Spatial Movement Patterns and Local Co-Occurrence of Nutria Individuals in Association with Habitats Using Geo-Self-Organizing Map (Geo-SOM)

Nutrias (Myocastor coypus) were imported to South Korea for farming in 1985; individuals escaped captivity and established wild populations in natural ecosystems in the late 1990s. Numerous studies have focused on their monitoring and management; however, information on the continuous movement of individuals is not available. In this study, telemetry data from field conditions were used to identify the nearest-neighbor distances of individuals in association with environmental factors, including plant type, land cover, and biological parameters. The minimum nearest-neighbor distances for the different sexes were, overall, according to the minimum distances for the same sex. Local co-occurrences of individuals, either of the same or different sex, were seasonal. Tall grasslands, followed by herbaceous vegetation, were associated with the co-occurrence of different sexes. Conversely, floating-leaved hydrophytes, followed by xeric herbaceous vegetation, were correlated with the co-occurrence of the same sex. Local female–male co-occurrences were negatively associated with male–male co-occurrences but not with female–female co-occurrences, suggesting male dominance in group formations. Movement and co-occurrence information extracted using Geo-self-organizing maps furthers our understanding of population dispersal and helps formulate management strategies for nutria populations.

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An update on the distribution of the coypu, Myocastor coypus, in Asia and Africa through published literature, citizen-science and online platforms

Mammalian Biology ◽

10.1007/s42991-021-00207-1 ◽

2022 ◽

Author(s):

Luca Pedruzzi ◽

Anna Schertler ◽

Silvia Giuntini ◽

Ivan Leggiero ◽

Emiliano Mori

Keyword(s):

Management Strategies ◽

Spatially Explicit ◽

Asian Countries ◽

Natural Ecosystems ◽

Myocastor Coypus ◽

Video Sharing ◽

Data Gaps ◽

Southern Russia ◽

Warm Temperate ◽

Modelling Approach

AbstractThe coypu, Myocastor coypus, has been introduced worldwide for fur farming and is widely recognized as one of the most invasive alien mammals of the world, affecting natural ecosystems, crops and possibly human health. Here we present a comprehensive up-to-date review of its distribution and status in Asia and Africa. Using a multi-source approach, we collected occurrences from published literature as well as from online biodiversity platforms (e.g. GBIF, iNaturalist), video sharing platforms, and local experts. Additionally, we used an ensemble modelling approach to predict the climatic suitability across Africa and Asia. We present an updated distribution map, including a total of 1506 spatially explicit records from 1973 to 2021, covering 1 African and 16 Asian countries. We find evidence for current populations in Kenya and five new countries since the last review of (Carter and Leonard, Wildl Soc Bull 30:162–175, 2002): Iran, Jordan, Lebanon, Uzbekistan, and Vietnam, and identify main clusters of coypu occurrence in Western (including Transcaucasia) and East Asia. We show that warm temperate and Mediterranean areas on both continents are predicted to be climatically suitable for the coypu and highlight not only areas of possible spread, but also potential data gaps, i.e. with high suitability and low availability of concrete information (e.g. China, Southern Russia). We emphasize the importance of citizen involvement and the urgency for coypu-targeted studies in data-poor regions to obtain a clear picture of the geographical distribution and to better address management strategies.

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Parallel-elementary-stream architecture for nearest-neighbor-search-based self-organizing map

2016 13th IEEE International Conference on Solid-State and Integrated Circuit Technology (ICSICT) ◽

10.1109/icsict.2016.7998991 ◽

2016 ◽

Author(s):

Fengwei An ◽

Xiangyu Zhang ◽

Lei Chen ◽

Hans Jurgen Mattausch

Keyword(s):

Nearest Neighbor ◽

Nearest Neighbor Search ◽

Self Organizing Map ◽

Neighbor Search ◽

Stream Architecture ◽

Self Organizing

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Approximate nearest neighbor search using self-organizing map clustering for face recognition system

2014 International Computer Science and Engineering Conference (ICSEC) ◽

10.1109/icsec.2014.6978186 ◽

2014 ◽

Cited By ~ 1

Author(s):

Paitoon Yodkhad ◽

Aram Kawewong ◽

Karn Patanukhom

Keyword(s):

Face Recognition ◽

Nearest Neighbor ◽

Recognition System ◽

Nearest Neighbor Search ◽

Self Organizing Map ◽

Approximate Nearest Neighbor Search ◽

Approximate Nearest Neighbor ◽

Neighbor Search ◽

Face Recognition System ◽

Self Organizing

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Detection of Brain Tumor Using K-Nearest Neighbor (KNN) Based Classification Model and Self Organizing Map (SOM) Algorithm

International Journal of Neural Networks and Advanced Applications ◽

10.46300/91016.2020.7.6 ◽

2020 ◽

Vol 7 ◽

Keyword(s):

