ORCID Profile
0000-0002-4717-1056
Current Organisations
University of Granada
,
The University of Newcastle
,
University of Jaen
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Publisher: IEEE
Date: 11-2018
Publisher: Elsevier BV
Date: 09-2016
Publisher: Journal of Artificial Societies and Social Simulation
Date: 2019
DOI: 10.18564/JASSS.4008
Publisher: Elsevier BV
Date: 09-2012
Publisher: IEEE
Date: 04-2011
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: Elsevier BV
Date: 2011
Publisher: IEEE
Date: 06-2017
Publisher: Elsevier BV
Date: 2017
Publisher: Springer Science and Business Media LLC
Date: 24-12-2019
DOI: 10.1038/S41598-019-55384-4
Abstract: In this paper, we present an evolutionary trust game, taking punishment and protection into consideration, to investigate the formation of trust in the so-called sharing economy from a population perspective. This sharing economy trust model comprises four types of players: a trustworthy provider, an untrustworthy provider, a trustworthy consumer, and an untrustworthy consumer. Punishment in the form of penalty for untrustworthy providers and protection in the form of insurance for consumers are mechanisms adopted to prevent untrustworthy behaviour. Through comprehensive simulation experiments, we evaluate dynamics of the population for different initial population setups and effects of having penalty and insurance in place. Our results show that each player type influences the ‘existence’ and ‘survival’ of other types of players, and untrustworthy players do not necessarily dominate the population even when the temptation to defect (i.e., to be untrustworthy) is high. Additionally, we observe that imposing a heavier penalty or having insurance for all consumers (trustworthy and untrustworthy) can be counterproductive for promoting trustworthiness in the population and increasing the global net wealth. Our findings have important implications for understanding trust in the context of the sharing economy, and for clarifying the usefulness of protection policies within it.
Publisher: IEEE
Date: 11-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2018
Publisher: Springer Science and Business Media LLC
Date: 11-09-2017
Publisher: SAGE Publications
Date: 09-07-2019
Abstract: Artificial intelligence (AI) has proven to be useful in many applications from automating cars to providing customer service responses. However, though many firms want to take advantage of AI to improve marketing, they lack a process by which to execute a Marketing AI project. This article discusses the use of AI to provide support for marketing decisions. Based on the established Cross-Industry Standard Process for Data Mining (CRISP-DM) framework, it creates a process for managers to use when executing a Marketing AI project and discusses issues that might arise. It explores how this framework was used to develop three cutting-edge Marketing AI applications.
Publisher: Elsevier BV
Date: 07-2020
Publisher: Springer Berlin Heidelberg
Date: 2008
Publisher: Springer International Publishing
Date: 17-10-2014
Publisher: Springer International Publishing
Date: 2018
Publisher: Elsevier BV
Date: 12-2019
Publisher: Elsevier BV
Date: 12-2020
Publisher: Springer Science and Business Media LLC
Date: 10-09-2014
Publisher: Elsevier BV
Date: 07-2015
Publisher: Elsevier BV
Date: 09-2020
Publisher: Elsevier BV
Date: 09-2010
Publisher: Informa UK Limited
Date: 08-08-2021
Publisher: Elsevier BV
Date: 10-2018
Publisher: Wiley
Date: 20-10-2017
Abstract: A cost-effective hexagonal sphericon hematite with predominant (110) facets for the oxygen evolution reaction (OER) is demonstrated. Sequential incorporation of near-atomic uniformly distributed Ce species and Ni nanoparticles into selected sites of the hematite induces a complex synergistic integration phenomenon that enhances the overall catalytic OER performance. This cheap hexagonal sphericon hematite (Fe ≈ 98%) only needs a small overpotential (η) of 0.34 V to reach 10 mA cm
Publisher: Elsevier BV
Date: 08-2011
Publisher: Elsevier BV
Date: 10-2013
Publisher: Springer Berlin Heidelberg
Date: 2010
Publisher: IEEE
Date: 06-2011
Publisher: Elsevier BV
Date: 07-2023
Publisher: Elsevier BV
Date: 02-2024
Publisher: Wiley
Date: 27-06-2012
DOI: 10.1002/JEMT.22091
Abstract: A novel method for authenticating pollen grains in bright-field microscopic images is presented in this work. The usage of this new method is clear in many application fields such as bee-keeping sector, where laboratory experts need to identify fraudulent bee pollen s les against local known pollen types. Our system is based on image processing and one-class classification to reject unknown pollen grain objects. The latter classification technique allows us to tackle the major difficulty of the problem, the existence of many possible fraudulent pollen types, and the impossibility of modeling all of them. Different one-class classification paradigms are compared to study the most suitable technique for solving the problem. In addition, feature selection algorithms are applied to reduce the complexity and increase the accuracy of the models. For each local pollen type, a one-class classifier is trained and aggregated into a multiclassifier model. This multiclassification scheme combines the output of all the one-class classifiers in a unique final response. The proposed method is validated by authenticating pollen grains belonging to different Spanish bee pollen types. The overall accuracy of the system on classifying fraudulent microscopic pollen grain objects is 92.3%. The system is able to rapidly reject pollen grains, which belong to nonlocal pollen types, reducing the laboratory work and effort. The number of possible applications of this authentication method in the microscopy research field is unlimited.
