CompareMLA Novel Approach to Supporting Preliminary Data Analysis Decision Making

  1. Antonio Jesús Fernández-García 1
  2. Juan Carlos Preciado 2
  3. Álvaro E. Prieto 2
  4. Fernando Sánchez-Figueroa 2
  5. Juan D. Gutiérrez 2
  1. 1 Universidad Internacional de La Rioja
    info

    Universidad Internacional de La Rioja

    Logroño, España

    ROR https://ror.org/029gnnp81

  2. 2 Universidad de Extremadura
    info

    Universidad de Extremadura

    Badajoz, España

    ROR https://ror.org/0174shg90

Revista:
IJIMAI

ISSN: 1989-1660

Ano de publicación: 2022

Volume: 7

Número: 4

Páxinas: 225-238

Tipo: Artigo

DOI: 10.9781/IJIMAI.2021.08.001 DIALNET GOOGLE SCHOLAR lock_openDialnet editor

Outras publicacións en: IJIMAI

Obxectivos de Desenvolvemento Sustentable

Resumo

Over the last years, works related to accessible technologies have increased both in number and in quality. This work presents a series of articles which explore different trends in the field of accessible video games for the blind or visually impaired. Reviewed articles are distributed in four categories covering the following subjects: (1) video game design and architecture, (2) video game adaptations, (3) accessible games as learning tools or treatments and (4) navigation and interaction in virtual environments. Current trends in accessible game design are also analysed, and data is presented regarding keyword use and thematic evolution over time. As a conclusion, a relative stagnation in the field of human-computer interaction for the blind is detected. However, as the video game industry is becoming increasingly interested in accessibility, new research opportunities are starting to appear

