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Domestic Violence Prevention System

  • Samuel Gallego Chimeno
  • Joaquín Delgado Fernández
  • Sergio Márquez SánchezEmail author
  • Pablo Pueyo Ramón
  • Óscar Mauricio Salazar Ospina
  • Marcel Vicente Muñoz
  • Aarón González Hernández
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 802)

Abstract

Domestic violence is a common problem in society. This type of violence can be understood as a behaviour pattern in the form of physical and/or sexual abuse, threats, coercion, intimidation, isolation, emotional or economic abuse exercised in the field of family life against any member who forms its nucleus. Currently, numerous efforts have been made to mitigate this type of violence, on a social, legal, technological or any other level. However, this is a problem that is difficult to control due to the diversity of ways in which this pattern of behavior can be expressed and the large number of repeat offenders. In this context, it is necessary to take advantage of the benefits that technology brings to detect this type of problem early and take corrective action in time. Based on the above, this work proposes the development of a system supported by intelligent services to detect cases of violence in homes with a history of violence. The experimental results obtained from the implementation of the case study show that the incorporation of intelligent services into early domestic violence prevention systems can help to control cases of recidivism and take corrective action in advance, thus mitigating the consequences and in many cases helping to save lives.

Keywords

Domestic violence Watson Violence prevention systems Threats Violent action Domestic problems 

