Real Data Distribution Research Articles

Attention-enhanced conditional-diffusion-based data synthesis for data augmentation in machine fault diagnosis

Data scarcity and class imbalance are pervasive challenges in machine fault diagnosis, impeding the development and broad adaptation of accurate and reliable deep-learning-based fault detection systems. To address these issues, we propose a novel attention-enhanced conditioning-guided diffusion-based approach for synthesizing additional training data. By generating noisy motor-current signals and employing a diffusion process to reduce noise levels, we obtain synthetic data that closely resembles the statistical properties and patterns of real-world data. The quality of the synthesized samples is assessed using a four-layer, pre-trained convolutional neural network, achieving an impressive classification rate of 96.39%. This demonstrates the effectiveness of our attention-enhanced conditioned diffusion-based model in synthesizing critical features for fault detection, making it well-suited for the introduced fault detection task. To further investigate the quality of the synthesized data samples, we implement a specific training strategy that uses the generated samples as training data. The trained diffusion model generates samples per class, which are then used to train a simple four-layer convolutional neural network for fault classification. Remarkably, the model achieves an accuracy of 98.96% when evaluated with real inverter signals, indicating that the generated samples effectively capture the distribution of real data. For further evaluation, multiple scenarios are created confirming the value of the additionally synthesized samples for real-world bearing fault detection. Our approach presents a well-suited solution to the data scarcity and class imbalance problem in machine fault diagnosis. The proposed method enhances the accuracy and reliability of fault diagnosis systems, offering valuable insights for various real-world condition monitoring applications.

HOT-GAN: Hilbert Optimal Transport for Generative Adversarial Network.

Generative adversarial network (GAN) has achieved remarkable success in generating high-quality synthetic data by learning the underlying distributions of target data. Recent efforts have been devoted to utilizing optimal transport (OT) to tackle the gradient vanishing and instability issues in GAN. They use the Wasserstein distance as a metric to measure the discrepancy between the generator distribution and the real data distribution. However, most optimal transport GANs define loss functions in Euclidean space, which limits their capability in handling high-order statistics that are of much interest in a variety of practical applications. In this article, we propose a computational framework to alleviate this issue from both theoretical and practical perspectives. Particularly, we generalize the optimal transport-based GAN from Euclidean space to the reproducing kernel Hilbert space (RKHS) and propose Hilbert Optimal Transport GAN (HOT-GAN). First, we design HOT-GAN with a Hilbert embedding that allows the discriminator to tackle more informative and high-order statistics in RKHS. Second, we prove that HOT-GAN has a closed-form kernel reformulation in RKHS that can achieve a tractable objective under the GAN framework. Third, HOT-GAN's objective enjoys the theoretical guarantee of differentiability with respect to generator parameters, which is beneficial to learn powerful generators via adversarial kernel learning. Extensive experiments are conducted, showing that our proposed HOT-GAN consistently outperforms the representative GAN works.

Constraint-Aware Learning for Fractional Flow Reserve Pullback Curve Estimation from Invasive Coronary Imaging.

Estimation of the fractional flow reserve (FFR) pullback curve from invasive coronary imaging is important for the intraoperative guidance of coronary intervention. Machine/deep learning has been proven effective in FFR pullback curve estimation. However, the existing methods suffer from inadequate incorporation of intrinsic geometry associations and physics knowledge. In this paper, we propose a constraint-aware learning framework to improve the estimation of the FFR pullback curve from invasive coronary imaging. It incorporates both geometrical and physical constraints to approximate the relationships between the geometric structure and FFR values along the coronary artery centerline. Our method also leverages the power of synthetic data in model training to reduce the collection costs of clinical data. Moreover, to bridge the domain gap between synthetic and real data distributions when testing on real-world imaging data, we also employ a diffusion-driven test-time data adaptation method that preserves the knowledge learned in synthetic data. Specifically, this method learns a diffusion model of the synthetic data distribution and then projects real data to the synthetic data distribution at test time. Extensive experimental studies on a synthetic dataset and a real-world dataset of 382 patients covering three imaging modalities have shown the better performance of our method for FFR estimation of stenotic coronary arteries, compared with other machine/deep learning-based FFR estimation models and computational fluid dynamics-based model. The results also provide high agreement and correlation between the FFR predictions of our method and the invasively measured FFR values. The plausibility of FFR predictions along the coronary artery centerline is also validated.

