Enhancing body fat prediction with WGAN-GP data augmentation and XGBoost algorithm.

Optimizing starch content prediction in kudzu: Integrating hyperspectral imaging and deep learning with WGAN-GP

Carbohydrate-protein supplementation often improves endurance performance. However, effectiveness varies significantly among individuals due to unique personal characteristics. This study aimed to develop a predictive machine learning framework for personalized supplementation, with a core methodological novelty in applying a Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) to address the critical issue of data scarcity. Based on 231 rowing trials, the framework utilized 46 input features covering baseline characteristics and dietary intakes. Rowing distance was the performance outcome. The machine learning pipeline first utilized a hybrid feature selection method (correlation analysis, model-based importance, and domain knowledge). Following a comparative evaluation, WGAN-GP was utilized for data augmentation. Finally, several regression models (XGBoost, SVR, and MLP) were trained to predict rowing performance. The top-performing model was used to construct a personalized supplement recommendation framework. Feature selection identified 21 key indicators from 46 initial inputs. The XGBoost model, enhanced with WGAN-GP data augmentation, demonstrated the most robust performance, achieving a strong predictive accuracy (R² = 0.53) coupled with high stability. Body weight, explosive power, and nutritional inputs were key performance predictors. This study demonstrates that a data-augmented machine learning approach can effectively model individual responses to supplementation. The developed framework provides a data-driven pathway for creating personalized nutritional strategies to optimize athletic performance.Supplementary InformationThe online version contains supplementary material available at 10.1038/s41598-025-23989-7.

Recently, generative adversarial networks (GANs) are widely applied to increase the amounts of imbalanced input samples in fault diagnosis. However, the existing GAN-based methods have convergence difficulties and training instability, which affect the fault diagnosis efficiency. This paper develops a novel framework for imbalanced fault classification based on Wasserstein generative adversarial networks with gradient penalty (WGAN-GP), which interpolates randomly between the true and generated samples to ensure that the transition region between the true and false samples satisfies the Lipschitz constraint. The process of feature learning is visualized to show the feature extraction process of WGAN-GP. To verify the availability of the generated samples, a stacked autoencoder (SAE) is set to classify the enhanced dataset composed of the generated samples and original samples. Furthermore, the exhibition of the loss curve indicates that WGAN-GP has better convergence and faster training speed due to the introduction of the gradient penalty. Three bearing datasets are employed to verify the effectiveness of the developed framework, and the results show that the proposed framework has an excellent performance in mechanical fault diagnosis under the imbalanced training dataset.

ECG databases are usually highly imbalanced due to the abundance of Normal ECG and scarcity of abnormal cases. As such, deep learning classifiers trained on imbalanced datasets usually perform poorly, especially on minor classes. One solution is to generate realistic synthetic ECG signals using Generative Adversarial Networks (GAN) to augment imbalanced datasets. In this study, we combined conditional GAN with WGAN-GP and developed AC-WGAN-GP in 1D form for the first time to be applied on MIT-BIH Arrhythmia dataset. We investigated the impact of data augmentation on arrhythmia classification. Two models were employed for ECG generation: (i) unconditional GAN; Wasserstein GAN with gradient penalty (WGAN-GP) is trained on each class individually; (ii) conditional GAN; one Auxiliary Classifier WGAN-GP (AC-WGAN-GP) model is trained on all classes and then used to generate synthetic beats in all classes. Two scenarios are defined for each case: (a) unscreened; all the generated synthetic beats were used, and (b) screened; only high-quality beats are selected and used, based on their Dynamic Time Warping (DTW) distance to a designated template. The state-of-the-art ResNet classifier (EcgResNet34) is trained on each of the four augmented datasets and the performance metrics (precision/recall/F1-Score micro- and macro-averaged, confusion matrices, multiclass precision-recall curves) were compared with those of the original imbalanced case. We also used a simple metric Net Improvement. All the three metrics show consistently that unconditional GAN with raw generated data creates the best improvements.

