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An Efficient Prediction Method for Coronary Heart Disease Risk Based on Two Deep Neural Networks Trained on Well-Ordered Training Datasets

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IEEE ACCESS 2021年 9卷 135210-135223页

作者： Amarbayasgalan, Tsatsral Pham, Van-Huy Theera-Umpon, Nipon Piao, Yongjun Ryu, Keun Ho Chungbuk Natl Univ Sch Elect & Comp Engn Database & Bioinformat Lab Cheongju 28644 South Korea Ton Duc Thang Univ Fac Informat Technol Ho Chi Minh City 700000 Vietnam Chiang Mai Univ Biomed Engn Inst Chiang Mai 50200 Thailand Chiang Mai Univ Fac Engn Dept Elect Engn Chiang Mai 50200 Thailand Nankai Univ Sch Med Tianjin 300071 Peoples R China Tianjin Cent Hosp Gynecol Obstet Tianjin Key Lab Human Dev & Reprod Regulat Tianjin 300199 Peoples R China

This study proposes an efficient prediction method for coronary heart disease risk based on two deep neural networks trained on well-ordered training datasets. Most real datasets include an irregular subset with higher variance than most data, and predictive models do not learn well from these datasets. While most existing prediction models learned from the whole or randomly sampled training datasets, our suggested method draws up training datasets by separating regular and highly biased subsets to build accurate prediction models. We use a two-step approach to prepare the training dataset: (1) divide the initial training dataset into two groups, commonly distributed and highly biased using Principal Component Analysis, (2) enrich the highly biased group by variational autoencoders. Then, two deep neural network classifiers learn from the isolated training groups separately. The well-organized training groups enable a chance to build more accurate prediction models. When predicting the risk of coronary heart disease from the given input, only one appropriate model is selected based on the reconstruction error on the Principal Component Analysis model. Dataset used in this study was collected from the Korean National Health and Nutritional Examination Survey. We have conducted two types of experiments on the dataset. The first one proved how Principal Component Analysis and variational autoencoder models of the proposed method improves the performance of a single deep neural network. The second experiment compared the proposed method with existing machine learning algorithms, including Naive Bayes, Random Forest, K-Nearest Neighbor, Decision Tree, Support Vector Machine, and Adaptive Boosting. The experimental results show that the proposed method outperformed conventional machine learning algorithms by giving the accuracy of 0.892, specificity of 0.840, precision of 0.911, recall of 0.920, f-measure of 0.915, and AUC of 0.882.

关键词： Training Heart Principal component analysis Predictive models Diseases Deep learning Support vector machines Coronary heart disease deep neural network machine learning principal component analysis reconstruction error variational autoencoder

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Performance-Based Generative Design for Parametric Modeling of Engineering Structures Using Deep Conditional Generative Models

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AUTOMATION IN CONSTRUCTION 2023年 156卷

作者： Bucher, Martin Juan Jose Kraus, Michael Anton Rust, Romana Tang, Siyu Swiss Fed Inst Technol Stefano Franscini Pl CH-8093 Zurich Switzerland

Parametric Modeling, Generative Design, and Performance-Based Design have gained increasing attention in the AEC field as a way to create a wide range of design variants while focusing on performance attributes rather than building codes. However, the relationships between design parameters and performance attributes are often very complex, resulting in a highly iterative and unguided process. In this paper, we argue that a more goal-oriented design process is enabled by an inverse formulation that starts with performance attributes instead of design parameters. A Deep Conditional Generative Design workflow is proposed that takes a set of performance attributes and partially defined design features as input and produces a complete set of design parameters as output. A model architecture based on a Conditional variational autoencoder is presented along with different approximate posteriors, and evaluated on four different case studies. Compared to Genetic Algorithms, our method proves superior when utilizing a pre-trained model.

