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The most common causes of spine fractures, or vertebral column fractures (VCF), are traumas like falls, injuries from sports, or accidents. CT scans are affordable and effective at detecting VCF types in an accurate manner. VCF type identification in cervical, thoracic, and lumbar (C3-L5) regions is limited and sensitive to inter-observer variability. To solve this problem, this work introduces an autonomous approach for identifying VCF type by developing a novel ensemble model of Vision Transformers (ViT) and best-performing deep learning (DL) models. It assists orthopaedicians in easy and early identification of VCF types. The performance of numerous fine-tuned DL architectures, including VGG16, ResNet50, and DenseNet121, was investigated, and an ensemble classification model was developed to identify the best-performing combination of DL models. A ViT model is also trained to identify VCF. Later, the best-performing DL models and ViT were fused by weighted average technique for type identification. To overcome data limitations, an extended Deep Convolutional Generative Adversarial Network (DCGAN) and Progressive Growing Generative Adversarial Network (PGGAN) were developed. The VGG16-ResNet50-ViT ensemble model outperformed all ensemble models and got an accuracy of 89.98%. Extended DCGAN and PGGAN augmentation increased the accuracy of type identification to 90.28% and 93.68%, respectively. This demonstrates efficacy of PGGANs in augmenting VCF images. The study emphasizes the distinctive contributions of the ResNet50, VGG16, and ViT models in feature extraction, generalization, and global shape-based pattern capturing in VCF type identification. CT scans collected from a tertiary care hospital are used to validate these models.

Conventional radar systems are often unable to produce highly accurate results for target classification and identification via linear frequency modulation (LFM) signals. The potential of artificial intelligence, particularly deep learning, has been applied in various fields, which promotes utilizing them in the context of target classification in radar systems. However, to train deep learning models for this task, large datasets of LFM radar signals are required, which are practically difficult to obtain due to the time, effort, and involved high cost. Therefore, the presented work spots the light on utilizing the recent one-dimensional generative adversarial network (GAN) and Wasserstein GAN (WGAN) models to synthesize a large time-series LFM signal dataset from a reference smaller one. Moreover, the work fairly judges the generated LFM signals realistic via a decent qualitative and quantitative analysis, unlike other studies which rely solely on qualitative evaluation by human observers. The proposed study outcome reveals the WGAN’s efficiency in synthesizing high-quality LFM signals while reducing the training time and resource requirements.

There have been major advances in the field of data science in the last few decades, and these have been utilized for different engineering disciplines and applications. Artificial intelligence (AI), machine learning (ML) and deep learning (DL) algorithms have been utilized for civil structural health Monitoring (SHM) especially for damage detection applications using sensor data. Although ML and DL methods show superior learning skills for complex data structures, they require plenty of data for training. However, in SHM, data collection from civil structures can be expensive and time taking; particularly getting useful data (damage associated data) can be challenging. The objective of this study is to address the data scarcity problem for damage detection applications. This paper employs 1-D Wasserstein Deep Convolutional Generative Adversarial Networks using Gradient Penalty (1-D WDCGAN-GP) for synthetic labelled acceleration data generation. Then, the generated data is augmented with varying ratios for the training data set of a 1-D deep convolutional neural network (1-D DCNN) for damage detection application. The damage detection results show that the 1-D WDCGAN-GP can be successfully utilized to tackle data scarcity in vibration-based damage detection applications of civil structures.

Image generation is a hot topic in the field of machine learning and computer vision. As a representative of its algorithm, the Generative Adversarial Network (GAN) has the problem of mode collapse in practice. The proposed Dual Discriminator Weighted Mixture Generative Adversarial Network (D2WMGAN) approach can cope with this problem. On the one hand, the D2WMGAN uses the mixed distribution of multiple generators to approximate the real distribution, in order to prevent the extreme situation that multiple generators learn the same distribution and generate the same class of samples, with a classifier to play games with generators to make different generators learn different distributions. On the other hand, the objective function of D2WMGAN weights the Kullback–Leibler (KL) divergence and the reverse KL divergence, and uses their complementary characteristics to improve the quality and diversity of samples from the generators. Then, the theoretical conditional optimality of the D2WMGAN is proved theoretically, which shows that multiple generators can learn the real data distribution in the case of the optimal discriminator and classifier. Finally, extensive experiments are conducted on a large amount of synthetic data and real-world large-scale datasets (such as, CIFAR-10 and MNIST), and the commonly used GAN evaluation indicators (Wasserstein distance, JS divergence, Inception score, and Frechet Inception Distance) are introduced for comparative analysis. Experimental results show that the proposed D2WMGAN approach can better learn multiple mode data, generate rich realistic samples, and effectively solve the problem of mode collapse.

Generative adversarial network (GAN) is one of the most promising methods for unsupervised learning in recent years. GAN works via adversarial training concept and has shown excellent performance in the fields image synthesis, image super-resolution, video generation, image translation, etc. Compared with classical algorithms, quantum algorithms have their unique advantages in dealing with complex tasks, quantum machine learning (QML) is one of the most promising quantum algorithms with the rapid development of quantum technology. Specifically, Quantum generative adversarial network (QGAN) has shown the potential exponential quantum speedups in terms of performance. Meanwhile, QGAN also exhibits some problems, such as barren plateaus, unstable gradient, model collapse, absent complete scientific evaluation system, etc. How to improve the theory of QGAN and apply it that have attracted some researcher. In this paper, we comprehensively and deeply review recently proposed GAN and QAGN models and their applications, and we discuss the existing problems and future research trends of QGAN.