Publisher: Elsevier BV
Date: 06-2020
Publisher: MDPI AG
Date: 04-12-2022
DOI: 10.3390/SU142316188
Abstract: The emergence and spread of COVID-19 has severely impacted the tourism industry worldwide. In order to limit the effect of new pandemics or any unforeseen crisis, coordinated actions need to be adopted among tourism stakeholders. In this paper, we use an evolutionary game model to analyze the conditions that promote cooperation among different stakeholders in a tourism network to control high-risk crises. A data s le of 280 EU regions is used to define the tourism network of regions with a heterogeneous dependence on tourism. The results show that cooperation is helped by the existence of a structured tourism network. Moreover, cooperation is enhanced when coordination groups include small numbers of participants and when they are formed according to the similarity of tourism dependence.
Publisher: IEEE
Date: 06-2007
Publisher: Springer Berlin Heidelberg
Date: 2010
Publisher: IEEE
Date: 06-2017
Publisher: Elsevier BV
Date: 05-2017
Publisher: Elsevier BV
Date: 04-2021
Publisher: IEEE
Date: 07-2020
Publisher: IEEE
Date: 12-2015
Publisher: Elsevier BV
Date: 09-2019
Publisher: Elsevier BV
Date: 11-2019
Publisher: Elsevier BV
Date: 2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2018
Publisher: Elsevier BV
Date: 11-2013
Publisher: Elsevier BV
Date: 03-2012
Publisher: Springer Science and Business Media LLC
Date: 03-03-2021
DOI: 10.1038/S41598-021-84604-Z
Abstract: The current COVID-19 pandemic has impacted millions of people and the global economy. Tourism has been one the most affected economic sectors because of the mobility restrictions established by governments and uncoordinated actions from origin and destination regions. The coordination of restrictions and reopening policies could help control the spread of virus and enhance economies, but this is not an easy endeavor since touristic companies, citizens, and local governments have conflicting interests. We propose an evolutionary game model that reflects a collective risk dilemma behind these decisions. To this aim, we represent regions as players, organized in groups and consider the perceived risk as a strict lock-down and null economic activity. The costs for regions when restricting their mobility are heterogeneous, given that the dependence on tourism of each region is erse. Our analysis shows that, for both large populations and the EU NUTS2 case study, the existence of heterogeneous costs enhances global agreements. Furthermore, the decision on how to group regions to maximize the regions’ agreement of the population is a relevant issue for decision makers to consider. We find out that a layout of groups based on similar costs of cooperation boosts the regions’ agreements and avoid the risk of having a total lock-down and a negligible tourism activity. These findings can guide policy makers to facilitate agreements among regions to maximize the tourism recovery.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2021
Publisher: The Royal Society
Date: 05-2022
DOI: 10.1098/RSOS.212000
Abstract: We present an evolutionary game model that integrates the concept of tags, trust and migration to study how trust in social and physical groups influence cooperation and migration decisions. All agents have a tag, and they gain or lose trust in other tags as they interact with other agents. This trust in different tags determines their trust in other players and groups. In contrast to other models in the literature, our model does not use tags to determine the cooperation/defection decisions of the agents, but rather their migration decisions. Agents decide whether to cooperate or defect based purely on social learning (i.e. imitation from others). Agents use information about tags and their trust in tags to determine how much they trust a particular group of agents and whether they want to migrate to that group. Comprehensive experiments show that the model can promote high levels of cooperation and trust under different game scenarios, and that curbing the migration decisions of agents can negatively impact both cooperation and trust in the system. We also observed that trust becomes scarce in the system as the ersity of tags increases. This work is one of the first to study the impact of tags on trust in the system and migration behaviour of the agents using evolutionary game theory.
