Machine Learning Methods Applied in Diversity Studies

Dear Colleagues,

Protecting our ecosystems with sustainable diversity of flora and fauna requires continuous observation, advanced evaluation methods, and data management. In recent decades, data collection has become more and more powerful, applying several kinds of remote sensors and measurement techniques based on network connections. In this way, several types of big data have become available, the analysis of which needs machine learning (ML) techniques. This Special Issue of Diversity is dedicated to the methodical approach of diversity issues including but not limited to data mining, supervised and unsupervised ML, classification and regression trees (CART), artificial neural networks (ANN), deep learning (DL), Bayesian models, artificial intelligence, dynamic programming, support vector machines, Markov Chain Monte Carlo (MCMC) method, hidden Markov Models (HMM), advanced algorithms and statistical methods etc. employed in conservation biology, bioinformatics, population monitoring, species recognition, environmental protection, degradation and invasion monitoring, habitat quality assessment methods, diversity assessment methods, climate change effect studies, risk assessment and analysis, etc., using any kind of programming languages (JavaScript, R, Python, C# etc.).

Dr. Márta Ladányi
Guest Editor

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