MCS-GAN: A Different Understanding for Generalization of Deep Forgery Detection

After several years of development, deep synthesis technology has made significant progress in image and video synthesis. Deep forgery represented by Deepfakes has become a research hotspot, which is used as a tool for disinformation attacks. The current strongly discriminative models can have good performance on specific datasets, even close to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$100\%$</tex-math></inline-formula> accuracy. Unfortunately, since a specific discriminative method only fits a specific data distribution, and different forgery methods or datasets have different data distributions. These methods fail to achieve high performance in cross-dataset detection. In response to this problem and focusing on the actual situation, we adjust the strong generalization detection across the dataset to the generalization detection of unseen fake video. We propose Multi-Crise-Cross Attention and StyleGANv2 Generative Adversarial Network (MCS-GAN). Firstly, we built a Generative Adversarial Network (GAN) framework to learn the distribution of real face data and generate corresponding face images. Secondly, to break the high stitch between the fake region and the background, the model needs to have strong enough feature analysis and pixel restoration capabilities. Therefore, we propose a generator consisting of a Multi-Crise-Cross-Attention (MC) encoder and a StyleGANv2 (SG2) decoder. Finally, to avoid the situation where as long as a face is normal or different faces are abnormal, we set a latent space encoding discriminator and increase the ratio of latent space vector, so as to detect anomaly generated by the forgery operation acting on latent space. We conduct some model generalization experiments on videos on the Internet and some popular deepfake databases. The results show that the accuracy of our method is better compared with the best methods.

Enhancement Methods of Hydropower Unit Monitoring Data Quality Based on the Hierarchical Density-Based Spatial Clustering of Applications with a Noise-Wasserstein Slim Generative Adversarial Imputation Network with a Gradient Penalty.

In order to solve low-quality problems such as data anomalies and missing data in the condition monitoring data of hydropower units, this paper proposes a monitoring data quality enhancement method based on HDBSCAN-WSGAIN-GP, which improves the quality and usability of the condition monitoring data of hydropower units by combining the advantages of density clustering and a generative adversarial network. First, the monitoring data are grouped according to the density level by the HDBSCAN clustering method in combination with the working conditions, and the anomalies in this dataset are detected, recognized adaptively and cleaned. Further combining the superiority of the WSGAIN-GP model in data filling, the missing values in the cleaned data are automatically generated by the unsupervised learning of the features and the distribution of real monitoring data. The validation analysis is carried out by the online monitoring dataset of the actual operating units, and the comparison experiments show that the clustering contour coefficient (SCI) of the HDBSCAN-based anomaly detection model reaches 0.4935, which is higher than that of the other comparative models, indicating that the proposed model has superiority in distinguishing between the valid samples and anomalous samples. The probability density distribution of the data filling model based on WSGAIN-GP is similar to that of the measured data, and the KL dispersion, JS dispersion and Hellinger's distance of the distribution between the filled data and the original data are close to 0. Compared with the filling methods such as SGAIN, GAIN, KNN, etc., the effect of data filling with different missing rates is verified, and the RMSE error of data filling with WSGAIN-GP is lower than that of other comparative models. The WSGAIN-GP method has the lowest RMSE error under different missing rates, which proves that the proposed filling model has good accuracy and generalization, and the research results in this paper provide a high-quality data basis for the subsequent trend prediction and state warning.

