scholarly journals Deep Learning and Handcrafted Features for Virus Image Classification

2020 ◽  
Vol 6 (12) ◽  
pp. 143
Author(s):  
Loris Nanni ◽  
Eugenio De Luca ◽  
Marco Ludovico Facin ◽  
Gianluca Maguolo
Keyword(s):  
Neural Network ◽  
Electron Microscopy ◽  
Deep Neural Network ◽  
State Of The Art ◽  
Support Vector ◽  
Transmission Electron ◽  
Vector Machines ◽  
Multiple Support ◽  
Multiple Support Vector Machines

In this work, we present an ensemble of descriptors for the classification of virus images acquired using transmission electron microscopy. We trained multiple support vector machines on different sets of features extracted from the data. We used both handcrafted algorithms and a pretrained deep neural network as feature extractors. The proposed fusion strongly boosts the performance obtained by each stand-alone approach, obtaining state of the art performance.

scholarly journals An Analysis of State-of-the-art Activation Functions For Supervised Deep Neural Network

2021 ◽  
Author(s):  
Anh Nguyen ◽  
Khoa Pham ◽  
Dat Ngo ◽  
Thanh Ngo ◽  
Lam Pham
Keyword(s):  
Neural Network ◽  
Supervised Classification ◽  
Deep Neural Network ◽  
State Of The Art ◽  
Network Architectures ◽  
Activation Functions ◽  
Scene Classification ◽  
Learning Network ◽  
Deep Learning Network

This paper provides an analysis of state-of-the-art activation functions with respect to supervised classification of deep neural network. These activation functions comprise of Rectified Linear Units (ReLU), Exponential Linear Unit (ELU), Scaled Exponential Linear Unit (SELU), Gaussian Error Linear Unit (GELU), and the Inverse Square Root Linear Unit (ISRLU). To evaluate, experiments over two deep learning network architectures integrating these activation functions are conducted. The first model, basing on Multilayer Perceptron (MLP), is evaluated with MNIST dataset to perform these activation functions.Meanwhile, the second model, likely VGGish-based architecture, is applied for Acoustic Scene Classification (ASC) Task 1A in DCASE 2018 challenge, thus evaluate whether these activation functions work well in different datasets as well as different network architectures.

scholarly journals SPECTRAL-SPATIAL CLASSIFICATION OF HYPERSPECTRAL IMAGERY USING NEURAL NETWORK ALGORITHM AND HIERARCHICAL SEGMENTATION

2019 ◽  
Vol XLII-2/W12 ◽  
pp. 1-5
Author(s):  
D. Akbari ◽  
M. Moradizadeh ◽  
M. Akbari
Keyword(s):  
Neural Network ◽  
Spatial Information ◽  
Hyperspectral Data ◽  
Support Vector ◽  
Washington Dc ◽  
Hierarchical Segmentation ◽  
Vector Machines ◽  
Neural Network Algorithm ◽  
New Framework

<p><strong>Abstract.</strong> This paper describes a new framework for classification of hyperspectral images, based on both spectral and spatial information. The spatial information is obtained by an enhanced Marker-based Hierarchical Segmentation (MHS) algorithm. The hyperspectral data is first fed into the Multi-Layer Perceptron (MLP) neural network classification algorithm. Then, the MHS algorithm is applied in order to increase the accuracy of less-accurately classified land-cover types. In the proposed approach, the markers are extracted from the classification maps obtained by MLP and Support Vector Machines (SVM) classifiers. Experimental results on Washington DC Mall hyperspectral dataset, demonstrate the superiority of proposed approach compared to the MLP and the original MHS algorithms.</p>

scholarly journals Training Multiple Support Vector Machines for Personalized Web Content Filters

2013 ◽  
Vol E96.D (11) ◽  
pp. 2376-2384
Author(s):  
Dung Duc NGUYEN ◽  
Maike ERDMANN ◽  
Tomoya TAKEYOSHI ◽  
Gen HATTORI ◽  
Kazunori MATSUMOTO ◽  
...  
Keyword(s):  
Support Vector Machines ◽  
Support Vector ◽  
Web Content ◽  
Vector Machines ◽  
Multiple Support ◽  
Multiple Support Vector Machines

A Research of MM-SVM Technique for Fault Diagnosis

2014 ◽  
Vol 556-562 ◽  
pp. 2633-2637
Author(s):  
Hong Yin ◽  
Shu Qiang Yang ◽  
Guo Ming Li ◽  
Ping Yin ◽  
Song Chang Jin
Keyword(s):  
Fault Diagnosis ◽  
Satellite Telemetry ◽  
Fault Identification ◽  
Support Vector ◽  
Physical Relationship ◽  
Vector Machines ◽  
Diagnosis Method ◽  
Multiple Support ◽  
Multiple Support Vector Machines ◽  
Satellite Equipment

With the satellite development of our country, higher accuracy and stability are requires, which makes the control systems becoming more complex and requiring more telemetry parameters. Data mining techniques do not consider the physical relationship between the various components, but use of satellite telemetry parameters of the satellite states the purpose of fault identification. In this paper, we give a model based on multiple support vector machines (MM-SVM) technology satellite fault diagnosis method. The experiment shows that our method is effective in satellite equipment fault diagnosis

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