José M. Molina

5.0k total citations
288 papers, 2.7k citations indexed

About

José M. Molina is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Computer Networks and Communications. According to data from OpenAlex, José M. Molina has authored 288 papers receiving a total of 2.7k indexed citations (citations by other indexed papers that have themselves been cited), including 167 papers in Artificial Intelligence, 76 papers in Computer Vision and Pattern Recognition and 34 papers in Computer Networks and Communications. Recurrent topics in José M. Molina's work include Multi-Agent Systems and Negotiation (32 papers), Metaheuristic Optimization Algorithms Research (31 papers) and Target Tracking and Data Fusion in Sensor Networks (30 papers). José M. Molina is often cited by papers focused on Multi-Agent Systems and Negotiation (32 papers), Metaheuristic Optimization Algorithms Research (31 papers) and Target Tracking and Data Fusion in Sensor Networks (30 papers). José M. Molina collaborates with scholars based in Spain, Brazil and Iran. José M. Molina's co-authors include Jesús Garcı́a, Antonio Berlanga, Miguel Á. Patricio, Javier Carbó, Luis Martí, David Griol, Nayat Sánchez-Pi, Zoraida Callejas, Jorge Dávila and Juan Gómez‐Romero and has published in prestigious journals such as SHILAP Revista de lepidopterología, Expert Systems with Applications and IEEE Access.

In The Last Decade

José M. Molina

264 papers receiving 2.5k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
José M. Molina Spain 27 1.1k 590 372 282 255 288 2.7k
Lakhmi C. Jain Australia 29 1.3k 1.1× 935 1.6× 328 0.9× 246 0.9× 423 1.7× 204 3.5k
Yu‐Jun Zheng China 30 1.2k 1.0× 460 0.8× 313 0.8× 284 1.0× 264 1.0× 112 3.1k
Tarik A. Rashid Iraq 33 1.5k 1.3× 452 0.8× 532 1.4× 83 0.3× 241 0.9× 193 3.8k
Kagan Tumer United States 25 1.4k 1.3× 365 0.6× 376 1.0× 201 0.7× 285 1.1× 126 2.5k
Xiao‐Zhi Gao Finland 33 1.7k 1.5× 671 1.1× 405 1.1× 109 0.4× 448 1.8× 185 4.3k
Iztok Fister Slovenia 31 1.9k 1.7× 587 1.0× 397 1.1× 105 0.4× 422 1.7× 133 4.0k
Zheng Xu China 29 1.2k 1.0× 582 1.0× 589 1.6× 130 0.5× 130 0.5× 158 3.0k
Lakhmi C. Jain Australia 29 1.0k 0.9× 353 0.6× 232 0.6× 60 0.2× 281 1.1× 226 2.8k
Qi Kang China 31 1.3k 1.1× 313 0.5× 246 0.7× 125 0.4× 538 2.1× 142 3.0k
Kalyan Veeramachaneni United States 21 1.3k 1.1× 365 0.6× 471 1.3× 154 0.5× 179 0.7× 82 2.5k

Countries citing papers authored by José M. Molina

Since Specialization
Citations

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

Fields of papers citing papers by José M. Molina

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by José M. Molina. 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 José M. Molina. The network helps show where José M. Molina may publish in the future.

Co-authorship network of co-authors of José M. Molina

This figure shows the co-authorship network connecting the top 25 collaborators of José M. Molina. A scholar is included among the top collaborators of José M. Molina 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 José M. Molina. José M. Molina 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.
Patricio, Miguel Á., et al.. (2024). Analyzing feature importance with neural-network-derived trees. Neural Computing and Applications. 37(5). 3419–3433. 1 indexed citations
3.
Garcı́a, Jesús, et al.. (2023). Context learning from a ship trajectory cluster for anomaly detection. Neurocomputing. 563. 126920–126920. 10 indexed citations
4.
Molina, José M., et al.. (2021). Minimum Relevant Features to Obtain Explainable Systems for Predicting Cardiovascular Disease Using the Statlog Data Set. Applied Sciences. 11(3). 1285–1285. 15 indexed citations
5.
Dimou, Anastasios, et al.. (2021). Towards Unsupervised Knowledge Extraction. e-Archivo (Carlos III University of Madrid). 1 indexed citations
6.
Ocaña, Juan, et al.. (2019). Relevancia del margen de resección positivo en adenocarcinoma ductal de páncreas y otros factores pronósticos. Cirugía Española. 98(2). 85–91. 4 indexed citations
7.
Garcı́a, Jesús, et al.. (2016). Quality-of-service metrics for evaluating sensor fusion systems without ground truth. International Conference on Information Fusion. 2251–2258. 4 indexed citations
8.
Griol, David, José M. Molina, & Jesús Garcı́a. (2015). Fusion of sentiment analysis and emotion recognition to model the user's emotional state. International Conference on Information Fusion. 814–822.
9.
Molina, José M., et al.. (2015). Percepción de los estudiantes universitarios de la Región de Murcia sobre los valores educativos que pueden transmitir los Juegos Olímpicos. Aplicaciones prácticas.. POLI-RED (Revistas Digitales Politécnicas) (La Universidad Politécnica de Madrid). 284–296. 1 indexed citations
10.
Piñera, Pascual, et al.. (2013). Guía española de la enfermedad pulmonar obstructiva crónica (GesEPOC). Diagnóstico y tratamiento hospitalario de la agudización. Emergencias. 25(4). 301–317. 8 indexed citations
11.
Martí, Enrique, Jesús Garcı́a, & José M. Molina. (2011). Neighborhood-based regularization of proposal distribution for improving resampling quality in particle filters. e-Archivo (Carlos III University of Madrid). 1–8. 1 indexed citations
12.
Molina, José M., et al.. (2008). Analysis of data fusion architectures and techniques in the development of an A-SMGCS Surveillance prototype. e-Archivo (Carlos III University of Madrid).
13.
Corchado, Juan M., et al.. (2008). International Symposium on Distributed Computing and Artificial Intelligence 2008 (DCAI08). Springer eBooks. 1 indexed citations
14.
Molina, José M., Juan Córdoba, Ángel Gil de Miguel, & M Gobernado. (2007). Epidemiología de las resistencias genotípicas del VIH-1 en Valencia. Estudio de cuatro años. Hispana. 20(3). 346–353. 2 indexed citations
15.
Sánchez-Pi, Nayat, et al.. (2006). Reputation in User Profiling for a Context-aware MultiAgent System.. 4 indexed citations
16.
Gutiérrez, Germán, Araceli Sanchis, Pedro Isasi, José M. Molina, & Inés M. Galván. (2005). NON-DIRECT ENCODING METHOD BASED ON CELLULAR AUTOMATA TO DESIGN NEURAL NETWORK ARCHITECTURES. Computing and Informatics / Computers and Artificial Intelligence. 24(3). 225–247. 5 indexed citations
17.
Camacho, David, Ricardo Aler, Daniel Borrajo, & José M. Molina. (2005). A multi-agent architecture for intelligent gathering systems. AI Communications. 18(1). 15–32. 17 indexed citations
18.
Carbó, Javier, José M. Molina, & Jorge Dávila. (2005). Fuzzy referral based cooperation in social networks of agents. AI Communications. 18(1). 1–13. 16 indexed citations
19.
Sanchis, Araceli, et al.. (1999). Fuzzy colour distance applied to region growing in image processing.. European Society for Fuzzy Logic and Technology Conference. 259–262. 1 indexed citations
20.
Molina, José M. & Vicente Matellán Olivera. (1996). Robots autónomos : arquitecturas y control. 19–24. 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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