scholarly journals Multistage Feature Complimentary Network for Single-Image Deraining

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Kangying Wang ◽  
Minghui Wang
Keyword(s):  
Neural Network ◽  
Image Analysis ◽  
Recurrent Neural Network ◽  
Data Sets ◽  
Single Image ◽  
Visual Effects ◽  
Rain Removal ◽  
Feature Complementarity ◽  
Vision Algorithm ◽  
Image Details

Rain will cause the occlusion and blur of background and target objects and affect the image visual effect and subsequent image analysis. Aiming at the problem of insufficient rain removal in the current rain removal algorithm, in order to improve the accuracy of computer vision algorithm in the process of rain removal, this paper proposes a multistage framework based on progressive restoration combined with recurrent neural network and feature complementarity technology to remove rain streak from single images. Firstly, the encoder-decoder subnetwork is adapted to learn multiscale information and extract richer rain features. Secondly, the original resolution image restored by decoder is used to preserve refined image details. Finally, we use the effective information of the previous stage to guide the rain removal of the next stage by the recurrent neural network. The final experimental results show that a multistage feature complementarity network performs well on both synthetic rainy data sets and real-world rainy data sets can remove rain more completely, preserve more background details, and achieve better visual effects compared with some popular single-image deraining methods.

scholarly journals Latent representation of the human pan-celltype epigenome through a deep recurrent neural network

2021 ◽  
Author(s):  
Kevin B. Dsouza ◽  
Adam Y. Li ◽  
Vijay K. Bhargava ◽  
Maxwell W. Libbrecht
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Short Term Memory ◽  
Replication Timing ◽  
Cell Types ◽  
Data Sets ◽  
Deep Recurrent Neural Network ◽  
Long Short Term Memory ◽  
Latent Representations

AbstractThe availability of thousands of assays of epigenetic activity necessitates compressed representations of these data sets that summarize the epigenetic landscape of the genome. Until recently, most such representations were celltype specific, applying to a single tissue or cell state. Recently, neural networks have made it possible to summarize data across tissues to produce a pan-celltype representation. In this work, we propose Epi-LSTM, a deep long short-term memory (LSTM) recurrent neural network autoencoder to capture the long-term dependencies in the epigenomic data. The latent representations from Epi-LSTM capture a variety of genomic phenomena, including gene-expression, promoter-enhancer interactions, replication timing, frequently interacting regions and evolutionary conservation. These representations outperform existing methods in a majority of cell-types, while yielding smoother representations along the genomic axis due to their sequential nature.

scholarly journals Multiattentive Recurrent Neural Network Architecture for Multilingual Readability Assessment

2019 ◽  
Vol 7 ◽  
pp. 421-436 ◽  
Author(s):  
Ion Madrazo Azpiazu ◽  
Maria Soledad Pera
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Network Architecture ◽  
Text Structure ◽  
Reading Level ◽  
Data Sets ◽  
Neural Network Architecture ◽  
Using Data ◽  
Word Attention ◽  
Readability Assessment

We present a multiattentive recurrent neural network architecture for automatic multilingual readability assessment. This architecture considers raw words as its main input, but internally captures text structure and informs its word attention process using other syntax- and morphology-related datapoints, known to be of great importance to readability. This is achieved by a multiattentive strategy that allows the neural network to focus on specific parts of a text for predicting its reading level. We conducted an exhaustive evaluation using data sets targeting multiple languages and prediction task types, to compare the proposed model with traditional, state-of-the-art, and other neural network strategies.

scholarly journals DAN: Deep Attention Neural Network for News Recommendation

2019 ◽  
Vol 33 ◽  
pp. 5973-5980 ◽  
Author(s):  
Qiannan Zhu ◽  
Xiaofei Zhou ◽  
Zeliang Song ◽  
Jianlong Tan ◽  
Li Guo
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Real World ◽  
Attention Mechanism ◽  
Experimental Results ◽  
Data Sets ◽  
Challenging Problem ◽  
World News ◽  
Sequential Information ◽  
News Recommendation

With the rapid information explosion of news, making personalized news recommendation for users becomes an increasingly challenging problem. Many existing recommendation methods that regard the recommendation procedure as the static process, have achieved better recommendation performance. However, they usually fail with the dynamic diversity of news and user’s interests, or ignore the importance of sequential information of user’s clicking selection. In this paper, taking full advantages of convolution neural network (CNN), recurrent neural network (RNN) and attention mechanism, we propose a deep attention neural network DAN for news recommendation. Our DAN model presents to use attention-based parallel CNN for aggregating user’s interest features and attention-based RNN for capturing richer hidden sequential features of user’s clicks, and combines these features for new recommendation. We conduct experiment on real-world news data sets, and the experimental results demonstrate the superiority and effectiveness of our proposed DAN model.

scholarly journals An Intrusion Detection System for Network Security Using Recurrent Neural Network

2021 ◽  
Author(s):  
Kishor P. Jadhav ◽  
Mohit Gangwar
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Recurrent Neural Network ◽  
Detection System ◽  
Machine Learning Algorithms ◽  
Data Sets ◽  
Data Set ◽  
Unauthorized Access ◽  
Network Protection ◽  
Signature Detection

To maintain the security of vulnerable network is the most essential thing in network system; for network protection or to eliminate unauthorized access of internal as well as external connections, various architectures have been suggested. Various existing approaches has developed different approaches to detect suspicious attacks on victimized machines; nevertheless, an external user develops malicious behaviour and gains unauthorized access to victim machines via such a behaviour framework, referred to as malicious activity or Intruder. A variety of supervised machine algorithms and soft computing algorithms have been developed to distinguish events in real-time as well as synthetic network log data. On the benchmark data set, the NLSKDD most commonly used data set to identify the Intruder. In this paper, we suggest using machine learning algorithms to identify intruders. A signature detection and anomaly detection are two related techniques that have been suggested. In the experimental study, the Recurrent Neural Network (RNN) algorithm is demonstrated with different data sets, and the system’s output is demonstrated in a real-time network context.