Referencias bibliográficas

  • I. H. Witten, E. Frank, M. A. Hall, “Introduction to weka,” in Data Mining: Practical Machine Learning Tools and Techniques (Third Edition), The Morgan Kaufmann Series in Data Management Systems, Boston: Morgan Kaufmann, 2011, pp. 403 – 406, third edition ed., doi: https://doi. org/10.1016/B978-0-12-374856-0.00010-9.
  • S. Lang, F. Bravo-Marquez, C. Beckham, M. Hall, E. Frank, “Wekadeeplearning4j: A deep learning package for weka based on deeplearning4j,” Knowledge-Based Systems, vol. 178, pp. 48 – 50, 2019, doi: https://doi.org/10.1016/j.knosys.2019.04.013.
  • J. Demšar, T. Curk, A. Erjavec, Č. Gorup, T. Hočevar, M. Milutinovič, M. Možina, M. Polajnar, M. Toplak, A. Starič, M. Štajdohar, L. Umek, L. Žagar, J. Žbontar, M. Žitnik, B. Zupan, “Orange: Data mining toolbox in python,” Journal of Machine Learning Research, vol. 14, pp. 2349–2353, 2013.
  • M. R. Berthold, N. Cebron, F. Dill, T. R. Gabriel, T. Kötter, T. Meinl, P. Ohl, K. Thiel, B. Wiswedel, “Knime - the konstanz information miner: Version 2.0 and beyond,” SIGKDD Explor. Newsl., vol. 11, p. 26–31, Nov. 2009, doi: 10.1145/1656274.1656280.
  • I. Mierswa, M. Wurst, R. Klinkenberg, M. Scholz, T. Euler, “Yale: Rapid prototyping for complex data mining tasks,” in Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’06, New York, NY, USA, 2006, p. 935–940, Association for Computing Machinery.
  • A. Jovic, K. Brkic, N. Bogunovic, “An overview of free software tools for general data mining,” in 2014 37th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), 2014, pp. 1112–1117.
  • X. He, K. Zhao, X. Chu, “Automl: A survey of the state-of- the-art,” Knowledge-Based Systems, vol. 212, p. 106622, 2021, doi: https://doi. org/10.1016/j.knosys.2020.106622.
  • H. Song, P. Flach, “Efficient and robust model benchmarks with item response theory and adaptive testing,” International Journal of Interactive Multimedia and Artificial Intelligence, vol. 6, pp. 110–118, 2021, doi: https://doi.org/10.9781/ijimai.2021.02.009.
  • Microsoft, “Powerbi automated machine learning.” https://docs.microsoft. com/en-us/power-bi/transform-model/dataflows/dataflows-machinelearning-integration. Online; last accessed 2 April 2021.
  • M. Ali, PyCaret: An open source, low-code machine learning library in Python, July 2020. PyCaret version 2.3.
  • Google, “Cloud automl.” https://cloud.google.com/automl. Online; last accessed 2 April 2021.
  • H. Robles-Berumen, A. Zafra, H. M. Fardoun, S. Ventura, “Leac: An efficient library for clustering with evolutionary algorithms,” KnowledgeBased Systems, vol. 179, pp. 117 – 119, 2019, doi: https://doi.org/10.1016/j. knosys.2019.05.008.
  • D. Charte, F. Herrera, F. Charte, “Ruta: Implementations of neural autoencoders in r,” Knowledge-Based Systems, vol. 174, pp. 4 – 8, 2019, doi: https://doi.org/10.1016/j.knosys.2019.01.014.
  • E. Real, C. Liang, D. R. So, Q. V. Le, “Automl-zero: Evolving machine learning algorithms from scratch,” 2020.
  • C. M. University, “Turi graphlab create.” https://turi.com/. Online; last accessed 2 April 2021.
  • G. van Rossum, the Python Software Foundation, “Python programming language.” https://www.python.org/. Online; last accessed 2 April 2021.
  • F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, E. Duchesnay, “Scikitlearn: Machine learning in Python,” Journal of Machine Learning Research, vol. 12, pp. 2825–2830, 2011.
  • R Core Team, R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, 2013.
  • A. Gupta, K. Ghanshala, R. C. Joshi, “Machine learning classifier approach with gaussian process, ensemble boosted trees, svm, and linear regression for 5g signal coverage mapping,” International Journal of Interactive Multimedia and Artificial Intelligence, vol. 6, pp. 156–163, 2021, doi: https://doi.org/10.9781/ijimai.2021.03.004.
  • A. J. Fernández-García, L. Iribarne, A. Corral, J. Criado, J. Z. Wang, “A recommender system for component- based applications using machine learning techniques,” Knowledge-Based Systems, vol. 164, pp. 68–84, 2019, doi: https://doi.org/10.1016/j.knosys.2018.10.019.
  • A. J. Fernández-García, R. Rodríguez-Echeverría, J. C. Preciado, J. M. C. Manzano, F. Sánchez-Figueroa, “Creating a recommender system to support higher education students in the subject enrollment decision,” IEEE Access, vol. 8, pp. 189069–189088, 2020, doi: 10.1109/ ACCESS.2020.3031572.
  • T. H.-Y. Chiu, C. Wu, R. C. C.-H. Chen, “A generalized wine quality prediction framework by evolutionary algorithms,” International Journal of Interactive Multimedia and Artificial Intelligence, doi: https://doi. org/10.9781/ijimai.2021.04.006.
  • K. M. Ting, Confusion Matrix, pp. 260–260. Boston, MA: Springer US, 2017.
  • A. Leff, J. T. Rayfield, “Web-application development using the model/ view/controller design pattern,” in Proceedings Fifth IEEE International Enterprise Distributed Object Computing Conference, Sep. 2001, pp. 118– 127.
  • W. McKinney, “pandas: a foundational python library for data analysis and statistics,” Python for High Performance and Scientific Computing, vol. 14, 2011.
  • S. Hellegouarch, CherryPy Essentials: Rapid Python Web Application Development Design, Develop, Test, and Deploy Your Python Web Applications Easily. Packt Publishing, 2007.
  • M. Bohanec, V. Rajkovič, “Knowledge acquisition and explanation for multi-attribute decision,” in 8th International Workshop Expert Systems and Their Applications, 1988.
  • D. Dua, C. Graff, “UCI machine learning repository,” 2017. [Online]. Available: http://archive.ics.uci.edu/ml.
  • A. Tsanas, A. Xifara, “Accurate quantitative estimation of energy performance of residential buildings using statistical machine learning tools,” Energy and Buildings, vol. 49, pp. 560 – 567, 2012, doi: https://doi. org/10.1016/j.enbuild.2012.03.003.
  • J. Lewis, M. Fowler, “Microservices: a definition of this new architectural term.” http://martinfowler.com/articles/microservices.html, 2014.
Fonte de datos: Dialnet