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Background: Health-related patient-reported outcomes (HR-PROs) are crucial for assessing the quality of life among individuals experiencing low back pain. However, manual data entry from paper forms, while convenient for patients, imposes a considerable tallying burden on collectors. In this study, we developed a deep learning (DL) model capable of automatically reading these paper forms., Methods: We employed the Japanese Orthopaedic Association Back Pain Evaluation Questionnaire, a globally recognized assessment tool for low back pain. The questionnaire comprised 25 low back pain-related multiple-choice questions and three pain-related visual analog scales (VASs). We collected 1305 forms from an academic medical center as the training set, and 483 forms from a community medical center as the test set. The performance of our DL model for multiple-choice questions was evaluated using accuracy as a categorical classification task. The performance for VASs was evaluated using the correlation coefficient and absolute error as regression tasks., Result: In external validation, the mean accuracy of the categorical questions was 0.997. When outputs for categorical questions with low probability (threshold: 0.9996) were excluded, the accuracy reached 1.000 for the remaining 65 % of questions. Regarding the VASs, the average of the correlation coefficients was 0.989, with the mean absolute error being 0.25., Conclusion: Our DL model demonstrated remarkable accuracy and correlation coefficients when automatic reading paper-based HR-PROs during external validation., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

In this paper, a scalable end-to-end tree boosting system called XGBoost has been applied for predicting the magnitude of an earthquake from the early part of earthquake waveform data. This model uses the features extracted from the early P wave phase of the records as an input. The model's effectiveness has been verified by using data on earthquakes occurring in the Eurasian plate of Japan Islands from 1996 to 2021. Feature engineering has given 29 new features identified from the early P wave phase of the record, which show a high correlation with the magnitude of an earthquake. The comparison of predicted and actual magnitude shows that a trained XGboost model, which uses a single input record for magnitude prediction, gives an average prediction error of 0.004 ± 0.57 for earthquakes in the test dataset. In contrast, the average prediction error of –1.1 ± 0.80 and –0.65 ± 0.69 has been obtained for the magnitude estimated from conventional τc and Pd methods using the same test dataset. It is further seen that the average predicted magnitude of a single earthquake of magnitude 4.5 and 6.1 (MJMA) obtained by using multiple nearfield records using XGBoost model is 4.58 ± 0.33 and 6.32 ± 0.29, which is close to the actual magnitude of the earthquake. The results presented in this paper clearly show that the structured data can be effectively used by complex machine learning or deep learning models to predict earthquake magnitude from single or multiple records. [ABSTRACT FROM AUTHOR]

Japan is one of the countries with the most frequent natural disasters in the world and is faced with various threats of natural disasters every year, which significantly impact Japan's social economy and people's lives. A great deal of information about disasters is preserved in Japanese literature. Interpreting and organizing this information help us to analyze the regularity of disasters and understand the preventive measures of ancient people. This paper aims to organize, analyze and save disaster data by collecting various information about disasters. Then a disaster spatiotemporal database system is constructed by using deep learning, image processing, and database technology. The system consists of two parts, namely, the disaster database and disaster website. The disaster database is the core of the whole system, which saves the disaster data after organizing and summarizing. The database collects disaster information from various sources, including key information such as disaster type, time, location, scale, and scope of impact. The Disaster website is the system's user interface, providing an interactive platform for users to access and use disaster data easily. The website has many functions, including search, visual display, disaster information query, etc. We also make a detailed analysis of the collected data, aiming to predict the causes and occurrence rules of disasters so as to achieve the target of disaster prediction. [ABSTRACT FROM AUTHOR]

Accurate traffic volume prediction plays a crucial role in urban traffic control by relieving congestion through improved regulation of traffic volume. Network‐level traffic volume prediction and detector failure have rarely been considered in the literature. This paper proposes a framework based on long short‐term memory and the multilayer perceptron that can predict network‐level traffic volumes even with detector failure. A profile model learns the profile of the detector's signature (traffic pattern). Detectors with similar profiles are considered to have similar traffic patterns and are grouped into a cluster. Failed detectors can obtain reference information from similar detectors in the same cluster without additional information. A predictive model is developed for each cluster. The proposed method is validated using Japan Road Traffic Information Center data for three cities. The computational results indicate that the proposed method performs well both on typical days and atypical days (the COVID‐19 lockdown period and the 2021 Tokyo Olympics). Further, it considers detector reliability: the increase in mean absolute error is less than 1 veh/5 min when the probability of detector failure increases to 20%. [ABSTRACT FROM AUTHOR]