CDGAN-BERT: Adversarial constraint and diversity discriminator for semi-supervised text classification

Semi-supervised generative adversarial learning (SS-GAN) significantly improves the model’s performance with limited labeled data, especially on text classification. However, these SS-GAN-like works pay more attention to the more real properties of the generated samples and ignore their adversarial nature, which may prevent the model from learning more higher-quality data representations. And the existing single discriminator may produce discrimination ambiguity when discriminating against tasks with different discriminative properties (simultaneously identifying real or fake and classifying categories), which may harm the model’s classification performance. In this paper, we propose a novel CDGAN-BERT, an SS-GAN-based architecture with the adversarial constraint and a diversity discriminator. Specifically, CDGAN-BERT first focuses on adversarial constraint, which calculates the model’s intrinsic state representation with the reciprocal of Mean Squared Error (MSE), further keeping adversarial of the generated samples relative to the real data distribution. Then, a diversity discriminator is designed to improve the model’s classification performance by alleviating discriminative ambiguity, which refines tasks with different discriminative attributes by adding an additional authenticity discriminator. We validate our model on 6 text datasets and achieve significant improvements, especially with limited supervision. The experimental results show that our method outperforms or gets comparable results to other state-of-the-art semi-supervised learning methods on several datasets. Especially for the datasets of QC-Fine and QC-Coarse with limited labeled data, our CDGAN-BERT achieves the best average Micro-F1 scores of 72.5% and 92.037%, respectively.

Reconstructing Close Human Interactions from Multiple Views

This paper addresses the challenging task of reconstructing the poses of multiple individuals engaged in close interactions, captured by multiple calibrated cameras. The difficulty arises from the noisy or false 2D keypoint detections due to inter-person occlusion, the heavy ambiguity in associating keypoints to individuals due to the close interactions, and the scarcity of training data as collecting and annotating motion data in crowded scenes is resource-intensive. We introduce a novel system to address these challenges. Our system integrates a learning-based pose estimation component and its corresponding training and inference strategies. The pose estimation component takes multi-view 2D keypoint heatmaps as input and reconstructs the pose of each individual using a 3D conditional volumetric network. As the network doesn't need images as input, we can leverage known camera parameters from test scenes and a large quantity of existing motion capture data to synthesize massive training data that mimics the real data distribution in test scenes. Extensive experiments demonstrate that our approach significantly surpasses previous approaches in terms of pose accuracy and is generalizable across various camera setups and population sizes. The code is available on our project page: https://github.com/zju3dv/CloseMoCap.

Dynamic model-assisted transferable network for liquid rocket engine fault diagnosis using limited fault samples

The accurate detection and diagnosis of faults in Liquid Rocket Engines (LREs) are critical for ensuring space mission safety. However, the limited availability of actual fault samples and the diversification of potential faults present significant challenges in achieving precise diagnosis. To overcome these obstacles, we propose a dynamic model-assisted transfer learning approach. In this study, we first modularize the LRE and establish a dynamic model based on the mathematical principles of each module. Subsequently, we artificially established a fault module model, injected faults into the normal model, and simulated various fault modes to expand the fault sample library. Leveraging this augmented dataset in combination with a limited number of actual fault samples, we employ transfer learning to fine-tune a Convolutional Neural Network (CNN). Compared with other classic methods, the migrated CNN effectively adapts to the distribution of real data and significantly improves the accuracy of LRE fault diagnosis.

