shown

Vasa, Rajesh. (2025). CLOUD-NATIVE MIDDLEWARE: AI AS THE DRIVING FORCE BEHIND DIGITAL TRANSFORMATION. INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING & TECHNOLOGY. 16(1). 3358–3374.

Yang, Lu‐Xing, Gang Li, Robin Doss, et al.. (2025). Mitigating malware prevalence in networks with arbitrary topologies: a Flip-It cyber game approach integrated with epidemic modeling. Information Sciences. 726. 122753–122753.

Gao, Jinlong, et al.. (2025). Machine learning algorithms enhance the accuracy of radiographic diagnosis of dental caries: a comparative study. Dentomaxillofacial Radiology. 54(8). 632–641.

Li, Ziyuan, et al.. (2025). Supervised machine learning for corrosion assessment of multi-principal element alloys using experimental and generative datasets. npj Materials Degradation. 9(1). 1 indexed citations

Vasa, Rajesh, et al.. (2025). Descriptor: Deakin IoT Traffic (D-IoT). 2. 8–16. 2 indexed citations

Fuller‐Tyszkiewicz, Matthew, Allan Jones, Rajesh Vasa, et al.. (2025). Artificial Intelligence Software to Accelerate Screening for Living Systematic Reviews. Clinical Child and Family Psychology Review. 3 indexed citations

Maddison, Ralph, Rebecca Nourse, Reza Daryabeygi‐Khotbehsara, et al.. (2025). Digital Home-Based Self-Monitoring System for People with Heart Failure: Protocol for Development of SmartHeart and Evaluation of Feasibility and Acceptability. JMIR Research Protocols. 14. e62964–e62964. 3 indexed citations

Gao, Jinlong, et al.. (2024). Australian Dentist's Knowledge and Perceptions of Factors Affecting Radiographic Interpretation. International Dental Journal. 74(3). 589–596. 4 indexed citations

Han, Jin, Alexis E. Whitton, Leonard Hoon, et al.. (2024). Passive sensing data predicts stress in university students: a supervised machine learning method for digital phenotyping. Frontiers in Psychiatry. 15. 1422027–1422027. 2 indexed citations

10.

Barnett, Scott, et al.. (2024). Comparative analysis of real issues in open-source machine learning projects. Empirical Software Engineering. 29(3).

11.

Grundy, John, et al.. (2023). Case study of designing and evaluating an independent open learner model tool. Monash University Research Portal (Monash University). 8(1).

12.

Barnett, Scott, Shobi Sivathamboo, Piero Perucca, et al.. (2023). EEG datasets for seizure detection and prediction— A review. Epilepsia Open. 8(2). 252–267. 47 indexed citations

13.

Thudumu, Srikanth, et al.. (2023). UAV Dynamic Object Tracking with Lightweight Deep Vision Reinforcement Learning. Algorithms. 16(5). 227–227. 6 indexed citations

14.

Abdelrazek, Mohamed, et al.. (2023). Robustness Attributes to Safeguard Machine Learning Models in Production. 1–9. 2 indexed citations

15.

Barnett, Scott, Shobi Sivathamboo, Piero Perucca, et al.. (2023). EEG based automated seizure detection – A survey of medical professionals. Epilepsy & Behavior. 149. 109518–109518. 13 indexed citations

16.

Abdelrazek, Mohamed, et al.. (2017). Methods of distributed processing for combat simulation data generation. 1 indexed citations

17.

Fernando, Niroshinie, et al.. (2016). Examining digital assisted living: towards a case study of smart homes for the elderly. Deakin Research Online (Deakin University). 1–11. 9 indexed citations

18.

Barnett, Scott, Rajesh Vasa, & John Grundy. (2015). Bootstrapping mobile app development. International Conference on Software Engineering. 2. 657–660. 15 indexed citations

19.

Sukunesan, Suku, et al.. (2014). Mobile learning in corporate businesses: A review of literature focusing on journal articles. Swinburne Research Bank (Swinburne University of Technology). 1–10. 1 indexed citations

20.

Vasa, Rajesh, et al.. (2005). Detecting structural changes in object oriented software systems. Figshare. 463–470. 13 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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