Due to the extremely irregular nonlinear degradation of images obtained in real underwater environments, it is difficult for existing underwater image enhancement methods to stably restore degraded underwater images, thus making it challenging to improve the efficiency of marine work. We propose a hybrid algorithm model for underwater image color restoration and texture enhancement, termed HA-Net. First, we introduce a dynamic color correction algorithm based on depth estimation to restore degraded images and mitigate color attenuation in underwater images by calculating the depth of targets and backgrounds. Then, we propose a multi-scale U-Net to enhance the network’s feature extraction capability and introduce a parallel attention module to capture image spatial information, thereby improving the model’s accuracy in recognizing deep semantics such as fine texture. Finally, we propose a global information compensation algorithm to enhance the output image’s integrity and boost the network’s learning ability. Experimental results on synthetic standard data sets and real data demonstrate that our method produces images with clear texture and bright colors, outperforming other algorithms in both subjective and objective evaluations, making it more suitable for real marine environments.

Underwater autonomous driving devices, such as autonomous underwater vehicles (AUVs), rely on visual sensors, but visual images tend to produce color aberrations and a high turbidity due to the scattering and absorption of underwater light. To address these issues, we propose the Dense Residual Generative Adversarial Network (DRGAN) for underwater image enhancement. Firstly, we adopt a multi-scale feature extraction module to obtain a range of information and increase the receptive field. Secondly, a dense residual block is proposed, to realize the interaction of image features and ensure stable connections in the feature information. Multiple dense residual modules are connected from beginning to end to form a cyclic dense residual network, producing a clear image. Finally, the stability of the network is improved via adjustment to the training with multiple loss functions. Experiments were conducted using the RUIE and Underwater ImageNet datasets. The experimental results show that our proposed DRGAN can remove high turbidity from underwater images and achieve color equalization better than other methods.

Pneumonia has long been a significant concern in global public health. With the advancement of convolutional neural networks (CNNs), new technological methods have emerged to address this challenge. However, the application of CNNs to pneumonia diagnosis still faces several critical issues. First, the datasets used for training models often suffer from insufficient sample sizes and imbalanced class distributions, leading to reduced classification performance. Second, although CNNs can automatically extract features and make decisions from complex image data, their interpretability is relatively poor, limiting their widespread use in clinical diagnosis to some extent. To address these issues, a novel weighted cross-entropy loss function is proposed, which calculates weights via an inverse proportion exponential function to handle data imbalance more efficiently. Additionally, we employ a transfer learning approach that combines pretrained CNN model parameter fine-tuning to improve classification performance. Finally, we introduce the gradient-weighted class activation mapping method to enhance the interpretability of the model’s decisions by visualizing the image regions of focus. The experimental results indicate that our proposed approach significantly enhances CNN performance in pneumonia diagnosis tasks. Among the four selected models, the accuracy rates improved to over 90%, and visualized results were provided.

With the rapid development of the global economy, air pollution, which restricts sustainable development and threatens human health, has become an important focus of environmental governance worldwide. The modeling and reliable prediction of air quality remain substantial challenges because uncertainties residing in emissions data are unknown and the dynamic processes are not well understood. A number of machine learning approaches have been used to predict air quality to help alleviate air pollution, since accurate air quality estimation may result in significant social-economic development. From this perspective, a novel air quality estimation approach is proposed, which consists of two components: newly-designed dendritic neural regression (DNR) and customized scale-free network-based differential evolution (SFDE). The DNR can adaptively utilize spatio-temporal information to capture the nonlinear correlation between observations and air pollutant concentrations. Since the landscape of the weight space in DNR is vast and multimodal, SFDE is used as the optimization algorithm due to its powerful search ability. Extensive experimental results demonstrate that the proposed approach can provide stable and reliable performances in the estimation of both PM_2.5 and PM₁₀ concentrations, being significantly better than several commonly-used machine learning algorithms, such as support vector regression and long short-term memory.

Underwater imagery exhibits markedly more severe visual degradation than their terrestrial counterparts, manifesting as pronounced color aberration, diminished contrast and luminosity, and spatially non-uniform haze. To surmount these challenges, we propose the graph-aware framework for underwater image enhancement (GA-UIE), integrating specialized modules for color correction and texture restoration, a unified framework that explicitly utilizes the intrinsic graph information of underwater images to achieve high-fidelity color restoration and texture enhancement. The proposed algorithm is architected in three synergistic stages: (1) graph feature generation, which distills color and texture graph feature priors from the underwater image; (2) graph-aware enhancement, performing joint color restoration and texture sharpening under explicit graph priors; and (3) graph-aware fusion, harmoniously aggregating the graph-aware color and texture joint representations to yield the final visually coherent output. Comprehensive quantitative evaluations reveal that the output from our novel framework achieves the significant scores across a broad spectrum of metrics, including PSNR, SSIM, LPIPS, UCIQE, and UIQM on the UIEB and U45 datasets. These results decisively exceed those of all existing benchmark techniques, thereby validating the method’s exceptional efficacy in the enhancement of underwater imagery.

Assessing building energy consumption is of paramount significance in sustainability and energy efficiency (EE) studies. The development of an accurate EE prediction model is pivotal for optimizing energy resources and facilitating effective building planning. Traditional physical modeling approaches are encumbered by high complexity and protracted modeling cycles. In this paper, we introduce a novel evolutionary dendritic neural regression (EDNR) model tailored to forecasting residential building EE. Acknowledging the vast landscape and complexity of the EDNR weight space, coupled with the inherent susceptibility of traditional optimization algorithms to local optima, we propose a complex network-guided strategy-based differential evolution algorithm for training the EDNR model. This strategy adeptly strikes a balance between exploration and exploitation during the search process, significantly enhancing the predictive and generalization capacities of EDNR. To our knowledge, this study represents the inaugural application of dendritic neural regression in real-world prediction scenarios. Extensive experimental findings demonstrate the efficacy of EDNR in accurately predicting building EE with commendable performance. Furthermore, the results of two nonparametric statistical tests affirm the validity and stability of EDNR. Consequently, our proposed methodology exhibits high potential and competitiveness in machine learning applications within the energy domain.