Use Of Generative Adversarial Networks Research Articles

Conditional Generative Adversarial Networks with Optimized Machine Learning for Fault Detection of Triplex Pump in Industrial Digital Twin

In recent years, digital twin (DT) technology has garnered significant interest from both academia and industry. However, the development of effective fault detection and diagnosis models remains challenging due to the lack of comprehensive datasets. To address this issue, we propose the use of Generative Adversarial Networks (GANs) to generate synthetic data that replicate real-world data, capturing essential features indicative of health-related information without directly referencing actual industrial DT systems. This paper introduces an intelligent fault detection and diagnosis framework for industrial triplex pumps, enhancing fault recognition capabilities and offering a robust solution for real-time industrial applications within the DT paradigm. The proposed framework leverages Conditional GANs (CGANs) alongside the Harris Hawk Optimization (HHO) as a metaheuristic method to optimize feature selection from input data to enhance the performance of machine learning (ML) models such as Bagged Ensemble (BE), AdaBoost (AD), Support Vector Machine (SVM), K-Nearest Neighbors (KNNs), Decision Tree (DT), and Naive Bayes (NB). The efficacy of the approach is evaluated using key performance metrics such as accuracy, precision, recall, and F-measure on a triplex pump dataset. Experimental results indicate that hybrid-optimized ML algorithms (denoted by “ML-HHO”) generally outperform or match their classical counterparts across these metrics. BE-HHO achieves the highest accuracy at 95.24%, while other optimized models also demonstrate marginal improvements, highlighting the framework’s effectiveness for real-time fault detection in DT systems, where SVM-HHO attains 94.86% accuracy, marginally higher than SVM’s 94.48%. KNN-HHO outperforms KNNs with 94.73% accuracy compared to 93.14%. Both DT-HHO and DT achieve 94.73% accuracy, with DT-HHO exhibiting slightly better precision and recall. NB-HHO and NB show near-equivalent performance, with NB-HHO at 94.73% accuracy versus NB’s 94.6%. Overall, the optimized algorithms demonstrate consistent, albeit marginal, improvements over their classical versions.

Generative Adversarial Network Based Music Generation

Music has been an integral part of human civilization personally and culturally. Historically, music has been generated using various instruments, or natural sounds like water drops, or unconventional musical instruments like metal or glass-wares. At present, technologies like Musical Instrument Digital Interface (MIDI) are used to generate music electronically. This research investigates the use of Generative Adversarial Networks (GANs) for beginner-friendly music production. This model uses Long Short-Term Memory (LSTM) generator and Patch GAN as discriminator for the GAN architecture. The generator consists of input layer, embedding layer, LSTM layer and generates output with a SoftMax function. Similarly, the discriminator consists of a convolution layer, the output of which is averaged by the global average pooling layer, and output is generated by the sigmoid function. The model is trained on Maestro MIDI dataset. We make the process understandable by delving into the implementation specifics and outlining the fundamental concepts of music. Our effective model highlights the potential of GANs in music composition by producing cohesive music. After training for 50 epochs, the model exhibited a remarkable precision of 91.82 percent. This project uses the combination of Artificial Intelligence (AI) with music theory to provide intriguing new opportunities in the field of music. The model can be beneficial for different industries like gaming, music, entertainment, education, etc.

Transformer-inspired stacked-GAN for hyperspectral target detection

ABSTRACT Hyperspectral images (HSI) can provide abundant spectral feature information and have been successfully used in the field of object detection. The use of Generative Adversarial Network (GAN) to reconstruct the background spectral to achieve differential detection has shown excellent performance in hyperspectral object detection (HTD) tasks. However, due to factors such as noise interference and the complexity of the background, such algorithms also face many challenges such as lack of background sample mixing, poor reconstruction effect, and generation of out-of-class samples. A Transformer-inspired stacked GAN network (STGAN) is proposed to solve these problems. First, in the data preparation phase, we adopted a method of extracting pure background samples at a deep level. Then, in the encoder module, the background spectral features are mined using a Transformer-Encoder with Position Coding and Multihead Self-Attention Mechanism (MHSA). Finally, the same GAN is stacked based on the above network structure, and the latent feature mapping of the first GAN is used as input to reduce the out-of-class samples and achieve a more detailed reconstruction of the background. The comparison experiments of our model with other algorithms on datasets in different scenarios prove its advanced performance.

