scholarly journals How Spectral Properties and Machine Learning Can Categorize Twin Species - Based on Diachrysia Genus

2021 ◽  
Author(s):  
Krzysztof Dyba ◽  
Roman Wąsala ◽  
Jan Piekarczyk ◽  
Elżbieta Gabała ◽  
Magdalena Gawlak ◽  
...  
Keyword(s):  
Machine Learning ◽  
Spectral Properties ◽  
High Stability ◽  
Stochastic Approach ◽  
Reflectance Spectroscopy ◽  
Dorsal Side ◽  
Species Classification ◽  
Small Set ◽  
Discriminant Analyses ◽  
New Evidence

Abstract Confirmation of distinctiveness or taxonomic affinity awaits new evidence in many twin species. In our work we used noninvasive point reflectance spectroscopy in the range from 400 to 2100 nm coupled with machine learning to study scales on the brown and golden iridescent areas on the dorsal side of the forewing of Diachrysia chrysitis and D. stenochrysis. The basis for the study a statistically significant collection of 95 specimens gathered during 23 years in Poland. The numerical part of an experiment included two independent discriminant analyses: stochastic and deterministic. The more sensitive stochastic approach achieved average compliance with the species classification made by entomologists at the level of 99-100%. It demonstrated high stability against the different configurations of training and validation sets, hence strong predictors of Diachrysia siblings distinctiveness. Both methods resulted in the same small set of relevant features, where minimal fully discriminating subsets of wavelengths were three for glass scales on the golden area and four for the brown. The differences between species in scales primarily concern their major components and ultrastructure. In melanin-absent glass scales, this is mainly chitin configuration, while in melanin-present brown scales, melanin reveals as an additional factor.

scholarly journals Birds Sound Classification Based on Machine Learning Algorithms

2021 ◽  
pp. 1-11
Author(s):  
Aska E. Mehyadin ◽  
Adnan Mohsin Abdulazeez ◽  
Dathar Abas Hasan ◽  
Jwan N. Saeed
Keyword(s):  
Machine Learning ◽  
Noise Suppression ◽  
Bird Species ◽  
Machine Learning Algorithms ◽  
Data Sets ◽  
Learning Technology ◽  
Species Classification ◽  
Data Set ◽  
Sound Classification ◽  
Mel Frequency Cepstral Coefficient

The bird classifier is a system that is equipped with an area machine learning technology and uses a machine learning method to store and classify bird calls. Bird species can be known by recording only the sound of the bird, which will make it easier for the system to manage. The system also provides species classification resources to allow automated species detection from observations that can teach a machine how to recognize whether or classify the species. Non-undesirable noises are filtered out of and sorted into data sets, where each sound is run via a noise suppression filter and a separate classification procedure so that the most useful data set can be easily processed. Mel-frequency cepstral coefficient (MFCC) is used and tested through different algorithms, namely Naïve Bayes, J4.8 and Multilayer perceptron (MLP), to classify bird species. J4.8 has the highest accuracy (78.40%) and is the best. Accuracy and elapsed time are (39.4 seconds).

scholarly journals Overview of Smart Aquaculture System: Focusing on Applications of Machine Learning and Computer Vision

Electronics ◽  
2021 ◽  
Vol 10 (22) ◽  
pp. 2882
Author(s):  
Thi Thu Em Vo ◽  
Hyeyoung Ko ◽  
Jun-Ho Huh ◽  
Yonghoon Kim
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Sustainable Development ◽  
Species Classification ◽  
The Sustainable Development ◽  
Aquaculture System ◽  
Aquaculture Industry ◽  
Intelligent Technology ◽  
Applications Of Machine Learning ◽  
Aquaculture Production

Smart aquaculture is nowadays one of the sustainable development trends for the aquaculture industry in intelligence and automation. Modern intelligent technologies have brought huge benefits to many fields including aquaculture to reduce labor, enhance aquaculture production, and be friendly to the environment. Machine learning is a subdivision of artificial intelligence (AI) by using trained algorithm models to recognize and learn traits from the data it watches. To date, there are several studies about applications of machine learning for smart aquaculture including measuring size, weight, grading, disease detection, and species classification. This review provides and overview of the development of smart aquaculture and intelligent technology. We summarized and collected 100 articles about machine learning in smart aquaculture from nearly 10 years about the methodology, results as well as the recent technology that should be used for development of smart aquaculture. We hope that this review will give readers interested in this field useful information.

scholarly journals Tree Species Classification Using Hyperion and Sentinel-2 Data with Machine Learning in South Korea and China

2019 ◽  
Vol 8 (3) ◽  
pp. 150 ◽  
Author(s):  
Joongbin Lim ◽  
Kyoung-Min Kim ◽  
Ri Jin
Keyword(s):  
Machine Learning ◽  
Tree Species ◽  
North Korea ◽  
Training Data ◽  
Machine Learning Techniques ◽  
Species Classification ◽  
Tree Species Classification ◽  
Hyperion Data ◽  
Study Sites ◽  
Sentinel 2