As an emerging field that aims to bridge the gap between human activities and computing systems, human-centered computing (HCC) in cloud, edge, fog has had a huge impact on the artificial intelligence algorithms. The quantum generative adversarial network (QGAN) is considered to be one of the quantum machine learning algorithms with great application prospects, which also should be improved to conform to the human-centered paradigm. The generation process of QGAN is relatively random and the generated model does not conform to the human-centered concept, so it is not quite suitable for real scenarios. In order to solve these problems, a hybrid quantum-classical conditional generative adversarial network (QCGAN) algorithm is proposed, which is a knowledge-driven human–computer interaction computing mode that can be implemented in cloud. The purposes of stabilizing the generation process and realizing the interaction between human and computing process are achieved by inputting artificial conditional information in the generator and discriminator. The generator uses the parameterized quantum circuit with an all-to-all connected topology, which facilitates the tuning of network parameters during the training process. The discriminator uses the classical neural network, which effectively avoids the “input bottleneck” of quantum machine learning. Finally, the BAS training set is selected to conduct experiment on the quantum cloud computing platform. The result shows that the QCGAN algorithm can effectively converge to the Nash equilibrium point after training and perform human-centered classification generation tasks.

In a complex industrial environment, it is difficult to obtain hot rolled strip steel surface defect images. Moreover, there is a lack of effective identification methods. In response to this, this paper implements accurate classification of strip steel surface defects based on generative adversarial network and attention mechanism. Firstly, a novel WGAN model is proposed to generate new surface defect images from random noises. By expanding the number of samples from 1360 to 3773, the generated images can be further used for training classification algorithm. Secondly, a Multi-SE-ResNet34 model integrating attention mechanism is proposed to identify defects. The accuracy rate on the test set is 99.20%, which is 6.71%, 4.56%, 1.88%, 0.54% and 1.34% higher than AlexNet, VGG16, ShuffleNet v2 1×, ResNet34, and ResNet50, respectively. Finally, a visual comparison of the features extracted by different models using Grad-CAM reveals that the proposed model is more calibrated for feature extraction. Therefore, it can be concluded that the proposed methods provide a significant reference for data augmentation and classification of strip steel surface defects.

Infertility has emerged as a significant health issue impacting individuals’ lives. In prior investigations, image classification has been applied to identify morphologic abnormalities associated with infertility issues. However, the limited data availability has impeded high performance. In the field of image augmentation techniques, particularly concerning generative adversarial networks (GANs), an alternative approach can encounter a significant issue known as mode collapse. This phenomenon arises when the generator consistently produces a restricted set of identical or highly similar images, which may negatively affect the overall performance and accuracy of the model. Consequently, the aim of this study is to mitigate mode collapse by employing loss‐based ensemble GAN framework, formulated based on the integration of two distinct GAN models. In addition, a comprehensive analysis is carried out using an expanded approach involving three GAN models in conjunction with a spatial augmentation technique. The Shifted Window Transformer model achieves 95.37% accuracy on the HuSHeM dataset, outperforming other classification models. This finding shows enhanced accuracy relative to earlier studies using the identical dataset.

A significant role is played by medical images in diagnosing diseases and planning the course of treatment. Noise can potentially degrade the quality of images which can lead to misdiagnosis. One of the oldest challenges in computer vision for restoring images that have been corrupted is image denoising. Generative adversarial networks (GANs) are among the most extensively used deep learning methods for various computer vision tasks. Utilizing an innovative quantum adversarial denoising architecture, denoised image samples are produced from a noisy distribution. In this paper, the authors employ an architecture of quantum deep convolutional generative adversarial networks (QDCGAN) for denoising medical images. The architecture of the DCGAN (deep convolutional generative adversarial networks) is augmented with a quantum computing layer to enhance the performance through quantum-generated inputs. The research is performed on the BraTS dataset via the TensorFlow Quantum platform. The study demonstrates that QDCGAN outperforms traditional methods. The proposed method achieves a better PSNR (peak signal-to-noise ratio) and SSIM (structural similarity index measure) value. The study underscores its effectiveness in improving the diagnostic quality of medical images with an 3.4% enhancement in SSIM and 7.35% in PSNR over existing methods, thereby offering tangible benefits for healthcare practitioners and patients alike.

The widespread use of automated decision processes in many areas of our society raises serious ethical issues with respect to the fairness of the process and the possible resulting discrimination. To solve this issue, we propose a novel adversarial training approach called GANSan for learning a sanitizer whose objective is to prevent the possibility of any discrimination (i.e., direct and indirect) based on a sensitive attribute by removing the attribute itself as well as the existing correlations with the remaining attributes. Our method GANSan is partially inspired by the powerful framework of generative adversarial networks (in particular Cycle-GANs), which offers a flexible way to learn a distribution empirically or to translate between two different distributions. In contrast to prior work, one of the strengths of our approach is that the sanitization is performed in the same space as the original data by only modifying the other attributes as little as possible, thus preserving the interpretability of the sanitized data. Consequently, once the sanitizer is trained, it can be applied to new data locally by an individual on their profile before releasing it. Finally, experiments on real datasets demonstrate the effectiveness of the approach as well as the achievable trade-off between fairness and utility.