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Diversity
Special Issues
Machine Learning Methods Applied in Diversity Studies
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Machine Learning Methods Applied in Diversity Studies

A special issue of Diversity (ISSN 1424-2818). This special issue belongs to the section "Biodiversity Conservation".

Deadline for manuscript submissions: closed (20 September 2021) | Viewed by 8178

Special Issue Editor

Dr. Márta Ladányi

E-Mail Website
Guest Editor
Department of Applied Statistics, Institute of Mathematics and Basic Science, Hungarian University of Agriculture and Life Sciences, Villányi út 29–43, H-1118 Budapest, Hungary
Interests: mathematical modeling; applied statistics; multivariate methods; applied machine learning methods

Special Issue Information

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

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Diversity is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2100 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial neural networks
  • bayesian models
  • big data
  • bioinformatics
  • classification and regression trees
  • climate change
  • diversity assessment
  • population monitoring
  • remote sensing
  • machine learning

 

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Published Papers (2 papers)

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Research

16 pages, 1568 KiB  
Article
The Effect of Grapevine Variety and Wine Region on the Primer Parameters of Wine Based on 1H NMR-Spectroscopy and Machine Learning Methods
by Ágnes Diána Nyitrainé Sárdy, Márta Ladányi, Zsuzsanna Varga, Áron Pál Szövényi and Réka Matolcsi
Diversity 2022, 14(2), 74; https://doi.org/10.3390/d14020074 - 21 Jan 2022
Cited by 17 | Viewed by 3258
Abstract
Nuclear magnetic resonance (NMR) spectroscopy is an innovative method for wine analysis. Every grapevine variety has a unique structural formula, which can be considered as the genetic fingerprint of the plant. This specificity appears in the composition of the final product (wine). In [...] Read more.
Nuclear magnetic resonance (NMR) spectroscopy is an innovative method for wine analysis. Every grapevine variety has a unique structural formula, which can be considered as the genetic fingerprint of the plant. This specificity appears in the composition of the final product (wine). In the present study, the originality of Hungarian wines was investigated with 1H NMR-spectroscopy considering 861 wine samples of four varieties (Cabernet Sauvignon, Blaufränkisch, Merlot, and Pinot Noir) that were collected from two wine regions (Villány, Eger) in 2015 and 2016. The aim of our analysis was to classify these varieties and region and to select the most important traits from the observed 22 ones (alcohols, sugars, acids, decomposition products, biogene amines, polyphenols, fermentation compounds, etc.) in order to detect their effect in the identification. From the tested four classification methods—linear discriminant analysis (LDA), neural networks (NN), support vector machines (SVM), and random forest (RF)—the last two were the most successful according to their accuracy. Based on 1000 runs for each, we report the classification results and show that NMR analysis completed with machine learning methods such as SVM or RF might be a successfully applicable approach for wine identification. Full article
(This article belongs to the Special Issue Machine Learning Methods Applied in Diversity Studies)
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20 pages, 6649 KiB  
Article
Deep Learning Applied to SEM Images for Supporting Marine Coralline Algae Classification
by Giulia Piazza, Cecile Valsecchi and Gabriele Sottocornola
Diversity 2021, 13(12), 640; https://doi.org/10.3390/d13120640 - 3 Dec 2021
Cited by 17 | Viewed by 4252
Abstract
The classification of coralline algae commonly relies on the morphology of cells and reproductive structures, along with thallus organization, observed through Scanning Electron Microscopy (SEM). Nevertheless, species identification based on morphology often leads to uncertainty, due to their general plasticity. Evolutionary and environmental [...] Read more.
The classification of coralline algae commonly relies on the morphology of cells and reproductive structures, along with thallus organization, observed through Scanning Electron Microscopy (SEM). Nevertheless, species identification based on morphology often leads to uncertainty, due to their general plasticity. Evolutionary and environmental studies featured coralline algae for their ecological significance in both recent and past Oceans and need to rely on robust taxonomy. Research efforts towards new putative diagnostic tools have recently been focused on cell wall ultrastructure. In this work, we explored a new classification tool for coralline algae, using fine-tuning pretrained Convolutional Neural Networks (CNNs) on SEM images paired to morphological categories, including cell wall ultrastructure. We considered four common Mediterranean species, classified at genus and at the species level (Lithothamnion corallioides, Mesophyllum philippii, Lithophyllum racemus, Lithophyllum pseudoracemus). Our model produced promising results in terms of image classification accuracy given the constraint of a limited dataset and was tested for the identification of two ambiguous samples referred to as L. cf. racemus. Overall, explanatory image analyses suggest a high diagnostic value of calcification patterns, which significantly contributed to class predictions. Thus, CNNs proved to be a valid support to the morphological approach to taxonomy in coralline algae. Full article
(This article belongs to the Special Issue Machine Learning Methods Applied in Diversity Studies)
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