Application of artificially intelligent systems for the identification of discrete fossiliferous levels.

ABSTRACT: The separation of discrete fossiliferous levels within an archaeological or paleontological site with no clear stratigraphic horizons has historically been carried out using qualitative approaches, relying on two-dimensional transversal and longitudinal projection planes. Analyses of this type, however, can often be conditioned by subjectivity based on the perspective of the analyst. This study presents a novel use of Machine Learning algorithms for pattern recognition techniques in the automated separation and identification of fossiliferous levels. This approach can be divided into three main steps including: (1) unsupervised Machine Learning for density based clustering (2) expert-in-the-loop Collaborative Intelligence Learning for the integration of geological data followed by (3) supe

SUBMITTER: Martin-Perea DM 

PROVIDER: S-EPMC7071820 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature