Adaptive elitist differential evolution extreme learning machines on big data: Intelligent recognition of invasive species

Abstract

One of the direct consequences of climate change lies in the spread of invasive species, which constitute a serious and rapidly worsening threat to ecology, preservation of natural biodiversity and protection of flora and fauna. It can even be a potential threat to the health of humans. These species, do not appear to have serious morphological variations, despite their strong biological differences. Due to this fact their identification process is often quite difficult. The need to protect the environment and safeguard public health, requires the development of advanced methods for early and accurate identification of some particularly dangerous invasive species, in order to plan and apply specific and effective management measures. The aim of this study is to create an advanced computer vision system for the automatic recognition of invasive or other unknown species, based on their phenotypes. More specifically, this research proposes an innovative and very effective Extreme Learning Machine (ELM) model, which is optimized by the Adaptive Elitist Differential Evolution algorithm (AEDE). The AEDE is an improved version of the differential evolution (DE) algorithm and it is proper for big data resolution. Feature selection is done by using deep learning Convolutional Neural Networks. A Geo Location system is used to detect the invasive species by Comparing with the local species of the region under research.