scholarly journals Support vector subset scan for spatial pattern detection

2021 ◽  
Vol 157 ◽  
pp. 107149
Author(s):  
Dylan Fitzpatrick ◽  
Yun Ni ◽  
Daniel B. Neill
Keyword(s):  
Spatial Pattern ◽  
Pattern Detection ◽  
Support Vector

scholarly journals MEMGENE: Spatial pattern detection in genetic distance data

2014 ◽  
Vol 5 (10) ◽  
pp. 1116-1120 ◽  
Author(s):  
Paul Galpern ◽  
Pedro R. Peres-Neto ◽  
Jean Polfus ◽  
Micheline Manseau
Keyword(s):  
Genetic Distance ◽  
Spatial Pattern ◽  
Pattern Detection ◽  
Distance Data

Detection of spatial pattern at several scales simultaneously

1975 ◽  
Vol 7 (03) ◽  
pp. 449-450
Author(s):  
Roger Mead
Keyword(s):  
Spatial Pattern ◽  
Plant Communities ◽  
Random Distribution ◽  
Small Sample ◽  
Pattern Detection ◽  
Ecological Research ◽  
Valid Method ◽  
Distributional Properties ◽  
The Individual ◽  
Plant Ecological

Detection of the different scales of pattern in plant communities is an important area of plant ecological research, and various tests of pattern have been devised. The method of pattern detection which is ecologically most meaningful is that due to Greig-Smith (1952) but, until now, this has suffered from the lack of valid tests of significance for the individual scales of pattern, once the overall departure from a random distribution has been established. Various tests which partially or completely overcome this deficiency are discussed and exemplified and their small sample distributional properties examined. It is concluded that a set of tests, based on randomisation arguments, provides a fully valid method of testing simultaneously for pattern at various scales.

scholarly journals HSV image classification of ancient script on copper Kintamani inscriptions using GLRCM and SVM

2020 ◽  
Vol 8 (2) ◽  
pp. 94-99 ◽  
Author(s):  
Christina Purnama Yanti ◽  
I Gede Andika
Keyword(s):  
Texture Features ◽  
Processing Technique ◽  
Pattern Detection ◽  
Image Processing Technique ◽  
Support Vector ◽  
Digital Form ◽  
Physical Damage ◽  
Window Method ◽  
Sliding Window Method

The problem of inscription physical damage as one of the historical heritages can be overcome using an image processing technique. The purpose of this study is to design a segmentation application for ancient scripts on inscriptions to recognize the character patterns on the inscriptions in digital form. The preprocessing was carried out to convert images from RGB to HSV. The application used the gray level run length matrix (GLRLM) to extract texture features and the support vector machine (SVM) method to classify the results. The inscription image segmentation was carried out through the pattern detection process using the sliding window method. The application obtained 88.32 % of accuracy, 0.87 of precision, and 0.94 of sensitivity.

Detection of spatial pattern at several scales simultaneously

1975 ◽  
Vol 7 (3) ◽  
pp. 449-450
Author(s):  
Roger Mead
Keyword(s):  
Spatial Pattern ◽  
Plant Communities ◽  
Random Distribution ◽  
Small Sample ◽  
Pattern Detection ◽  
Ecological Research ◽  
Valid Method ◽  
Distributional Properties ◽  
The Individual ◽  
Plant Ecological

Detection of the different scales of pattern in plant communities is an important area of plant ecological research, and various tests of pattern have been devised. The method of pattern detection which is ecologically most meaningful is that due to Greig-Smith (1952) but, until now, this has suffered from the lack of valid tests of significance for the individual scales of pattern, once the overall departure from a random distribution has been established. Various tests which partially or completely overcome this deficiency are discussed and exemplified and their small sample distributional properties examined. It is concluded that a set of tests, based on randomisation arguments, provides a fully valid method of testing simultaneously for pattern at various scales.

scholarly journals ECG-Based Subject Identification Using Common Spatial Pattern and SVM

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Turky N. Alotaiby ◽  
Saleh A. Alshebeili ◽  
Latifah M. Aljafar ◽  
Waleed M. Alsabhan
Keyword(s):  
Spatial Pattern ◽  
Error Rate ◽  
Support Vector ◽  
Svm Classifier ◽  
Identification System ◽  
Equal Error Rate ◽  
Common Spatial Pattern ◽  
Identification Rate ◽  
Subject Identification ◽  
Signal Segment