Soft Margin Spectral Normalization for GANs

In this paper, we explore the use of Generative Adversarial Networks (GANs) to speed up the simulation process while ensuring that the generated results are consistent in terms of physics metrics. Our main focus is the application of spectral normalization for GANs to generate electromagnetic calorimeter (ECAL) response data, which is a crucial component of the LHCb. We propose an approach that allows to balance between model’s capacity and stability during training procedure, compare it with previously published ones and study the relationship between proposed method’s hyperparameters and quality of generated objects. We show that the tuning of normalization method’s hyperparameters boosts the quality of generative model.

Using AutoML and generative AI to predict the type of wildfire propagation in Canadian conifer forests

Accurate prediction of wildfire behaviour is critical for safe and effective fire management and suppression. This study focused on developing and evaluating machine learning (ML) models based on a dataset collected of outdoor experimental fires in Canadian conifer forests. Binary classification models (surface fire vs. crown fire) and multi-class fire type models (surface, passive crown, active crown) were created using ensemble tree methods (Random Forest, XGBoost) and automated ML (AutoGluon, TabPFN). Generative adversarial networks (GAN) were used to generate synthetic data to overcome imbalances in the type of fire distribution. The results show automated ML methods applied to the binary problem to perform best of all tested methods, with an overall accuracy of 91%. TabPFN had the highest accuracy for the multi-class problem, with the use of GAN improving model fit. Results show overfitting issues with some of the ML models, highlighting the need of independent evaluation when ML models are developed. The TabPFN models have potential for application in supporting fire management, namely in fuel management and identifying situations with high fire spread and intensity potential that can impact firefighter safety.

Use of generative-adversarial networks when creating content

The application of generative-adversarial networks in the creation of content is studied. Monitoring of training, analysis of architectures, determination of internal processes at the level of layers, research of properties of latent space, and interaction with it are carried out. Variants of using the specified networks in image generation are considered. Special attention is paid to practical implementation aspects, including selecting optimal parameters and data processing. The difference between a classifier and a discriminator is formulated. The principles of generative-adversarial networks and their influence on the efficiency and quality of generated images are studied. The advantages and limitations of using GANs in content creation are considered.

Patient-specific placental vessel segmentation with limited data

A major obstacle in applying machine learning for medical fields is the disparity between the data distribution of the training images and the data encountered in clinics. This phenomenon can be explained by inconsistent acquisition techniques and large variations across the patient spectrum. The result is poor translation of the trained models to the clinic, which limits their implementation in medical practice. Patient-specific trained networks could provide a potential solution. Although patient-specific approaches are usually infeasible because of the expenses associated with on-the-fly labeling, the use of generative adversarial networks enables this approach. This study proposes a patient-specific approach based on generative adversarial networks. In the presented training pipeline, the user trains a patient-specific segmentation network with extremely limited data which is supplemented with artificial samples generated by generative adversarial models. This approach is demonstrated in endoscopic video data captured during fetoscopic laser coagulation, a procedure used for treating twin-to-twin transfusion syndrome by ablating the placental blood vessels. Compared to a standard deep learning segmentation approach, the pipeline was able to achieve an intersection over union score of 0.60 using only 20 annotated images compared to 100 images using a standard approach. Furthermore, training with 20 annotated images without the use of the pipeline achieves an intersection over union score of 0.30, which, therefore, corresponds to a 100% increase in performance when incorporating the pipeline. A pipeline using GANs was used to generate artificial data which supplements the real data, this allows patient-specific training of a segmentation network. We show that artificial images generated using GANs significantly improve performance in vessel segmentation and that training patient-specific models can be a viable solution to bring automated vessel segmentation to the clinic.

A Comparative Analysis of the Novel Conditional Deep Convolutional Neural Network Model, Using Conditional Deep Convolutional Generative Adversarial Network-Generated Synthetic and Augmented Brain Tumor Datasets for Image Classification.

