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Continual development necessitates innovation. One must discover seeds of innovation and then concentrate resources on these seeds. To do so, one must recognize technology trends and then adopt and execute appropriate innovation strategies. This study used advanced change point detection method to investigate leading indicators that represent changes in technology trends. We examine patents, papers, and newspaper articles in Japan and US for 55 technologies. Results suggest that patents can be more appropriate as leading indicators than either papers or newspapers. This result can contribute to appropriate innovation strategies for planning and updating, and can provide tools that are useful to decision-makers. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive analysis of the social media posts of prefectural governors in Japan during the COVID-19 pandemic. It investigates the correlation between social media activity levels, governors' characteristics, and engagement metrics. To predict citizen engagement of a specific tweet, machine learning models (MLMs) are trained using three feature sets. The first set includes variables representing profile- and tweet-related features. The second set incorporates word embeddings from three popular models, while the third set combines the first set with one of the embeddings. Additionally, seven classifiers are employed. The best-performing model utilizes the first feature set with FastText embedding and the XGBoost classifier. This study aims to collect governors' COVID-19-related tweets, analyze engagement metrics, investigate correlations with governors' characteristics, examine tweet-related features, and train MLMs for prediction. This paper's main contributions are twofold. Firstly, it offers an analysis of social media engagement by prefectural governors during the COVID-19 pandemic, shedding light on their communication strategies and citizen engagement outcomes. Secondly, it explores the effectiveness of MLMs and word embeddings in predicting tweet engagement, providing practical implications for policymakers in crisis communication. The findings emphasize the importance of social media engagement for effective governance and provide insights into factors influencing citizen engagement. [ABSTRACT FROM AUTHOR]

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]

