Climate change, a global problem, is now impacting human life and nature in many sectors. To reduce the severity of the impacts, General Circulation Models (GCMs) are used for predicting future climate. The prediction output of a GCM requires a downscaling process to increase its spatial resolution before projecting on local area. In order to downscale the output to a higher spatial resolution (less than 20 km), a statistical method is typically considered. By using this method, a large amount of historical observed data, up to 30 years, is essential. In some areas, the historical data is insufficient. Hence, the statistical method may not be suitable to downscale the output on the area which lacks the required data. Hence, this research aims to explore a high spatial resolution downscaling process that is able to provide a valid and high accuracy result in the Thailand area with a limitation in quantity of historical data. In this research, a combination of an interpolation and machine learning model called `IDW-ANN’ is proposed for downscaling the data under the condition. The prediction of temperature and precipitation from a GCM, IPSL-CM6A-LR in CMIP6 is downscaled by the proposed combination into a 1 km spatial resolution. After the performance evaluation, the IDW-ANN downscaling process showed good accuracy (RMSE, MAE, and R²) and valid downscaled results. The future climate situation in Thailand, in particular temperature, and precipitation level, in 2040 and 2100 under two scenarios of SSPs (SSP1-2.6 and SSP3-7.0) is also projected at 1 km resolution by using IDW-ANN. From the projection, the level of precipitation sums, and temperature seem to be increased in most of Thailand in all future scenarios.

Arrhythmia, which is characterized by irregular heart rhythms, can lead to life-threatening conditions by disrupting the circulatory system. Thus, early arrhythmia detection is crucial for timely and appropriate patient treatment. Machine learning models have been developed to classify arrhythmia using electrocardiogram (ECG) data, which effectively capture the patterns associated with different abnormalities and achieve high classification performance. However, these models face challenges in terms of input coverage and robustness against data imbalance issues. Typically, existing methods employ a single cardiac cycle as the input, possibly overlooking the intervals between cycles, potentially resulting in the loss of critical temporal information. In addition, limited samples for rare arrhythmia types restrict the involved model’s ability to effectively learn, frequently resulting in low classification accuracy. Furthermore, the classification performance of existing methods on unseen data is not satisfactory owing to insufficient generalizability. To address these limitations, this research proposes a convolutional neural network (CNN) model for arrhythmia classification that incorporates two specialized modules. First, the proposed model utilizes images of three consecutive cardiac cycles as the input to expand the learning scope. Second, we implement a focal loss (FL) function during model training to prioritize minority classes. The experimental results demonstrate that the proposed model outperforms existing methods without requiring data preprocessing. The integration of multicycle ECG images and the FL function substantially enhances the model’s ability to capture ECG patterns, particularly for minority classes. In addition, our model exhibits satisfactory classification performance on unseen data from new patients. These findings suggest that the proposed model is a promising tool for practical application in arrhythmia classification tasks.

The ear is an effective biometric feature for personal identification. Although numerous studies have attempted personal identification using frontal-view images of the ear, only a few have attempted personal identification using multi-angle-captured ear images. To expand the extant literature and facilitate future biometric authentication technologies, we explore the feasibility of personal identification using multidirectionally captured ear images and attempted to identify the direction-independent feature points that contribute to the identification process. First, we construct a convolutional neural network model for personal identification based on multi-angle-captured ear images, after which we conduct identification experiments. We obtained high identification accuracies, exceeding 0.980 for all the evaluation metrics, confirming the feasibility of personal identification using multi-angle-captured ear images. Further, we performed Gradient-weighted Class Activation Mapping to visualize the feature points that contribute to the identification process, identifying the helix region of the ear as a key feature point. Notably, the contribution ratios for ear images in which the inner ear was visible and not visible are 97.5% and 56.0%, respectively. These findings indicate the feasibility of implementing personal identification using multi-angle-captured ear images for applications, such as surveillance and access control systems. These findings will promote the development of future biometric authentication technologies.

Cardiovascular diseases (CVDs) are the leading cause of global mortality, highlighting the need for efficient and reliable automated electrocardiogram (ECG) analysis. While deep learning methods have achieved high classification accuracy, their large model sizes and computational demands limit clinical deployment. This study proposes a lightweight multiscale framework, the FPN–ECA–ELM, integrating a feature pyramid network (FPN), efficient channel attention (ECA), and an extreme learning machine (ELM) for automated CVD classification using standard 12-lead ECG images. The FPN enables efficient multiscale feature fusion by combining feature maps from different network depths to generate high-resolution semantically enriched representations. ECA performs channel-wise feature recalibration, and the ELM replaces conventional fully connected layers, further reducing computational cost. Under an inter-patient evaluation protocol, the model achieved 87.08% accuracy and 87.07% weighted F1-score for binary classification, and 78.06% accuracy and 78.34% weighted F1-score for five-class classification, demonstrating competitive classification performance. The model contains only 1.73 million parameters, with a size of 6.59 MB, requiring 0.21 GFLOPs, and achieves an inference time of 0.69 ms per sample. These results illustrate a favorable balance between accuracy and efficiency, supporting practical deployment in resource-constrained clinical and edge-computing environments.