Rear‐lamp tracking at night is a research topic in night vision that is essential for Advanced Driver Assistance Systems (ADAS). Most current computer vision‐based methods address this problem using color features because rear lamps are lit during night driving. However, such methods are sufficiently robust in complex environments owing to the lack of feature diversity. On the other hand, as there is no off‐the‐shelf dataset, the application of deep learning to nighttime rear‐lamp tracking has been sparsely explored. To tackle the above issues, in this paper, we propose hierarchical rear‐lamp tracking at nighttime (H‐RTN) and create a novel dataset for rear‐lamp tracking. Specifically, the H‐RTN consists of a rough detection hierarchy (R‐hierarchy), an accurate detection hierarchy (A‐hierarchy), and an optimization hierarchy (O‐hierarchy). The R‐hierarchy determines the region of interest (ROI) containing the target rear lamps, the A‐hierarchy samples the rear‐lamp candidates in the ROI, and the O‐hierarchy selects the best pair of candidates as the final location of the target rear lamps. The experimental results show that H‐RTN outperforms the alternative existing methods. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Obstructive sleep apnea (OSA) is a common sleep disorder, and polysomnography (PSG) is the gold standard for diagnosing OSA. However, patients often have to wait long before receiving the costly PSG test in a hospital. Hence, low‐cost and easy‐to‐use portable screening tools were developed for predicting OSA. Based on our recent study, this paper presents a feature extraction method with multiple receptive fields applied to the input electrocardiography (ECG) signals of a model to improve its performance in OSA severity classification. This work also employs unsegmented ECG signals as input to keep all the advantages from our original approach. The proposed model achieves an overall accuracy of 59.49% for four‐level OSA severity classification, giving an improvement of approximately 2% compared to our original work. The effectiveness of the proposed method is demonstrated. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

In recent years, the automatic analysis of natural environment images acquired with unmanned aerial vehicles (UAV) has rapidly gained popularity. UAVs are specially important in mountainous forests where access is difficult and large areas need to be surveyed. In Zao mountains in northeastern Japan, regenerated fir saplings are competing with sub-alpine vegetation shrubs after a severe fir tree mortality caused by bark beetle infestation. A detailed survey of vegetation distribution is key to improve our understanding of species succession and the influence of climate change in that process. To that end, we evaluated the suitability of deep-learning-based automatic image classification of UAV images in order to map sub-alpine vegetation succession in large areas and the potential of fir regeneration. In order to assess the contribution of this technology in this research field, we first conducted an observer study to assess the difficulty for humans of the task of classifying vegetation from images. Afterwards, we compared the observers' accuracy to four state-of-the art deep learning networks for automatic image classification. The best observer accuracy of 55% demonstrates the limitations of species classification using only images. Furthermore, a detailed analysis of the sources of error showed that even though humans could differentiate between deciduous and evergreen species with an accuracy of 96%, identifying the correct species within each group proved much more challenging. In contrast, deep learning networks achieved accuracy values in the range of 70–80% for species classification, clearly demonstrating capabilities beyond human experts. Our experiments also indicated that the performance of these networks was significantly influenced by the similarity between the datasets used to fine-tune them and evaluate them. This fact highlights the importance of building publicly available images databases to further improve the results. Nevertheless, the results presented in this paper show that the analysis of UAV-acquired with deep learning networks can usher in a new type of large-scale study, spanning tenths or even hundreds of hectares with high spatial resolution (of a few cms per pixel), providing the ability to assess challenging vegetation dynamics problems that go beyond the ability of conventional fieldwork methodologies. • UAV Shrub image classification. • Deep Learning vs human expert comparison. • Focus on Practical use. [ABSTRACT FROM AUTHOR]