关键词： Deep generative modeling Performance-based design Generative design variational autoencoder Deep generative design Artificial intelligence

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Multiagent Hierarchical Cognition Difference Policy for Multiagent Cooperation

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ALGORITHMS 2021年第3期14卷 98页

作者： Wang, Huimu Liu, Zhen Yi, Jianqiang Pu, Zhiqiang Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Chinese Acad Sci Inst Automat Beijing 100190 Peoples R China

Multiagent cooperation is one of the most attractive research fields in multiagent systems. There are many attempts made by researchers in this field to promote cooperation behavior. However, several issues still exist, such as complex interactions among different groups of agents, redundant communication contents of irrelevant agents, which prevents the learning and convergence of agent cooperation behaviors. To address the limitations above, a novel method called multiagent hierarchical cognition difference policy (MA-HCDP) is proposed in this paper. It includes a hierarchical group network (HGN), a cognition difference network (CDN), and a soft communication network (SCN). HGN is designed to distinguish different underlying information of diverse groups' observations (including friendly group, enemy group, and object group) and extract different high-dimensional state representations of different groups. CDN is designed based on a variational auto-encoder to allow each agent to choose its neighbors (communication targets) adaptively with its environment cognition difference. SCN is designed to handle the complex interactions among the agents with a soft attention mechanism. The results of simulations demonstrate the superior effectiveness of our method compared with existing methods.

关键词： multiagent system deep reinforcement learning variational autoencoder attention mechanism

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Cost-Sensitive variational Autoencoding Classifier for Imbalanced Data Classification

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ALGORITHMS 2022年第5期15卷 139-139页

作者： Liu, Fen Qian, Quan Shanghai Univ Sch Comp Engn & Sci Shanghai 200444 Peoples R China Shanghai Univ Mat Genome Inst Shanghai 200444 Peoples R China Zhejiang Lab Hangzhou 311100 Peoples R China

Classification is among the core tasks in machine learning. Existing classification algorithms are typically based on the assumption of at least roughly balanced data classes. When performing tasks involving imbalanced data, such classifiers ignore the minority data in consideration of the overall accuracy. The performance of traditional classification algorithms based on the assumption of balanced data distribution is insufficient because the minority-class samples are often more important than others, such as positive samples, in disease diagnosis. In this study, we propose a cost-sensitive variational autoencoding classifier that combines data-level and algorithm-level methods to solve the problem of imbalanced data classification. Cost-sensitive factors are introduced to assign a high cost to the misclassification of minority data, which biases the classifier toward minority data. We also designed misclassification costs closely related to tasks by embedding domain knowledge. Experimental results show that the proposed method performed the classification of bulk amorphous materials well.

关键词： variational autoencoder imbalanced data classification cost-sensitive learning

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Prediction of rare feature combinations in population synthesis: Application of deep generative modelling

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TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES 2020年 120卷 102787-102787页

作者： Garrido, Sergio Borysov, Stanislav S. Pereira, Francisco C. Rich, Jeppe Tech Univ Denmark Dept Technol Management & Econ DTU DK-2800 Lyngby Denmark

Population synthesis is concerned with the generation of agents for agent-based modelling in many fields, such as economics, transportation, ecology and epidemiology. When the number of attributes describing the agents and/or their level of detail becomes large, survey data cannot densely support the joint distribution of the attributes in the population due to the curse of dimensionality. It leads to a situation where many attribute combinations are missing from the sample data while such combinations exist in the real population. In this case, it becomes essential to consider methods that are able to impute such missing information effectively. In this paper, we propose to use deep generative latent models. These models are able to learn a compressed representation of the data space, which when projected back to the original space, leads to an effective way of imputing information in the observed data space. Specifically, we employ the Wasserstein Generative Adversarial Network (WGAN) and the variational autoencoder (VAE) for a large-scale population synthesis application. The models are applied to a Danish travel survey with a feature-space of more than 60 variables and trained and tested using cross validation. A new metric that applies to the evaluation of generative models in an unsupervised setting is proposed. It is based on the ability to generate diverse yet valid synthetic attribute combinations by comparing if the models can recover missing combinations (sampling zeros) while keeping truly impossible combinations (structural zeros) models at a minimum. For a low dimensional experiment, the VAE, the marginal sampler and the fully random sampler generate 5%, 21% and 26% more structural zeros per sampling zero when compared to the WGAN. For a high dimensional case, these figures increase to 44%, 2217% and 170440% respectively. This research directly supports the development of agent-based systems and in particular cases where detailed socio-economic or geog

关键词： Population synthesis Generative modeling Deep learning Generative Adversarial Networks variational autoencoder Transportation modeling Agent-based modeling

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Prediction of the microstructure of cold-compacted Astaloy 85Mo with deep generative models

