scholarly journals Comparison of two convolutional neural network models for automated classification of brain cancer types

2021 ◽  
Author(s):  
M. S. Fuad ◽  
C. Anam ◽  
K. Adi ◽  
G. Dougherty
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Brain Cancer ◽  
Network Models ◽  
Automated Classification ◽  
Neural Network Models ◽  
Cancer Types

Classification of first quality fancy cashew kernels using four deep convolutional neural network models

2020 ◽  
Vol 43 (12) ◽  
Author(s):  
Sriram K. Vidyarthi ◽  
Samrendra K. Singh ◽  
Rakhee Tiwari ◽  
Hong‐Wei Xiao ◽  
Rewa Rai
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Models ◽  
Deep Convolutional Neural Network ◽  
Neural Network Models

scholarly journals Classification of particle height in a hopper bin from limited discharge data using convolutional neural network models

2018 ◽  
Vol 339 ◽  
pp. 615-624 ◽  
Author(s):  
Shaohua Chen ◽  
Laurent A. Baumes ◽  
Aytekin Gel ◽  
Manogna Adepu ◽  
Heather Emady ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Models ◽  
Neural Network Models ◽  
Discharge Data

Efficient Imbalanced Multimedia Concept Retrieval by Deep Learning on Spark Clusters

2020 ◽  
pp. 274-294
Author(s):  
Yilin Yan ◽  
Min Chen ◽  
Saad Sadiq ◽  
Mei-Ling Shyu
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Imbalanced Data ◽  
Network Models ◽  
Multimedia Data ◽  
Neural Network Models ◽  
Minority Class ◽  
Imbalanced Data Classification

The classification of imbalanced datasets has recently attracted significant attention due to its implications in several real-world use cases. The classifiers developed on datasets with skewed distributions tend to favor the majority classes and are biased against the minority class. Despite extensive research interests, imbalanced data classification remains a challenge in data mining research, especially for multimedia data. Our attempt to overcome this hurdle is to develop a convolutional neural network (CNN) based deep learning solution integrated with a bootstrapping technique. Considering that convolutional neural networks are very computationally expensive coupled with big training datasets, we propose to extract features from pre-trained convolutional neural network models and feed those features to another full connected neutral network. Spark implementation shows promising performance of our model in handling big datasets with respect to feasibility and scalability.

Efficient Imbalanced Multimedia Concept Retrieval by Deep Learning on Spark Clusters

2017 ◽  
Vol 8 (1) ◽  
pp. 1-20 ◽  
Author(s):  
Yilin Yan ◽  
Min Chen ◽  
Saad Sadiq ◽  
Mei-Ling Shyu
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Imbalanced Data ◽  
Network Models ◽  
Multimedia Data ◽  
Neural Network Models ◽  
Minority Class ◽  
Imbalanced Data Classification

The classification of imbalanced datasets has recently attracted significant attention due to its implications in several real-world use cases. The classifiers developed on datasets with skewed distributions tend to favor the majority classes and are biased against the minority class. Despite extensive research interests, imbalanced data classification remains a challenge in data mining research, especially for multimedia data. Our attempt to overcome this hurdle is to develop a convolutional neural network (CNN) based deep learning solution integrated with a bootstrapping technique. Considering that convolutional neural networks are very computationally expensive coupled with big training datasets, we propose to extract features from pre-trained convolutional neural network models and feed those features to another full connected neutral network. Spark implementation shows promising performance of our model in handling big datasets with respect to feasibility and scalability.

scholarly journals Wood microscopic image identification method based on convolution neural network

BioResources ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. 4986-4999
Author(s):  
Ziyu Zhao ◽  
Xiaoxia Yang ◽  
Zhedong Ge ◽  
Hui Guo ◽  
Yucheng Zhou
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Recognition Accuracy ◽  
Network Models ◽  
Identification Accuracy ◽  
Technical Solution ◽  
Wood Identification ◽  
Neural Network Models ◽  
Identification Method

To prevent the illegal trade of precious wood in circulation, a wood species identification method based on convolutional neural network (CNN), namely PWoodIDNet (Precise Wood Specifications Identification) model, is proposed. In this paper, the PWoodIDNet model for the identification of rare tree species is constructed to reduce network parameters by decomposing convolutional kernel, prevent overfitting, enrich the diversity of features, and improve the performance of the model. The results showed that the PWoodIDNet model can effectively improve the generalization ability, the characterization ability of detail features, and the recognition accuracy, and effectively improve the classification of wood identification. PWoodIDNet was used to analyze the identification accuracy of microscopic images of 16 kinds of wood, and the identification accuracy reached 99%, which was higher than the identification accuracy of several existing classical convolutional neural network models. In addition, the PWoodIDNet model was analyzed to verify the feasibility and effectiveness of the PWoodIDNet model as a wood identification method, which can provide a new direction and technical solution for the field of wood identification.

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