scholarly journals End-to-end Deep Learning Methods for Automated Damage Detection in Extreme Events at Various Scales

2021 ◽  
Author(s):  
Yongsheng Bai ◽  
Halil Sezen ◽  
Alper Yilmaz
Keyword(s):  
Deep Learning ◽  
Damage Detection ◽  
Extreme Events ◽  
Learning Methods ◽  
End To End

scholarly journals Automatic Multi-task Learning System for Abnormal Network Traffic Detection

2018 ◽  
Vol 13 (04) ◽  
pp. 4 ◽  
Author(s):  
He Huang ◽  
Haojiang Deng ◽  
Jun Chen ◽  
Luchao Han ◽  
Wei Wang
Keyword(s):  
Deep Learning ◽  
Network Traffic ◽  
Learning Systems ◽  
Learning System ◽  
Traffic Classification ◽  
Learning Methods ◽  
Web 3.0 ◽  
Task Learning ◽  
Knowledge Models ◽  
End To End

Since the last decade of the 20th century, the Internet had become flourishing, which drew great interest in the detection of abnormal network traffic. Particular-ly, it’s impossible to manually detect the abnormal patterns from enormous traffic flow in real time. Therefore, multiple machine learning methods are adopted to solve this learning problem. Those methods differ in mathematical models, knowledge models, application scenarios and target flows. In recent years, as a consequence of the technological breakthrough of Web 3.0, the traditional types of traffic classifiers are getting outdated and people start to focus on deep learning methods. Deep learning provides the potential for end-to-end learning systems to automatically learn the abnormal patterns without massive feature engineering, saving plenty of detecting time. In this study, to further save both memory and times of learning systems, we propose a novel multi-task learning system based on convolutional neural network, which can simultaneously solve the tasks of malware detection, VPN-capsulation recognition and Trojan classification. To the best of our knowledge, it’s the first time to apply an end-to-end multi-task learn-ing system in traffic classification. In order to validate this method, we establish experiments on public malware dataset CTU-13 and VPN traffic dataset ISCX. Our system found a synergy among all these tasks and managed to achieve the state-of-the-art output for most of the experiments.

Deep learning methods for damage detection of jacket-type offshore platforms

2021 ◽  
Vol 154 ◽  
pp. 249-261
Author(s):  
Xingxian Bao ◽  
Tongxuan Fan ◽  
Chen Shi ◽  
Guanlan Yang
Keyword(s):  
Deep Learning ◽  
Damage Detection ◽  
Offshore Platforms ◽  
Learning Methods

scholarly journals End-to-End Adversarial Memory Network for Cross-domain Sentiment Classification

2017 ◽  
Author(s):  
Zheng Li ◽  
Yu Zhang ◽  
Ying Wei ◽  
Yuxiang Wu ◽  
Qiang Yang
Keyword(s):  
Deep Learning ◽  
Domain Adaptation ◽  
Sentiment Classification ◽  
Classification Error ◽  
Target Domain ◽  
Learning Methods ◽  
Cross Domain ◽  
Memory Network ◽  
End To End ◽  
Two Parameter

Domain adaptation tasks such as cross-domain sentiment classification have raised much attention in recent years. Due to the domain discrepancy, a sentiment classifier trained in a source domain may not work well when directly applied to a target domain. Traditional methods need to manually select pivots, which behave in the same way for discriminative learning in both domains. Recently, deep learning methods have been proposed to learn a representation shared by domains. However, they lack the interpretability to directly identify the pivots. To address the problem, we introduce an end-to-end Adversarial Memory Network (AMN) for cross-domain sentiment classification. Unlike existing methods, our approach can automatically capture the pivots using an attention mechanism. Our framework consists of two parameter-shared memory networks: one is for sentiment classification and the other is for domain classification. The two networks are jointly trained so that the selected features minimize the sentiment classification error and at the same time make the domain classifier indiscriminative between the representations from the source or target domains. Moreover, unlike deep learning methods that cannot tell us which words are the pivots, our approach can offer a direct visualization of them. Experiments on the Amazon review dataset demonstrate that our approach can significantly outperform state-of-the-art methods.

scholarly journals DETECTING CRACKS AND SPALLING AUTOMATICALLY IN EXTREME EVENTS BY END-TO-END DEEP LEARNING FRAMEWORKS

2021 ◽  
Vol V-2-2021 ◽  
pp. 161-168
Author(s):  
Y. Bai ◽  
H. Sezen ◽  
A. Yilmaz
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Extreme Events ◽  
Learning Approaches ◽  
Field Investigations ◽  
High Resolution Images ◽  
End To End ◽  
Structural Failures ◽  
Learning Frameworks ◽  
2D Images

Abstract. In this paper, we develop and implement end-to-end deep learning approaches to automatically detect two important types of structural failures, cracks and spalling, of buildings and bridges in extreme events such as major earthquakes. A total of 2,229 images were annotated, and are used to train and validate three newly developed Mask Regional Convolutional Neural Networks (Mask R-CNNs). In addition, three sets of public images for different disasters were used to test the accuracy of these models. For detecting and marking these two types of structural failures, one of proposed methods can achieve an accuracy of 67.6% and 81.1%, respectively, on low- and high-resolution images collected from field investigations. The results demonstrate that it is feasible to use the proposed end-to-end method for automatically locating and segmenting the damage using 2D images which can help human experts in cases of disasters.

Data Science in Economics: Comprehensive Review of Advanced Machine Learning and Deep Learning Methods

2020 ◽  
Author(s):  
Saeed Nosratabadi ◽  
Amir Mosavi ◽  
Puhong Duan ◽  
Pedram Ghamisi ◽  
Filip Ferdinand ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Data Science ◽  
Learning Methods ◽  
Comprehensive Review

Sequence-Based Deep Learning Frameworks on Enhancer-Promoter Interactions Prediction

2020 ◽  
Vol 26 ◽  
Author(s):  
Xiaoping Min ◽  
Fengqing Lu ◽  
Chunyan Li
Keyword(s):  
Gene Expression ◽  
Deep Learning ◽  
Dna Sequences ◽  
Experimental Methods ◽  
Learning Methods ◽  
Comprehensive Review ◽  
Genomic Features ◽  
Disease Mechanisms ◽  
Evaluation Strategies ◽  
Learning Frameworks

: Enhancer-promoter interactions (EPIs) in the human genome are of great significance to transcriptional regulation which tightly controls gene expression. Identification of EPIs can help us better deciphering gene regulation and understanding disease mechanisms. However, experimental methods to identify EPIs are constrained by the fund, time and manpower while computational methods using DNA sequences and genomic features are viable alternatives. Deep learning methods have shown promising prospects in classification and efforts that have been utilized to identify EPIs. In this survey, we specifically focus on sequence-based deep learning methods and conduct a comprehensive review of the literatures of them. We first briefly introduce existing sequence-based frameworks on EPIs prediction and their technique details. After that, we elaborate on the dataset, pre-processing means and evaluation strategies. Finally, we discuss the challenges these methods are confronted with and suggest several future opportunities.

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