Classification of Relationship in Argumentation Using Graph Convolutional Network

2020 ◽  
pp. 60-71
Author(s):  
Dimmy Magalhães ◽  
Aurora Pozo
Keyword(s):  
Convolutional Network

scholarly journals Convolutional Neural Network-Based Artificial Intelligence for Classification of Protein Localization Patterns

Biomolecules ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 264
Author(s):  
Kaisa Liimatainen ◽  
Riku Huttunen ◽  
Leena Latonen ◽  
Pekka Ruusuvuori
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Convolutional Neural Network ◽  
High Throughput ◽  
Protein Function ◽  
Protein Localization ◽  
Convolutional Network ◽  
High Throughput Analysis ◽  
Fully Convolutional Network

Identifying localization of proteins and their specific subpopulations associated with certain cellular compartments is crucial for understanding protein function and interactions with other macromolecules. Fluorescence microscopy is a powerful method to assess protein localizations, with increasing demand of automated high throughput analysis methods to supplement the technical advancements in high throughput imaging. Here, we study the applicability of deep neural network-based artificial intelligence in classification of protein localization in 13 cellular subcompartments. We use deep learning-based on convolutional neural network and fully convolutional network with similar architectures for the classification task, aiming at achieving accurate classification, but importantly, also comparison of the networks. Our results show that both types of convolutional neural networks perform well in protein localization classification tasks for major cellular organelles. Yet, in this study, the fully convolutional network outperforms the convolutional neural network in classification of images with multiple simultaneous protein localizations. We find that the fully convolutional network, using output visualizing the identified localizations, is a very useful tool for systematic protein localization assessment.

scholarly journals A Convolutional Network for the Classification of Sleep Stages

Proceedings ◽  
2018 ◽  
Vol 2 (18) ◽  
pp. 1174 ◽  
Author(s):  
Isaac Fernández-Varela ◽  
Elena Hernández-Pereira ◽  
Vicente Moret-Bonillo
Keyword(s):  
Sleep Medicine ◽  
Sleep Stages ◽  
Kappa Index ◽  
Night Sleep ◽  
Convolutional Network ◽  
Sleep Study ◽  
Convolutional Networks ◽  
Sleep Studies ◽  
Automatic Methods

The classification of sleep stages is a crucial task in the context of sleep medicine. It involves the analysis of multiple signals thus being tedious and complex. Even for a trained physician scoring a whole night sleep study can take several hours. Most of the automatic methods trying to solve this problem use human engineered features biased for a specific dataset. In this work we use deep learning to avoid human bias. We propose an ensemble of 5 convolutional networks achieving a kappa index of 0.83 when classifying 500 sleep studies.

Qualitative classification of waste textiles based on near infrared spectroscopy and the convolutional network

2019 ◽  
Vol 90 (9-10) ◽  
pp. 1057-1066 ◽  
Author(s):  
Zhengdong Liu ◽  
Wenxia Li ◽  
Zihan Wei
Keyword(s):  
Deep Learning ◽  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Convolutional Network ◽  
The Sustainable Development ◽  
Softmax Classifier ◽  
Qualitative Classification ◽  
Reliable Classification

The recycling of waste textiles has become a growth point for the sustainable development of the textile and clothing industry. In addition, sorting is a key link in the follow-up recycling process. Since different fabrics are required to be processed by different technologies, manual sorting not only takes time and effort but also cannot achieve accurate and reliable classification. Based on the analysis of near infrared spectroscopy, the theory and methods of deep learning are used for the qualitative classification of waste textiles in order to complete the automatic fabric composition recognition in the sorting process. Firstly, a standard sample set is established by waveform clipping and normalization, and a Textile Recycling Net deep web suitable for near infrared spectroscopy is established. Then, a pixilated layer is used to facilitate the deep learning of features, and the multidimensional features of the spectrum are extracted by using the multi-layer convolutional and pooling layers. Finally, the softmax classifier is adopted to complete the qualitative classification. Experimental results show that the convolutional network classification method using normalized and pixelated near infrared spectroscopy can realize the automatic classification of several common textiles, such as cotton and polyester, and effectively improve the detection level and speed of fabric components.

