Small sample classification of hyperspectral image using model-agnostic meta-learning algorithm and convolutional neural network

A typical healthcare application for elderly people involves monitoring daily activities and providing them with assistance. Automatic analysis and classification of an image by the system is difficult compared to human vision. Several challenging problems for activity recognition from the surveillance video involving the complexity of the scene analysis under observations from irregular lighting and low-quality frames. In this article, the authors system use machine learning algorithms to improve the accuracy of activity recognition. Their system presents a convolutional neural network (CNN), a machine learning algorithm being used for image classification. This system aims to recognize and assist human activities for elderly people using input surveillance videos. The RGB image in the dataset used for training purposes which requires more computational power for classification of the image. By using the CNN network for image classification, the authors obtain a 79.94% accuracy in the experimental part which shows their model obtains good accuracy for image classification when compared with other pre-trained models.

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Classification of amyotrophic lateral sclerosis disease based on convolutional neural network and reinforcement sample learning algorithm

Health Information Science and Systems ◽

10.1007/s13755-017-0029-6 ◽

2017 ◽

Vol 5 (1) ◽

Cited By ~ 18

Author(s):

Abdulkadir Sengur ◽

Yaman Akbulut ◽

Yanhui Guo ◽

Varun Bajaj

Keyword(s):

Neural Network ◽

Amyotrophic Lateral Sclerosis ◽

Convolutional Neural Network ◽

Learning Algorithm ◽

Lateral Sclerosis

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Applied Machine Learning in Spiral Breast-CT: Can We Train a Deep Convolutional Neural Network for Automatic, Standardized and Observer Independent Classification of Breast Density?

Diagnostics ◽

10.3390/diagnostics12010181 ◽

2022 ◽

Vol 12 (1) ◽

pp. 181

Author(s):

Anna Landsmann ◽

Jann Wieler ◽

Patryk Hejduk ◽

Alexander Ciritsis ◽

Karol Borkowski ◽

...

Keyword(s):

Neural Network ◽

Machine Learning ◽

Convolutional Neural Network ◽

Real World ◽

Learning Algorithm ◽

Level Density ◽

Deep Convolutional Neural Network ◽

Good Correspondence ◽

Breast Ct

The aim of this study was to investigate the potential of a machine learning algorithm to accurately classify parenchymal density in spiral breast-CT (BCT), using a deep convolutional neural network (dCNN). In this retrospectively designed study, 634 examinations of 317 patients were included. After image selection and preparation, 5589 images from 634 different BCT examinations were sorted by a four-level density scale, ranging from A to D, using ACR BI-RADS-like criteria. Subsequently four different dCNN models (differences in optimizer and spatial resolution) were trained (70% of data), validated (20%) and tested on a “real-world” dataset (10%). Moreover, dCNN accuracy was compared to a human readout. The overall performance of the model with lowest resolution of input data was highest, reaching an accuracy on the “real-world” dataset of 85.8%. The intra-class correlation of the dCNN and the two readers was almost perfect (0.92) and kappa values between both readers and the dCNN were substantial (0.71–0.76). Moreover, the diagnostic performance between the readers and the dCNN showed very good correspondence with an AUC of 0.89. Artificial Intelligence in the form of a dCNN can be used for standardized, observer-independent and reliable classification of parenchymal density in a BCT examination.

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Airborne Network Traffic Identification Method under Small Training Samples

Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University ◽

10.1051/jnwpu/20203851129 ◽

2020 ◽

Vol 38 (5) ◽

pp. 1129-1137

Author(s):

Na Lyu ◽

Jiaxin Zhou ◽

Zhuo Chen ◽

Wu Chen

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Network Traffic ◽

Learning Algorithm ◽

Small Sample ◽

Fine Tuning ◽

Target Domain ◽

Data Set ◽

Traffic Identification ◽

Identification Method

Due to the high cost and difficulty of traffic data set acquisition and the high time sensitivity of traffic distribution, the machine learning-based traffic identification method is difficult to be applied in airborne network environment. Aiming at this problem, a method for airborne network traffic identification based on the convolutional neural network under small traffic samples is proposed. Firstly, the pre-training of the initial model for the convolutional neural network is implemented based on the complete data set in source domain, and then the retraining of the convolutional neural network is realized through the layer frozen based fine-tuning learning algorithm of convolutional neural network on the incomplete dataset in target domain, and the convolutional neural network model based feature representing transferring(FRT-CNN) is constructed to realize online traffic identification. The experiment results on the actual airborne network traffic dataset show that the proposed method can guarantee the accuracy of traffic identification under limited traffic samples, and the classification performance is significantly improved comparing with the existing small-sample learning methods.

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