Pneumonia is an inflammation of the lungs caused by pathogens or autoimmune diseases, with about 450 million patients worldwide each year. Chest X–ray analysis is the most common radiographic method used to diagnose pneumonia, and advances in deep learning have led to the availability of high-dimensional image, audio, and video data. Deep learning is being applied in many fields, including the medical field, where numerous researchers have attempted to use it for computer-aided diagnosis. Recently, with the appearance of generative adversarial networks, it is possible to generate plausible and realistic images. In this paper, we combined cycle Generative Adversarial Networks (GANs) and conditional GANs, which are extensions of GANs, to convert the domains between images and generate images of the intermediate domains. We conducted the domain change between pneumonia images and normal images by applying our framework to a Chest X–ray image dataset. We evaluated the domain change by redefining the ResNet152-based classifier, and we generated the pneumonia progression images by inputting a value between two domains in the conditional vector of the generator. We then evaluated the ability of the trained GANs by comparing the original dataset with the generated dataset, and generated plausible progression images of pneumonia.

Cardiovascular diseases are the leading cause of death globally. The ECG is the most commonly used tool for diagnosing cardiovascular diseases, and, recently, there are a number of attempts to use deep learning to analyze ECG. In this study, we propose a method for performing multi-label classification on standard ECG (12-lead with duration of 10 s) data. We used the ResNet model that can perform residual learning as a base model for classification in this work, and we tried to improve performance through SE-ResNet, which added squeeze and excitation blocks on the plain ResNet. As a result of the experiment, it was possible to induce overall performance improvement through squeeze and excitation blocks. In addition, the random k-labelsets (RAKEL) algorithm was applied to improve the performance in multi-label classification problems. As a result, the model that applied soft voting through the RAKEL algorithm to SE-ResNet-34 represented the best performance, and the average performances according to the number of label divisions k were achieved 0.99%, 88.49%, 92.43%, 90.54%, and 93.40% in exact match, accuracy, F1-score, precision, and recall, respectively.

Rapid and massive advances in deep learning have made it possible to address with issues with computer vision. In recent years, one type of generative model has emerged, generative adversarial networks (GAN), that enables creating realistic and plausible images. GAN allows for building competition models based on game theory that allows for modeling data probability distributions. Since the introduction of GAN, researchers have conducted many follow–up studies to apply and improve these models. In this article, using a global optimization technique called a genetic algorithm, we suggest the methodology restoring latent vector of pre–trained GAN and measure its performance as hyper–parameters and fitness functions; specifically, we utilize the mating and mutation rate as hyper–parameters of the genetic algorithms and use the mean squared error and the structural similarity as the fitness functions and evaluate their impact. We obtain image reconstruction results through experiment using the MNIST, Fashion–MNIST, Cifar10, and CelebA dataset, and we compare our method with the gradient descent method. We discuss limitations of these experiments and future research topics.

Portfolio optimization requires navigating complex trade-offs among conflicting financial objectives, including return maximization, risk mitigation, and drawdown control. Reinforcement Learning (RL) provides a dynamic framework for asset allocation, but manually specifying reward functions that generalize across diverse market regimes remains a significant challenge. Inverse Reinforcement Learning (IRL) offers a principled alternative by recovering reward signals from expert behavior. However, when demonstrations originate from multiple experts with divergent investment objectives, the collective expert distribution becomes inherently multi-modal. Multiple distinct actions may be simultaneously rational for a given market state, and conventional IRL frameworks are not designed to accommodate this complexity. We propose Multi-Expert Inverse Reinforcement Learning (ME-IRL), a framework that employs an Energy-Based Model (EBM) as a reward network to model this multi-modal collective expert distribution. Rather than specifying objectives a priori or aggregating expert behaviors at the action-level, ME-IRL performs reward-level inference that encodes interactions among conflicting financial objectives. This is achieved through a Transformer-based architecture utilizing cross-attention between state and action representations to explicitly model cross-sectional asset dependencies. Empirical evaluations across ETF and stock portfolios consistently demonstrate that ME-IRL out-performs traditional financial models and single- and multi-objective RL baselines in both return and risk-adjusted metrics across diverse market conditions.