In this paper, a nonfiducial electrocardiogram (ECG, the process of recording the electrical activity of the heart over a period of time using electrodes placed on the skin) identification system based on the common spatial pattern (CSP) feature extraction technique is presented. The single- and multilead ECG signals of each subject are divided into nonoverlapping segments, and different segment lengths (1, 3, 5, 7, 10, or 15 seconds) are investigated. Features are extracted from each signal segment through projection on a CSP projection matrix. The extracted features are then used to train a radial basis function kernel-based Support Vector Machine (SVM) classifier, which is then employed in the identification phase. The proposed identification system was evaluated on 10, 20, …, 200 reference subjects of the Physikalisch-Technische Bundesanstalt (PTB) ECG database. Using a single limb-based lead (I) with 200 reference subjects, the system achieved an identification rate of 95.15% and equal error rate of 0.1. The use of a single chest-based lead (V3) for 200 reference subjects resulted in an identification rate of 98.92% and equal error rate of 0.08.

scholarly journals Probabilistic Mapping and Spatial Pattern Analysis of Grazing Lawns in Southern African Savannahs Using WorldView-3 Imagery and Machine Learning Techniques

2020 ◽  
Vol 12 (20) ◽  
pp. 3357
Author(s):  
Kwame T. Awuah ◽  
Paul Aplin ◽  
Christopher G. Marston ◽  
Ian Powell ◽  
Izak P. J. Smit
Keyword(s):  
Machine Learning ◽  
Spatial Pattern ◽  
Pattern Analysis ◽  
Ecosystem Dynamics ◽  
Support Vector ◽  
Detection Accuracy ◽  
Spatial Pattern Analysis ◽  
Large Herbivores ◽  
Probabilistic Mapping ◽  
Grazing Lawn

Savannah grazing lawns are a key food resource for large herbivores such as blue wildebeest (Connochaetes taurinus), hippopotamus (Hippopotamus amphibius) and white rhino (Ceratotherium simum), and impact herbivore densities, movement and recruitment rates. They also exert a strong influence on fire behaviour including frequency, intensity and spread. Thus, variation in grazing lawn cover can have a profound impact on broader savannah ecosystem dynamics. However, knowledge of their present cover and distribution is limited. Importantly, we lack a robust, broad-scale approach for detecting and monitoring grazing lawns, which is critical to enhancing understanding of the ecology of these vital grassland systems. We selected two sites in the Lower Sabie and Satara regions of Kruger National Park, South Africa with mesic and semiarid conditions, respectively. Using spectral and texture features derived from WorldView-3 imagery, we (i) parameterised and assessed the quality of Random Forest (RF), Support Vector Machines (SVM), Classification and Regression Trees (CART) and Multilayer Perceptron (MLP) models for general discrimination of plant functional types (PFTs) within a sub-area of the Lower Sabie landscape, and (ii) compared model performance for probabilistic mapping of grazing lawns in the broader Lower Sabie and Satara landscapes. Further, we used spatial metrics to analyse spatial patterns in grazing lawn distribution in both landscapes along a gradient of distance from waterbodies. All machine learning models achieved high F-scores (F1) and overall accuracy (OA) scores in general savannah PFTs classification, with RF (F1 = 95.73±0.004%, OA = 94.16±0.004%), SVM (F1 = 95.64±0.002%, OA = 94.02±0.002%) and MLP (F1 = 95.71±0.003%, OA = 94.27±0.003%) forming a cluster of the better performing models and marginally outperforming CART (F1 = 92.74±0.006%, OA = 90.93±0.003%). Grazing lawn detection accuracy followed a similar trend within the Lower Sabie landscape, with RF, SVM, MLP and CART achieving F-scores of 0.89, 0.93, 0.94 and 0.81, respectively. Transferring models to the Satara landscape however resulted in relatively lower but high grazing lawn detection accuracies across models (RF = 0.87, SVM = 0.88, MLP = 0.85 and CART = 0.75). Results from spatial pattern analysis revealed a relatively higher proportion of grazing lawn cover under semiarid savannah conditions (Satara) compared to the mesic savannah landscape (Lower Sabie). Additionally, the results show strong negative correlation between grazing lawn spatial structure (fractional cover, patch size and connectivity) and distance from waterbodies, with larger and contiguous grazing lawn patches occurring in close proximity to waterbodies in both landscapes. The proposed machine learning approach provides a novel and robust workflow for accurate and consistent landscape-scale monitoring of grazing lawns, while our findings and research outputs provide timely information critical for understanding habitat heterogeneity in southern African savannahs.

scholarly journals Spatial Pattern Detection of Tuberculosis: A Case Study of Si Sa Ket Province, Thailand

2015 ◽  
Vol 12 (12) ◽  
pp. 16005-16018 ◽  
Author(s):  
Siriwan Hassarangsee ◽  
Nitin Tripathi ◽  
Marc Souris
Keyword(s):  
Spatial Pattern ◽  
Pattern Detection
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