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Francisco Herrera  - - - 
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Francisco Herrera

1051 shared publications

Andalusian Research Institute in Data Science and Computational Intelligence, University of Granada, 18071 Granada, Spain

Janusz Kacprzyk

759 shared publications

Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

Bernard De Baets

680 shared publications

Department of Data Analysis and Mathematical Modelling, Ghent University, Ghent, Belgium

Adel M. Alimi

474 shared publications

RGIM-Lab.: REsearch Groups in Intelligent Machines, National Engineering School of Sfax (ENIS), University of Sfax, Tunisia

Hisao Ishibuchi

454 shared publications

Southern University of Science and Technology, China

996
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Publication Record
Distribution of Articles published per year 
(1968 - 2018)
Total number of journals
published in
 
31
 
Publications See all
Article 0 Reads 4 Citations A review on trust propagation and opinion dynamics in social networks and group decision making frameworks Raquel Ureña, Gang Kou, Yucheng Dong, Francisco Chiclana, En... Published: 01 April 2019
Information Sciences, doi: 10.1016/j.ins.2018.11.037
DOI See at publisher website ABS Show/hide abstract
On-line platforms foster the communication capabilities of the Internet to develop large-scale influence networks in which the quality of the interactions can be evaluated based on trust and reputation. So far, this technology is well known for building trust and harnessing cooperation in on-line marketplaces, such as Amazon (www.amazon.com) and eBay (www.ebay.es). However, these mechanisms are poised to have a broader impact on a wide range of scenarios, from large scale decision making procedures, such as the ones implied in e-democracy, to trust based recommendations on e-health context or influence and performance assessment in e-marketing and e-learning systems. This contribution surveys the progress in understanding the new possibilities and challenges that trust and reputation systems pose. To do so, it discusses trust, reputation and influence which are important measures in networked based communication mechanisms to support the worthiness of information, products, services opinions and recommendations. The existent mechanisms to estimate and propagate trust and reputation, in distributed networked scenarios, and how these measures can be integrated in decision making to reach consensus among the agents are analysed. Furthermore, it also provides an overview of the relevant work in opinion dynamics and influence assessment, as part of social networks. Finally, it identifies challenges and research opportunities on how the so called trust based network can be leveraged as an influence measure to foster decision making processes and recommendation mechanisms in complex social networks scenarios with uncertain knowledge, like the mentioned in e-health and e-marketing frameworks.
Article 0 Reads 0 Citations Enabling Smart Data: Noise filtering in Big Data classification Diego García-Gil, Julián Luengo, Salvador García, Francisco ... Published: 01 April 2019
Information Sciences, doi: 10.1016/j.ins.2018.12.002
DOI See at publisher website
Article 0 Reads 0 Citations E2SAM: Evolutionary ensemble of sentiment analysis methods for domain adaptation Miguel López, Ana Valdivia, Eugenio Martínez-Cámara, M. Vict... Published: 01 April 2019
Information Sciences, doi: 10.1016/j.ins.2018.12.038
DOI See at publisher website
Article 0 Reads 0 Citations An integrated method for cognitive complex multiple experts multiple criteria decision making based on ELECTRE III with ... Huchang Liao, Xingli Wu, Xiaomei Mi, Francisco Herrera Published: 23 March 2019
Omega, doi: 10.1016/j.omega.2019.03.010
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The ELECTRE (ELimination Et Choix Traduisant la REalité, in French) is an effective multiple criteria decision making method based on comparative analysis. Among the family of the ELECTRE methods and their extensions, the ELECTRE III is widely used since it can tackle uncertain and imprecise information. The hesitant fuzzy linguistic term set can represent people's perceptions more comprehensively and flexibly than exact numbers especially in cognitive complex decision-making process. In this paper, we develop an integrated method based on the ELECTRE III to handle the cognitive complex multiple experts multiple criteria decision making problems in which the cognitive complex information is represented by hesitant fuzzy linguistic term sets and the outranking relations between alternatives are calculated by a novel score-function-based distance measure between hesitant fuzzy linguistic elements. A combinative weight-determining method involving both subjective and objective opinions of experts is introduced to derive the weights of criteria. After obtaining the ranking of alternatives from each experts’ decision matrix by the distillation algorithm, the weighted Borda rule is implemented to aggregate the rankings of alternatives regarding different experts. Some ordinal consensus measures are introduced to identify the reliability of the final ranking result. An application of hospital ranking in China is provided to validate the efficiency of the proposed method.
Article 0 Reads 0 Citations Detection of Fir Trees (Abies sibirica) Damaged by the Bark Beetle in Unmanned Aerial Vehicle Images with Deep Learning Anastasiia Safonova, Siham Tabik, Domingo Alcaraz-Segura, Al... Published: 16 March 2019
Remote Sensing, doi: 10.3390/rs11060643
DOI See at publisher website ABS Show/hide abstract
Invasion of the Polygraphus proximus Blandford bark beetle causes catastrophic damage to forests with firs (Abies sibirica Ledeb) in Russia, especially in Central Siberia. Determining tree damage stage based on the shape, texture and colour of tree crown in unmanned aerial vehicle (UAV) images could help to assess forest health in a faster and cheaper way. However, this task is challenging since (i) fir trees at different damage stages coexist and overlap in the canopy, (ii) the distribution of fir trees in nature is irregular and hence distinguishing between different crowns is hard, even for the human eye. Motivated by the latest advances in computer vision and machine learning, this work proposes a two-stage solution: In a first stage, we built a detection strategy that finds the regions of the input UAV image that are more likely to contain a crown, in the second stage, we developed a new convolutional neural network (CNN) architecture that predicts the fir tree damage stage in each candidate region. Our experiments show that the proposed approach shows satisfactory results on UAV Red, Green, Blue (RGB) images of forest areas in the state nature reserve “Stolby” (Krasnoyarsk, Russia).
Article 4 Reads 1 Citation Towards highly accurate coral texture images classification using deep convolutional neural networks and data augmentati... Anabel Gómez-Ríos, Siham Tabik, Julián Luengo, Asm Shihavudd... Published: 01 March 2019
Expert Systems with Applications, doi: 10.1016/j.eswa.2018.10.010
DOI See at publisher website
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