scholarly journals Deep Feature Fusion with Integration of Residual Connection and Attention Model for Classification of VHR Remote Sensing Images

2019 ◽  
Vol 11 (13) ◽  
pp. 1617 ◽  
Author(s):  
Jicheng Wang ◽  
Li Shen ◽  
Wenfan Qiao ◽  
Yanshuai Dai ◽  
Zhilin Li
Keyword(s):  
Remote Sensing ◽  
Feature Fusion ◽  
Learning Ability ◽  
Remote Sensing Images ◽  
Convolutional Network ◽  
Fully Convolutional Network ◽  
Attention Model ◽  
Low Level ◽  
Deep Feature

The classification of very-high-resolution (VHR) remote sensing images is essential in many applications. However, high intraclass and low interclass variations in these kinds of images pose serious challenges. Fully convolutional network (FCN) models, which benefit from a powerful feature learning ability, have shown impressive performance and great potential. Nevertheless, only classification results with coarse resolution can be obtained from the original FCN method. Deep feature fusion is often employed to improve the resolution of outputs. Existing strategies for such fusion are not capable of properly utilizing the low-level features and considering the importance of features at different scales. This paper proposes a novel, end-to-end, fully convolutional network to integrate a multiconnection ResNet model and a class-specific attention model into a unified framework to overcome these problems. The former fuses multilevel deep features without introducing any redundant information from low-level features. The latter can learn the contributions from different features of each geo-object at each scale. Extensive experiments on two open datasets indicate that the proposed method can achieve class-specific scale-adaptive classification results and it outperforms other state-of-the-art methods. The results were submitted to the International Society for Photogrammetry and Remote Sensing (ISPRS) online contest for comparison with more than 50 other methods. The results indicate that the proposed method (ID: SWJ_2) ranks #1 in terms of overall accuracy, even though no additional digital surface model (DSM) data that were offered by ISPRS were used and no postprocessing was applied.

scholarly journals A Spatiotemporal Convolutional Network for Multi-Behavior Recognition of Pigs

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2381
Author(s):  
Dan Li ◽  
Kaifeng Zhang ◽  
Zhenbo Li ◽  
Yifei Chen
Keyword(s):  
Network Architecture ◽  
Statistical Data ◽  
State Of The Art ◽  
Behavior Recognition ◽  
Convolutional Network ◽  
Test Set ◽  
Time Consumption ◽  
And Behavior ◽  
Human Eyes

The statistical data of different kinds of behaviors of pigs can reflect their health status. However, the traditional behavior statistics of pigs were obtained and then recorded from the videos through human eyes. In order to reduce labor and time consumption, this paper proposed a pig behavior recognition network with a spatiotemporal convolutional network based on the SlowFast network architecture for behavior classification of five categories. Firstly, a pig behavior recognition video dataset (PBVD-5) was built by cutting short clips from 3-month non-stop shooting videos, which was composed of five categories of pig’s behavior: feeding, lying, motoring, scratching and mounting. Subsequently, a SlowFast network based spatiotemporal convolutional network for the pig’s multi-behavior recognition (PMB-SCN) was proposed. The results of the networks with variant architectures of the PMB-SCN were implemented and the optimal architecture was compared with the state-of-the-art single stream 3D convolutional network in our dataset. Our 3D pig behavior recognition network showed a top-1 accuracy of 97.63% and a views accuracy of 96.35% on the test set of PBVD and a top-1 accuracy of 91.87% and a views accuracy of 84.47% on a new test set collected from a completely different pigsty. The experimental results showed that this network provided remarkable ability of generalization and possibility for the subsequent pig detection and behavior recognition simultaneously.

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