With the continuous development of intelligent refrigerators, users are having higher and higher requirements for smart management of refrigerator contents. When using traditional machine learning algorithms to deal with refrigerator ingredients classification, problems such as slow recognition speed and low accuracy would often arise. At the same time, the classification accuracy is easily affected by the location of the content. Therefore, this paper uses continuous asymmetric convolutions and depthwise separable convolutions to improve the original Inception model and builds a classification prediction network based on the improved Inception model. The steps to applying the said model is as follows: First, we put the Radio Frequency Identification (RFID) tag on the food in the refrigerator, and then we collect the antenna data RSSI received by each tag through the reader. Finally, the RSSI is preprocessed and entered into the classification network to determine the location of the label. The proposed classification network has the advantages of possessing a simple structure, easy expansion of network depth, and fewer training parameters. The experimental results show that compared with other machine learning algorithms and pure convolution networks, the lightweight network model based on the improved Inception structure demonstrates great superiority. Compared with the original Inception structure, the enhanced network model is more lightweight. The number of parameters is reduced by 50%, and the amount of float‐point operations is reduced by 34%. Meanwhile, the accuracy of the lightweight model has been improved by 0.6%. Thus the improved model has better prediction performance in the classification of refrigerator content. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Power line insulator defect identification usually suffers from complex backgrounds, small defect target sizes, and inconspicuous defect features. Traditional identification methods based on image processing, image analysis, and pattern classification have many limitations in solving the aforementioned problems. In recent decades, deep learning classification methods have gradually replaced traditional identification methods in the task of power line insulator defect identification. To accurately identify the locations of insulator defects, this paper proposes an insulator defect detection algorithm using an improved lightweight YOLOv4‐tiny network (ILYTN). First, the CBL (Conv‐BN‐LeakyReLU) modules of the backbone network are replaced with MobileViT blocks to enhance the feature extraction capability of the backbone network. Second, coordinate attention (CA) is introduced in the feature fusion part to improve the network's ability to focus on the location of defects. Finally, an EIOU (efficient intersection over union) loss function, instead of the original CIOU loss function, is used so that the convergence speed of the network can be improved. To verify the effectiveness of the proposed algorithm in this paper, the mViT‐yolo algorithm is compared with the mainstream Faster‐RCNN algorithm, SSD algorithm, YOLOv3 algorithm, and YOLOv4‐tiny algorithm. The experimental results show that the algorithm proposed in this paper outperforms all the above algorithms in terms of detection accuracy. Compared with the traditional YOLOv4‐tiny algorithm, the proposed algorithm increases the mean average precision (mAP) by 1.64%, the average precision (AP) of missing insulator defects by 0.32%, and the average precision (AP) of broken insulator defects by 4.96%. © 2023 The Authors. IEEJ Transactions on Electrical and Electronic Engineering published by Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Aiming at the problem of unnecessary waste of power resources in the power system caused by the uncertainty of wind farm output, and then affecting the economics of the system. This paper proposes a power system scheduling model based on wind power output forecasting errors. In the forecasting stage, in view of the defect that the Fruit Fly Optimization Algorithm (FOA) is easy to fall into the local optimal value, an Adaptive Mutation Fruit Fly Optimization Algorithm (AMFOA) based on the flavor concentration variance is proposed. And then, the parameters of Extreme Learning Machine (ELM) are optimized by AMFOA to predict the wind power output. In the scheduling phase, a limit scenario is established based on the obtained prediction error to reduce the fluctuation of the wind farm output. When modeling, the influence of the prediction error is considered in the power balance equation, and it is constructed to maximize the probability of its establishment. And then, incorporate it into the objective function. Based on this, a scheduling model is established and solved by AMFOA. Finally, an example is used to verify the accuracy of the proposed forecasting model and the economics of the scheduling model. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Due to the compact and complex internal structure of 110 kV three‐phase enclosed GIS disconnector, the voltage distribution between the contact breaks of each phase has an influence on the insulation performance of the GIS disconnector, and its internal insulation design is a difficult point to be solved. To solve this problem, this paper firstly establishes a three‐dimensional model of three‐phase enclosed GIS disconnecting switch and conducts a research on the distribution of electric field under a specific working condition, it is found that the outer corner of the static contact, the inner corner of the static arc contact, the inner corner of the outer end of the moving contact and the inner corner of the moving contact are the internal electric field concentration of the three‐phase enclosed GIS disconnector. Then, in order to further reduce the electric field strength and improve the insulation margin and performance, this paper proposes a method based on the combination of Extreme Learning Machine (ELM) and Multi‐objective Grasshopper Optimization Algorithm (MOGOA) to optimize the structural design of three‐phase enclosed GIS disconnector, and a ELM model is established, which takes the internal fillet radius of stationary arcing contact, the internal fillet radius of moving contact, the internal fillet radius of external end of dynamic and static contact, the radius of the outer corner of the two static side contacts and the opening distance of the grounding contact as the inputs, and takes the maximum electric field strength of the two static side contacts and the moving side contacts as outputs; Finally, the structural parameters are optimized by MOGOA algorithm. Compared to the pre‐optimization period, the electric field strengths of the bus bar static‐side contact, dynamic contact and static side contact of the B‐phase after optimization decreased from 20.2, 9.28 and 17.5 to 11.4, 6.65 and 11.5 kV/mm, respectively. The electric field strengths of the dynamic and static side contacts are significantly reduced, which is beneficial for improving the insulation performance of the three‐phase enclosed GIS disconnector. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Background: Bibliometric analysis is a mature method for quantitative evaluation of academic productivity. In view of the rapid development of research in the field of systemic lupus erythematosus (SLE) in the past decade, we used bibliometric methods to comprehensively analyze the literature in the field of SLE from 2013 to 2022. Methods: The relevant literature in the field of SLE from 2013 to 2022 was screened in the Web of Science Core Collection database. After obtaining and sorting out the data, CiteSpace and VOSviewer software were used to visualize the relevant data, and SPSS software was used for scientific statistics. Results: A total of 18,450 publications were included in this study. The number of articles published over the past 10 years has generally shown an upward trend, while Altmetric attention scores have also shown a clear upward trend in general and in most countries. Citation analysis and Altmetric analysis can mutually prove and supplement the influence of papers. The USA, China, Japan, Italy, and the UK are the most productive countries, but China and Japan are significantly inferior to other countries in terms of research influence. Four of the top ten authors are at the center of the collaboration network. LUPUS is the most contributing journal. The theme of systemic lupus erythematosus research mainly focuses on the pathogenesis, treatment, and management of SLE, and the emerging trend is related research on machine learning and immune cells. Conclusion: This study shows the research status of SLE, clarifies the main contributors in this field, discusses and analyzes the research hotspots and trends in this field, and provides reference for further research in this field to promote the development of SLE research. Key Points • Through bibliometric analysis, Altmetric analysis, and visual analysis, we reveal the global productivity characteristics of SLE-related papers in the past 10 years. • The number of global SLE-related studies has shown a significant increase, indicating that SLE is still a hot topic and deserves further study. • Citation analysis and Altmetric analysis can mutually prove and supplement the influence of papers, and the attention of related literature among non-professional researchers is increasing. • The theme of SLE research mainly focuses on the pathogenesis, treatment, and management of SLE. The emerging trend is machine learning and immune cells, which may provide new strategies for the diagnosis and treatment of SLE in the future. [ABSTRACT FROM AUTHOR]