On the requirements for statistical methods of data analysis (generalizing article)

Half a century activity in consulting researchers of various specialties, reviewing articles and books, opposing dissertations provided the possibility of getting acquainted with hundreds of specific studies on the development and application of statistical methods. A variety of shortcomings in conducting research and publishing the results of studies have been revealed, which hinder the perception of the data obtained, and in some cases cast doubt on the adequacy of the conclusions. Therefore, it appeared advisable to develop natural requirements for methods of data processing and presentation of the results of statistical data analysis. This study is devoted to an initial consideration of a number of formulations of such requirements. We proceed from the modern paradigm of applied statistics, based on non-parametric and non-numerical statistics which replace the primitive paradigm of the 19th century and the paradigm of the middle of the 20th century using parametric distribution systems. When describing and discussing the procedures for analyzing statistical data, it is necessary to start with probabilistic-statistical models for generating the data under study. It is necessary to proceed from the theory of measurements, according to which the first step in the data analysis is identification of the scales in which they are measured. Statistical inference must be invariant under the allowable transformations of data measurement scales. Since almost all distributions of real data are non-normal, a preference should be given to non-parametric formulations. The possibility of using parametric families of distributions must be carefully justified. In accordance with the theory of testing statistical hypothesis, both the null and alternative hypotheses must be specified. It is necessary to study the stability of the conclusions drawn from the model with respect to acceptable changes in the initial data and assumptions of the model. A number of further publications of the author will be devoted to the problems of developing a system of requirements for statistical models and methods.

Bidirectional-Feature-Learning-Based Adversarial Domain Adaptation with Generative Network

Studying domain adaptation is a recent research trend. Generally, many generative models that researchers have studied perform well on training data from a specific domain. However, their ability to be generalized to other domains might be limited. Therefore, a growing body of research has utilized domain adaptation techniques to address the problem of generative models being vulnerable to input from other domains. In this paper, we focused on generative models and representation learning. Generative models have received a lot of attention for their ability to generate various types of data such as images, music, and text. In particular, studies utilizing generative adversarial neural networks (GANs) and autoencoder structures have received a lot of attention. In this paper, we solved the domain adaptation problem by reconstructing real image data using an autoencoder structure. In particular, reconstructed image data, considered a type of noisy image data, are used as input data. How to reconstruct data by extracting features and selectively transforming them in order to reduce differences in characteristics between domains entails representative learning. Considering these research trends, this paper proposed a novel methodology combining bidirectional feature learning and generative networks to innovatively approach the domain adaptation problem. It could improve the adaptation ability by accurately simulating the real data distribution. The experimental results show that the proposed model outperforms the traditional DANN and ADDA. This demonstrates that combining bidirectional feature learning and generative networks is an effective solution in the field of domain adaptation. These results break new ground in the field of domain adaptation. They are expected to provide great inspiration for future research and applications. Finally, through various experiments and evaluations, we verify that the proposed approach outperforms the existing works. We conducted experiments for representative generative models and domain adaptation techniques and found that the proposed approach was effective in improving data and domain robustness. We hope to contribute to the development of domain-adaptive models that are robust to the domain.

STAR_outliers: a python package that separates univariate outliers from non-normal distributions

