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Classification of data on stacked autoencoder using modified sigmoid activation function

JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2023年第1期44卷 1-18页

作者： Kumar, Arvind Sodhi, Sartaj Singh Comp Sci & Engn USICT GGSIPU Delhi India

A Neural Network is one of the techniques by which we classify data. In this paper, we have proposed an effectively stacked autoencoder with the help of a modified sigmoid activation function. We have made a two-layer stacked autoencoder with a modified sigmoid activation function. We have compared our autoencoder to the existing autoencoder technique. In the existing autoencoder technique, we generally use the logsigmoid activation function. But in multiple cases using this technique, we cannot achieve better results. In that case, we may use our technique for achieving better results. Our proposed autoencoder may achieve better results compared to this existing autoencoder technique. The reason behind this is that our modified sigmoid activation function gives more variations for different input values. We have tested our proposed autoencoder on the iris, glass, wine, ovarian, and digit image datasets for comparison propose. The existing autoencoder technique has achieved 96% accuracy on the iris, 91% accuracy on wine, 95.4% accuracy on ovarian, 96.3% accuracy on glass, and 98.7% accuracy on digit (image) dataset. Our proposed autoencoder has achieved 100% accuracy on the iris, wine, ovarian, and glass, and 99.4% accuracy on digit (image) datasets. For more verification of the effeteness of our proposed autoencoder, we have taken three more datasets. They are abalone, thyroid, and chemical datasets. Our proposed autoencoder has achieved 100% accuracy on the abalone and chemical, and 96% accuracy on thyroid datasets.

关键词： autoencoder sigmoid activation function logsigmoid neural network classification stacked autoencoder

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Deep denoising autoencoder for seismic random noise attenuation

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GEOPHYSICS 2020年第4期85卷 V367-V376页

作者： Saad, Omar M. Chen, Yangkang Zhejiang Univ Sch Earth Sci Key Lab Geosci Big Data & Deep Resource Zhejiang Hangzhou 310027 Zhejiang Peoples R China NRIAG ENSN Lab Seismol Dept Helwan 11731 Egypt

Attenuation of seismic random noise is considered an important processing step to enhance the signal-to-noise ratio of seismic data. A new approach is proposed to attenuate random noise based on a deep-denoising autoencoder (DDAE). In this approach, the time-series seismic data are used as an input for the DDAE. The DDAE encodes the input seismic data to multiple levels of abstraction, and then it decodes those levels to reconstruct the seismic signal without noise. The DDAE is pretrained in a supervised way using synthetic data;following this, the pretrained model is used to denoise the field data set in an unsupervised scheme using a new customized loss function. We have assessed the proposed algorithm based on four synthetic data sets and two field examples, and we compare the results with several benchmark algorithms, such as f-x deconvolution (f-x deconv) and the f-x singular spectrum analysis (f-x SSA). As a result, our algorithm succeeds in attenuating the random noise in an effective manner.

关键词： deep learning autoencoder random noise attenuation deep denoising autoencoder local similarity

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Dual-Representation-Based autoencoder for Domain Adaptation

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IEEE TRANSACTIONS ON CYBERNETICS 2022年第8期52卷 7464-7477页

作者： Yang, Shuai Yu, Kui Cao, Fuyuan Wang, Hao Wu, Xindong Hefei Univ Technol Minist Educ Key Lab Knowledge Engn Big Data Hefei 230601 Peoples R China Hefei Univ Technol Sch Comp Sci & Informat Engn Hefei 230601 Peoples R China Shanxi Univ Sch Comp & Informat Technol Taiyuan 030006 Peoples R China Mininglamp Technol Mininglamp Acad Sci Beijing 100084 Peoples R China

Domain adaptation aims to facilitate the learning task in an unlabeled target domain by leveraging the auxiliary knowledge in a well-labeled source domain from a different distribution. Almost existing autoencoder-based domain adaptation approaches focus on learning domain-invariant representations to reduce the distribution discrepancy between source and target domains. However, there is still a weakness existing in these approaches: the class-discriminative information of the two domains may be damaged while aligning the distributions of the source and target domains, which makes the samples with different classes close to each other, leading to performance degradation. To tackle this issue, we propose a novel dual-representation autoencoder (DRAE) to learn dual-domain-invariant representations for domain adaptation. Specifically, DRAE consists of three learning phases. First, DRAE learns global representations of all source and target data to maximize the interclass distance in each domain and minimize the marginal distribution and conditional distribution of both domains simultaneously. Second, DRAE extracts local representations of instances sharing the same label in both domains to maintain class-discriminative information in each class. Finally, DRAE constructs dual representations by aligning the global and local representations with different weights. Using three text and two image datasets and 12 state-of-the-art domain adaptation methods, the extensive experiments have demonstrated the effectiveness of DRAE.

