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检索条件"主题词=sparse AutoEncoder"

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DSD-SVMs: Human Promoter Recognition Based on Multiple Deep Divergence Features 1

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13th International Conference on Intelligent Computing (ICIC)

作者： Xu, Wenxuan Bao, Wenzheng Yuan, Lin Jiang, ZhiChao Tongji Univ Inst Machine Learning & Syst Biol Coll Elect & Informat Engn Shanghai Peoples R China

ISBN: (数字)9783319633091

ISBN: (纸本)9783319633091;9783319633084

Accurate prediction and recognition of promoters remains a challenge in DNA sequence analysis. In this paper, the gene set firstly can be divided into two parts by CpG-island analysis. Then, in each part, a set of statistical divergence (SD) algorithms and sparse auto-encoders (SAEs) are integrated to optimize a series kinds of kmers and get multiple deep divergence features which compromises the merits of signal and context features. Extracted from the total possible combinations of kmers, the informative kmers can be selected by optimizing the differentiating extents of four sparse distributions based on promoter and non-promoters training samples. SAE in deep learning can convert the kmer feature based on SD into multiple deep divergence feature and reduce the dimension. Finally, multiple support vector machines and a bilevel decision model construct a human promoter recognition method called DSD-SVMs. Framework is flexible that it can integrate new features or new classification models freely. Experimental result shows the method has high sensitivity and specificity.

关键词： Promoter recognition Kmer CpG-island sparse autoencoder Statistical divergence Support vector machine

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Investigating the Computational Principles of Neural Dynamics Using Deep Learning Techniques

Investigating the Computational Principles of Neural Dynamic...

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作者： Alipour, Abolfazl Indiana University

学位级别：Ph.D., Doctor of Philosophy

One of the main goals of computational neuroscience is to explain why and how certain dynamics emerge in neural circuits. A promising approach to address this question is to train deep neural networks (DNNs) on ecologically relevant tasks and analyze their dynamics to see if they resemble the dynamics of their biological counterparts; and if they do, take this resemblance as supporting evidence that the modeled circuit is solving the same task. This approach allows us to understand why (for what task) and how (under what learning rules) specific dynamics arise. We have utilized this approach to test the proposed task-dynamics relationships in two of the most commonly studied systems in the brain, namely, the visual system and the memory system. In the visual system (chapters 1&2), we first performed a deep review of the two visual streams hypothesis (TVSH) and then tested the dorsal amnesia sub-hypothesis of this theory. According to this sub-hypothesis, the ventral visual stream has a longer memory compared to the dorsal stream, which stems from the tasks that this stream performs. To test this hypothesis, we trained identical networks to perform either dorsal stream or ventral stream tasks. We found that a DNN trained on orientation classification (a dorsal pathway task) develops a shorter memory while the same DNN trained on object classification (as a ventral pathway task) develops a longer memory, corroborating the dorsal amnesia sub-hypothesis. In the memory system (chapters 3&4), we first analyzed the predictive coding theory for the brain and argued that based on this theory, the entorhinal-hippocampal circuit must be tasked with the prediction of the inputs, and an autoencoder architecture is a reasonable first approximation for this circuit. We showed that a sparse autoencoder network that is tasked to reconstruct its inputs develops scale-invariant place cells, corroborating the hypothesis that hippocampal dynamics may emerge as a result of an input predi

关键词： Deep learning Hippocampus entorhinal cortex Predictive coding Scale invariant place cells sparse autoencoder Two visual streams hypothesis

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UAV Swarm Automatic Modulation Recognition with Multiple Interferences 23

UAV Swarm Automatic Modulation Recognition with Multiple Int...

引用

23rd IEEE International Conference on Communication Technology, ICCT 2023

作者： Ping, Jiarong Li, Sai Lin, Yunhang College of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics Nanjing211106 China

ISBN: (纸本)9798350325959

The complex factors such as Doppler frequency shift, multipath interference and atmospheric noise in the channel of unmanned aerial vehicle (UAV) swarm pose a huge challenge to the automatic modulation recognition. In response to the issue, this paper proposes an automatic modulation recognition method which is based on high-order cumulants and cyclic spectrum for UAV swarm communication signals. Firstly, a UAV swarm channel model interfered with multipath effect and Alpha noise is established. Secondly, the generalized cyclic spectral values and high-order cumulants are calculated from the received signals and extracted to comprise the sample of the UAV swarm communication signals. Finally, the samples are standardized and fed into a deep sparse autoencoder neural network (DSAEN) to achieve recognition of seven modulation types. The simulation results show that the proposed method improves recognition accuracy to over 97.5% in line of sight (LOS) scenes when Mixed Signal Noise Ratio (MSNR) is 0dB, showing excellent recognition performance in Low MSNR and high robustness to different LOS scenes. The combination of high-order cumulants and generalized cyclic spectral features greatly improves the automatic modulation recognition performance. The proposed method has significant value for automatic modulation recognition of UAV swarm communication signals. © 2023 IEEE.

