A CNN-Based Mosquito Classification Using Image Transformation of Wingbeat Features

2020 ◽  
Author(s):  
Jose Alvaro Luna-Gonzalez ◽  
Daniel Robles-Camarillo ◽  
Mariko Nakano-Miyatake ◽  
Humberto Lanz-Mendoza ◽  
Hector Perez-Meana
Keyword(s):  
Anopheles Arabiensis ◽  
World Wide ◽  
Color Image ◽  
Audio Features ◽  
Audio Feature ◽  
Representative Species ◽  
Audio Feature Extraction ◽  
Feature Values ◽  
Fully Connected

In this paper, a classification of mosquito’s specie is performed using mosquito wingbeats samples obtained by optical sensor. Six world-wide representative species of mosquitos, which are Aedes aegypti, Aedes albopictus, Anopheles arabiensis, Anopheles gambiae and Culex pipiens, Culex quinquefasciatus, are considered for classification. A total of 60,000 samples are divided equally in each specie mentioned above. In total, 25 audio feature extraction algorithms are applied to extract 39 feature values per sample. Further, each audio feature is transformed to a color image, which shows audio features presenting by different pixel values. We used a fully connected neural networks for audio features and a convolutional neural network (CNN) for image dataset generated from audio features. The CNN-based classifier shows 90.75% accuracy, which outperforms the accuracy of 87.18% obtained by the first classifier using directly audio features.

scholarly journals Freely scalable and reconfigurable optical hardware for deep learning

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Liane Bernstein ◽  
Alexander Sludds ◽  
Ryan Hamerly ◽  
Vivienne Sze ◽  
Joel Emer ◽  
...  
Keyword(s):  
Neural Network ◽  
Energy Consumption ◽  
Data Transfer ◽  
Power Delivery ◽  
Optical Data ◽  
Optical Multicast ◽  
Optical Neural Network ◽  
Fully Connected ◽  
Management Power

AbstractAs deep neural network (DNN) models grow ever-larger, they can achieve higher accuracy and solve more complex problems. This trend has been enabled by an increase in available compute power; however, efforts to continue to scale electronic processors are impeded by the costs of communication, thermal management, power delivery and clocking. To improve scalability, we propose a digital optical neural network (DONN) with intralayer optical interconnects and reconfigurable input values. The path-length-independence of optical energy consumption enables information locality between a transmitter and a large number of arbitrarily arranged receivers, which allows greater flexibility in architecture design to circumvent scaling limitations. In a proof-of-concept experiment, we demonstrate optical multicast in the classification of 500 MNIST images with a 3-layer, fully-connected network. We also analyze the energy consumption of the DONN and find that digital optical data transfer is beneficial over electronics when the spacing of computational units is on the order of $$>10\,\upmu $$ > 10 μ m.

Explainable Fully Connected Visual Words for the Classification of Skin Cancer Confocal Images

2020 ◽  
Author(s):  
Athanasios Kallipolitis ◽  
Alexandros Stratigos ◽  
Alexios Zarras ◽  
Ilias Maglogiannis
Keyword(s):  
Skin Cancer ◽  
Visual Words ◽  
Confocal Images ◽  
Fully Connected

Automatic classification of married couples' behavior using audio features

2010 ◽  
Author(s):  
Matthew Black ◽  
Athanasios Katsamanis ◽  
Chi-Chun Lee ◽  
Adam C. Lammert ◽  
Brian R. Baucom ◽  
...  
Keyword(s):  
Married Couples ◽  
Automatic Classification ◽  
Audio Features

Audio feature extraction using probability distribution function

2015 ◽  
Author(s):  
Suhaib A. ◽  
Khairunizam Wan ◽  
Azri A. Aziz ◽  
D. Hazry ◽  
Zuradzman M. Razlan ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Distribution Function ◽  
Probability Distribution ◽  
Probability Distribution Function ◽  
Audio Feature ◽  
Audio Feature Extraction

scholarly journals Modern knowledge on pathogenesis, diagnosis and treatment of helicobacter infection

2021 ◽  
pp. 15-26
Author(s):  
H. Yu. Kiselev ◽  
C. L. Gorlenko ◽  
Ya. A. El-Taravi ◽  
E. E. Porubayeva ◽  
E. V. Budanova
Keyword(s):  
Drug Resistance ◽  
Risk Factor ◽  
Peptic Ulcer ◽  
World Wide ◽  
International Agency ◽  
Proper Treatment ◽  
Non Invasive ◽  
Modern Knowledge ◽  
H Pylori

Since its discovery, H. pylori infection is known as one of the risk factor for the development of gastritis, peptic ulcer, GIT tumors and numerous other diseases such as psoriasis. Infection caused by H. pylori is posed as the top oncogene in the risk of the development of gastrocarcinoma (First class oncogene by Classification of International Agency for Research of Cancer). That is why the elaboration of fast and accurate methods of diagnosis (non-invasive methods especially) and proper treatment of Helicobacter infection is still very important. Throughout the time, knowledge about pathogenesis of Helicobacter infection have been expanded with the detection of adhesins, chemotaxins and multiple virulence factors related to invasion, adhesion and cytotoxicity of H. pylori. Invasive and non-invasive methods of diagnostics are currently being improved in effectiveness and accuracy. But still, due to different factors (e. g., dramatically increasing drug resistance), eradication of H. pylori remains big problem world-wide. Our review represents modern data on pathogenesis, diagnostics and treatment of Helicobacter infection.

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