Data Mining ◽

Brain Tumor ◽

Nearest Neighbor ◽

Classification Model ◽

Discrete Wavelet ◽

Self Organizing Map ◽

K Nearest Neighbor ◽

Data Set ◽

Novel Approach ◽

Self Organizing

Knowledge discovery is also known as Data mining in databases, in recent years that technique plays a major role in research area. Data mining in healthcare domain has noteworthy usage in real world. The mining method can enable the healthcare field for the enhancement of institutionalization of its administrations and become quicker with best in class technologies. Innovation utilization isn't restricted to basic leadership in undertakings, yet spread to different social statuses in all fields. In this paper a novel approach for the detection of brain tumor is proposed. The novel approach uses the classification technique of K-nearest neighbor (KNN) and for ignoring the error of the dataset image SOM (self-organizing map) algorithm has been used. Discrete wavelet transform (DWT) is used for transforming input image data set, in which RGB color of input data image has been converted into gray scale. Then it has been classified using KNN after that the error avoiding algorithm has been carried out. This will help to differentiate tumor cells and the normal cells. The presence of tumor in brain image is detected using parametric analysis by simulation.

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A fast prototype reduction method based on template reduction and visualization-induced self-organizing map for nearest neighbor algorithm

Applied Intelligence ◽

10.1007/s10489-013-0433-9 ◽

2013 ◽

Vol 39 (3) ◽

pp. 564-582 ◽

Cited By ~ 7

Author(s):

I-Jing Li ◽

Jia-Chian Chen ◽

Jiunn-Lin Wu

Keyword(s):

Nearest Neighbor ◽

Reduction Method ◽

Self Organizing Map ◽

Nearest Neighbor Algorithm ◽

Prototype Reduction ◽

Self Organizing

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BAYESIAN SELF-ORGANIZING MAP FOR DATA CLASSIFICATION AND CLUSTERING

International Journal of Wavelets Multiresolution and Information Processing ◽

10.1142/s0219691313500379 ◽

2013 ◽

Vol 11 (05) ◽

pp. 1350037 ◽

Cited By ~ 1

Author(s):

XIAOLIAN GUO ◽

HAIYING WANG ◽

DAVID H. GLASS

Keyword(s):

Learning Process ◽

Bayesian Learning ◽

Nearest Neighbor ◽

Information Criterion ◽

Gaussian Mixture ◽

Optimal Number ◽

Support Vector ◽

Self Organizing Map ◽

K Nearest Neighbor ◽

Self Organizing

The Bayesian self-organizing map (BSOM) has typically been used for density estimation. In this study, we implemented an adaptation of the model for performing unsupervized and supervised classification. In order to determine the optimal number of neurons to represent the given dataset during the learning process, an extended Bayesian learning process is proposed called the growing BSOM. It starts with two neurons and adds new neurons to the network via a process in which the neuron with the lowest individual log-likelihood is identified. The system has been tested using three synthetic datasets and one real dataset. The experimental results suggest that the BSOM-based approach can achieve better classification performance in comparisons to several widely-used models such as k-nearest neighbor (KNN), support vector machine (SVM) and Gaussian mixture model (GMM). By using the Bayesian information criterion (BIC) as a stopping criterion, the growing BSOM can model the data under study and estimate the number of clusters.

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Self-Organizing Map Neural Network-Based Nearest Neighbor Position Estimation Scheme for Continuous Crystal PET Detectors

IEEE Transactions on Nuclear Science ◽

10.1109/tns.2014.2347295 ◽

2014 ◽

Vol 61 (5) ◽

pp. 2446-2455 ◽

Cited By ~ 7

Author(s):

Yonggang Wang ◽

Deng Li ◽

Xiaoming Lu ◽

Xinyi Cheng ◽

Liwei Wang

Keyword(s):

Neural Network ◽

Nearest Neighbor ◽

Position Estimation ◽

Self Organizing Map ◽

Estimation Scheme ◽

Pet Detectors ◽

Self Organizing

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Application of Lattice Imaging Techniques to Amorphous Films

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100113664 ◽

1975 ◽

Vol 33 ◽

pp. 8-9

Author(s):

S. R. Herd ◽

P. Chaudhari

Keyword(s):

Nearest Neighbor ◽

Random Network ◽

Imaging Techniques ◽

Network Models ◽

Accurate Method ◽

Direct Transmission ◽

Lattice Imaging ◽

Transmission Electron ◽

Lattice Planes ◽

Nearest Neighbor Distances

Electron diffraction and direct transmission have been used extensively to study the local atomic arrangement in amorphous solids and in particular Ge. Nearest neighbor distances had been calculated from E.D. profiles and the results have been interpreted in terms of the microcrystalline or the random network models. Direct transmission electron microscopy appears the most direct and accurate method to resolve this issue since the spacial resolution of the better instruments are of the order of 3Å. In particular the tilted beam interference method is used regularly to show fringes corresponding to 1.5 to 3Å lattice planes in crystals as resolution tests.

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