检索结果-内蒙古大学图书馆

Early multi-cancer detection through deep learning: An anomaly detection approach using variational autoencoder

JOURNAL OF BIOMEDICAL INFORMATICS 2024年 160卷 104751页

作者： Sado, Innocent Tatchum Fitime, Louis Fippo Pelap, Geraud Fokou Tinku, Claude Meudje, Gaelle Mireille Bouetou, Thomas Univ Yaounde I Natl Adv Sch Engn Yaounde Dept Comp Engn Lab Informat Syst & Signal Proc Yaounde Cameroon Univ Dschang Dept Math & Comp Sci URIFIA Dschang Cameroon Smart Digital Strategy SARL Co Yaounde Cameroon

Cancer is a disease that causes many deaths worldwide. The treatment of cancer is first and foremost a matter of detection, a treatment that is most effective when the disease is detected at an early stage. With the evolution of technology, several computer-aided diagnosis tools have been developed around cancer;several image-based cancer detection methods have been developed too. However, cancer detection faces many difficulties related to early detection which is crucial for patient survival rate. To detect cancer early, scientists have been using transcriptomic data. However, this presents some challenges such as unlabeled data, a large amount of data, and image-based techniques that only focus on one type of cancer. The purpose of this work is to develop a deep learning model that can effectively detect as soon as possible, specifically in the early stages, any type of cancer as an anomaly in transcriptomic data. This model must have the ability to act independently and not be restricted to any specific type of cancer. To achieve this goal, we modeled a deep neural network (a variational autoencoder) and then defined an algorithm for detecting anomalies in the output of the variational autoencoder. The variational autoencoder consists of an encoder and a decoder with a hidden layer. With the TCGA and GTEx data, we were able to train the model for six types of cancer using the Adam optimizer with decay learning for training, and a two-component loss function. Asa result, we obtained the lowest value of accuracy 0.950, and the lowest value of recall 0.830. This research leads us to the design of a deep learning model for the detection of cancer as an anomaly in transcriptomic data.

关键词： variational autoencoder Anomaly detection Transcriptomic data Cancer Early detection

来源：评论

学校读者我要写书评

暂无评论

ADA-INCVAE: Improved data generation using variational autoencoder for imbalanced classification

引用

APPLIED INTELLIGENCE 2022年第3期52卷 2838-2853页

作者： Huang, Kai Wang, Xiaoguo Tongji Univ Coll Elect & Informat Engn Shanghai 201804 Peoples R China

Increasing the number of minority samples by data generation can effectively improve the performance of mining minority samples using a classifier in imbalanced problems. In this paper, we proposed an effective data generation algorithm for minority samples called the Adaptive Increase dimension of variational autoencoder (ADA-INCVAE). Complementary to prior studies, a theoretical study is conducted from the perspective of multi-task learning to solve the posterior collapse for VAE. Afterward, by using the theoretical support, it proposed a novel training method by increasing the dimension of data to avoid the occurrence of posterior collapse. Aiming at restricting the range of synthetic data for different minority samples, an adaptive reconstruction loss weight is proposed according to the distance distribution of majority samples around the minority class samples. In the data generation stage, the generation proportion of different sample points is determined by the local information of the minority class. The experimental results based on 12 imbalanced datasets indicated that the algorithm could help the classifier to effectively improve F1-measure and G-mean, which verifies the effectiveness of synthetic data generated by ADA-INCVAE.

关键词： Machine learning Imbalanced learning Data generation variational autoencoder

来源：评论

学校读者我要写书评

暂无评论

Joint Coding-Modulation for Digital Semantic Communications via variational autoencoder

引用

IEEE TRANSACTIONS ON COMMUNICATIONS 2024年第9期72卷 5626-5640页

作者： Bo, Yufei Duan, Yiheng Shao, Shuo Tao, Meixia Shanghai Jiao Tong Univ Dept Elect Engn Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Cooperat Medianet Innovat Ctr CMIC Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Sch Cyber Sci & Engn Shanghai 200240 Peoples R China