关键词： Semantics Noise reduction Manifolds Cats Adaptation models Feature extraction Task analysis autoencoder domain adaptation global representations local representations

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Anomaly detection for hydropower turbine unit based on variational modal decomposition and deep autoencoder

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ENERGY REPORTS 2021年 7卷 938-946页

作者： Wang, Hongteng Liu, Xuewei Ma, Liyong Zhang, Yong Huadian Elect Power Res Inst Co LTD Hangzhou 310030 Peoples R China Harbin Inst Technol Sch Informat Sci & Engn Weihai 264209 Peoples R China Harbin Inst Technol Sch Ocean Engn Weihai 264209 Peoples R China

Anomaly detection for hydropower turbine unit is a requirement for the safety of hydropower system. An unsupervised anomaly detection method employing variational modal decomposition (VMD) and deep autoencoder is proposed. VMD is employed to the data collected by multiple sensors to obtain the sub signal of each data. These sub signals in each time-period constitute two-dimensional data. The autoencoder based on convolutional neural network is used to complete unsupervised learning, and the reconstruction residual of autoencoder is used for anomaly detection. The experimental results show that the deep autoencoder can increase the interval between abnormal and normal data distribution, and VMD can effectively reduce the number of samples in the overlapping area. Compared with traditional autoencoder method, the proposed method improves the recall, precision and F1 scores by 0.140, 0.205 and 0.175, respectively. The proposed method achieves better anomaly detection performance than other methods. (C) 2021 The Author(s). Published by Elsevier Ltd.

关键词： Hydropower turbine Anomaly detection autoencoder Variational mode decomposition

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A Multi-Channel Spike Sorting Processor With Accurate Clustering Algorithm Using Convolutional autoencoder

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IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2021年第6期15卷 1441-1453页

作者： Seong, Changyu Lee, Wonjae Jeon, Dongsuk Seoul Natl Univ Res Inst Convergence Sci Grad Sch Convergence Sci & Technol Seoul 08826 South Korea Seoul Natl Univ Inter Univ Semicond Res Ctr Seoul 08826 South Korea

This paper presents a spike sorting processor based on an accurate spike clustering algorithm. The proposed spike sorting algorithm employs an L2-normalized convolutional autoencoder to extract features from the input, where the autoencoder is trained using the proposed spike sorting-aware loss. In addition, we propose a similarity-based K-means clustering algorithm that conditionally updates the means by observing the cosine similarity. The modified K-means algorithm exhibits better convergence and enables online clustering with higher classification accuracy. We implement a spike sorting processor based on the proposed algorithm using an efficient time-multiplexed hardware architecture in a 40-nm CMOS process. Experimental results show that the processor consumes 224.75 mu W/mm(2) when processing 16 input channels at 7.68 MHz and 0.55 V. Our design achieves 95.54% clustering accuracy, outperforming prior spike sorting processor designs.

关键词： Clustering algorithms Feature extraction Sorting Training Image reconstruction Real-time systems autoencoder neural recording real-time recording spike sorting

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A Wavelet-Based Memory autoencoder for Noncontact Fingerprint Presentation Attack Detection

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IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 2024年 19卷 8717-8730页

作者： Liu, Yi-Peng Yu, Hangtao Fang, Haonan Li, Zhanqing Chen, Peng Liang, Ronghua Zhejiang Univ Technol Coll Comp Sci & Technol Hangzhou 310023 Peoples R China

Fingerprint presentation attack detection (FPAD) is essential in fingerprint identification systems. Noncontact methods such as fingerprint biometrics are becoming popular because they are not affected by skin conditions and there are no hygiene issues. However, most of the existing noncontact FPAD methods are supervised methods with poor generalizability and poor performance during events such as unseen presentation attacks (PAs). Moreover, easily overlooked frequency domain information contributes to the fingerprint antispoofing task. Therefore, we propose a wavelet-based memory-augmented autoencoder that fully utilizes the frequency domain information. Specifically, the model first decomposes the input image into high- and low-frequency information and extracts features separately. Subsequently, we propose a frequency complementary connection (FCC) module to realize the fusion and complementation of frequency domain information at the feature level. Moreover, a memory distance expansion loss is proposed to keep the memory module diverse. Experiments are conducted to verify the effectiveness of the method. The code of our model is available on https://***/SuperIOyht/WaveMemAE.

关键词： Fingerprint recognition Feature extraction Frequency-domain analysis Image reconstruction Memory modules Image matching Biological system modeling Presentation attack detection biometrics noncontact fingerprint anomaly detection autoencoder wavelet frequency memory

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autoencoder Embedding of Task-Specific Information

Autoencoder Embedding of Task-Specific Information

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Conference on Medical Imaging: Image Perception, Observer Performance, and Technology Assessment

作者： Granstedt, Jason L. Zhou, Weimin Anastasio, Mark A. Washington Univ St Louis Dept Comp Sci & Engn St Louis MO 63130 USA Washington Univ St Louis Dept Elect & Syst Engn St Louis MO 63130 USA Washington Univ St Louis Dept Biomed Engn St Louis MO 63130 USA