Wildlife damage to agriculture is serious in Japan; therefore, it is important to understand changes in wildlife population sizes. Although several studies have been conducted to detect wildlife from drone images, behavioral changes (such as wildlife escaping when a drone approaches) have been confirmed. To date, the use of visible and near-infrared images has been limited to the daytime because many large mammals, such as sika deer (Cervus nippon), are crepuscular. However, it is difficult to detect wildlife in the thermal images of urban areas that are not open and contain various heat spots. To address this issue, a method was developed in a previous study to detect moving wildlife using pairs of time-difference thermal images. However, the user's accuracy was low. In the current study, two methods are proposed for extracting moving wildlife using pairs of airborne thermal images and deep learning models. The first method was to judge grid areas with wildlife using a deep learning classification model. The second method detected each wildlife species using a deep learning object detection model. The proposed methods were then applied to pairs of airborne thermal images. The classification test accuracies of "with deer" and "without deer" were >85% and >95%, respectively. The average precision of detection, precision, and recall were >85%. This indicates that the proposed methods are practically accurate for monitoring changes in wildlife populations and can reduce the person-hours required to monitor a large number of thermal remote-sensing images. Therefore, efforts should be made to put these materials to practical use. [ABSTRACT FROM AUTHOR]

Following recent advancements in medical laboratory technology, the analysis of high-resolution renal pathological images has become increasingly important in the diagnosis and prognosis prediction of chronic nephritis. In particular, deep learning has been widely applied to computer-aided diagnosis, with an increasing number of models being used for the analysis of renal pathological images. The diversity of renal pathological images and the imbalance between data acquisition and annotation have placed a significant burden on pathologists trying to perform reliable and timely analysis. Transfer learning based on contrastive pretraining is emerging as a viable solution to this dilemma. By incorporating unlabeled positive pretraining images and a small number of labeled target images, a transfer learning model is proposed for high-accuracy renal pathological image classification tasks. The pretraining dataset used in this study includes 5000 mouse kidney pathological images from the Open TG-GATEs pathological image dataset (produced by the Toxicogenomics Informatics Project of the National Institutes of Biomedical Innovation, Health, and Nutrition in Japan). The transfer training dataset comprises 313 human immunoglobulin A (IgA) chronic nephritis images collected at Fukushima Medical University Hospital. The self-supervised contrastive learning algorithm "Bootstrap Your Own Latent" was adopted for pretraining a residual-network (ResNet)-50 backbone network to extract glomerulus feature expressions from the mouse kidney pathological images. The self-supervised pretrained weights were then used for transfer training on the labeled images of human IgA chronic nephritis pathology, culminating in a binary classification model for supervised learning. In four cross-validation experiments, the proposed model achieved an average classification accuracy of 92.2%, surpassing the 86.8% accuracy of the original RenNet-50 model. In conclusion, this approach successfully applied transfer learning through mouse renal pathological images to achieve high classification performance with human IgA renal pathological images. [ABSTRACT FROM AUTHOR]

Understanding traditional culture is important. Various methods are used to achieve better cross-cultural understanding, and certain researchers have studied human behavior. However, behavior does not always represent a culture. Therefore, our study aims to understand Japanese greeting culture by classifying it through machine learning. Following are our study contributions. (1) The first study to analyze cultural differences in greeting gestures based on the politeness level of Japanese people by classifying them. (2) Classify Japanese greeting gestures eshaku, keirei, saikeirei, and waving hand. (3) Analyze the performance results of machine and deep learning. Our study noted that bowing and waving were the behaviors that could symbolize the culture in Japan. In conclusion, first, this is the first study to analyze the eshaku, keirei, saikeirei, and waving hand greeting gestures. Second, this study complements several human activity recognition studies that have been conducted but do not focus on behavior representing a culture. Third, according to our analysis, by using a small dataset, SVM and CNN methods provide better results than k-nearest neighbors (k-NN) with Euclidean distance, k-NN with DTW, logistic regression and LightGBM in classifying greeting gestures eshaku, keirei, saikeirei, and waving hand. In the future, we will investigate other behaviors from different perspectives using another method to understand cultural differences. [ABSTRACT FROM AUTHOR]