Recurrent Neural Network for Predicting Dielectric Mirror Reflectivity

2019 ◽  
Vol 23 (6) ◽  
pp. 1012-1018 ◽  
Author(s):  
Tomomasa Ohkubo ◽  
Ei-ichi Matsunaga ◽  
Junji Kawanaka ◽  
Takahisa Jitsuno ◽  
Shinji Motokoshi ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Recurrent Neural Network ◽  
Expert Knowledge ◽  
Thickness Distribution ◽  
Training Data ◽  
Generative Adversarial Networks ◽  
Data Sets ◽  
Dielectric Mirror ◽  
Dielectric Mirrors

Optical devices often achieve their maximum effectiveness by using dielectric mirrors; however, their design techniques depend on expert knowledge in specifying the mirror properties. This expertise can also be achieved by machine learning, although it is not clear what kind of neural network would be effective for learning about dielectric mirrors. In this paper, we clarify that the recurrent neural network (RNN) is an effective approach to machine-learning for dielectric mirror properties. The relation between the thickness distribution of the mirror’s multiple film layers and the average reflectivity in the target wavelength region is used as the indicator in this study. Reflection from the dielectric multilayer film results from the sequence of interfering reflections from the boundaries between film layers. Therefore, the RNN, which is usually used for sequential data, is effective to learn the relationship between average reflectivity and the thickness of individual film layers in a dielectric mirror. We found that a RNN can predict its average reflectivity with a mean squared error (MSE) less than 10-4 from representative thickness distribution data (10 layers with alternating refractive indexes 2.3 and 1.4). Furthermore, we clarified that training data sets generated randomly lead to over-learning. It is necessary to generate training data sets from larger data sets so that the histogram of reflectivity becomes a flat distribution. In the future, we plan to apply this knowledge to design dielectric mirrors using neural network approaches such as generative adversarial networks, which do not require the know-how of experts.

scholarly journals Analysis of Micro Facial Expression by Machine and Deep Learning Methods: Haar, CNN, and RNN

2021 ◽  
Vol 16 (1) ◽  
pp. 95-101
Author(s):  
Dibakar Raj Pant ◽  
Rolisha Sthapit
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Facial Expression ◽  
Facial Expressions ◽  
Recurrent Neural Network ◽  
Test Accuracy ◽  
Data Sets ◽  
Learning Methods ◽  
Facial Movements ◽  
Casme Ii

Facial expressions are due to the actions of the facial muscles located at different facial regions. These expressions are two types: Macro and Micro expressions. The second one is more important in computer vision. Analysis of micro expressions categorized by disgust, happiness, anger, sadness, surprise, contempt, and fear are challenging because of very fast and subtle facial movements. This article presents one machine learning method: Haar and two deep learning methods: Convolution Neural Network (CNN) and Recurrent Neural Network (RNN) to perform recognition of micro-facial expression analysis. First, Haar Cascade Classifier is used to detect the face as a pre-image-processing step. Secondly, those detected faces are passed through series of Convolutional Neural Network (CNN) layers for the features extraction. Thirdly, the Recurrent Neural Network (RNN) classifies micro facial expressions. Two types of data sets are used for training and testing of the proposed method: Chinese Academy of Sciences Micro-Expression II (CSAME II) and Spontaneous Actions and Micro-Movements (SAMM) database. The test accuracy of SAMM and CASME II are obtained as 84.76%, and 87% respectively. In addition, the distinction between micro facial expressions and non- micro facial expressions are analyzed by the ROC curve.

Research on behavior recognition based on feature fusion of automatic coder and recurrent neural network

2020 ◽  
Vol 39 (6) ◽  
pp. 8927-8935
Author(s):  
Bing Zheng ◽  
Dawei Yun ◽  
Yan Liang
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Behavior Pattern ◽  
Rapid Development ◽  
Video Data ◽  
Support Vector ◽  
Behavior Recognition ◽  
Learning Methods ◽  
The Impact ◽  
Internet Of Things Technology

Under the impact of COVID-19, research on behavior recognition are highly needed. In this paper, we combine the algorithm of self-adaptive coder and recurrent neural network to realize the research of behavior pattern recognition. At present, most of the research of human behavior recognition is focused on the video data, which is based on the video number. At the same time, due to the complexity of video image data, it is easy to violate personal privacy. With the rapid development of Internet of things technology, it has attracted the attention of a large number of experts and scholars. Researchers have tried to use many machine learning methods, such as random forest, support vector machine and other shallow learning methods, which perform well in the laboratory environment, but there is still a long way to go from practical application. In this paper, a recursive neural network algorithm based on long and short term memory (LSTM) is proposed to realize the recognition of behavior patterns, so as to improve the accuracy of human activity behavior recognition.

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