ISBN: (纸本)9781510625525

Task-based measures of image quality (IQ) quantify the ability of an observer to perform a specific task. Such measures are employed for assessing and optimizing medical imaging systems. Although the Bayesian ideal observer is optimal by definition, it is frequently both non-linear and intractable. In such cases, linear observers are commonly employed. However, the optimal linear observer, the Hotelling observer (HO), becomes intractable when considering large images. Channelized methods have become popular for reducing the dimensionality of image data. In this work, we propose a novel method for determining efficient channels by learning them with autoencoders (AEs). autoencoders are neural networks that can be employed to learn concise representations of data, frequently for the purposes of reducing dimensionality. We trained several AEs to encode task-specific information by modifying the standard loss function and examined the effect of hidden layer size and the use of tied/untied weights on the resulting representation accuracy. Subsequently, HOs were applied to both the original images and the dimensionality-reduced versions of them produced by the AEs. It was demonstrated that, for a suitable specification of the AE, the performance of the HO was relatively unaffected by the encoding of the image. However, the computational cost of inverting the covariance matrix was greatly reduced when the HO was applied with the encoded data due to its reduced dimensionality. Our findings suggest that AEs may represent an attractive alternative to the use of heuristic channels for reducing the dimensionality of image data when seeking to accurately approximate the performance of the HO on signal detection tasks.

关键词： autoencoder Hotelling Observer imaging system optimization numerical observers supervised learning task-based image quality

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autoencoder latent space: an empirical study

Autoencoder latent space: an empirical study

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IEEE Symposium Series on Computational Intelligence (IEEE SSCI)

作者： Lapenda, Leticia V. N. Monteiro, Rodrigo P. Bastos-Filho, Carmelo J. A. Univ Pernambuco UPE Comp Engn Dept Recife PE Brazil Fed Univ Pernambuco UFPE Elect Engn Dept Recife PE Brazil

ISBN: (纸本)9781728125473

Feature extraction is essential to many machine learning tasks. By extracting features, it is possible to reduce the dimensionality of datasets, focusing on the most relevant features and minimizing redundancy. autoencoders (AE) are neural network architectures commonly used for feature extraction. A usual metric used to evaluate AEs is the reconstruction error, which compares the AE output data with the original one. However, many applications depend on how the input representations in intermediate layers of AEs, i.e. the latent variables, are distributed. Therefore, additionally to the reconstruction error, an interesting measure - to study the latent variables - is the Kullback-Leibler divergence (KLD). This work analyzes how some variations on the AE training process impact the aforementioned measures. Those variations are: 1. the AE depth, 2. the AE middle layer architecture, and 3. the data setup used for training. Results have shown a possible relation between the KLD and the reconstruction error. In fact, lower errors have happened for higher KLDs and less compressed latent variables, i.e. more neurons on the AE middle layers.

关键词： artificial intelligence machine learning artificial neural networks feature extraction autoencoder latent space

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autoencoder based API Recommendation System for Android Programming 14

Autoencoder based API Recommendation System for Android Prog...

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14th International Conference on Computer Science and Education (ICCSE)

作者： Liu, Jinyang Qiu, Ye Ma, Zhiyi Wu, Zhonghai Peking Univ Acad Adv Interdisciplinary Studies Beijing Peoples R China Peking Univ Sch Elect Engn & Comp Sci Beijing Peoples R China Peking Univ Sch Software & Microelect Beijing Peoples R China

ISBN: (纸本)9781728118468

As a typical example of modern Information Technologies, Android platform and Apps are widely used by smartphone users all over the world. Thus, the research of designing models for assisting programmers in writing Android codes is of great importance and value, and recommending API usages is a stereotype task in this aspect. This paper applies autoencoder neural networks into the model of API recommendation system for Android programming, and designs new autoencoder based Android API recommendation system. This paper carries out experiments on the collected Android code dataset and verifies the effectiveness of the newly designed models compared with classical recommendation models.

关键词： Software Engineering Android Recommendation System autoencoder

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Pattern-Based Attention Recurrent autoencoder for Anomaly Detection in Air Quality Sensor Networks

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IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2024年第6期11卷 6372-6381页

作者： Allka, Xhensilda Ferrer-Cid, Pau Barcelo-Ordinas, Jose M. Garcia-Vidal, Jorge Univ Politecn Catalunya UPC Dept Comp Architecture Barcelona 08034 Spain

Sensor networks play an essential role in today's air quality monitoring platforms. Nevertheless, sensors often malfunction, leading to data anomalies. In this paper, an unsupervised pattern-based attention recurrent autoencoder for anomaly detection (PARAAD) is proposed to detect and locate anomalies in a network of air quality sensors. The novelty of the proposal lies in the use of temporal patterns, i.e., blocks of data, instead of point values. By looking at temporal patterns and through an attention mechanism, the architecture captures data dependencies in the feature space and latent space, enhancing the model's ability to focus on the most relevant parts. Its performance is evaluated with two categories of anomalies, bias fault and drift anomalies, and compared with baseline models such as a feed-forward autoencoder and a transformer architecture, as well as with models not based on temporal patterns. The results show that PARAAD achieves anomalous sensor detection and localization rates higher than 80%, outperforming existing baseline models in air quality sensor networks for both bias and drift faults.

关键词： Anomaly detection Monitoring Air quality Atmospheric modeling Data models Correlation Attention mechanisms monitoring networks anomaly detection Internet of Things autoencoder attention mechanism

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