A cross-region prediction model named SeisEML (an acronym for Seismological Ensemble Machine Learning) has been developed in this paper to predict the peak ground acceleration (PGA) at a given site during an earthquake. The SeisEML model consists of hybridized models, kernel-based algorithms, tree regression algorithms, and regression algorithms. The model ablation study is conducted to examine the performance and the selection of meta-machine learning models in the SeisEML. The training and testing dataset consists of 20852 and 6256 accelerograms recorded by the Kyoshin Network, Japan. The mean absolute error (MAE) and root mean square error (RMSE) have been utilized to compare the predicted peak ground acceleration (PGA) for the test data. The SeisEML model yields approximately half the MAE and RMSE values obtained with conventional attenuation relations. The SeisEML model has been used to compute Japan's iso acceleration contour map of three earthquakes ( M JMA 7.4, 6.6, and 6.1). The qualitative comparison of iso acceleration contours obtained from actual and predicted PGA using SeisEML clearly shows that the model can reliably predict the PGA distribution during an earthquake compared to the regional ground motion prediction equation (GMPE). The cross-region prediction was performed on the datasets of the Iranian earthquakes using SeisEML. The comparison of predicted and observed peak ground acceleration in terms of MAE and RMSE shows that the machine learning model's performance is superior to the regional attenuation relation. The predictions of PGA from the developed ML model indicate that this trained model has the potential for predicting regional and global scenarios with similar tectonic setups. The ML model developed in this paper can considerably enhance the reliability of PGA prediction for seismic hazard mapping of any region and can serve as a reliable substitute for GMPEs. [ABSTRACT FROM AUTHOR]

This paper compares the machine learning algorithm‐based prediction methods for the traction characteristics in linear traction induction motors operating at common working conditions, i.e. different slip and running velocity, with a symmetric or asymmetric secondary. These models provide a method for obtaining the dynamic characteristics in the motor that considers nonlinear effects. First, some analytical results for the prototype machine under different working conditions is calculated. Second, classification and feature extraction of traction characteristics results including thrust, transversal and vertical forces is made according to the different slip, running speed and lateral secondary displacement, and the results set is divided into training sets and test sets. Third, the prediction model established by different machine learning algorithms are analyzed and compared in principle. These algorithms in this paper mainly contain: artificial neural networks (ANNs), linear regression (LR), symbolic regression using GP, k‐Nearest Neighbour (kNN), random forests (RFRs). The machine learning algorithm‐based prediction methods are trained with the training set, and then the verified with the test set. Finally, this paper discusses the most optimal model for predicting traction characteristics. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Decision Intelligence is a methodology that integrates complex systems, machine learning, and decision analysis. It is increasingly interested in finding optimal solutions to the uncertainties noted in machine learning in today's complex AI systems. Furthermore, Decision Intelligence is a new engineering discipline that augments data science with theories from various sciences. Since decisions are made in all kinds of situations, they will become even more critical in a wide range of academic fields in the future. This paper introduces the ideas of proponents of Decision Intelligence, the promotion of digital decisioning toward automation, and trends in Western companies and Japan. Then, using risk management procedures, our examination of methods to ensure safety in the case of automated driving will be described. In addition, I will describe the challenges of each technology that promotes Decision Intelligence. I will also introduce a new synchronous AI that I am currently working on with the inventor of the challenge. This algorithm generation method is based on a metaphysical view of the synchronous nature of life activity. [ABSTRACT FROM AUTHOR]

Falling down is a common symptom of geriatric syndromes, and fractures and intracranial hemorrhages triggered by falling down lead to serious problems and impair life functioning. Moreover, it sometimes leads to a higher risk of death. In Japan in recent years, the number of fatalities from traffic accidents has been declining, whereas the number of fatalities from falls has been leveling off. In 2020, 8851 people died from falls, whereas the number of fatalities from traffic accidents was 2199. The number of fatalities among the elderly due to falls is approximately four times the number of fatalities from traffic accidents. Therefore, in this paper, we propose a system that analyzes the body by using Kinect, an infrared depth sensor for tracking a skeletal model of a user. In this study, the goal is for the predicted fall values from Kinect-measured data and the predicted fall values from motion-capture-measured data to be close to the predicted values, so that this technology can eventually be used in clinical practice. On the basis of information from the skeletal model, the system analyzes element indices such as the center of gravity and body tilt of people in need of nursing care when falling down. Then, it predicts the risk factor for falling down. This information is used for detecting warning signs for falling down in the early stages. Finally, this study will contribute to decreasing number of falls from the bed. [ABSTRACT FROM AUTHOR]