There are not currently any univariate outlier detection algorithms that transform and model arbitrarily shaped distributions to remove univariate outliers. Some algorithms model skew, even fewer model kurtosis, and none of them model bimodality and monotonicity. To overcome these challenges, we have implemented an algorithm for Skew and Tail-heaviness Adjusted Removal of Outliers (STAR_outliers) that robustly removes univariate outliers from distributions with many different shape profiles, including extreme skew, extreme kurtosis, bimodality, and monotonicity. We show that STAR_outliers removes simulated outliers with greater recall and precision than several general algorithms, and it also models the outlier bounds of real data distributions with greater accuracy.Background Reliably removing univariate outliers from arbitrarily shaped distributions is a difficult task. Incorrectly assuming unimodality or overestimating tail heaviness fails to remove outliers, while underestimating tail heaviness incorrectly removes regular data from the tails. Skew often produces one heavy tail and one light tail, and we show that several sophisticated outlier removal algorithms often fail to remove outliers from the light tail. Multivariate outlier detection algorithms have recently become popular, but having tested PyOD’s multivariate outlier removal algorithms, we found them to be inadequate for univariate outlier removal. They usually do not allow for univariate input, and they do not fit their distributions of outliership scores with a model on which an outlier threshold can be accurately established. Thus, there is a need for a flexible outlier removal algorithm that can model arbitrarily shaped univariate distributions.Results In order to effectively model arbitrarily shaped univariate distributions, we have combined several well-established algorithms into a new algorithm called STAR_outliers. STAR_outliers removes more simulated true outliers and fewer non-outliers than several other univariate algorithms. These include several normality-assuming outlier removal methods, PyOD’s isolation forest (IF) outlier removal algorithm (ACM Transactions on Knowledge Discovery from Data (TKDD) 6:3, 2012) with default settings, and an IQR based algorithm by Verardi and Vermandele that removes outliers while accounting for skew and kurtosis (Verardi and Vermandele, Journal de la Société Française de Statistique 157:90–114, 2016). Since the IF algorithm’s default model poorly fit the outliership scores, we also compared the isolation forest algorithm with a model that entails removing as many datapoints as STAR_outliers does in order of decreasing outliership scores. We also compared these algorithms on the publicly available 2018 National Health and Nutrition Examination Survey (NHANES) data by setting the outlier threshold to keep values falling within the main 99.3 percent of the fitted model’s domain. We show that our STAR_outliers algorithm removes significantly closer to 0.7 percent of values from these features than other outlier removal methods on average.Conclusions STAR_outliers is an easily implemented python package for removing outliers that outperforms multiple commonly used methods of univariate outlier removal.

Employing Generative Networks for Synthetic Phonocardiogram and Electrocardiogram Signal Creation: A Privacy-Ensured Approach to Data Augmentation in Heart Diagnostics

The diagnosis of various cardiac conditions necessitates meticulous analysis of Phonocardiogram (PCG) and Electrocardiogram (ECG) signals. In light of this, artificial intelligence and machine learning, coupled with computer-assisted diagnostic techniques, have been progressively integrated into modern healthcare systems, facilitating clinicians in making crucial diagnostic decisions. However, the effectiveness of these deep learning applications hinges on the availability of extensive training data, which exacerbates the risk of privacy violations. In response to this dilemma, research into methodologies for synthetic patient data generation has witnessed a surge. It has been observed that most attempts to generate synthetic ECG and PCG signals focus on modeling the statistical distributions of the available real training data, a process known as Data Augmentation. Among the various data augmentation techniques, Generative Adversarial Networks (GANs) have gained significant traction in recent years. This paper conducts an in-depth exploration and evaluation of GANs, specifically Deep Convolutional GANs and Conditional GANs, for the generation of synthetic ECG and PCG signals.

Long-range zero-shot generative deep network quantization

Quantization approximates a deep network model with floating-point numbers by the model with low bit width numbers, thereby accelerating inference and reducing computation. Zero-shot quantization, which aims to quantize a model without access to the original data, can be achieved by fitting the real data distribution through data synthesis. However, it has been observed that zero-shot quantization leads to inferior performance compared to post-training quantization with real data for two primary reasons: 1) a normal generator has difficulty obtaining a high diversity of synthetic data since it lacks long-range information to allocate attention to global features, and 2) synthetic images aim to simulate the statistics of real data, which leads to weak intra-class heterogeneity and limited feature richness. To overcome these problems, we propose a novel deep network quantizer called long-range zero-shot generative deep network quantization (LRQ). Technically, we propose a long-range generator (LRG) to learn long-range information instead of simple local features. To incorporate more global features into the synthetic data, we use long-range attention with large-kernel convolution in the generator. In addition, we also present an adversarial margin add (AMA) module to force intra-class angular enlargement between the feature vector and class center. The AMA module forms an adversarial process that increases the convergence difficulty of the loss function, which is opposite to the training objective of the original loss function. Furthermore, to transfer knowledge from the full-precision network, we also utilize decoupled knowledge distillation. Extensive experiments demonstrate that LRQ obtains better performance than other competitors.