Disease prediction is greatly challenged by the scarcity of datasets and privacy concerns associated with real medical data. An approach that stands out to circumvent this hurdle is the use of synthetic data generated using Generative Adversarial Networks (GANs). GANs can increase data volume while generating synthetic datasets that have no direct link to personal information. This study pioneers the use of GANs to create synthetic datasets and datasets augmented using traditional augmentation techniques for our binary classification task. The primary aim of this research was to evaluate the performance of our novel Conditional Deep Convolutional Neural Network (C-DCNN) model in classifying brain tumors by leveraging these augmented and synthetic datasets. We utilized advanced GAN models, including Conditional Deep Convolutional Generative Adversarial Network (DCGAN), to produce synthetic data that retained essential characteristics of the original datasets while ensuring privacy protection. Our C-DCNN model was trained on both augmented and synthetic datasets, and its performance was benchmarked against state-of-the-art models such as ResNet50, VGG16, VGG19, and InceptionV3. The evaluation metrics demonstrated that our C-DCNN model achieved accuracy, precision, recall, and F1 scores of 99% on both synthetic and augmented images, outperforming the comparative models. The findings of this study highlight the potential of using GAN-generated synthetic data in enhancing the training of machine learning models for medical image classification, particularly in scenarios with limited data available. This approach not only improves model accuracy but also addresses privacy concerns, making it a viable solution for real-world clinical applications in disease prediction and diagnosis.

The Use of Generative Adversarial Network and Graph Convolution Network for Neuroimaging-Based Diagnostic Classification.

Functional connectivity (FC) obtained from resting-state functional magnetic resonance imaging has been integrated with machine learning algorithms to deliver consistent and reliable brain disease classification outcomes. However, in classical learning procedures, custom-built specialized feature selection techniques are typically used to filter out uninformative features from FC patterns to generalize efficiently on the datasets. The ability of convolutional neural networks (CNN) and other deep learning models to extract informative features from data with grid structure (such as images) has led to the surge in popularity of these techniques. However, the designs of many existing CNN models still fail to exploit the relationships between entities of graph-structure data (such as networks). Therefore, graph convolution network (GCN) has been suggested as a means for uncovering the intricate structure of brain network data, which has the potential to substantially improve classification accuracy. Furthermore, overfitting in classifiers can be largely attributed to the limited number of available training samples. Recently, the generative adversarial network (GAN) has been widely used in the medical field for its generative aspect that can generate synthesis images to cope with the problems of data scarcity and patient privacy. In our previous work, GCN and GAN have been designed to investigate FC patterns to perform diagnosis tasks, and their effectiveness has been tested on the ABIDE-I dataset. In this paper, the models will be further applied to FC data derived from more public datasets (ADHD, ABIDE-II, and ADNI) and our in-house dataset (PTSD) to justify their generalization on all types of data. The results of a number of experiments show the powerful characteristic of GAN to mimic FC data to achieve high performance in disease prediction. When employing GAN for data augmentation, the diagnostic accuracy across ADHD-200, ABIDE-II, and ADNI datasets surpasses that of other machine learning models, including results achieved with BrainNetCNN. Specifically, in ADHD, the accuracy increased from 67.74% to 73.96% with GAN, in ABIDE-II from 70.36% to 77.40%, and in ADNI, reaching 52.84% and 88.56% for multiclass and binary classification, respectively. GCN also obtains decent results, with the best accuracy in ADHD datasets at 71.38% for multinomial and 75% for binary classification, respectively, and the second-best accuracy in the ABIDE-II dataset (72.28% and 75.16%, respectively). Both GAN and GCN achieved the highest accuracy for the PTSD dataset, reaching 97.76%. However, there are still some limitations that can be improved. Both methods have many opportunities for the prediction and diagnosis of diseases.

