Tom Wanyama

529 total citations
26 papers, 272 citations indexed

About

Tom Wanyama is a scholar working on Artificial Intelligence, Media Technology and Information Systems. According to data from OpenAlex, Tom Wanyama has authored 26 papers receiving a total of 272 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Artificial Intelligence, 8 papers in Media Technology and 6 papers in Information Systems. Recurrent topics in Tom Wanyama's work include Experimental Learning in Engineering (8 papers), Software Engineering Research (6 papers) and Advanced Software Engineering Methodologies (6 papers). Tom Wanyama is often cited by papers focused on Experimental Learning in Engineering (8 papers), Software Engineering Research (6 papers) and Advanced Software Engineering Methodologies (6 papers). Tom Wanyama collaborates with scholars based in Canada, China and United States. Tom Wanyama's co-authors include Ishwar Singh, Behrouz H. Far, Dan Centea, Zhen Gao, Weiran Shen and Brian W. Baetz and has published in prestigious journals such as Journal of Network and Computer Applications, Procedia Manufacturing and International journal of engineering education.

In The Last Decade

Tom Wanyama

25 papers receiving 250 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Tom Wanyama Canada	9	102	64	63	42	29	26	272
Néjib Moalla France	9	112 1.1×	77 1.2×	59 0.9×	29 0.7×	44 1.5×	29	256
Emil Blixt Hansen Denmark	6	124 1.2×	35 0.5×	41 0.7×	39 0.9×	26 0.9×	6	308
David Koonce United States	8	216 2.1×	30 0.5×	68 1.1×	34 0.8×	23 0.8×	26	317
Gülesin Sena Daş Türkiye	11	177 1.7×	24 0.4×	50 0.8×	36 0.9×	25 0.9×	23	347
Rúben Costa Portugal	10	42 0.4×	40 0.6×	67 1.1×	23 0.5×	33 1.1×	36	282
Mary Bone United States	7	56 0.5×	89 1.4×	69 1.1×	111 2.6×	29 1.0×	13	283
Tianyuan Xiao China	11	194 1.9×	31 0.5×	47 0.7×	20 0.5×	18 0.6×	44	313
Juergen Mangler Austria	11	89 0.9×	114 1.8×	54 0.9×	25 0.6×	27 0.9×	47	313
Adrien Bécue Portugal	7	151 1.5×	69 1.1×	62 1.0×	47 1.1×	73 2.5×	8	391
Gianfranco E. Modoni Italy	10	168 1.6×	39 0.6×	41 0.7×	13 0.3×	50 1.7×	20	286

Countries citing papers authored by Tom Wanyama

Since Specialization

Citations

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

Fields of papers citing papers by Tom Wanyama

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Tom Wanyama

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

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Wanyama, Tom & Brian W. Baetz. (2023). DESIGNING A LEARNING FACTORY FOR TEACHING COMPLEX INTEGRATION OF TECHNOLOGIES THAT SUPPORT INDUSTRY 4.0. INTED proceedings. 1. 681–687. 1 indexed citations

Centea, Dan, et al.. (2020). Using the SEPT Learning Factory for the Implementation of Industry 4.0: case of SMEs. Procedia Manufacturing. 45. 102–107. 8 indexed citations

Shen, Weiran, et al.. (2020). A Review of the Evolution of Deep Learning Architectures and Comparison of their Performances for Histopathologic Cancer Detection. Procedia Manufacturing. 46. 683–689. 6 indexed citations

Wanyama, Tom, et al.. (2018). Practice-Intensive Learning: An effective approach to enhance the fundamental skills of PLC beginners. International journal of engineering education. 34(4). 1236–1249. 1 indexed citations

Centea, Dan, et al.. (2018). SEPT Learning Factory for Industry 4.0 Education and Applied Research. Procedia Manufacturing. 23. 249–254. 63 indexed citations

Wanyama, Tom. (2017). Using industry 4.0 technologies to support teaching andlearning. International journal of engineering education. 33(2). 693–702. 8 indexed citations

Wanyama, Tom & Behrouz H. Far. (2017). Multi-Agent System For Irrigation Using Fuzzy Logic Algorithm And Open Platform Communication Data Access. Zenodo (CERN European Organization for Nuclear Research). 10 indexed citations

Wanyama, Tom, et al.. (2015). INTEGRATED HANDS-ON AND REMOTE PID TUNING LABORATORY. Proceedings of the Canadian Engineering Education Association (CEEA). 3 indexed citations

Singh, Ishwar, et al.. (2015). TEACHING NETWORK TECHNOLOGIES THAT SUPPORT INDUSTRY 4.0. Proceedings of the Canadian Engineering Education Association (CEEA). 3 indexed citations

10.

Singh, Ishwar, et al.. (2015). DESIGN OF A ROBOTIC ARM FOR TEACHING INTEGRATED DESIGN. Proceedings of the Canadian Engineering Education Association (CEEA). 5 indexed citations

11.

Wanyama, Tom & Ishwar Singh. (2013). A TRAINING DEMONSTRATION FOR EXPERIENTIAL LEARNING IN OPC BASED PROCESS AUTOMATION DATA ACCESS. Proceedings of the Canadian Engineering Education Association (CEEA). 3 indexed citations

12.

Wanyama, Tom. (2008). AN EMPIRICAL STUDY TO COMPARE THREE METHODS FOR SELECTING COTS SOFTWARE COMPONENTS. 9 indexed citations

13.

Wanyama, Tom & Behrouz H. Far. (2007). A Qualitative Reasoning Model for Tradeoff Analysis in Multiple Objective Decision Making. Journal of Advanced Computational Intelligence and Intelligent Informatics. 11(1). 11–20. 1 indexed citations

14.

Wanyama, Tom & Behrouz H. Far. (2006). A protocol for multi-agent negotiation in a group-choice decision making process. Journal of Network and Computer Applications. 30(3). 1173–1195. 25 indexed citations

15.

Wanyama, Tom & Behrouz H. Far. (2006). Using prediction to provide decision support for the elicitation of user preferences. 2076–2079.

16.

Wanyama, Tom, et al.. (2006). Towards providing decision support for COTS selection. 2. 908–911. 18 indexed citations

17.

Wanyama, Tom & Behrouz H. Far. (2006). Repositories for Cots Selection. 2416–2419. 4 indexed citations

18.

Wanyama, Tom. (2006). Decision support for COTS selection. PRISM (University of Calgary). 291–291. 9 indexed citations

19.

Wanyama, Tom. (2005). A Multi-Agent Framework for Conflict Analysis and Negotiation: Case of COTS Selection. IEICE Transactions on Information and Systems. E88-D(9). 2047–2058. 7 indexed citations

20.

Wanyama, Tom & Behrouz H. Far. (2003). Agent-Based Commercial Off-The-Shelf Software Components Evaluation Method. 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.

Explore authors with similar magnitude of impact