Automatic detection and classification of fish calls in the Northern Gulf of Mexico using energy detectors and a convolutional neural network

2019 ◽  
Vol 146 (4) ◽  
pp. 3025-3026
Author(s):  
Emily Waddell ◽  
Kaitlin E. Frasier ◽  
John Hildebrand ◽  
Ana Širović
Keyword(s):  
Neural Network ◽  
Gulf Of Mexico ◽  
Convolutional Neural Network ◽  
Automatic Detection ◽  
Northern Gulf Of Mexico ◽  
Energy Detectors

scholarly journals Automatic Detection and Classification of Rib Fractures on Thoracic CT Using Convolutional Neural Network: Accuracy and Feasibility

2020 ◽  
Vol 21 (7) ◽  
pp. 869 ◽  
Author(s):  
Qing-Qing Zhou ◽  
Jiashuo Wang ◽  
Wen Tang ◽  
Zhang-Chun Hu ◽  
Zi-Yi Xia ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Automatic Detection ◽  
Rib Fractures ◽  
Thoracic Ct

scholarly journals Automatic Detection and Classification of White Blood Cells in Blood Smear Images Using Convolutional Neural Network

2019 ◽  
Vol 16 (Special Issue) ◽  
Author(s):  
Ramin Nateghi ◽  
Mansoor Fatehi ◽  
Ali Sadeghitabar ◽  
Romana Khosravi ◽  
Fattane Pourakpour
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Blood Cells ◽  
Blood Smear ◽  
White Blood Cells ◽  
Automatic Detection

LENS CLASSIFICATION ACCORDING TO THE TYPE OF LIGHT SPOT USING A NEURAL NETWORK

2020 ◽  
Vol 2020 (4) ◽  
pp. 4-14
Author(s):  
Vladimir Budak ◽  
Ekaterina Ilyina
Keyword(s):  
Neural Network ◽  
Light Intensity ◽  
Convolutional Neural Network ◽  
Deep Convolutional Neural Network ◽  
Beam Angle ◽  
New Model ◽  
Technical Parameters ◽  
Transfer Training ◽  
Trained Network

The article proposes the classification of lenses with different symmetrical beam angles and offers a scale as a spot-light’s palette. A collection of spotlight’s images was created and classified according to the proposed scale. The analysis of 788 pcs of existing lenses and reflectors with different LEDs and COBs carried out, and the dependence of the axial light intensity from beam angle was obtained. A transfer training of new deep convolutional neural network (CNN) based on the pre-trained GoogleNet was performed using this collection. GradCAM analysis showed that the trained network correctly identifies the features of objects. This work allows us to classify arbitrary spotlights with an accuracy of about 80 %. Thus, light designer can determine the class of spotlight and corresponding type of lens with its technical parameters using this new model based on CCN.

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