Semantic communications have emerged as a new paradigm for improving communication efficiency by transmitting the semantic information of a source message that is most relevant to a desired task at the receiver. Most existing approaches typically utilize neural networks (NNs) to design end-to-end semantic communication systems, where NN-based semantic encoders output continuously distributed signals to be sent directly to the channel in an analog fashion. In this work, we propose a joint coding-modulation (JCM) framework for digital semantic communications by using variational autoencoder (VAE). Our approach learns the transition probability from source data to discrete constellation symbols, thereby avoiding the non-differentiability problem of digital modulation. Meanwhile, by jointly designing the coding and modulation process together, we can match the obtained modulation strategy with the operating channel condition. We also derive a matching loss function with information-theoretic meaning for end-to-end training. Experiments on image semantic communication validate the superiority of our proposed JCM framework over the state-of-the-art quantization-based digital semantic coding-modulation methods across a wide range of channel conditions, transmission rates, and modulation orders. Furthermore, its performance gap to analog semantic communication reduces as the modulation order increases while enjoying the hardware implementation convenience.

关键词： Semantics Symbols Digital modulation Artificial neural networks Receivers Probabilistic logic Decoding Semantic communications mutual information variational autoencoder digital modulation

来源：评论

学校读者我要写书评

暂无评论

Network intrusion detection based on conditional wasserstein variational autoencoder with generative adversarial network and one-dimensional convolutional neural networks

引用

APPLIED INTELLIGENCE 2023年第10期53卷 12416-12436页

作者： He, Jiaxing Wang, Xiaodan Song, Yafei Xiang, Qian Chen, Chen Air Force Engn Univ Changle East Rd Xian Shaanxi Peoples R China

There is a class-imbalance problem that the number of minority class samples is significantly lower than that of majority class samples in common network traffic datasets. Class-imbalance phenomenon will affect the performance of the classifier and reduce the robustness of the classifier to detect unknown anomaly detection. And the distribution of the continuous features in the dataset does not follow the Gaussian distribution, which will bring great difficulties to intrusion detection. We propose Conditional Wasserstein variational autoencoders with Generative Adversarial Network (CWVAEGAN) to solve the class-imbalance phenomenon, CWVAEGAN transform the original dataset through data preprocessing, and then use the improved VAEGAN to generate minority class samples. According to the CWVAEGAN model, an intrusion detection system based on CWVAEGAN and One-dimensional convolutional neural networks (1DCNN), namely CWVAEGAN-1DCNN, is established. By using the examples generated by CWVAEGAN, the problem of intrusion detection on class unbalanced data is solved. Specifically, CWVAEGAN-1DCNN consists of three modules: data preprocessing module, CWVAEGAN, and deep neural network. We evaluate the performance of CWVAEGAN-1DCNN on two benchmark datasets and compared it with the other 16 methods. Experiment results suggest that the performance of CWVAEGAN-1DCNN is better than class-balancing methods, and other advanced methods.

关键词： Intrusion detection system (IDS) Class-balancing method Generative adversarial network variational autoencoder Gaussian mixture model

来源：评论

学校读者我要写书评

暂无评论

Unsupervised Representation Disentanglement Using Cross Domain Features and Adversarial Learning in variational autoencoder Based Voice Conversion

引用

IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2020年第4期4卷 468-479页

作者： Huang, Wen-Chin Luo, Hao Hwang, Hsin-Te Lo, Chen-Chou Peng, Yu-Huai Tsao, Yu Wang, Hsin-Min Acad Sinica Inst Informat Sci Taipei 11529 Taiwan Nagoya Univ Grad Sch Informat Nagoya Aichi 4648601 Japan Acad Sinica Res Ctr Informat Technol Inst Informat Sci Taipei 11529 Taiwan