关键词： cumulants generalized cyclic spectrum modulation recognition sparse autoencoder UAV swarm

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Effects of deep neural network parameters on classification of bearing faults 42

Effects of deep neural network parameters on classification ...

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42nd Conference of the Industrial Electronics Society, IECON 2016

作者： Ahmed, H.O.A. Dennis Wong, M.L. Nandi, A.K. Department of Electronic and Computer Engineering Brunel University London UxbridgeUB8 3PH United Kingdom Faculty of Engineering Computing and Science Swinburne University of Technology Sarawak Campus Sarawak Malaysia College of Electronic and Information Engineering Tongji University Shanghai China

ISBN: (纸本)9781509034741

Automatic fault detection and classification for roller element bearings is an important issue for rotating machine condition monitoring. In this paper, we classify roller element bearings fault classes under two and three hidden layers' deep neural network framework based on sparse autoencoder. This allows us to learn and extract features for the bearing vibration samples in an unsupervised manner using the encoder part of the autoencoder. Then we form the deep neural network by stacking the encoders in each stage of the hidden layers together with the softmax layer. Classification performance using the full deep network and backpropagation compared, and effects of different deep neural network parameters on the classification accuracy are studied here. © 2016 IEEE.

关键词： Bearing Fault Classification Deep Neural Network (DNN) Machine Condition Monitoring sparse autoencoder

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DETECTION OF LUMEN AND MEDIA-ADVENTITIA BORDERS IN IVUS IMAGES USING sparse AUTO-ENCODER NEURAL NETWORK

DETECTION OF LUMEN AND MEDIA-ADVENTITIA BORDERS IN IVUS IMAG...

引用

International Symposium on Biomedical Imaging

作者： Shengran Su Zhifan Gao Heye Zhang Qiang Lin William Kongto Hau Shuo Li College of Sciences Zhejiang University of Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences LiKaShing Faculty of Medicine University of Hong Kong University of Western Ontario

ISBN: (纸本)9781509011735

This paper describes an artificial neural network (ANN) method that employs a feature-learning algorithm to detect the lumen and MA borders in intravascular ultrasound (IVUS) images. Three types of imaging features including spatial, neighboring, and gradient features were used as the input features to the neural network, and then the different vascular layers were distinguished using two sparse autoencoders and one softmax classifier. To smooth the lumen and MA borders detected by the ANN method, we used the active contour model. The performance of our approach was compared with the manual drawing method and another existing method on 538 IVUS images from six subjects. Results showed that our approach had a high correlation (r= 0.9284~ 0.9875 for all measurements) and good agreement (bias= 0.0148~ 0.4209 mm) with the manual drawing method, and small detection error (lumen border: 0.0928 + 0.0935 mm, MA border: 0.1056+0.1088 mm). The average time to process each image was 14 + 4.6 seconds. The obtained results indicate that our proposed approach can be used to efficiently and accurately detect the lumen and MA borders in IVUS images.

关键词： Intravascular image Deep neural network Image segmentation sparse autoencoder

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A Vehicle Type Recognition Method based on sparse Auto Encoder

A Vehicle Type Recognition Method based on Sparse Auto Encod...

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2015 International Conference on Computer Information Systems and Industrial Applications(CISIA2015)

作者： H.L.Rong Y.X.Xia Wu Han University of Technology

ISBN: (纸本)9781510805750

In recent years,feature learning methods based on unsupervised learning get more and more *** now,Unsupervised feature learning has been applied to solve many problems such as detection,recognition and *** this paper,we propose a deep feature learning method based on sparse autoencoder to recognize vehicle types and to improve the classification accuracy *** we used sparse autoencoder to generate the convolutional kernel and used the convolutional kernel to generate convolutional *** pooling was *** repeated the network several times to construct a deep feature learning *** improve performance,we also combined the feature learned in different layer to form a new feature vector and applied PCA to reduce the *** we used softmax to recognize the vehicle *** the local receive field,we can reduce the *** experiment shows that our method can improve the performance a little.