Publisher: Elsevier BV
Date: 02-2023
Publisher: Elsevier BV
Date: 09-2013
Publisher: IEEE
Date: 11-2009
Publisher: Springer Science and Business Media LLC
Date: 06-03-2019
Publisher: IEEE
Date: 03-2009
Publisher: SAGE Publications
Date: 10-2017
DOI: 10.1509/JMR.15.0443
Abstract: Marketers must constantly decide how to implement word-of-mouth (WOM) programs, and a well-developed decision support system (DSS) can provide them valuable assistance in doing so. The authors propose an agent-based framework that aggregates social network–level in idual interactions to guide the construction of a successful DSS for WOM. The framework presents a set of guidelines and recommendations to (1) involve stakeholders, (2) follow a data-driven iterative modeling approach, (3) increase validity through automated calibration, and (4) understand the DSS behavior. This framework is applied to build a DSS for a freemium app in which premium users discuss the product with their social network and promote its viral adoption. After its validation, the agent-based DSS forecasts the aggregate number of premium sales over time and the most likely users to become premium in the near future. The experiments show how the DSS can help managers by forecasting premium conversions and increasing the number of premiums through targeting and implementing reward policies.
Publisher: Hindawi Limited
Date: 26-11-2019
DOI: 10.1002/INT.22211
Publisher: IEEE
Date: 06-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: Elsevier BV
Date: 10-2014
Publisher: Elsevier BV
Date: 04-2012
DOI: 10.1016/J.COMPBIOMED.2011.12.003
Abstract: In this paper we address the problem of recognising the movement intentions of patients restricted to a medical bed. The developed recognition system will be used to implement a natural human-machine interface to move a medical bed by means of the slight movements of patients with reduced mobility. Our proposal uses pressure map sequences as input and presents a novel system based on artificial neural networks to recognise the movement intentions. The system analyses each pressure map in real-time and classifies the raw information into output classes which represent these intentions. The complexity of the recognition problem is high because of the multiple body characteristics and distinct ways of communicating intentions. To address this problem, a complete processing chain was developed consisting of image processing algorithms, a knowledge extraction process, and a multilayer perceptron (MLP) classification model. Different configurations of the MLP have been investigated and quantitatively compared. The accuracy of our approach is high, obtaining an accuracy of 87%. The model was compared with five well-known classification paradigms. The performance of a reduced model, obtained by through feature selection algorithms, was found to be better and less time-consuming than the original model. The whole proposal has been validated with real patients in pre-clinical tests using the final medical bed prototype. The proposed approach produced very promising results, outperforming existing classification approaches. The excellent behaviour of the recognition system will enable its use in controlling the movements of the bed, in several degrees of freedom, by the patient with his/her own body.
Publisher: Springer Science and Business Media LLC
Date: 04-03-2010
Publisher: IEEE
Date: 07-2016
Publisher: MDPI AG
Date: 12-03-2020
DOI: 10.3390/SU12062221
Abstract: This paper introduces a decision support tool for sustainable intermodal chains with seaborne transport, in which the optimization of a multi-objective model enables conflicting objectives to be handled simultaneously. Through the assessment of ‘door-to-door’ transport in terms of costs, time, and environmental impact, the most suitable maritime route and the optimized fleet are jointly calculated to maximize the opportunities for success of intermodal chains versus trucking. The resolution of the model through NSGA-II algorithms permits to obtain Pareto fronts that offer groups of optimized solutions. This is not only useful to make decisions in the short term, but also to establish long-term strategies through assessment of the frontiers’ behavior obtained when a sensitivity analysis is undertaken. Thus, consequences of transport policies on intermodal performance can be analyzed. A real-life case is studied to test the usefulness of the model. From the application case, not only the most suitable Motorway of the Seas with their optimized fleets are identified for Chile, but also significant general findings are provided for both policy makers and heads of ports to promote the intermodal option regardless of their geographical locations.
Publisher: Elsevier BV
Date: 06-2022
Publisher: Elsevier BV
Date: 2016
No related grants have been discovered for Manuel Chica Serrano.