Precise charging time prediction can effectively mitigate the inconvenience to drivers induced by inevitable charging behavior throughout trips. Although the effectiveness of the machine learning (ML) algorithm in predicting future outcomes has been established in a variety of applications (transportation sector), the investigation into electric vehicle (EV) charging time prediction is almost new. This calls for the investigation of the ML algorithm to predict EV charging time. The study developed an EV charging time prediction model based on two years of charging event data collected from 500 EVs in Japan. To predict EV charging time, this paper employed three ML algorithms: extreme learning machine (ELM), feed-forward neural network (FFNN), and support vector regression (SVR). Furthermore, ML algorithms parameters are optimized by a metaheuristic techniques: the gray wolf optimizer (GWO), particle swarm optimizer (PSO), and genetic algorithm (GA) to achieve higher accuracy and robustness. The prediction results reveal that GWO-based ML models yielded better results compared to other models. [ABSTRACT FROM AUTHOR]

Since its beginning in 1992 in Japan, International Symposium on Semiconductor Manufacturing (ISSM) has provided unique opportunities to share the best practices of semiconductor manufacturing technologies for professionals. At the symposiums, semiconductor manufacturing professionals discussed the technologies developed to meet the worldwide requirements for advanced manufacturing. It is becoming crucial to re-examine semiconductor manufacturing in terms of fundamental principles to improve the performance of semiconductor devices. Moreover, utilizing artificial intelligence and machine learning technologies to improve semiconductor manufacturing have become a new challenge. These manufacturing technology challenges are showing the need for drastic revolutionary concept and stronger collaborative efforts to find solutions to the precompetitive challenges. [ABSTRACT FROM AUTHOR]

Cirrhosis is a liver disease resulting from abnormal continuation of fibrosis, and ultrasound imaging is widely used for cirrhosis diagnosis because of its non‐invasiveness. However, due to unclear appearances of cirrhosis on ultrasound images, diagnoses are difficult and individual results possibly differ depending on the physician's experience. Recently, computer‐aided diagnostic systems using image processing and machine learning have been developed to help physicians detect cirrhosis as a 'Second opinion'. Some related studies have focused on a scenario where physicians set ROIs (Region of Interests) manually because selecting reliable ROIs for training a classifier and classification of patients is indispensable. But, the accuracy of such systems depends inherently on the quality of ROIs, and thus the workloads of physicians increase. In this paper, we propose a reliability evaluation method (REM) for each ROI based on its posterior probability and relationship to peripheral ROIs. The assumption of our proposal is that reliable regions of cirrhosis and normal can be observed in certain regions predominantly. We evaluated the effectiveness of the REM and its optimization for practical use. Experimental results showed that our proposed method curated reliable ROIs and improved classification performance in terms of AUC (Area Under the Curve). © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

A research project on chemical AI, called the Molecular Cybernetics Project, was launched in Japan in 2021 with the goal of creating a molecular machine that can learn a type of conditioned reflex through the process of classical conditioning. In this project, we have developed a learning method for the network of such learning molecular machines, which is reported in this paper. First, as a model of a learning molecular machine, we formulate a logic gate that can learn conditioned reflex and introduce the network of the logic gates. Then we derive a key principle for learning, called the flipping principle, by which we present a learning algorithm for the network to realize a desired function. [ABSTRACT FROM AUTHOR]