关键词： vehicle type recognition sparse autoencoder multilevel features fusing

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Vertical Axis Detection for Sport Video Analytics

Vertical Axis Detection for Sport Video Analytics

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International Conference on Digital Image Computing - Techniques and Applications (DICTA)

作者： Zeng, Rui Lakemond, Ruan Denman, Simon Sridharan, Sridha Fookes, Clinton Morgan, Stuart Queensland Univ Technol Speech Audio Image & Video Technol Lab Brisbane Qld Australia Imaginat Technol King Langley England Australian Inst Sport Canberra ACT Australia

ISBN: (纸本)9781509028962

When processing video, it is normally assumed that cameras are vertically oriented such that people appear upright, which helps simplify subsequent processing such as person detection. In real situations, due to the need to provide maximum coverage of the viewing space, cameras are usually placed with arbitrary orientations so the apparent vertical axis of the videos captured may not correspond to the true vertical direction of the captured scene. To rectify this situation, we propose a classification-based system, which normalizes the video compensating for the camera orientation. We demonstrate the performance of the system for outdoor sports video. Our system works as follows: From an arbitrary set of sports videos, we first automatically create a training/testing image dataset, in which players have various orientations. Our classifier is a stacked autoencoder connected to a softmax output layer, which is trained using this dataset for estimating the orientation of players. The orientation of an input video is normalized according to the orientations of player patches, whose angles of orientation are estimated by the above trained classifier. The experiments conducted on hockey field video dataset show that the proposed system is able to estimate the true vertical axis of an input video accurately.

关键词： sparse autoencoder video orientation normalisation human detection

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Fuzzy Rule Reduction using sparse Auto-Encoders

Fuzzy Rule Reduction using Sparse Auto-Encoders

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IEEE International Conference on Fuzzy Systems

作者： Rahul K. Sevakula Nishchal K. Verma Department of Electrical Engineering Indian Institute of Technology Kanpur

ISBN: (纸本)9781467374293

Fuzzy Rule based regression, classification and control have found great use in modern applications due to its simplicity, flexibility and capability. A key issue in all such methods is the computation time. Computational complexity of training and testing is linearly dependent on the size of fuzzy rule base and the respective fuzzy rule space is exponentially dependent on data dimensionality. sparse Auto-Encoders (SAs) have become popular in giving compact feature representations for image, audio and speech data and have helped in giving state of the art pattern recognition performances in most of the domains. These feature representation are learnt in an unsupervised fashion and are found to give higher order building blocks with which the data is seemingly made of. This paper proposes a method where SAs are used for getting compact feature representation of input data and if needed with reduced dimensionality. The regular fuzzy rule based models are then learnt from data in the new feature space. The method was tested for Regression and Classification problems, giving impressive results in both. The method with Regression problem gave comparable performance with almost half the number of rules and with Classification problem it gave improvement in classification accuracy by 2.67% while reducing the size of fuzzy rule base by 11.25 times and 7.5 times by number.

关键词： sparse autoencoder Fuzzy rule reduction Deep learning Deep belief networks Dimensionality reduction

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Network Anomaly Detection using Threshold-based sparse 20

Network Anomaly Detection using Threshold-based Sparse

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Proceedings of the 11th International Conference on Advances in Information Technology

作者： May Thet Tun Dim En Nyaung Myat Pwint Phyu University of Information Technology Yangon Myanmar

ISBN: (纸本)9781450377591

Nowadays, cyber-attacks have been dramatically increased due to the rapid development of Internet-based services. The current network anomaly detection solutions such as Firewall, Snort, honeypots are useful, but they are insufficient to deal with zero-day attacks. At present, unsupervised deep learning technologies focus on increasing the performance of anomaly-based network attack detection in the recent research area. The normal and attack transactions can be distinguished by the threshold in the network anomaly detection using a sparse autoencoder. The threshold value estimation is not a non-trivial task. The key factor of the threshold value which draws a line between normal and attack highly depends on the accuracy of the network anomaly detection. In this paper, the network anomaly detection based on a sparse autoencoder with a percentile-based threshold selection algorithm is proposed. The results of the proposed system have been validated concerning the accuracy, precision, recall and false positive rate. As a result, the experimental results on UNSW-NB15 and NSL-KDD datasets have shown that the proposed system provides higher accuracy and outperforms the previous related works in recent years.

关键词： Chi-squared feature selection Deep learning Network anomaly detection Percentile threshold approach sparse autoencoder

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A sparse feature representation for genetic data analysis

A sparse feature representation for genetic data analysis

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Hua-Hao Liu Pei-Jie Huang Pi-Yuan Lin Wen-Hu Lin Pei-Heng Qi Chong-Hua Song College of Mathematics and Informatics South China Agricultural University Guangzhou China

ISBN: (纸本)9781467372220

Feature representation is one of the key research issues in machine learning. In some applications with high dimensionality of data, e.g. genomie microarray data, obtaining a good feature representation with effective dimensionality reduction still remains a challenge. In this paper, instead of selecting a subset of original features by feature selection, we use sparse autoencoder to find a reconstructed feature representation for genetic data analysis. The performance of our proposed method is empirically evaluated using one of the genomie microarray dataset provided in ASU Feature Selection Repository. The results show that the proposed method yield better classification accuracy than some representative feature selection methods.

关键词： Feature representation Genetic data analysis Machine learning sparse autoencoder Machine Learning Artificial Intelligence Feature extraction genetic information Data analysis dimensionality reduction Microarray

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