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COMPUTATIONAL MATERIALS SCIENCE 2024年 242卷

作者： Schenk, Oliver Becker, Marco Deng, Yuanbin Niemietz, Philipp Bergs, Thomas Broeckmann, Christoph Chair & Inst Mat Applicat Mech Engn Augustinerbach 4 D-52062 Aachen Germany Rhein Westfal TH Aachen Mfg Technol Inst MTI Campus Blvd 30 D-52074 Aachen Germany Fraunhofer Inst Prod Technol IPT Steinbachstr 17 D-52074 Aachen Germany

In powder metallurgy, iron -based powders are compacted to so-called green bodies, which undergo a subsequent sintering process. The microstructure of the green bodies affects the sintering process and the resulting properties of the produced component. Assessing the microstructure experimentally is time consuming and costly, while simulation approaches rely on simplifications that significantly affect relevant microstructural information. This work investigates the suitability of deep generative models to synthetically generate realistic micrographs of green bodies as an alternative to experiments and simulations. For that purpose experiments comparing Generative Adversarial Neural Networks and variational autoencoders were conducted. The two deep learning frameworks were used to train models which produce synthetic micrographs in dependence of two parameters, namely powder particle size and green body porosity. The data utilized to train and evaluate the models was acquired from compacted water -atomized Astaloy 85Mo via scanning electron microscopy. Besides visual inspection, the trained models were evaluated via quantitative metrics. Distributions of relevant microstructural parameters such as pore perimeter or minimum and maximum pore feret diameter of the synthetically generated micrographs were compared to those of experimentally acquired micrographs. The results indicate that Generative Adversarial Networks represent a promising approach to train models, which produce synthetic micrographs that capture relevant properties of green body micrographs in dependence of given parameters such as powder particle size. In contrast, the variational autoencoders trained for this study did not capture microstructural properties well.

关键词： Generative models Generative Adversarial Network variational autoencoder Micrograph Powder metallurgy Microstructure

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A Lightweight Unsupervised Intrusion Detection Model Based on variational Auto-Encoder

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SENSORS 2023年第20期23卷 8407页

作者： Ren, Yi Feng, Kanghui Hu, Fei Chen, Liangyin Chen, Yanru Sichuan Univ Sch Comp Sci Chengdu 610065 Peoples R China Sichuan Univ Inst Ind Internet Res Chengdu 610065 Peoples R China

With the gradual integration of internet technology and the industrial control field, industrial control systems (ICSs) have begun to access public networks on a large scale. Attackers use these public network interfaces to launch frequent invasions of industrial control systems, thus resulting in equipment failure and downtime, production data leakage, and other serious harm. To ensure security, ICSs urgently need a mature intrusion detection mechanism. Most of the existing research on intrusion detection in ICSs focuses on improving the accuracy of intrusion detection, thereby ignoring the problem of limited equipment resources in industrial control environments, which makes it difficult to apply excellent intrusion detection algorithms in practice. In this study, we first use the spectral residual (SR) algorithm to process the data;we then propose the improved lightweight variational autoencoder (LVA) with autoregression to reconstruct the data, and we finally perform anomaly determination based on the permutation entropy (PE) algorithm. We construct a lightweight unsupervised intrusion detection model named LVA-SP. The model as a whole adopts a lightweight design with a simpler network structure and fewer parameters, which achieves a balance between the detection accuracy and the system resource overhead. Experimental results on the ICSs dataset show that our proposed LVA-SP model achieved an F1-score of 84.81% and has advantages in terms of time and memory overhead.

关键词： industrial control systems intrusion detection variational autoencoder

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Layer-constrained variational autoencoding kernel density estimation model for anomaly detection

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KNOWLEDGE-BASED SYSTEMS 2020年 196卷 105753-105753页

作者： Lv, Peng Yu, Yanwei Fan, Yangyang Tang, Xianfeng Tong, Xiangrong Ocean Univ China Dept Comp Sci & Technol Qingdao 266100 Shandong Peoples R China Yantai Univ Sch Comp & Control Engn Yantai 264005 Shandong Peoples R China Shanghai Jiao Tong Univ Shanghai Key Lab Adv High Temp Mat & Precis Formi Shanghai 200240 Peoples R China Penn State Univ Coll Informat Sci & Technol University Pk PA 16802 USA