Performance Evaluation and Validation of Intelligent Security Mechanism in Software Defined Network

Network attacks are discovered using intrusion detection systems (IDS), one of the most crucial security solutions. Machine learning techniques-based intrusion detection approaches have been rapidly created as a result of the widespread use of standard machine learning algorithms in the security field. Unfortunately, as technology has advanced and there have been faults in the machine learning-based intrusion detection system, the system has consistently failed to fulfill the standards for cyber security. Generative adversarial networks (GANs) have drawn a lot of interest recently and have been utilized widely in anomaly detection due to their enormous capacity for learning difficult high-dimensional real time data distribution. Traditional machine learning algorithms for intrusion detection have a number of drawbacks that deep learning techniques can significantly mitigate. With the help of a real time dataset, this work suggests employing GANs and its variants to detect network intrusions in SDN. The feasibility and comparison results are also presented. For different kinds of datasets, the BiGAN outcomes outperform the GAN.

An optimized method for variational autoencoders based on Gaussian cloud model

Variational Autoencoders is one of the most valuable generative models in the field of unsupervised learning. Due to its own construction characteristics, Variational Autoencoders has insufficient precision for high-resolution image reconstruction. In this paper, the priori variant model of Variational Autoencoders based on the Gaussian Cloud Model is proposed to optimize the sampling method of latent variables, network structure and loss function. First, the Gaussian Cloud Model is used to replace the prior distribution of Variational Autoencoders. Second, the sampling process is changed into two consecutive Gaussian distributions. Finally, a new loss function based on the envelope curve of the Gaussian Cloud Model is presented for approximating the real data distribution. The method is evaluated qualitatively and quantitatively on several datasets to fully demonstrate the correctness and effectiveness of the method.

GH-Feat: Learning Versatile Generative Hierarchical Features From GANs.

Recent years witness the tremendous success of generative adversarial networks (GANs) in synthesizing photo-realistic images. GAN generator learns to compose realistic images and reproduce the real data distribution. Through that, a hierarchical visual feature with multi-level semantics spontaneously emerges. In this work we investigate that such a generative feature learned from image synthesis exhibits great potentials in solving a wide range of computer vision tasks, including both generative ones and more importantly discriminative ones. We first train an encoder by considering the pre-trained StyleGAN generator as a learned loss function. The visual features produced by our encoder, termed as Generative Hierarchical Features (GH-Feat), highly align with the layer-wise GAN representations, and hence describe the input image adequately from the reconstruction perspective. Extensive experiments support the versatile transferability of GH-Feat across a range of applications, such as image editing, image processing, image harmonization, face verification, landmark detection, layout prediction, image retrieval, etc. We further show that, through a proper spatial expansion, our developed GH-Feat can also facilitate fine-grained semantic segmentation using only a few annotations. Both qualitative and quantitative results demonstrate the appealing performance of GH-Feat. Code and models are available at https://genforce.github.io/ghfeat/.

Unsupervised Discovery and Manipulation of Continuous Disentangled Factors of Variation

Learning a disentangled representation of a distribution in a completely unsupervised way is a challenging task that has drawn attention recently. In particular, much focus has been put in separating factors of variation (i.e., attributes) within the latent code of a Generative Adversarial Network (GAN). Achieving that permits control of the presence or absence of those factors in the generated samples by simply editing a small portion of the latent code. Nevertheless, existing methods that perform very well in a noise-to-image setting often fail when dealing with a real data distribution, i.e., when the discovered attributes need to be applied to real images. However, some methods are able to extract and apply a style to a sample but struggle to maintain its content and identity, while others are not able to locally apply attributes and end up achieving only a global manipulation of the original image. In this article, we propose a completely (i.e., truly ) unsupervised method that is able to extract a disentangled set of attributes from a data distribution and apply them to new samples from the same distribution by preserving their content. This is achieved by using an image-to-image GAN that maps an image and a random set of continuous attributes to a new image that includes those attributes. Indeed, these attributes are initially unknown and they are discovered during training by maximizing the mutual information between the generated samples and the attributes’ vector. Finally, the obtained disentangled set of continuous attributes can be used to freely manipulate the input samples. We prove the effectiveness of our method over a series of datasets and show its application on various tasks, such as attribute editing, data augmentation, and style transfer.