Diffusion-based Wasserstein generative adversarial network for blood cell image augmentation

Seismic inverse modeling method based on generative adversarial networks

The high-penetration integration of renewable energy into port power systems is challenged by the stochastic volatility of wind–solar generation and dynamic load demands. To address this, this study proposes a data-driven bi-level coordinated planning framework for port wind–solar-storage systems, integrating a Wasserstein generative adversarial network with gradient penalty (WGAN-GP) and hybrid secretary bird optimization algorithm (SBOA) for solution seeking. The WGAN-GP-K-Means++ framework is adopted to capture the high-dimensional spatiotemporal correlations under the uncertainty of source ports and loads, and to generate the wind and solar resource scenarios for typical day. Subsequently, a bi-level planning model is constructed: the upper layer optimizes the siting and sizing of distributed generation and energy storage to minimize the life-cycle net present value, while the lower layer minimizes annual operating costs through multi-scenario dispatch. To resolve the resulting complex mixed-integer programming problem, a nested SBOA-Gurobi algorithm is developed. Case study of a Guangxi port demonstrates that the proposed approach reduces life-cycle cost by 44.94% relative to the baseline grid-connected scheme and exhibits superior convergence stability compared with GA, GRSO, and WOA. Additionally, sensitivity analysis quantifies the impact of electricity pricing policies, shore power utilization rates, and discount rate on the system’s economic benefits. This study provides a decision-support tool for the low-carbon transition and economic planning of port energy systems.

It is possible to generate artificial EEG signals using generative adversarial networks (GANs), but the physiological plausibility of these signals is not always considered, even though plausibility is important for the trustworthiness of generated EEG. Here, for the first time, we investigate how two key factors, input noise type (white vs. 1/f) and event-related potential (ERP)-based penalties, affect the plausibility of EEG trials generation, using a Wasserstein GAN with gradient penalty (WGAN-GP). ERP penalties were introduced as loss terms to penalize excessive high-frequency oscillations, thereby suppressing them during training. We evaluated physiological plausibility through visual inspection of ERP waveforms and power spectra (PS), statistical comparisons of EEG features (bandpower, entropy, P3 amplitude and latency, Petrosian fractal dimension, and Hjorth complexity), dimensional similarity by principal component analysis, t-distributed stochastic neighbor embedding, kernel density estimation, and decomposition of periodic and aperiodic components. Results show that WGAN-GP models using 1/f noise input preserved spectral characteristics better than white noise models, which introduced high-frequency oscillations. ERP-based penalties reduced these oscillations in white noise models, especially the 0.5-5Hz bandpass-filtered ERP penalty, improving ERP waveforms and PS. However, ERP penalties with 1/f noise models sometimes disrupted the PS. In summary, using white noise and a 0.5-5Hz passband penalty best reproduced ERP waveforms and PS, while using 1/f noise and a 0.5-3Hz passband penalty achieved the most physiologically plausible feature distributions. There is a trade-off between learning time and frequency domain features. Input noise and ERP penalties must be aligned with the intended application.

Mineral prospectivity mapping (MPM) is a powerful approach for identifying mineralization zones with high potential for economically viable mineral deposits. This study proposes a hybrid framework combining a Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP), a Convolutional Neural Network (CNN) and a Fuzzy-Kernel Extreme Learning Machine (FKELM) to address the challenges of imbalanced and uncertain datasets in mineral exploration. The approach was applied to the G.R. Halli gold prospect, in the Chitradurga Schist Belt, Western Dharwar Craton, India, using nine geochemical pathfinder elements. WGAN-GP generated high-quality negative samples, balancing the dataset and reducing overfitting. Compared with Support Vector Machines, Gradient Boosting, and a baseline CNN, FKELM (AUC = 0.976, accuracy = 92%) and WGAN-GP + CNN (AUC = 0.973, accuracy = 91%) showed superior performance and produced geologically coherent prospectivity maps. Promising gold targets were delineated, closely aligned with known mineralized zones and geochemical anomalies. This hybrid framework provides a robust, cost-effective, and scalable MPM solution for structurally controlled geological tracts, insufficient data terrains, and integration with additional geoscience datasets for other complex mineral systems.

Automated classification of human gait, a critical indicator of neuromuscular health, is often hindered by the dependence of supervised machine learning on extensive labeled pathological datasets, which are scarce and difficult to obtain. This paper explores the feasibility of a paradigm towards unsupervised learning, proposing a framework based on a Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP). The WGAN-GP is trained exclusively on healthy gait patterns from a single young subject's shankmounted inertial measurement unit (IMU) to build a model of healthy movement. The framework utilizes a reconstruction-based anomaly detection strategy, where abnormalities are quantified by the magnitude of the error when attempting to reconstruct a new gait cycle from the learned healthy model. Evaluated using real-world data from healthy subjects (both young and old) and for individuals with Parkinson's disease, the model showed strong performance, achieving an area under the curve (AUC) of 0.96. Notably, the framework also demonstrated sensitivity to non-pathological, age-related variations in gait. This feasibility study demonstrates the ability of the proposed WGAN-GP-based unsupervised detection method as a data-efficient and generalizable alternative. Consequently, it paves the way for future validation on larger clinical datasets to characterize mobility impairments in various disorders and age groups.