Federated Learning (FL) is a distributed machine learning paradigm that enables multiple data holders to collaborate on building machine learning models while preserving the privacy of their data. FL can be categorized as horizontal or vertical, depending on the distribution characteristics of the data. Specifically, horizontal FL uses data partitioned in the sample space, whereas vertical FL uses data partitioned in the feature space. Traditional vertical FL methods aim to facilitate collaboration among clients to infer a single global target. However, these methods may be impractical because each client often has a unique target to be inferred. In this paper, we propose a novel vertical FL method, called personalized vertical federated learning (pvFed), which addresses this limitation by allowing each client to perform inferences specific to their individual task. To the best of our knowledge, no existing method currently addresses this limitation. The objective of pvFed is to construct a global model that generates a representation vector to support client inference. The global model, constructed using distillation and dimensionality reduction, takes a sample ID common to all clients as input and outputs a sample‐specific representation vector. Clients utilize the intermediate representation of their own model and the representation vectors output by the global model for inference. Because these vectors are not dependent on client‐specific tasks, clients can repurpose them for any additional tasks. Our experiments, conducted on two distinct data types—image and tabular data sets, under a vertical partitioning where each client had its own specific task, demonstrated the efficacy of vectors generated by the global model in pvFed for client inference. © 2024 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Facial expression is one of the most direct and effective ways to recognize emotions, widely used in human‐computer interaction, affective computing, and other research fields. Expression recognition can be divided into discrete expression classification and continuous dimensional emotion recognition. Most of the existing multi‐dimensional emotional estimation only considers the data under laboratory conditions. In this paper, facial emotion estimation is performed based on real‐world images and combined with the advantages of multi‐task learning and attention mechanism. We improve the multi‐task attention network (MTAN) from two aspects: task and feature. At the aspect of the task, the multi‐task collaborative attention network (MTCAN), which is based on task correlation, is proposed to solve task deviation in multi‐task learning. At the aspect of the feature, based on MTCAN, we came up with MTACN, which used the self‐attention mechanism to measure the importance of each attention module for each specific task. Then, we can capture the local‐to‐global connection in one step and fully exploit the feature within different levels of each task. Experimental results on the AffectNet dataset show that the performance of the model is significantly better than the original network, and the Root‐mean‐square error and consistency correlation coefficient results are superior to other existing models. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Recently, the whole world was hit by COVID‐19 pandemic that led to health emergency everywhere. During the peak of the early waves of the pandemic, medical and healthcare departments were overwhelmed by the number of COVID‐19 cases that exceeds their capacity. Therefore, new rules and techniques are urgently required to help in receiving, filtering and diagnosing patients. One of the decisive steps in the fight against COVID‐19 is the ability to detect patients early enough and selectively put them under special care. Symptoms of this disease can be observed in chest X‐rays. However, it is sometimes difficult and tricky to differentiate "only" pneumonia patients from COVID‐19 patients. Machine‐learning can be very helpful in carrying out this task. In this paper, we tackle the problem of COVID‐19 diagnostics following a data‐centric approach. For this purpose, we construct a diversified dataset of chest X‐ray images from publicly available datasets and by applying data augmentation techniques. Then, we employ a transfer learning approach based on a pre‐trained convolutional neural network (DenseNet‐169) to detect COVID‐19 in chest X‐ray images. In addition to that, we employ Gradient‐weighted Class Activation Mapping (GradCAM) to provide visual inspection and explanation of the predictions made by our deep learning model. The results were evaluated against various metrics such as sensitivity, specificity, Positive Predictive Value (PPV), Negative Predictive Value (NPV) and the confusion matrix. The resulting models has achieved an average detection accuracy close to 98.82%. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Peak ground acceleration (PGA) is a key parameter used in earthquake early warning systems to measure the ground motion strength and initiate emergency protocols at major projects. The traditional P -wave peak displacement-dependent PGA prediction model (Pd-PGA model) tends to underestimate the PGA for large earthquakes because it cannot make full use of the fault continuity rupture information hidden in the time-varying process of ground motion. In this paper, a continuous PGA prediction long short-term memory (LSTM) neural network model is proposed. The model takes eight sequential features of stations that are proxies of the energy and other physical parameters as input and provides the recorded PGA at the station as the target output. A total of 5961 records from 119 earthquakes recorded by the Japanese Strong-Motion Earthquake Network (K-NET) in Japan are used to train the neural network and 3433 records from 73 earthquakes are used as the test set to verify the model's generalization ability. The results show that within the same data set, the residuals of the predicted PGA for the proposed model are smaller than those of the Pd-PGA model and that the problem of PGA underestimation is resolved. The prediction accuracy also improves with increasing sequence length, which indicates that the LSTM neural network learns the rules hidden in the time series. To further verify the model's generalization ability, the model performance is analyzed for an M 7.3 earthquake that was not included in the training or test data sets. The results show that the residuals of the predicted PGA for the event are consistent with those for the test data set, indicating that the model has good generalization ability. [ABSTRACT FROM AUTHOR]

