American Journal of Artificial Intelligence


Volume 4, Issue 2, December 2020

  • Spanish-Turkish Low-Resource Machine Translation: Unsupervised Learning vs Round-Tripping

    Tianyi Xu, Ozge Ilkim Ozbek, Shannon Marks, Sri Korrapati, Benyamin Ahmadnia

    Issue: Volume 4, Issue 2, December 2020
    Pages: 42-49
    Received: 28 May 2020
    Accepted: 18 June 2020
    Published: 23 July 2020
    Abstract: The quality of data-driven Machine Translation (MT) strongly depends on the quantity as well as the quality of the training dataset. However, collecting a large set of training parallel texts is not easy in practice. Although various approaches have already been proposed to overcome this issue, the lack of large parallel corpora still poses a major... Show More
  • The Taxonomy of Living Organisms Using Self-organizing Map

    Adebayo Rotimi Philip

    Issue: Volume 4, Issue 2, December 2020
    Pages: 50-61
    Received: 30 May 2020
    Accepted: 15 June 2020
    Published: 7 September 2020
    Abstract: The Self Organizing Map (SOM) is an unsupervised network algorithm that projects high dimensional data into low dimensional maps. The projection preserves the topology of the data so that similar data items are mapped to nearby locations on the map. The algorithm has been so popular because of its application in Computer Science and other areas; it... Show More
  • A Neural Network Scheme for Monetary Policy Rate Validation in Nigeria

    Oloruntoba Samuel Ogundele, Augustine Ujunwa, Aminu Ado Mohammed

    Issue: Volume 4, Issue 2, December 2020
    Pages: 62-72
    Received: 4 October 2020
    Accepted: 12 November 2020
    Published: 11 December 2020
    Abstract: This research work is an exploratory study that tried to examine the viability of adopting artificial neural network (ANN), an aspect of machine learning in the analysis of monetary data for the design and validation of monetary policy from both optimistic and normative approach. Methodologically, the research is motivated by the work of [33] which... Show More