Generative adversarial networks for overlapped and imbalanced problems in impact damage classification

This study presents a novel approach using Wasserstein Generative Adversarial Networks with Gradient Penalty (WGAN-GP) to generate synthetic electroencephalography (EEG) and electrocardiogram (ECG) waveforms. The synthetic EEG data represent concentration and relaxation mental states, while the synthetic ECG data correspond to normal and abnormal states. By addressing the challenges of limited biophysical data, including privacy concerns and restricted volunteer availability, our model generates realistic synthetic waveforms learned from real data. Combining real and synthetic datasets improved classification accuracy from 92% to 98.45%, highlighting the benefits of dataset augmentation for machine learning performance. The WGAN-GP model achieved 96.84% classification accuracy for synthetic EEG data representing relaxation states and optimal accuracy for concentration states when classified using a fusion of convolutional neural networks (CNNs). A 50% combination of synthetic and real EEG data yielded the highest accuracy of 98.48%. For EEG signals, the real dataset consisted of 60-s recordings across four channels (TP9, AF7, AF8, and TP10) from four individuals, providing approximately 15,000 data points per subject per state. For ECG signals, the dataset contained 1200 real samples, each comprising 140 data points, representing normal and abnormal states. WGAN-GP outperformed a basic generative adversarial network (GAN) in generating reliable synthetic data. For ECG data, a support vector machine (SVM) classifier achieved an accuracy of 98% with real data and 95.8% with synthetic data. Synthetic ECG data improved the random forest (RF) classifier’s accuracy from 97% with real data alone to 98.40% when combined with synthetic data. Statistical significance was assessed using the Wilcoxon signed-rank test, demonstrating the robustness of the WGAN-GP model. Techniques such as discrete wavelet transform, downsampling, and upsampling were employed to enhance data quality. This method shows significant potential in addressing biophysical data scarcity and advancing applications in assistive technologies, human-robot interaction, and mental health monitoring, among other medical applications.

Platelets play a crucial role in diagnosing and detecting various diseases, influencing the progression of conditions and guiding treatment options. Accurate identification and classification of platelets are essential for these purposes. The present study aims to create a synthetic database of platelet images using Generative Adversarial Networks (GANs) and validate its effectiveness by comparing it with datasets of increasing sizes generated through traditional augmentation techniques. Starting from an initial dataset of 71 platelet images, the dataset was expanded to 141 images (Level 1) using random oversampling and basic transformations and further to 1463 images (Level 2) through extensive augmentation (rotation, shear, zoom). Additionally, a synthetic dataset of 300 images was generated using a Wasserstein GAN with Gradient Penalty (WGAN-GP). Eight pre-trained deep learning models (DenseNet121, DenseNet169, DenseNet201, VGG16, VGG19, InceptionV3, InceptionResNetV2, and AlexNet) and two custom CNNs were evaluated across these datasets. Performance was measured using accuracy, precision, recall, and F1-score. On the extensively augmented dataset (Level 2), InceptionV3 and InceptionResNetV2 reached 99% accuracy and 99% precision/recall/F1-score, while DenseNet201 closely followed, with 98% accuracy, precision, recall and F1-score. GAN-augmented data further improved DenseNet’s performance, demonstrating the potential of GAN-generated images in enhancing platelet classification, especially where data are limited. These findings highlight the benefits of combining traditional and GAN-based augmentation techniques to improve classification performance in medical imaging tasks.

Adversarial unpaired disentanglement network (AUDNet): Precise analysis of severely overlapping CS2/SO2 signals in UV-DOAS.

Generative adversarial networks (GAN) are currently a hotly debated research topic in the field of machine vision; however, they possess various shortcomings that cannot be overlooked, such as unstable generated samples, collapsed modes, and slow convergence. The present paper combines the advantages of the super-resolution GAN model (SRGAN) and Wasserstein GAN with gradient penalty (WGAN-GP) and proposes a hybrid model, known as super-resolution Wasserstein generative adversarial network with gradient penalty (SRWGAN-GP). The generator of SRWGAN-GP utilizes the residuals block and Wasserstein distance to increase the resolution of the generated samples and solve the shortcomings of the unstable GAN training. Besides, the discriminator of SRWGAN-GP consists of a local discriminator and a global discriminator to enhance image recognition capacity. Finally, the discriminator is extracted for image recognition. The results of comparative experiments indicate that this method can effectively enhance the accuracy of image recognition.