The tide gauge measurements from global navigation satellite system reflectometry (GNSS-R) observables are considered to be a promising alternative to the traditional tide gauges in the present days. In the present paper, we deliver a comparative analysis of tide-gauge (TG) measurements retrieved by quasi-zenith satellite system-reflectometry (QZSS-R) and the legacy TG recordings with additional observables from other constellations viz. GPS-R and GLONASS-R. The signal-to-noise ratio data of QZSS (L1, L2, and L5 signals) retrieved at the P109 site of GNSS Earth Observation Network in Japan (37.815° N; 138.281° E; 44.70 m elevation in ellipsoidal height) during 01 October 2019 to 31 December 2019. The results from QZSS observations at L1, L2, and L5 signals show respective correlation coefficients of 0.8712, 0.6998, and 0.8763 with observed TG measurements whereas the corresponding root means square errors were 4.84 cm, 4.26 cm, and 4.24 cm. The QZSS-R signals revealed almost equivalent precise results to that of GPS-R (L1, L2, and L5 signals) and GLONASS-R (L1 and L2 signals). To reconstruct the tidal variability for QZSS-R measurements, a machine learning technique, i.e., kernel extreme learning machine (KELM) is implemented that is based on variational mode decomposition of the parameters. These KELM reconstructed outcomes from QZSS-R L1, L2, and L5 observables provide the respective correlation coefficients of 0.9252, 0.7895, and 0.9146 with TG measurements. The mean errors between the KELM reconstructed outcomes and observed TG measurements for QZSS-R, GPS-R, and GLONASS-R very often lies close to the zero line, confirming that the KELM-based estimates from GNSS-R observations can provide alternative unbiased estimations to the traditional TG measurement. The proposed method seems to be effective, foreseeing a dense tide gauge estimations with the available QZSS-R along with other GNSS-R observables., (© 2022. The Author(s).)

In this paper, we address the task of segmenting small organs (i.e., the pancreas) from abdominal CT scans. As the target often occupies a relatively small region in the input image, deep neural networks can be easily confused by complex and variable backgrounds. We propose a method that uses a parallel multi‐scale network with an attention mechanism for pancreas segmentation, which can better grasp the balance between the semantic segmentation, classification, and localization tasks. We use a parallel network to connect the feature maps between different bottleneck layers, which contain rich semantic information and complete spatial information. We apply an attention module to enhance the key features of semantic information. Then, we fuse the two modules and apply the fused module as attention information on the feature map to ensure the full fusion between contextual semantic information and spatial information, thereby improving segmentation accuracy. We conduct extensive experiments on the NIH pancreas segmentation data set. In particular, our model achieves a mean coefficient Dice of 86.6. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Shikoku island, southwestern Japan lies in the western Nankai Trough and showcases along-strike segmentation of slow earthquake behavior. Whether the spatial variation of tremor behavior reflects the regional differences in structure/source properties and how much such differences can be recognized by the seismic signals themselves are two questions addressed in this paper. Taking advantage of advanced methods in recognizing and classifying signals using machine learning approaches, we attempt to answer them by conducting signal classification experiments in Shikoku. Based on the tremor catalog from 1 June 2014 to 31 March 2015, the tremors recorded in four different areas were treated as different classes and segmented into 60-s-long signals. The number of tremors in four different areas (A to D, from west to east) reached 15,000, 31,000, 10,000, and 16,000, respectively. To efficiently distinguish between tremors from different areas, we applied a k-nearest neighbor (k-NN) classifier with Fisher's class separability criteria to select the optimal feature subset. The resulting classification performance reached more than 90% at all 12 stations. We further designed a triangle test to select the features that can better represent the differences in source properties between areas. We found that the most efficient features were associated with (1) the number of peaks in the temporal evolution of discrete Fourier transforms and (2) the energy distribution in the autocorrelation function (ACF). To match the difference in behavior revealed by the ACF, the size of the tremor zone, which mainly controls how long the seismic energy lasts in a tremor episode, was determined to be largest in Area B and smallest in Area C. The heterogeneity of the asperities in a tremor zone, which may control how spiky the tremor signals developed over time, was determined to be strong in Areas B and C. Together with previously documented variations in slow earthquake behavior in the same area, we finally propose a conceptual model that provides a better understanding of the regional differences in the tremor source properties in Shikoku, Japan. [ABSTRACT FROM AUTHOR]