References

  1. 1.
    Carrillo Calderón, M.E.: Agentes virtuales con capacidades cognitivas utilizando IBM Watson (Bachelor’s thesis) (2017)Google Scholar
  2. 2.
  3. 3.
    Fernandes, F., Gomes, L., Morais, H., Silva, M., Vale, Z., Corchado, J.M.: Dynamic energy management method with demand response interaction applied in an office building. In: de la Prieta, F., et al. (eds.) Trends in Practical Applications of Scalable Multi-Agent Systems, the PAAMS Collection. AISC, vol. 473, pp. 69–82. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-40159-1_6CrossRefGoogle Scholar
  4. 4.
    Dang, N.C., De la Prieta, F., Corchado, J.M., Moreno, M.N.: Framework for retrieving relevant contents related to fashion from online social network data. In: Omatu, S., et al. (eds.) Trends in Practical Applications of Scalable Multi-Agent Systems, the PAAMS Collection. AISC, vol. 473, pp. 335–347. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-40159-1_28CrossRefGoogle Scholar
  5. 5.
    Chamoso, P., De la Prieta, F., De Paz, F., Corchado, J.M.: Swarm agent-based architecture suitable for internet of things and smartcities. In: Omatu, S., et al. (eds.) Distributed Computing and Artificial Intelligence, 12th International Conference. AISC, vol. 373, pp. 21–29. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-19638-1_3CrossRefGoogle Scholar
  6. 6.
    Casado-Vara, R., Chamoso, P., De la Prieta, F., Prieto, J., Corchado, J.M.: Non-linear adaptive closed-loop control system for improved efficiency in IoT-blockchain management. Inf. Fusion 49, 227–239 (2019)CrossRefGoogle Scholar
  7. 7.
    González-Briones, A., Chamoso, P., Yoe, H., Corchado, J.M.: GreenVMAS: virtual organization based platform for heating greenhouses using waste energy from power plants. Sensors 18(3), 861 (2018)CrossRefGoogle Scholar
  8. 8.
    Casado-Vara, R., Prieto-Castrillo, F., Corchado, J.M.: A game theory approach for cooperative control to improve data quality and false data detection in WSN. Int. J. Robust Nonlinear Control 28(16), 5087–5102 (2018)MathSciNetzbMATHCrossRefGoogle Scholar
  9. 9.
    Morente-Molinera, J.A., Kou, G., González-Crespo, R., Corchado, J.M., Herrera-Viedma, E.: Solving multi-criteria group decision making problems under environments with a high number of alternatives using fuzzy ontologies and multi-granular linguistic modelling methods. Knowl. Based Syst. 137, 54–64 (2017)CrossRefGoogle Scholar
  10. 10.
    Li, T., Sun, S., Bolić, M., Corchado, J.M.: Algorithm design for parallel implementation of the SMC-PHD filter. Signal Process. 119, 115–127 (2016).  https://doi.org/10.1016/j.sigpro.2015.07.013CrossRefGoogle Scholar
  11. 11.
    Chamoso, P., Rodríguez, S., de la Prieta, F., Bajo, J.: Classification of retinal vessels using a collaborative agent-based architecture. AI Commun. 31(5), 427–444 (2018). PreprintMathSciNetCrossRefGoogle Scholar
  12. 12.
    Chamoso, P., González-Briones, A., Rodríguez, S., Corchado, J.M.: Tendencies of technologies and platforms in smart cities: a state-of-the-art review. Wirel. Commun. Mob. Comput. 2018, 17 (2018)CrossRefGoogle Scholar
  13. 13.
    Gonzalez-Briones, A., Prieto, J., De La Prieta, F., Herrera-Viedma, E., Corchado, J.M.: Energy optimization using a case-based reasoning strategy. Sensors (Basel) 18(3), 865 (2018).  https://doi.org/10.3390/s18030865CrossRefGoogle Scholar
  14. 14.
    Gonzalez-Briones, A., Chamoso, P., De La Prieta, F., Demazeau, Y., Corchado, J.M.: Agreement technologies for energy optimization at home. Sensors (Basel) 18(5), 1633 (2018).  https://doi.org/10.3390/s18051633CrossRefGoogle Scholar
  15. 15.
    Gazafroudi, A.S., Corchado, J.M., Kean, A., Soroudi, A.: Decentralized flexibility management for electric vehicles. IET Renew. Power Gener. (2019). http://ietdl.org/t/IBgIPb
  16. 16.
    Gazafroudi, A.S., Soares, J., Ghazvini, M.A.F., Pinto, T., Vale, Z., Corchado, J.M.: Stochastic interval-based optimal offering model for residential energy management systems by household owners. Int. J. Electr. Power Energy Syst. 105, 201–219 (2019)CrossRefGoogle Scholar
  17. 17.
    Durik, B.O.: Organisational metamodel for large-scale multi-agent systems: first steps towards modelling organisation dynamics. ADCAIJ Adv. Distrib. Comput. Artif. Intell. J. 6(3), 17–27 (2017). ISSN: 2255-2863Google Scholar
  18. 18.
    Bremer, J., Lehnhoff, S.: Decentralized coalition formation with agent-based combinatorial heuristics. ADCAIJ Adv. Distrib. Comput. Artif. Intell. J. 6(3), 29–44 (2017). ISSN: 2255-2863Google Scholar
  19. 19.
    Munera, E., Poza-Lujan, J.-L., Posadas-Yagüe, J.-L., Simó-Ten, J.-E., Blanes, F.: Integrating smart resources in ROS-based systems to distribute services. ADCAIJ Adv. Distrib. Comput. Artif. Intell. J. 6(1), 13–19 (2017). ISSN: 2255-2863Google Scholar
  20. 20.
    Omatu, S., Wada, T., Rodríguez, S., Chamoso, P., Corchado, J.M.: Multi-agent technology to perform odor classification. In: Ramos, C., Novais, P., Nihan, C.E., Corchado Rodríguez, J.M. (eds.) Ambient Intelligence - Software and Applications. AISC, vol. 291, pp. 241–252. Springer, Cham (2014).  https://doi.org/10.1007/978-3-319-07596-9_27CrossRefGoogle Scholar
  21. 21.
    Román, J.A., Rodríguez, S., Corchado, J.M.: Improving intelligent systems: specialization. In: Corchado, J.M., et al. (eds.) PAAMS 2014. CCIS, vol. 430, pp. 378–385. Springer, Cham (2014).  https://doi.org/10.1007/978-3-319-07767-3_34CrossRefGoogle Scholar
  22. 22.
    Oliver, M., Molina, J.P., Fernández-Caballero, A., González, P.: Collaborative computer-assisted cognitive rehabilitation system. ADCAIJ Adv. Distrib. Comput. Artif. Intell. J. 6(3), 57–74 (2017)Google Scholar
  23. 23.
    Griol, D., Molina, J.M.: Simulating heterogeneous user behaviors to interact with conversational interfaces. ADCAIJ Adv. Distrib. Comput. Artif. Intell. J. 5(4), 59–69 (2016)Google Scholar
  24. 24.
    Desquesnes, G., Lozenguez, G., Doniec, A., Duviella, E.: Planning large systems with MDPs: case study of inland waterways supervision. ADCAIJ Adv. Distrib. Comput. Artif. Intell. J. 5(4), 71–84 (2016)Google Scholar
  25. 25.
    Griol, D., Molina, K.: Measuring the differences between human-human and human-machine dialogs. ADCAIJ Adv. Distrib. Comput. Artif. Intell. J.4(2), 99–112 (2015)Google Scholar
  26. 26.
    Alvarado-Pérez, J.C., Peluffo-Ordóñez, D.H., Therón, R.: Bridging the gap between human knowledge and machine learning. ADCAIJ Adv. Distrib. Comput. Artif. Intell. J.4(1), 54–64 (2015)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Samuel Gallego Chimeno
    • 1
  • Joaquín Delgado Fernández
    • 1
  • Sergio Márquez Sánchez
    • 1
    Email author
  • Pablo Pueyo Ramón
    • 1
  • Óscar Mauricio Salazar Ospina
    • 2
  • Marcel Vicente Muñoz
    • 1
  • Aarón González Hernández
    • 1
  1. 1.BISITE Digital Innovation HubUniversity of Salamanca, Edificio I+D+i Universidad de SalamancaSalamancaSpain
  2. 2.Departamento de Ciencias de la Computación y la DecisiónUniversidad Nacional de Colombia – Sede MedellínMedellínColombia

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