Unsupervised techniques typically rely on the probability density distribution of the data to detect anomalies, where objects with low probability density are considered to be abnormal. However, modeling the density distribution of high dimensional data is known to be hard, making the problem of detecting anomalies from high-dimensional data challenging. The state-of-the-art methods solve this problem by first applying dimension reduction techniques to the data and then detecting anomalies in the low dimensional space. Unfortunately, the low dimensional space does not necessarily preserve the density distribution of the original high dimensional data. This jeopardizes the effectiveness of anomaly detection. In this work, we propose a novel high dimensional anomaly detection method called LAKE. The key idea of LAKE is to unify the representation learning capacity of layer-constrained variational autoencoder with the density estimation power of kernel density estimation (KDE). Then a probability density distribution of the high dimensional data can be learned, which is able to effectively separate the anomalies out. LAKE successfully consolidates the merits of the two worlds, namely layer-constrained variational autoencoder and KDE by using a probability density-aware strategy in the training process of the autoencoder. Extensive experiments on six public benchmark datasets demonstrate that our method significantly outperforms the state-of-the-art methods in detecting anomalies and achieves up to 37% improvement in F-1 score. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Anomaly detection variational autoencoder Kernel density estimation Layer constraint Deep learning

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Research on Insider Threat Detection Method Based on variational Autoencoding

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电脑学刊 2021年第4期32卷 201-210页

作者： Zhenjiang Zhang Lulu Zhao Yang Zhang Hongde Zhou Wei Li

In recent years, internal attacks have posed a serious threat to the security of individuals, companies and even the country. Machine learning is currently a common method of insider threat detection. However, this technology requires a series of complex feature engineering, which has certain limitations in practical applications. This paper comprehensively considers the user's business operation behavior data and internal psychological data, and establishes an internal threat detection model to analyze their potential associations. The main tasks are as follows: In order to improve the fine-grained features of heterogeneous behavior log data and accurately reflect user behavior attributes, a session-based full feature extraction method is proposed. In this method, combined with a variational autoencoder, a long and shortterm memory variational autoencoder (LVE) model is proposed. Taking into account the time characteristics of user behavior, a long and short-term memory network is used in the codec part, that is, input data, generate hidden variables, and then restore output data through hidden variables. The results show that this method improves the recall rate compared with other algorithms. Finally, the main work and improvement prospects are summarized.

关键词： detection efficiency insider threat user behavior analysis variational autoencoder

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BioDSNN: a dual-stream neural network with hybrid biological knowledge integration for multi-gene perturbation response prediction

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BRIEFINGS IN BIOINFORMATICS 2024年第1期26卷 bbae617页

作者： Tan, Yuejun Xie, Linhai Yang, Hong Zhang, Qingyuan Luo, Jinyuan Zhang, Yanchun Guangzhou Univ Cyberspace Inst Adv Technol Guangzhou 510000 Peoples R China Zhejiang Normal Univ Sch Comp Sci & Technol Jinhua 321000 Peoples R China Natl Ctr Prot Sci Beijing State Key Lab Prote Beijing 100000 Peoples R China Int Acad Phronesis Med Guangzhou 510000 Peoples R China

Studying the outcomes of genetic perturbation based on single-cell RNA-seq data is crucial for understanding genetic regulation of cells. However, the high cost of cellular experiments and single-cell sequencing restrict us from measuring the full combination space of genetic perturbations and cell types. Consequently, a bunch of computational models have been proposed to predict unseen combinations based on existing data. Among them, generative models, e.g. variational autoencoder and diffusion models, have the superiority in capturing the perturbed data distribution, but lack a biologically understandable foundation for generalization. On the other side of the spectrum, Gene Regulation Networks or gene pathway knowledge have been exploited for more reasonable generalization enhancement. Unfortunately, they do not reach a balanced processing of the two data modalities, leading to a degraded fitting ability. Hence, we propose a dual-stream architecture. Before the information from two modalities are merged, the sequencing data are learned with a generative model while three types of knowledge data are comprehensively processed with graph networks and a masked transformer, enforcing a deep understanding of single-modality data, respectively. The benchmark results show an approximate 20% reduction in terms of mean squared error, proving the effectiveness of the model.

关键词： genetic perturbation predict biological knowledge masked attention variational autoencoder

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