Humpback whale song analysis based on automatic classification performance

2020 ◽  
Vol 148 (4) ◽  
pp. 2597-2597
Author(s):  
Carlos A. Rueda ◽  
John P. Ryan
Keyword(s):  
Humpback Whale ◽  
Automatic Classification ◽  
Classification Performance ◽  
Song Analysis

Automatic classification of humpback whale social calls

2017 ◽  
Vol 141 (5) ◽  
pp. 3605-3605 ◽  
Author(s):  
Irina Tolkova ◽  
Lisa Bauer ◽  
Antonella Wilby ◽  
Ryan Kastner ◽  
Kerri Seger
Keyword(s):  
Humpback Whale ◽  
Automatic Classification ◽  
Social Calls

scholarly journals Gastrointestinal Disease Classification in Endoscopic Images Using Attention-Guided Convolutional Neural Networks

2021 ◽  
Vol 11 (23) ◽  
pp. 11136
Author(s):  
Zenebe Markos Lonseko ◽  
Prince Ebenezer Adjei ◽  
Wenju Du ◽  
Chengsi Luo ◽  
Dingcan Hu ◽  
...  
Keyword(s):  
Data Augmentation ◽  
Spatial Information ◽  
Gastrointestinal Disease ◽  
Confusion Matrix ◽  
Automatic Classification ◽  
Classification Performance ◽  
Attention Mechanism ◽  
Disease Classification ◽  
Matrix Analysis

Gastrointestinal (GI) diseases constitute a leading problem in the human digestive system. Consequently, several studies have explored automatic classification of GI diseases as a means of minimizing the burden on clinicians and improving patient outcomes, for both diagnostic and treatment purposes. The challenge in using deep learning-based (DL) approaches, specifically a convolutional neural network (CNN), is that spatial information is not fully utilized due to the inherent mechanism of CNNs. This paper proposes the application of spatial factors in improving classification performance. Specifically, we propose a deep CNN-based spatial attention mechanism for the classification of GI diseases, implemented with encoder–decoder layers. To overcome the data imbalance problem, we adapt data-augmentation techniques. A total of 12,147 multi-sited, multi-diseased GI images, drawn from publicly available and private sources, were used to validate the proposed approach. Furthermore, a five-fold cross-validation approach was adopted to minimize inconsistencies in intra- and inter-class variability and to ensure that results were robustly assessed. Our results, compared with other state-of-the-art models in terms of mean accuracy (ResNet50 = 90.28, GoogLeNet = 91.38, DenseNets = 91.60, and baseline = 92.84), demonstrated better outcomes (Precision = 92.8, Recall = 92.7, F1-score = 92.8, and Accuracy = 93.19). We also implemented t-distributed stochastic neighbor embedding (t–SNE) and confusion matrix analysis techniques for better visualization and performance validation. Overall, the results showed that the attention mechanism improved the automatic classification of multi-sited GI disease images. We validated clinical tests based on the proposed method by overcoming previous limitations, with the goal of improving automatic classification accuracy in future work.

scholarly journals A Dynamic Multi-Scale Network for EEG Signal Classification

2021 ◽  
Vol 14 ◽  
Author(s):  
Guokai Zhang ◽  
Jihao Luo ◽  
Letong Han ◽  
Zhuyin Lu ◽  
Rong Hua ◽  
...  
Keyword(s):  
Receptive Fields ◽  
Automatic Classification ◽  
Classification Performance ◽  
Signal Classification ◽  
Great Success ◽  
Eeg Signal ◽  
Multi Scale ◽  
Key Factor ◽  
Scale Network ◽  
Eeg Signal Classification

Accurate and automatic classification of the speech imagery electroencephalography (EEG) signals from a Brain-Computer Interface (BCI) system is highly demanded in clinical diagnosis. The key factor in designing an automatic classification system is to extract essential features from the original input; though many methods have achieved great success in this domain, they may fail to process the multi-scale representations from different receptive fields and thus hinder the model from achieving a higher performance. To address this challenge, in this paper, we propose a novel dynamic multi-scale network to achieve the EEG signal classification. The whole classification network is based on ResNet, and the input signal first encodes the features by the Short-time Fourier Transform (STFT); then, to further improve the multi-scale feature extraction ability, we incorporate a dynamic multi-scale (DMS) layer, which allows the network to learn multi-scale features from different receptive fields at a more granular level. To validate the effectiveness of our designed network, we conduct extensive experiments on public dataset III of BCI competition II, and the experimental results demonstrate that our proposed dynamic multi-scale network could achieve promising classification performance in this task.