Generative Adversarial Network for Overcoming Occlusion in Images: A Survey

Although current computer vision systems are closer to the human intelligence when it comes to comprehending the visible world than previously, their performance is hindered when objects are partially occluded. Since we live in a dynamic and complex environment, we encounter more occluded objects than fully visible ones. Therefore, instilling the capability of amodal perception into those vision systems is crucial. However, overcoming occlusion is difficult and comes with its own challenges. The generative adversarial network (GAN), on the other hand, is renowned for its generative power in producing data from a random noise distribution that approaches the samples that come from real data distributions. In this survey, we outline the existing works wherein GAN is utilized in addressing the challenges of overcoming occlusion, namely amodal segmentation, amodal content completion, order recovery, and acquiring training data. We provide a summary of the type of GAN, loss function, the dataset, and the results of each work. We present an overview of the implemented GAN architectures in various applications of amodal completion. We also discuss the common objective functions that are applied in training GAN for occlusion-handling tasks. Lastly, we discuss several open issues and potential future directions.

Hardware-accelerated Real-time Drift-awareness for Robust Deep Learning on Wireless RF Data

Proactive and intelligent management of network resource utilization (RU) using deep learning (DL) can significantly improve the efficiency and performance of the next generation of wireless networks. However, variations in wireless RU are often affected by uncertain events and change points due to the deviations of real data distribution from that of the original training data. Such deviations, which are known as dataset drifts, can subsequently lead to a shift in the corresponding decision boundary degrading the DL model prediction performance. To address these challenges, we present hardware-accelerated real-time radio frequency (RF) analytics and drift-awareness modules for robust DL predictions. We have prototyped the proposed design on a Zynq-7000 System-on-Chip that contains an FPGA and an embedded ARM processor. We have used Xilinx Vivado design suite for synthesis and analysis of the HDL design for the proposed solution. To detect dataset drifts, the proposed solution adopts a distance-based technique on FPGA to quantify in real-time the change between the prediction distribution obtained from DL predictions and data distribution of input streaming samples. Using various performance metrics, we have extensively evaluated the performance of the proposed solution and shown that it can significantly improve the DL model robustness in the presence of dataset drifts.

Tabular Data Generation to Improve Classification of Liver Disease Diagnosis

Liver diseases are among the most common diseases worldwide. Because of the high incidence and high mortality rate, these diseases diagnoses are vital. Several elements harm the liver. For instance, obesity, undiagnosed hepatitis infection, and alcohol abuse. This causes abnormal nerve function, bloody coughing or vomiting, insufficient kidney function, hepatic failure, jaundice, and liver encephalopathy.. The diagnosis of this disease is very expensive and complex. Therefore, this work aims to assess the performance of various machine learning algorithms at decreasing the cost of predictive diagnoses of chronic liver disease. In this study, five machine learning algorithms were employed: Logistic Regression, K-Nearest Neighbor, Decision Tree, Support Vector Machine, and Artificial Neural Network (ANN) algorithm. In this work, we examined the effects of the increased prediction accuracy of Generative Adversarial Networks (GANs) and the synthetic minority oversampling technique (SMOTE). Generative opponents’ networks (GANs) are a mechanism to produce artificial data with a distribution close to real data distribution. This is achieved by training two different networks: the generator, which seeks to produce new and real samples, and the discriminator, which classifies the augmented samples using supervised classifications. Statistics show that the use of increased data slightly improves the performance of the classifier.