An effective approach for voice conversion (VC) is to disentangle linguistic content from other components in the speech signal. The effectiveness of variational autoencoder (VAE) based VC (VAE-VC), for instance, strongly relies on this principle. In our prior work, we proposed a cross-domain VAE-VC (CDVAE-VC) framework, which utilized acoustic features of different properties, to improve the performance of VAE-VC. We believed that the success came from more disentangled latent representations. In this article, we extend the CDVAE-VC framework by incorporating the concept of adversarial learning, in order to further increase the degree of disentanglement, thereby improving the quality and similarity of converted speech. More specifically, we first investigate the effectiveness of incorporating the generative adversarial networks (GANs) with CDVAE-VC. Then, we consider the concept of domain adversarial training and acid an explicit constraint to the latent representation, realized by a speaker classifier, to explicitly eliminate the speaker information that resides in the latent code. Experimental results confirm that the degree of disentanglement of the learned latent representation can he enhanced by both GANs and the speaker classifier. Meanwhile, subjective evaluation results in terms of quality and similarity scores demonstrate the effectiveness of our proposed methods.

关键词： Voice conversion unsupervised learning disentangled representation variational autoencoder adversarial learning cross domain features

来源：评论

学校读者我要写书评

暂无评论

Metamaterial-based realization for thermal transparency: A conditional variational autoencoder approach

引用

PHYSICA B-CONDENSED MATTER 2024年 684卷

作者： Liu, Bin Cai, Haoyang Wang, Yixi LeShan Normal Univ Dept Elect Informat & Artificial Intelligence LeShan 614099 Peoples R China Fudan Univ Dept Phys State Key Lab Surface Phys Shanghai 200438 Peoples R China Fudan Univ Key Lab Micro & Nano Photon Struct MOE Shanghai 200438 Peoples R China

The advances in thermal metamaterials and their applications have revolutionized how we can manipulate thermal transport behavior. The challenging inverse design problems of utilizing thermal metamaterialbased structures to achieve desired thermal transport behavior are increasingly being tackled by data-driven, machine learning-based approaches. The explosive progress in generative AI is permeating the field of material design by offering new perspectives to address the inverse design problems. In this paper, we propose a simple yet effective method of training a generative conditional variational autoencoder to find the design parameters for a thermal metamaterial-based system with a periodic interparticle arrangement to achieve thermal transparency, which is one of the most desirable and interesting thermal transport behaviors. Our work attests to the predictive power of a generative model with a relatively small number of parameters for the purpose of tackling inverse design problems to achieve thermal transport behavior manipulation.

关键词： Thermal metamaterial Thermal transparency Machine learning variational autoencoder

来源：评论

学校读者我要写书评

暂无评论

A variational autoencoder for a semiconductor fault detection model robust to process drift due to incomplete maintenance

引用

JOURNAL OF INTELLIGENT MANUFACTURING 2023年第2期34卷 529-540页

作者： Kim, Youngju Lee, Hoyeop Kim, Chang Ouk Yonsei Univ Dept Ind Engn Seoul 03722 South Korea

In the semiconductor manufacturing field, few studies on fault detection (FD) models have considered process drift due to incomplete maintenance. Process drift refers to the shift in sensor measurements over time due to tool aging, and it leads to defective production when it is severe. Tool maintenance is conducted regularly to prevent defects. However, when it is performed improperly, tool aging accelerates, and the drift increases. In this paper, we propose an FD model robust to process drift by modeling process drift with a variational autoencoder (VAE). Because process drift is characterized by time-varying information, the proposed model encodes some time-varying information through separate hidden layers. By adopting a strategy that combines information separately encoded in a feature vector, the proposed model successfully models process drift. With actual chemical vapor deposition process data, we were able to generate many virtual datasets that incorporate process drift with various drift characteristics, such as patterns, degrees, and speeds. The proposed model outperformed four comparison FD methods on these datasets.