Remote sensing (RS) has been used to monitor inaccessible regions. It is considered a useful technique for deriving important environmental information from inaccessible regions, especially North Korea. In this study, we aim to develop a tree species classification model based on RS and machine learning techniques, which can be utilized for classification in North Korea. Two study sites were chosen, the Korea National Arboretum (KNA) in South Korea and Mt. Baekdu (MTB; a.k.a., Mt. Changbai in Chinese) in China, located in the border area between North Korea and China, and tree species classifications were examined in both regions. As a preliminary step in developing a classification algorithm that can be applied in North Korea, common coniferous species at both study sites, Korean pine (Pinus koraiensis) and Japanese larch (Larix kaempferi), were chosen as targets for investigation. Hyperion data have been used for tree species classification due to the abundant spectral information acquired from across more than 200 spectral bands (i.e., hyperspectral satellite data). However, it is impossible to acquire recent Hyperion data because the satellite ceased operation in 2017. Recently, Sentinel-2 satellite multispectral imagery has been used in tree species classification. Thus, it is necessary to compare these two kinds of satellite data to determine the possibility of reliably classifying species. Therefore, Hyperion and Sentinel-2 data were employed, along with machine learning techniques, such as random forests (RFs) and support vector machines (SVMs), to classify tree species. Three questions were answered, showing that: (1) RF and SVM are well established in the hyperspectral imagery for tree species classification, (2) Sentinel-2 data can be used to classify tree species with RF and SVM algorithms instead of Hyperion data, and (3) training data that were built in the KNA cannot be used for the tree classification of MTB. Random forests and SVMs showed overall accuracies of 0.60 and 0.51 and kappa values of 0.20 and 0.00, respectively. Moreover, combined training data from the KNA and MTB showed high classification accuracies in both regions; RF and SVM values exhibited accuracies of 0.99 and 0.97 and kappa values of 0.98 and 0.95, respectively.

scholarly journals Morphological diversity of the stone crayfish − traditional and geometric morphometric approach

2019 ◽  
pp. 1 ◽  
Author(s):  
Leona Lovrenčić ◽  
Vjera Pavić ◽  
Stefan Majnarić ◽  
Lucija Abramović ◽  
Mišel Jelić ◽  
...  
Keyword(s):  
Morphological Variability ◽  
Morphological Characteristics ◽  
Morphological Diversity ◽  
Dorsal Side ◽  
Small Scale ◽  
Geometric Morphometric ◽  
Discriminant Analyses ◽  
Stone Crayfish ◽  
Multivariate Discriminant ◽  
First Time

Austropotamobius torrentium is one of four native European crayfish species inhabiting Croatian freshwaters. Existence of eight divergent monophyletic mtDNA phylogroups was described within A. torrentium; six of them are distributed in Croatia, with the highest genetic diversity established in its northern-central Dinaric region. Recent small-scale study of the stone crayfish morphological variability indicated significant differences among different phylogroups. In the present study larger sample size, covering populations from five phylogroups, was analysed with the aim of determining whether there are morphological characteristics that reliably separate stone crayfish from different phylogroups. Aiming this, 245 stone crayfish were analysed through traditional (TM) and, for the first time, geometric morphometric (GM) analyses. Multivariate discriminant analyses included 24 TM characteristics per crayfish, while GM comprised analyses of 22 landmarks on the dorsal side of cephalon. Both methods revealed congruent results, and significant differences among phylogroups in analysed features were obtained, with the cephalon shape contributing the most to crayfish discrimination. Research confirmed that both approaches, combined with statistical methods, are useful in distinguishing and separating crayfish phylogroups. Findings of present study are compatible with the previous molecular findings; stone crayfish present several distinct evolutionary lineages whose species status are currently undefined and require urgent clarification.

scholarly journals Investigating the role of Simpson’s paradox in the analysis of top-ranked features in high-dimensional bioinformatics datasets

2019 ◽  
Vol 21 (2) ◽  
pp. 421-428 ◽  
Author(s):  
Alex A Freitas
Keyword(s):  
Machine Learning ◽  
High Dimensional ◽  
Feature Ranking ◽  
Ranking Methods ◽  
Simpson’S Paradox ◽  
Small Set ◽  
Simpson's Paradox ◽  
High Dimensional Datasets ◽  
Class Variable

Abstract An important problem in bioinformatics consists of identifying the most important features (or predictors), among a large number of features in a given classification dataset. This problem is often addressed by using a machine learning–based feature ranking method to identify a small set of top-ranked predictors (i.e. the most relevant features for classification). The large number of studies in this area has, however, an important limitation: they ignore the possibility that the top-ranked predictors occur in an instance of Simpson’s paradox, where the positive or negative association between a predictor and a class variable reverses sign upon conditional on each of the values of a third (confounder) variable. In this work, we review and investigate the role of Simpson’s paradox in the analysis of top-ranked predictors in high-dimensional bioinformatics datasets, in order to avoid the potential danger of misinterpreting an association between a predictor and the class variable. We perform computational experiments using four well-known feature ranking methods from the machine learning field and five high-dimensional datasets of ageing-related genes, where the predictors are Gene Ontology terms. The results show that occurrences of Simpson’s paradox involving top-ranked predictors are much more common for one of the feature ranking methods.

scholarly journals Extended-wavelength diffuse reflectance spectroscopy with a machine-learning method for in vivo tissue classification

PLoS ONE ◽  
2019 ◽  
Vol 14 (10) ◽  
pp. e0223682 ◽  
Author(s):  
Ulf Dahlstrand ◽  
Rafi Sheikh ◽  
Cu Dybelius Ansson ◽  
Khashayar Memarzadeh ◽  
Nina Reistad ◽  
...  
Keyword(s):  
Machine Learning ◽  
Diffuse Reflectance ◽  
Diffuse Reflectance Spectroscopy ◽  
Reflectance Spectroscopy ◽  
Machine Learning Method ◽  
Learning Method ◽  
Tissue Classification
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