Automatic hierarchical classification of silhouettes of 3D objects

2002 ◽  
Author(s):  
Y. Gdalyahu ◽  
D. Weinshall
Keyword(s):  
Hierarchical Classification ◽  
3D Objects

scholarly journals A Medical Cloud-Based Platform for Respiration Rate Measurement and Hierarchical Classification of Breath Disorders

Sensors ◽  
2014 ◽  
Vol 14 (6) ◽  
pp. 11204-11224 ◽  
Author(s):  
Atena Fekr ◽  
Majid Janidarmian ◽  
Katarzyna Radecka ◽  
Zeljko Zilic
Keyword(s):  
Respiration Rate ◽  
Hierarchical Classification ◽  
Rate Measurement ◽  
Medical Cloud ◽  
Respiration Rate Measurement

scholarly journals GPCRTree: online hierarchical classification of GPCR function

2008 ◽  
Vol 1 (1) ◽  
pp. 67 ◽  
Author(s):  
Matthew N Davies ◽  
Andrew Secker ◽  
Mark Halling-Brown ◽  
David S Moss ◽  
Alex A Freitas ◽  
...  
Keyword(s):  
Hierarchical Classification

VirusTaxo: Taxonomic classification of virus genome using multi-class hierarchical classification by k-mer enrichment

2021 ◽  
Author(s):  
Rajan Saha Raju ◽  
Abdullah Al Nahid ◽  
Preonath Shuvo ◽  
Rashedul Islam
Keyword(s):  
Genome Sequence ◽  
Rna Viruses ◽  
Hierarchical Classification ◽  
Classification Problem ◽  
Virus Genome ◽  
Taxonomic Classification ◽  
Dna Viruses ◽  
Dna And Rna ◽  
Full Length Genome

AbstractTaxonomic classification of viruses is a multi-class hierarchical classification problem, as taxonomic ranks (e.g., order, family and genus) of viruses are hierarchically structured and have multiple classes in each rank. Classification of biological sequences which are hierarchically structured with multiple classes is challenging. Here we developed a machine learning architecture, VirusTaxo, using a multi-class hierarchical classification by k-mer enrichment. VirusTaxo classifies DNA and RNA viruses to their taxonomic ranks using genome sequence. To assign taxonomic ranks, VirusTaxo extracts k-mers from genome sequence and creates bag-of-k-mers for each class in a rank. VirusTaxo uses a top-down hierarchical classification approach and accurately assigns the order, family and genus of a virus from the genome sequence. The average accuracies of VirusTaxo for DNA viruses are 99% (order), 98% (family) and 95% (genus) and for RNA viruses 97% (order), 96% (family) and 82% (genus). VirusTaxo can be used to detect taxonomy of novel viruses using full length genome or contig sequences.AvailabilityOnline version of VirusTaxo is available at https://omics-lab.com/virustaxo/.

scholarly journals ACCURACY ASSESSMENTS OF PAN-SHARPENED IMAGE FOR BENTHIC HABITATS MAPPING

2017 ◽  
Vol 4 (1) ◽  
pp. 27 ◽  
Author(s):  
Pramaditya Wicaksono ◽  
Faza Adhimah
Keyword(s):  
Spatial Resolution ◽  
Classification Scheme ◽  
Spectral Response ◽  
Hierarchical Classification ◽  
Visual Interpretation ◽  
Benthic Habitats ◽  
Visual Appearance ◽  
Accuracy Assessments

Image-sharpening process integrates lower spatial resolution multispectral bands with higher spatial resolution panchromatic band to produce multispectral bands with finer spatial detail called pan-sharpened image. Although the pan-sharpened image can greatly assist the process of information extraction using visual interpretation, the benefit and setback of using pan-sharpened image on the accuracy of digital classification for mapping remain unclear. This research aimed at 1) highlighting the issue of using pan-sharpened image to perform benthic habitats mapping and 2) comparing the accuracy of benthic habitats mapping using original and pan-sharpened bands. In this study, Quickbird image was used and Kemujan Island was selected as the study area. Two levels of hierarchical classification scheme of benthic habitats were constructed based on the composition of in situ benthic habitats. PC Spectral sharpening method was applied on Quickbird image. Image radiometric corrections, PCA transformation, and image classifications were performed on both original and pan-sharpened image. The results showed that the accuracy of benthic habitats classification of pan-sharpened image (maximum overall accuracy 64.28% and 73.30% for per-pixel and OBIA, respectively) was lower than the original image (73.46% and 73.10%, respectively). The main setback of using pan-sharpened image is the inability to correct the sunglint, hence adversely affects the process of water column correction, PCA transformation and image classification. This is mainly because sunglint do not only affect object’s spectral response but also the texture of the object. Nevertheless, the pan-sharpened image can still be used to map benthic habitats using visual interpretation and digital image processing. Pan-sharpened image will deliver better classification accuracy and visual appearance especially when the sunglint is low.

scholarly journals Technology networks: the autocatalytic origins of innovation

2018 ◽  
Vol 5 (6) ◽  
pp. 172445 ◽  
Author(s):  
Lorenzo Napolitano ◽  
Evangelos Evangelou ◽  
Emanuele Pugliese ◽  
Paolo Zeppini ◽  
Graham Room
Keyword(s):  
Positive Reinforcement ◽  
Hierarchical Classification ◽  
Source Node ◽  
Directed Network ◽  
The Core ◽  
Source Field ◽  
Patenting Activity ◽  
Positive Spillovers

We analyse the autocatalytic structure of technological networks and evaluate its significance for the dynamics of innovation patenting. To this aim, we define a directed network of technological fields based on the International Patents Classification, in which a source node is connected to a receiver node via a link if patenting activity in the source field anticipates patents in the receiver field in the same region more frequently than we would expect at random. We show that the evolution of the technology network is compatible with the presence of a growing autocatalytic structure, i.e. a portion of the network in which technological fields mutually benefit from being connected to one another. We further show that technological fields in the core of the autocatalytic set display greater fitness, i.e. they tend to appear in a greater number of patents, thus suggesting the presence of positive spillovers as well as positive reinforcement. Finally, we observe that core shifts take place whereby different groups of technology fields alternate within the autocatalytic structure; this points to the importance of recombinant innovation taking place between close as well as distant fields of the hierarchical classification of technological fields.

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