Current deep learning-based medical image classification methods face challenges in effectively learning correct classification boundaries when dealing with imbalanced data. Traditional data augmentation methods often suffer from insufficient diversity, leading to limited performance improvement. Based on this, this paper proposes a Boundary and Diversity-aware Generative Adversarial Network (BDGAN) for data augmentation, focusing on class boundaries and intra-class diversity. First, to enhance the diversity of generated samples, we design a multi-generator GAN architecture, where each generator learns and generates different data patterns. Second, to further generate more diverse and higher-quality samples, we introduce mutual exclusion loss and Hausdorff loss. Finally, for downstream classification tasks, we design a sampling method based on One-Class SVM (OCS), enabling the GAN to focus more on training and generating boundary samples during the training process. Experimental results on two real-world medical image datasets demonstrate that the proposed method can generate more diverse and higher-quality augmented samples, effectively improving the performance of downstream classification tasks.

Addressing the persistent challenge of learning from imbalanced datasets is crucial in advancing machine learning applications. Standard machine learning algorithms typically assume that the input data is balanced, and they often struggle to effectively learn the distribution of minority class data when dealing with imbalanced data. To address this, our study designed an improved Generative Adversarial Networks (GANs) model, named MDGAN, for tabular sample synthesis to augment samples and balance the data distribution. MDGAN employs a multi-generator and multi-discriminator structure to capture non-connected subspace manifolds, thereby better fitting the complete data distribution. To enhance the diversity among the multiple generators, an exclusive loss among generators was designed, ensuring that each generator produces data of different modalities. Additionally, a contrastive loss was introduced to ensure that the generated samples better fit the minority class distribution and are separated from the majority class distribution, preventing blurred classification boundaries. Qualitative and quantitative tests were conducted on 25 real datasets, and the experimental results indicate that MDGAN outperforms traditional classical models and current advanced oversampling models.

Infrared small target detection (IRSTD) based on deep learning has received extensive research and application. However, deep learning models require a large amount of data to perform well, and the collection and standardization of infrared small target data is challenging, limiting the applicability of such models. To address this issue, this study proposes a data augmentation scheme for infrared small targets based on Generative Adversarial Networks (GANs). The proposed method is a two-step approach: the first step is the generation of clean backgrounds, and the second is the adaptive fusion of targets and backgrounds. In the background generation stage, we first use the Fast Marching Method (FMM) to fill background targets and obtain clean backgrounds. Then, we design a multi-generator and multi-discriminator GAN model (MGD-GAN) to generate high-quality and diverse background images. In the adaptive target-background fusion stage, we propose a dual-discriminator GAN network (FusionGAN), which allows the target mask to be adaptively fused with the background pixels. By combining real targets with generated backgrounds, new infrared small target images are generated, achieving the goal of data augmentation. Experiments conducted across three different scenarios demonstrate that the proposed data augmentation scheme effectively enhances the performance of both traditional and advanced detection models.

In medical data processing, the issue of data imbalance often leads to biased learning outcomes in machine learning methods. Generative Adversarial Networks (GAN) can generate realistic and diverse augmented samples to balance sample distribution. However, the problems of intra-class imbalance and sparse isolated samples often affect the performance of GAN models. Based on this, this paper proposes a multi-generator GAN model architecture, named Diversity Multi-Generator GAN (DM-GAN). This model combines self-attention mechanisms and diversity loss functions to improve the quality and diversity of generated samples. Specifically, we designed multiple independently trained generators to capture more sample patterns and introduced self-attention modules in both the generator and discriminator to enhance the model’s ability to capture image details. Additionally, we proposed an improved generator loss function that combines mode-seeking loss and mutual exclusion loss. By encouraging the generation of different samples and reducing sample overlap, this approach enhances the diversity and coverage of generated samples. Experimental results on two real-world medical image datasets demonstrate that DM-GAN shows significant advantages in handling intra-class imbalance and isolated sample issues. Compared to existing methods, our approach not only achieves better results in terms of the quality and diversity of generated samples but also effectively improves the performance of downstream classification tasks.

Internet of Things (IoT) devices are large in number, widely distributed, weak in protection ability, and vulnerable to various malicious attacks. Intrusion detection technology can provide good protection for network equipment. However, the normal traffic and abnormal traffic in the network are usually imbalanced. Imbalanced samples will seriously affect the performance of machine learning detection algorithm. Therefore, this paper proposes an intrusion detection method based on data augmentation, namely TMG-IDS. We name the proposed data augmentation model TMG-GAN, which is a data augmentation method based on generative adversarial networks (GAN). First, TMG-GAN has a multi-generator structure, which can be used to generate different types of attack data simultaneously. Second, we increase the classifier structure, which can optimize the generator and discriminator more efficiently based on the classification loss. Third, we calculate the cosine similarity between the generated samples and the original samples and other types of generated samples as a generator loss, which can further improve the quality of generated samples and reduce the class overlap area between the distributions of various generated samples. We conduct extensive experiments on two intrusion detection datasets, CICIDS2017 and UNSW-NB15. The experimental results show that compared with the advanced oversampling algorithm and the latest intrusion detection algorithm, the proposed TMG-IDS method has a good detection effect under the three indicators of Precision, Recall and F1-score.

With the rapid development of the cloud computing, personal health record (PHR) has attracted great attention of many researchers all over the world recently. However, PHR, which is often outsourced to be stored at a third party, has many security and efficiency issues. Therefore, the study of secure and efficient Personal Health Record Scheme to protect users' privacy in PHR files is of great significance. In this paper, we present a secure and efficient Personal Health Record scheme called SE-PHR. In the SE-PHR scheme, we divide the users into personal domain (PSD) and public domain (PUD) logically. In the PSD, the Key-Aggregate Encryption called KAE is exploited. For the users of PUD, we use outsource-able multi-authority attribute-based encryption (MA-ABE) to largely eliminate the overhead for users and support efficient attribute revocation without updating the user's private key. Our scheme also presents a new algorithm which enables dynamic modification of access policies. Function and performance testing results show the security and efficiency of the proposed SE-PHR.