关键词： Process drift Fault detection Incomplete maintenance variational autoencoder Deep learning

来源：评论

学校读者我要写书评

暂无评论

Process monitoring using variational autoencoder for high-dimensional nonlinear processes

引用

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2019年 83卷 13-27页

作者： Lee, Seulki Kwak, Mingu Tsui, Kwok-Leung Kim, Seoung Bum Korea Univ Sch Ind Management Engn 145 Anam Ro Seoul 02841 South Korea City Univ Hong Kong Dept Syst Engn & Engn Management Hong Kong 999077 Peoples R China

In many industries, statistical process monitoring techniques play a key role in improving processes through variation reduction and defect prevention. Modern large-scale industrial processes require appropriate monitoring techniques that can efficiently address high-dimensional nonlinear processes. Such processes have been successfully monitored with several latent variable-based methods. However, because these monitoring methods use Hotelling's T-2 statistics in the reduced space, a normality assumption underlies the construction of these tools. This assumption has limited the use of latent variable-based monitoring charts in both nonlinear and nonnormal situations. In this study, we propose a variational autoencoder (VAE) as a monitoring method that can address both nonlinear and nonnormal situations in high-dimensional processes. VAE is appropriate for T-2 charts because it causes the reduced space to follow a multivariate normal distribution. The effectiveness and applicability of the proposed VAE-based chart were demonstrated through experiments on simulated data and real data from a thin-film-transistor liquid-crystal display process.

关键词： Statistical process monitoring variational autoencoder High-dimensional process Nonlinear process Multivariate control chart

来源：评论

学校读者我要写书评

暂无评论

EEG-Based Adaptive Driver-Vehicle Interface Using variational autoencoder and PI-TSVM

引用

IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING 2019年第10期27卷 2025-2033页

作者： Bi, Luzheng Zhang, Jingwei Lian, Jinling Beijing Inst Technol Sch Mech Engn Beijing 100081 Peoples R China

Event-related potential (ERP)-based driver-vehicle interfaces (DVIs) have been developed to provide a communication channel for people with disabilities to drive a vehicle. However, they require a tedious and time-consuming training procedure to build the decoding model, which can translate EEG signals into commands. In this paper, to address this problem, we propose an adaptive DVI by using a new semi-supervised algorithm. The decoding model of the proposed DVI is first built with a small labeled training set, and then gradually improved by updating the proposed semi-supervised decoding model with new collected unlabeled EEG signals. In our semi-supervised algorithm, independent component analysis (ICA) and Kalman smoother are first used to improve the signal-to-noise ratio (SNR). After that, variational autoencoder is applied to provide a robust feature representation of EEG signals. Finally, a prior information-based transductive support vector machine (PI-TSVM) classifier is developed to translate these features into commands. Experimental results show that the proposed DVI can significantly reduce the training effort. After a short updating, its performance can be close to that of the supervised DVI requiring a lengthy training procedure. This work is vital for advancing the application of these DVIs.

关键词： Brain-controlled vehicle EEG semi-supervised learning variational autoencoder transductive support vector machine

来源：评论

学校读者我要写书评

暂无评论

Unsupervised machinery prognostics approach based on wavelet packet decomposition and variational autoencoder

引用

JOURNAL OF THE BRAZILIAN SOCIETY OF MECHANICAL SCIENCES AND ENGINEERING 2024年第2期46卷 1-15页

作者： de Godoi, Leonardo Franco Nobrega, Euripedes Guilherme de Oliveira Univ Estadual Campinas Sch Mech Engn Dept Computat Mech Mendeleyev St BR-13083860 Campinas SP Brazil

The prognosis of rotating machinery has been very prominent in recent years thanks to the advances in digital signal processing and intelligent systems. Unsupervised machine learning methods have been adopted along with signal processing techniques in both time and frequency domain to build indicators that describe the degradation of mechanical systems. This paper proposes a novel method for generating a degradation indicator to estimate the remaining useful life of rotating machinery critical components, based on a beta variational autoencoder neural network that processes statistical distributions in a feature hyperspace whose coordinates combine time-domain analysis and wavelet packet decomposition features from vibration signals. Indicators are obtained using bearing data from a publicly available dataset, aiming to enhance the observability of monotonic trends, and are used to assess different hyperparameter configurations of the proposed methodology. Based on the comparison with recently published results on the same dataset, the proposed method produced more robust indicators capable of detecting early changes in degradation processes, determining more accurate RUL estimates.

关键词： Prognostics Rotating machine Machine learning variational autoencoder Wavelet packet decomposition

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：