scholarly journals Mining Rules for Satellite Imagery Using Evolutionary Classification Tree

2014 ◽  
Author(s):  
L.C. Lien ◽  
Y.N. Liu ◽  
M.Y. Cheng ◽  
I-C. Yeh
Keyword(s):  
Satellite Imagery ◽  
Classification Tree

Profiling older community-dwellers with hypovitaminosis D: A classification tree analysis

2020 ◽  
Vol 90 (3-4) ◽  
pp. 195-199 ◽  
Author(s):  
Gaelle Chevallereau ◽  
Mathilde Legeay ◽  
Guillaume T. Duval ◽  
Spyridon N. Karras ◽  
Bruno Fantino ◽  
...  
Keyword(s):  
Vitamin D ◽  
Classification Tree ◽  
Hypovitaminosis D ◽  
Vitamin D Supplementation ◽  
Community Dwelling ◽  
Gait Disorders ◽  
Hydroxyvitamin D ◽  
Chaid Analysis ◽  
Clinical Profiles ◽  
Community Dwellers

Abstract. Despite the high prevalence of hypovitaminosis D in older adults, universal vitamin D supplementation is not recommended due to potential risk of intoxication. Our aim here was to determine the clinical profiles of older community-dwellers with hypovitaminosis D. The perspective is to build novel strategies to screen for and supplement those with hypovitaminosis D. A classification tree (CHAID analysis) was performed on multiple datasets standardizedly collected from 1991 older French community-dwelling volunteers ≥ 65 years in 2009–2012. Hypovitaminosis D was defined as serum 25-hydroxyvitamin D ≤ 50 nmol/L. CHAID analysis retained 5 clinical profiles of older community-dwellers with different risks of hypovitaminosis D up to 87.3%, based on various combinations of the following characteristics: polymorbidity, obesity, sadness and gait disorders. For instance, the probability of hypovitaminosis D was 1.42-fold higher [95CI: 1.27–1.59] for those with polymorbidity and gait disorders compared to those with no polymorbidity, no obesity and no sadness. In conclusion, these easily-recordable measures may be used in clinical routine to identify older community-dwellers for whom vitamin D supplementation should be initiated.

LambdaNet: A Fully Convolutional Architecture for Directional Change Detection

2020 ◽  
Vol 2020 (8) ◽  
pp. 114-1-114-7
Author(s):  
Bryan Blakeslee ◽  
Andreas Savakis
Keyword(s):  
Change Detection ◽  
Satellite Imagery ◽  
Classification Scheme ◽  
Deep Architecture ◽  
Binary Process ◽  
Directional Change ◽  
Class Labels ◽  
Image Pairs

Change detection in image pairs has traditionally been a binary process, reporting either “Change” or “No Change.” In this paper, we present LambdaNet, a novel deep architecture for performing pixel-level directional change detection based on a four class classification scheme. LambdaNet successfully incorporates the notion of “directional change” and identifies differences between two images as “Additive Change” when a new object appears, “Subtractive Change” when an object is removed, “Exchange” when different objects are present in the same location, and “No Change.” To obtain pixel annotated change maps for training, we generated directional change class labels for the Change Detection 2014 dataset. Our tests illustrate that LambdaNet would be suitable for situations where the type of change is unstructured, such as change detection scenarios in satellite imagery.

Speaker Identity Recognition by Acoustic and Visual Data Fusion through Personal Privacy for Smart Care and Service Applications

2020 ◽  
Vol 64 (4) ◽  
pp. 40404-1-40404-16
Author(s):  
I.-J. Ding ◽  
C.-M. Ruan
Keyword(s):  
Face Detection ◽  
Speaker Recognition ◽  
Visual Information ◽  
Classification Tree ◽  
Gaussian Mixture ◽  
Recognition Method ◽  
Indoor Space ◽  
Identity Recognition ◽  
Visual Identity ◽  
Speaker Classification

Abstract With rapid developments in techniques related to the internet of things, smart service applications such as voice-command-based speech recognition and smart care applications such as context-aware-based emotion recognition will gain much attention and potentially be a requirement in smart home or office environments. In such intelligence applications, identity recognition of the specific member in indoor spaces will be a crucial issue. In this study, a combined audio-visual identity recognition approach was developed. In this approach, visual information obtained from face detection was incorporated into acoustic Gaussian likelihood calculations for constructing speaker classification trees to significantly enhance the Gaussian mixture model (GMM)-based speaker recognition method. This study considered the privacy of the monitored person and reduced the degree of surveillance. Moreover, the popular Kinect sensor device containing a microphone array was adopted to obtain acoustic voice data from the person. The proposed audio-visual identity recognition approach deploys only two cameras in a specific indoor space for conveniently performing face detection and quickly determining the total number of people in the specific space. Such information pertaining to the number of people in the indoor space obtained using face detection was utilized to effectively regulate the accurate GMM speaker classification tree design. Two face-detection-regulated speaker classification tree schemes are presented for the GMM speaker recognition method in this study—the binary speaker classification tree (GMM-BT) and the non-binary speaker classification tree (GMM-NBT). The proposed GMM-BT and GMM-NBT methods achieve excellent identity recognition rates of 84.28% and 83%, respectively; both values are higher than the rate of the conventional GMM approach (80.5%). Moreover, as the extremely complex calculations of face recognition in general audio-visual speaker recognition tasks are not required, the proposed approach is rapid and efficient with only a slight increment of 0.051 s in the average recognition time.

Mask R-CNN based segmentation method for satellite imagery of photovoltaics generation systems

2020 ◽  
Author(s):  
SiMing Liang ◽  
FengYang Qi ◽  
YiFan Ding ◽  
Rui Cao ◽  
Qiang Yang ◽  
...  
Keyword(s):  
Satellite Imagery ◽  
Segmentation Method
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