Generating reality and silencing debate: Synthetic data as discursive device

In addition to tapping data from users’ behavioral surplus, by drawing on generative adversarial networks, data for artificial intelligence is now increasingly being generated through artificial intelligence. With this new method of producing data synthetically, the data economy is not only shifting from “data collection” to “data generation.” Synthetic data is also being employed to address some of the most pressing ethical concerns around artificial intelligence. It thereby comes with the sociotechnical imaginary that social problems can be cut out of artificial intelligence, separating training data from real persons. In response to this technical solutionism, this commentary aims to initiate a critical debate about synthetic data that goes beyond misuse scenarios such as the use of generative adversarial networks to create deep fakes or dark patterns. Instead, on a more general level, we seek to complicate the idea of “solving,” i.e., “closing” and thus “silencing” the ethico-political debates for which synthetic data is supposed to be a solution by showing how synthetic data itself is political. Drawing on the complex connections between recent uses of synthetic data and public debates about artificial intelligence, we therefore propose to consider and analyze synthetic data not only as a technical device but as a discursive one as well. To this end, we shed light on their relationship to three pillars that we see associated with them (a) algorithmic bias, (b) privacy, (c) platform economy.

Gan-based data augmentation to improve breast ultrasound and mammography mass classification

Data imbalance is a common problem in breast cancer diagnosis, to address this challenge, the research explores the use of Generative Adversarial Networks (GANs) to generate synthetic medical data. Various GAN methods, including Wasserstein GAN with Gradient Penalty (WGAN-GP), Cycle GAN, Conditional GAN, and Spectral Normalization GAN (SNGAN), were tested for data augmentation in breast regions of interest (ROIs) using mammography and ultrasound databases. The study employed real, synthetic, and hybrid ROIs (128x128 pixels) to train a Resnet network for classifying as benign (B) or malignant (M) classes. The quality and diversity of the synthetic data were assessed using several metrics: Fréchet Inception Distance (FID), Kernel Inception Distance (KID), Structural Similarity Index (SSIM), Multi-Scale SSIM (MS-SSIM), Blind Reference Image Spatial Quality Evaluator (BRISQUE), Naturalness Image Quality Evaluator (NIQE), and Perception-based Image Quality Evaluator (PIQE).Results revealed that the SNGAN model (FID = 52.89) was most effective for augmenting mammography data, while CGAN (FID = 116.03) excelled with ultrasound data. Cycle GAN and WGAN-GP, though demonstrating lower KID values, did not perform better than SNGAN and CGAN. The lower average MS-SSIM values suggested that SNGAN and CGAN produced a high diversity of synthetic images. However, lower SSIM, BRISQUE, NIQE, and PIQE values indicated poor quality in both real and synthetic images. Classification results showed high accuracy without data augmentation in both US (93.1 %B/94.9 %M) and mammography (80.9 %B/76.9 %M). The research concludes that preprocessing and characterizing ROIs by abnormality type is crucial to generate diverse synthetic data and improve accuracy in the classification process using combined GANs and CNN models.

Applications and implementation of generative artificial intelligence in cardiovascular imaging with a focus on ethical and legal considerations: what cardiovascular imagers need to know!

Abstract Machine learning (ML) and deep learning (DL) have potential applications in medicine. This overview explores the applications of AI in cardiovascular imaging, focusing on echocardiography, cardiac MRI (CMR), coronary CT angiography (CCTA), and CT morphology and function. AI, particularly DL approaches like convolutional neural networks, enhances standardization in echocardiography. In CMR, undersampling techniques and DL-based reconstruction methods, such as variational neural networks, improve efficiency and accuracy. ML in CCTA aids in diagnosing coronary artery disease, assessing stenosis severity, and analyzing plaque characteristics. Automatic segmentation of cardiac structures and vessels using AI is discussed, along with its potential in congenital heart disease diagnosis and 3D printing applications. Overall, AI integration in cardiovascular imaging shows promise for enhancing diagnostic accuracy and efficiency across modalities. The growing use of Generative Adversarial Networks in cardiovascular imaging brings substantial advancements but raises ethical concerns. The “black box” problem in DL models poses challenges for interpretability crucial in clinical practice. Evaluation metrics like ROC curves, image quality, clinical relevance, diversity, and quantitative performance assess GAI models. Automation bias highlights the risk of unquestioned reliance on AI outputs, demanding careful implementation and ethical frameworks. Ethical considerations involve transparency, respect for persons, beneficence, and justice, necessitating standardized evaluation protocols. Health disparities emerge if AI training lacks diversity, impacting diagnostic accuracy. AI language models, like GPT-4, face hallucination issues, posing ethical and legal challenges in healthcare. Regulatory frameworks and ethical governance are crucial for fair and accountable AI. Ongoing research and development are vital to evolving AI ethics.