Aiming at the problem of calculation speed for distribution transformer on‐line fault diagnosis, and the shortage of on‐line monitoring type and routine test data, an on‐line fault diagnosis model of distribution transformer based on parallel big data stream and transfer learning is established. First, the fault identification feasibility based on the present on‐line monitoring data of distribution transformer is analyzed, and the main indicators of on‐line fault identification are proposed. Second, the on‐line fault identification method based on ARIMA is proposed, on this basis, the on‐line fault identification model of distribution transformer based on big data stream is established, and the distribution transformers that possess hidden fault are selected, which would be diagnosed further. In order to improve the efficiency, the model is completed on Storm platform in parallel form. Then, the distribution transformer fault diagnosis indicator system is construct. In order to achieve fault diagnosis of the above selected distribution transformers, the effective fault information from other distribution transformers is extracted using the transfer learning algorithm TrAdaBoost, which is used as auxiliary data for the fault diagnosis tool training of the distribution transformer to be diagnosed. At the same time, the model is completed on Storm platform to improve the efficiency. Finally, based on the distribution transformer fault data, the distribution transformer fault diagnosis is simulated, and results show that the fault diagnosis is accurate and efficient. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Machine learning (ML) has become a rising sophisticated technological application trend in the electrical industry in recent years. Such innovation provides optional methodologies for many existing applications, such as power and load profile forecasting, reliability evaluation, substation behavior detection and state observation of electrical equipment, and so on. This paper presents a review of various supervised and unsupervised ML techniques and applications for electrical power systems, including generation, transmission, distribution and micro‐grid. The algorithms and applications are mainly summarized from IEEE journals and the interest of this paper shows the roles and developments of most used algorithms and its corresponding extensions and performance in different applications. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

The integration of artificial intelligence into various aspects of daily life is developing at a rapid pace in Japan. Discussions to govern applications of artificial intelligence to the field of social infrastructure are also critical and need to match the rapid pace of development. However, the legal implications and risks of applying artificial intelligence to the management of lifelines such as drinking water supply and wastewater treatment have not yet been fully explored. This paper reviews the existing legislations and ongoing discussions on governance regarding applications of artificial intelligence to water and wastewater management. Based on the review, we discuss the ability of legislative frameworks in Japan to respond to the applications of artificial intelligence, as well as identifying potential gaps and challenges thereof, including access to accurate data, demarcation of rights and responsibilities, risk hedging and risk management, monitoring and evaluation, and handling of intellectual property rights. This paper concludes with key recommendations to national and local governments to support the application of artificial intelligence in the field of water and wastewater. [ABSTRACT FROM AUTHOR]

Heave motion of ships is a complex nonlinear dynamic process and cannot be accurately forecasted using a single prediction model. In this paper, an effective combined forecasting method is proposed to perform ship's heave motion prediction. The proposed method combines back propagation neural network (BPNN), autoregressive model (AR) and extreme learning machine (ELM) through an induced ordered weighted averaging (IOWA) operator. The prediction accuracy is selected as the induced variable and the prediction results are sorted according to prediction accuracy and IOWA operator assigns larger weights to the position with the smallest prediction error. The optimal weights are determined by maximizing the B‐mode relational degree. Experimental results demonstrate its effectiveness of the proposed method. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Object tracking tasks often face problems such as illumination and appearance changes due to various scenarios. To effectively solve such problems, single object tracking algorithms in recent years have improved the stability of the tracker by introducing online learning, reducing tracking drift, and tracking objects stably even with large deformations. However, on the one hand, employing online learning increases computation costs, causing the tracker to run slower. On the other hand, because it learns from the tracking results, the tracking template will only be correctly updated if the object is accurately tracked. Any incorrect update will result in tracking failure. In this paper, we propose a filtering algorithm for online learning sample processing in object tracking to remedy these issues. Our method can select valid training samples and discard unnecessary training samples in object tracking. Numerous experiments demonstrate that our algorithm improves the tracking accuracy, reduces the computation complexity, and improves the running speed and run‐time. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Real estate price prediction takes an important role in the economy that can drive up and down the stock prices and even generate disruptive economic events. Many researchers have tried to understand the pricing mechanism with machine learning techniques such as support vector machine, neural network, random forest, and AdaBoost. The boundary problem, on the other hand, makes the pricing scheme more complicated, and this trend is accelerated especially in the situation of population decline in Japan. In this paper, we discuss how we could approach the boundary problem in real estate prediction. We propose a new comprehensive inference model extending and adapting XGBoost to the domain that has the boundary conditions problem by utilizing the distance between the instances in the domain data set to make the layers of bumpy boundaries smooth for more accurate predictions and robustness against the domain data set. The experiments result showed our proposed method performed well on both hypothetical data sets and actual real estate price data. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Accurate and reliable fault location technology is essential to the stable operation of the modular multilevel converter‐high voltage direct current (MMC‐HVDC) system. Aiming at the difficulty of locating high‐resistance ground faults on MMC‐HVDC transmission lines, this paper proposes a method for fault location of transmission lines based on wavelet transform and deep belief network (DBN). First, the wavelet transform is used to decompose the original single pole ground fault voltage waveform, and then the high‐frequency and low‐frequency components obtained are used as training samples to train different DBN models, the final fault location results are obtained by superimposing the outputs of each model at last. The ±250 kV double‐ended MMC‐HVDC system model is established by using PSCAD/EMTDC, which can simulate the faults of different positions and different transition resistances. In order to verify the fault location performance of the proposed method, it is compared with two machine learning fault location methods. The results show that this method can accurately and reliably locate the single pole‐to‐ground fault of the transmission line with transition resistance of up to 10 000 Ω at low sampling frequency of 20 kHz. © 2020 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

