The photoplethysmography (PPG) signal contains various information that is related to CVD (cardiovascular disease). The remote PPG (rPPG) is a method that can measure a PPG signal using a face image taken with a camera, without a PPG device. Deep learning-based rPPG methods can be classified into three main categories. First, there is a 3D CNN approach that uses a facial image video as input, which focuses on the spatio-temporal changes in the facial video. The second approach is a method that uses a spatio-temporal map (STMap), and the video image is pre-processed using the point where it is easier to analyze changes in blood flow in time order. The last approach uses a preprocessing model with a dichromatic reflection model. This study proposed the concept of an axis projection network (APNET) that complements the drawbacks, in which the 3D CNN method requires significant memory; the STMap method requires a preprocessing method; and the dyschromatic reflection model (DRM) method does not learn long-term temporal characteristics. We also showed that the proposed APNET effectively reduced the network memory size, and that the low-frequency signal was observed in the inferred PPG signal, suggesting that it can provide meaningful results to the study when developing the rPPG algorithm.

Existing CNN-based super resolution methods have low emphasis on high-frequency features, resulting in poor performance for contours and textures. To solve this problem, this paper proposes single image super resolution using an attention mechanism that emphasizes high-frequency features and a feature extraction process with different depths. In order to emphasize the high-frequency features of the channel and space, it is composed of CSBlock that combines channel attention and spatial attention. Attention block using 10 CSBlocks was used for high-frequency feature extraction. In order to extract various features with different degrees of feature emphasis from insufficient low-resolution features, features were extracted from structures connected with different numbers of attention blocks. The extracted features were expanded through sub-pixel convolution to create super resolution images, and learning was performed through $L_1$ loss. Compared to the existing deep learning method, it showed improved results in several high-frequency features such as small object outlines and line patterns. In PSNR and SSIM, it showed about 11% to 26% improvement over the existing Bicubic interpolation and about 1 to 2% improvement over VDSR and EDSR.

Image quality plays a critical role in medical image analysis, significantly impacting diagnostic outcomes. Sharp and detailed images are essential for accurate diagnoses, but acquiring high-resolution medical images often demands sophisticated and costly equipment. To address this challenge, this study proposes a convolutional neural network (CNN)-based super-resolution architecture, utilizing a melanoma dataset to enhance image resolution through deep learning techniques. The proposed model incorporates a convolutional self-attention block that combines channel and spatial attention to emphasize important image features. Channel attention uses global average pooling and fully connected layers to enhance high-frequency features within channels. Meanwhile, spatial attention applies a single-channel convolution to emphasize high-frequency features in the spatial domain. By integrating various attention blocks, feature extraction is optimized and further expanded through subpixel convolution to produce high-quality super-resolution images. The model uses L1 loss to generate realistic and smooth outputs, outperforming existing deep learning methods in capturing contours and textures. Evaluations with the ISIC 2020 dataset—containing 33126 training and 10982 test images for skin lesion analysis—showed a 1–2% improvement in peak signal-to-noise ratio (PSNR) compared to very deep super-resolution (VDSR) and enhanced deep super-resolution (EDSR) architectures.

Automatic detection of deceptive intent in negotiation dialogue remains difficult because deceptive utterances are rare, context-dependent, and pragmatically subtle. This study develops a deployment-oriented evaluation pipeline for negotiation analytics using the Diplomacy corpus and compares sparse, dense, and imbalance-aware neural models under a unified protocol. The pipeline integrates context-window benchmarking, validation-based threshold selection, 10-seed robustness analysis, model-agnostic explanation case studies, and controlled perturbation stress tests. Across binary speaker-intention and receiver-perception tasks, contextualized inputs consistently outperform isolated utterances, confirming that deception-related interpretation is inherently sequential. The sparse term frequency–inverse document frequency (TF-IDF) model remains the strongest and most efficient overall benchmark, whereas stronger imbalance-aware neural baselines can improve minority deceptive-instance sensitivity at substantially higher computational cost. Error analysis further shows that socially mediated deception-quadrant prediction is markedly harder than direct binary in-tent prediction, with many failures collapsing toward majority straightforward cases. Controlled perturbation tests show that sparse modeling is especially stable under light-weight surface corruption, while neural robustness remains architecture-dependent. The main contribution is therefore a practical decision framework for selecting among efficient sparse monitoring, dense baselines, and minority-sensitive neural detection under operational constraints.

Predicting near-future monetization in virtual livestreaming remains methodologically challenging because paid-support events are sparse, temporally dependent, and vulnerable to leakage under inappropriate evaluation designs. This study develops a leakage-guarded, window-based machine-learning framework for predicting next-window Superchat occurrence from VTuber live-chat dynamics. Public VTuber live-chat and Superchat logs were reconstructed into non-overlapping five-minute windows, and features were organized into audience activity, member composition, message intensity, donation-state information, and short-horizon dynamic groups. To reduce optimistic bias, the primary evaluation used video-level grouped splitting and compared a strict setting that excluded direct current-window donation-state variables with an extended donation-state-aware setting. HistGradientBoosting achieved the strongest performance. In the strict setting, it reached PR-AUC = 0.899, ROC-AUC = 0.920, F1 = 0.822, and Brier score = 0.171, while the extended setting produced only modest additional gains. Additional zero-chat sensitivity, repeated grouped split, channel-level robustness, graph-proxy baseline, feature-ablation, and calibration analyses supported the stability and interpretability of the framework. The results suggest that next-window Superchat occurrence can be predicted from participation breadth, chat activity, message intensity, and temporally shifted behavioral dynamics under leakage-aware evaluation.