Deep learning‐ and image processing‐based methods for automatic estimation of leaf herbivore damage

Abstract Quantifying the intensity of leaf herbivory pressure is crucial for understanding the interaction between plants and herbivores in both applied and basic science. Visual estimates and digital analysis have been commonly used to estimate leaf herbivore damage but are time‐consuming which limits the amount of data that can be collected and prevent answering big picture questions that require large‐scale sampling of herbivory pressure. Recent developments in deep learning have provided a potential tool for automatic collection of ecological data from various sources. However, most applications have focused on identification and counting, and there is a lack of deep learning tools for quantitative estimation of leaf herbivore damage. Here, we trained generative adversarial networks (GANs) to predict the intact status of damaged leaves and applied image processing technique to estimate the area and percentage of leaf damage. We first described procedures for collecting leaf images, training GAN models, predicting intact leaves and calculating leaf area, with a Python package provided to enable hands‐on application of these procedures. Then, we collected a large leaf data set to train a universal deep learning model and developed an online app HerbiEstim to allow direct use of pretrained models to estimate herbivory damage of leaves. We tested these methods using both simulated and real leaf damage data. The procedures provided in our study greatly improved the efficiency of leaf herbivore damage estimation. Our test demonstrated that the reconstruction of damaged leaf image resembled the ground‐truth image with a similarity of 98.8%. The estimation of leaf herbivore damage exhibited a high accuracy with an averaged root mean square error of 1.6% and had a general applicability to different plant taxa and leaf shapes. Overall, our work demonstrated the feasibility of applying deep learning techniques to quantify leaf herbivory intensity. The use of GANs allows automatic estimation of leaf damage, representing a major advantage of the method. The Python package and the online app with pre‐trained models will facilitate the use of our method for the analysis of large data sets of plant–herbivore interactions.

Fin-GAN: forecasting and classifying financial time series via generative adversarial networks

We investigate the use of Generative Adversarial Networks (GANs) for probabilistic forecasting of financial time series. To this end, we introduce a novel economics-driven loss function for the generator. This newly designed loss function renders GANs more suitable for a classification task, and places them into a supervised learning setting, whilst producing full conditional probability distributions of price returns given previous historical values. Our approach moves beyond the point estimates traditionally employed in the forecasting literature, and allows for uncertainty estimates. Numerical experiments on equity data showcase the effectiveness of our proposed methodology, which achieves higher Sharpe Ratios compared to classical supervised learning models, such as LSTMs and ARIMA.

An analysis of the impact of content-generation-based AI design tools on the visual arts

Abstract With the development of artificial intelligence, the use of generative adversarial networks in deep learning can generate good visual art based on content, and at the same time, it can reduce the problems of noisy texture, transition migration, and image distortion that exist in content-based image generation. The article proposes a multi-view image generation architecture (DrawGAN) based on generative adversarial networks and builds an AI design tool on this basis to explore the effect and analysis of the design tool for content generation based on generative adversarial networks on visual art. Through experimental testing, the model proposed in this paper, after the constraints of the relevant loss function, the generated image content is compatible with the input image, the style is consistent with the style of the target image, and the image generated by the base DrawGAN has a high degree of reproducibility. In addition, the average score of visual art images generated using the model proposed in this paper is 8.25, which is 2.26 points higher than that of images generated by traditional methods. To sum up, the DrawGAN model proposed in this paper is a valuable tool in the generation of visual art images.

Augmenting Blood Test and Periodontal Examination Data with Generative Adversarial Networks for Enhanced Dementia Risk Prediction.

This study investigates the effectiveness of data augmentation to improve dementia risk prediction using deep neural networks (DNNs). Previous research has shown that basic blood test data were cost-effective and crucial in predicting cognitive function, as indicated by mini-mental state examination (MMSE) scores. However, creating models that can accommodate various conditions is a significant challenge due to constraints related to blood test and MMSE results, such as high costs, limited sample size, and missing data from specific tests not conducted in certain facilities. Periodontal examinations have also emerged as a cost-effective tool for mass screening. To address these issues, this study explores the use of generative adversarial networks (GANs) for generating synthesised data from blood test and periodontal examination results. We used DNNs with four hidden layers to compare prediction accuracy between real and GAN-synthesised data from 108 participants at Nihon University Itabashi Hospital. The GAN-synthesised DNNs achieved a mean absolute error (MAE) of 1.91±0.30 compared to 2.04±0.37 for real data, indicating improved accuracy with synthesised data. Importantly, synthesised data showcased enhanced robustness against missing important variables including age information, and better managed data imbalances. Considering the difficulties in amassing extensive medical data, the augmentation approach is promising in refining dementia risk prediction.

