M.J. del Moral
- Management Science and Operations Research top 1%
- Artificial Intelligence top 5%
- Computational Theory and Mathematics top 5%
- Control and Systems Engineering top 10%
- Statistics and Probability top 5%
- Co-authors
- Enrique Herrera‐ViedmaJuan Miguel Tapia GarcíaFrancisco ChiclanaMaría A. MartínezMariano J. ValderramaJuan Antonio Morente-MolineraFrancisco Javier CabrerizoIgnacio Javier Pérez
- Topics
- Multi-Criteria Decision Making (10 papers)Bayesian Modeling and Causal Inference (5 papers)Data Management and Algorithms (3 papers)
- Cited by
- Management Science and Operations ResearchComputational Theory and MathematicsArtificial Intelligence
- Partner nations
- SpainUnited KingdomJordan
In The Last Decade
M.J. del Moral
10 papers receiving 599 citations
Peers
Comparison fields: 5 of 80
- Management Science and Operations Research 482
- Artificial Intelligence 298
- Computational Theory and Mathematics 170
- Control and Systems Engineering 80
- Statistics and Probability 72
Countries citing papers authored by M.J. del Moral
This map shows the geographic impact of M.J. del Moral'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 M.J. del Moral with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites M.J. del Moral more than expected).
Fields of papers citing papers by M.J. del Moral
This network shows the impact of papers produced by M.J. del Moral. 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 M.J. del Moral. The network helps show where M.J. del Moral may publish in the future.
Co-authorship network of co-authors of M.J. del Moral
This figure shows the co-authorship network connecting the top 25 collaborators of M.J. del Moral. A scholar is included among the top collaborators of M.J. del Moral 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 M.J. del Moral. M.J. del Moral 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 | 1 | |
| 3 | 121 | |
| 4 | 11 | |
| 5 | 3 | |
| 6 | 6 | |
| 7 | 9 | |
| 8 | 290 | |
| 9 | 131 | |
| 10 | 29 | |
| 11 | 8 |
About M.J. del Moral
M.J. del Moral is a scholar working on Management Science and Operations Research, Signal Processing and Artificial Intelligence, having authored 11 papers that have together received 609 indexed citations. Recurring topics across this work include Multi-Criteria Decision Making (10 papers), Bayesian Modeling and Causal Inference (5 papers) and Data Management and Algorithms (3 papers). The work is most often cited by research in Management Science and Operations Research (482 citations), Computational Theory and Mathematics (170 citations) and Artificial Intelligence (298 citations). M.J. del Moral has collaborated with scholars based in Spain, United Kingdom and Jordan. Frequent co-authors include Enrique Herrera‐Viedma, Juan Miguel Tapia García, Francisco Chiclana, María A. Martínez, Mariano J. Valderrama, Juan Antonio Morente-Molinera, Francisco Javier Cabrerizo and Ignacio Javier Pérez. Their work appears in journals such as Expert Systems with Applications, Information Sciences and International Journal of Forecasting.
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.