Interpreting HVEM of muscle-cell impulse networks

1992 ◽  
Vol 50 (1) ◽  
pp. 126-127
Author(s):  
Paul DeCosta ◽  
Kyugon Cho ◽  
Stephen Shemlon ◽  
Heesung Jun ◽  
Stanley M. Dunn
Keyword(s):  
Structural Analysis ◽  
Muscle Cell ◽  
Electron Micrographs ◽  
Relative Size ◽  
Automatic Classification ◽  
Nerve Fibers ◽  
Analysis Algorithm ◽  
Structural Networks

Introduction: The analysis and interpretation of electron micrographs of cells and tissues, often requires the accurate extraction of structural networks, which either provide immediate 2D or 3D information, or from which the desired information can be inferred. The images of these structures contain lines and/or curves whose orientation, lengths, and intersections characterize the overall network.Some examples exist of studies that have been done in the analysis of networks of natural structures. In, Sebok and Roemer determine the complexity of nerve structures in an EM formed slide. Here the number of nodes that exist in the image describes how dense nerve fibers are in a particular region of the skin. Hildith proposes a network structural analysis algorithm for the automatic classification of chromosome spreads (type, relative size and orientation).

The Sound of Enemies and Friends in the Neighborhood

2011 ◽  
Vol 58 (6) ◽  
pp. 454-463 ◽  
Author(s):  
Diane Pecher ◽  
Inge Boot ◽  
Saskia van Dantzig ◽  
Carol J. Madden ◽  
David E. Huber ◽  
...  
Keyword(s):  
Word Recognition ◽  
Visual Word Recognition ◽  
Neighborhood Effects ◽  
Classification Performance ◽  
Visual Word ◽  
Orthographic Neighborhood ◽  
Semantic Classification ◽  
Semantic Class ◽  
Orthographic Neighbors ◽  
Target Words

Previous studies (e.g., Pecher, Zeelenberg, & Wagenmakers, 2005) found that semantic classification performance is better for target words with orthographic neighbors that are mostly from the same semantic class (e.g., living) compared to target words with orthographic neighbors that are mostly from the opposite semantic class (e.g., nonliving). In the present study we investigated the contribution of phonology to orthographic neighborhood effects by comparing effects of phonologically congruent orthographic neighbors (book-hook) to phonologically incongruent orthographic neighbors (sand-wand). The prior presentation of a semantically congruent word produced larger effects on subsequent animacy decisions when the previously presented word was a phonologically congruent neighbor than when it was a phonologically incongruent neighbor. In a second experiment, performance differences between target words with versus without semantically congruent orthographic neighbors were larger if the orthographic neighbors were also phonologically congruent. These results support models of visual word recognition that assume an important role for phonology in cascaded access to meaning.

An Automatic Classification Method for Involuntary and Two Types of Voluntary Blinks

2016 ◽  
Vol 136 (9) ◽  
pp. 1350-1358 ◽  
Author(s):  
Hironobu Sato ◽  
Kiyohiko Abe ◽  
Shoichi Ohi ◽  
Minoru Ohyama
Keyword(s):  
Automatic Classification ◽  
Classification Method

Humpback Whale Seawater Entry Videos

2017 ◽  
Author(s):  
Maria Clara Iruzun Martins ◽  
Carolyn Miller ◽  
Philip K. Hamilton ◽  
Jooke Robbins ◽  
Daniel Zitterbart ◽  
...  
Keyword(s):  
Humpback Whale
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