Methods of applied utilization of generative adversarial networks in graphic data processing

The paper explores an important area of artificial intelligence — Generative Adversarial Networks (GANs), which are used to create high-quality artificial data samples. GANs have undergone significant development and application in various sectors, including the processing of graphical data. The report focuses on the practical use of GANs and their architecture. It discusses the fundamental principles of GAN operation, highlights the advantages and disadvantages, including issues with training, vanishing gradients, and convergence oscillations, and describes measures to overcome these problems. It also examines current research in the field of GANs and their applications in various domains, including cybersecurity, medicine, forensics, and computer vision. Practical results from the report's authors regarding their own GAN experiments, optimization, and architecture improvements are presented. The research aims to analyze the architectural features of GANs to enhance their training process

Delphi-based visual scenarios: An innovative use of generative adversarial networks

In the Futures Studies context, the Delphi-based scenario (DBS) is a valuable method for setting future-oriented strategies and actions by gathering expert opinions in multiple iterative rounds, in order to make better decisions in the present. However, one of the main challenges is to find a suitable representation of the scenarios both for evaluation and decision-making. The development of future scenarios requires high creativity and simple textual descriptions could not be enough to grab the attention of the experts and decision-makers, due to a probable lack of communication and/or technical language. Since the textual descriptions concern the future, the human mind may have difficulty imagining events that have not already happened. With the spread of AI models, different implementations can support the DBS process, such as the use of Generative Adversarial Networks (GANs) to generate specific images starting from the narratives of the scenarios. In this paper, we propose a new method for the development of Delphi-based Visual Scenarios (DBVS) adopting GANs, with the aim to support the experts and decision-makers in planning and communication, in order to have immediate images of what the future could be like. We apply this method with regard to the future of Italian families.

Implementation of Synthesize GAN Model to Detect Outlier in National Stock Exchange Time Series Multivariate Data

This research work explores a novel approach for identifying outliers in stock related time series multivariate datasets, using Generative Adversarial Networks (GANs). The proposed framework harnesses the power of GANs to create synthetic data points that replicate the statistical characteristics of genuine stock related time series. The use of Generative Adversarial Networks to generate tabular data has become more important in a number of industries, including banking, healthcare, and data privacy. The process of synthesizing tabular data with GANs is also provided in this paper. It involves several critical steps, including data collection, preprocessing, and exploration, as well as the design and training using Generator and Discriminator networks. While the discriminator separates genuine samples from synthetic ones, the generator is in charge of producing synthetic data. Generating high quality tabular data with GANs is a complex task, but it has the potential to facilitate data generation in various domains while preserving data privacy and integrity. The results from the experiments confirm that the GAN framework is useful for detecting outliers. The model demonstrates its proficiency in identifying outliers within stock-related time series data. In comparison, our proposed work also examines the statistics and machine learning models in related application fields.

Comparison of Deep Convolutional GAN and Progressive GAN for facial image generation

Generative Adversarial Networks (GANs) have generated realistic and diverse facial images with promising results. This work demonstrates a technique for creating facial pictures using GANs and evaluates the effectiveness of several GAN designs and training approaches. The CelebA dataset is leveraged for training and evaluating the GAN model, and employ a variety of evaluation metrics, such as the Structural Similarity Index (SSIM) to assess the quality and diversity of the generated images. Progressive GAN outperforms Deep Convolutional GAN in terms of image quality and diversity, and conditional GAN training is more effective than standard GAN training for generating facial images with specific attributes. The combination of Progressive GAN and conditional GAN training produces facial images of the utmost quality and diversity. The findings contribute to a broader comprehension of the use of GANs for generating facial images and have ramifications for a variety of applications, from facial recognition to virtual reality.