This paper presents a method for creating machine learning models, specifically a gradient boosting model and a random forest model, to forecast real GDP growth. This study focuses on the real GDP growth of Japan and produces forecasts for the years from 2001 to 2018. The forecasts by the International Monetary Fund and Bank of Japan are used as benchmarks. To improve out-of-sample prediction, the cross-validation process, which is designed to choose the optimal hyperparameters, is used. The accuracy of the forecast is measured by mean absolute percentage error and root squared mean error. The results of this paper show that for the 2001–2018 period, the forecasts by the gradient boosting model and random forest model are more accurate than the benchmark forecasts. Between the gradient boosting and random forest models, the gradient boosting model turns out to be more accurate. This study encourages increasing the use of machine learning models in macroeconomic forecasting. [ABSTRACT FROM AUTHOR]

Introduction: Degenerative Cervical Myelopathy (DCM) is a common and disabling condition, with a relatively modest research capacity. In order to accelerate knowledge discovery, the AO Spine RECODE-DCM project has recently established the top priorities for DCM research. Uptake of these priorities within the research community will require their effective dissemination, which can be supported by identifying key opinion leaders (KOLs). In this paper, we aim to identify KOLs using artificial intelligence. We produce and explore a DCM co-authorship network, to characterise researchers' impact within the research field., Methods: Through a bibliometric analysis of 1674 scientific papers in the DCM field, a co-authorship network was created. For each author, statistics about their connections to the co-authorship network (and so the nature of their collaboration) were generated. Using these connectedness statistics, a neural network was used to predict H-Index for each author (as a proxy for research impact). The neural network was retrospectively validated on an unseen author set., Results: DCM research is regionally clustered, with strong collaboration across some international borders (e.g., North America) but not others (e.g., Western Europe). In retrospective validation, the neural network achieves a correlation coefficient of 0.86 (p<0.0001) between the true and predicted H-Index of each author. Thus, author impact can be accurately predicted using only the nature of an author's collaborations., Discussion: Analysis of the neural network shows that the nature of collaboration strongly impacts an author's research visibility, and therefore suitability as a KOL. This also suggests greater collaboration within the DCM field could help to improve both individual research visibility and global synergy., Competing Interests: Have read the journal’s policy and the authors of this manuscript have the following competing interests: BMD is a National Institute for Health Research (NIHR) Clinical Doctoral Research Fellow. This report is independent research arising from a NIHR doctoral research fellowship. The views expressed in this publication are those of the authors and not necessarily those of the NHS, the National Institute for Health Research or the Department of Health and Social Care. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

In this paper, we explore the use of seismicity data on a single-station basis in site response characterization. We train a supervised deep-learning model, SeismAmp, to recognize and separate seismic site response with reference to seismological bedrock (VS  = 3.45 km s−1) in a broad frequency range (0.2–20 Hz) directly from single-station earthquake recordings (features) in Japan. Ground-truth data are homogeneously created using a classical multistation approach—generalized spectral inversion at a total number of 1725 sites. We demonstrate that site response can be reliably separated from single-station seismograms in an end-to-end approach. When SeismAmp is tested at new sites in both Japan (in-domain) and Europe (cross-domain), it achieves the lowest standard deviation among all tested single-station techniques. We also find that horizontal-to-vertical spectral ratio (HVSR) is not the optimal use of single-station recordings. The individual components of each record carry salient information on site response, especially at high frequencies. However, part of the information is lost in HVSR. SeismAmp could lead to improved site-specific earthquake hazard prediction in cases where recordings are available or can be collected at target sites. It is also a convenient tool to remove repeatable site effects from ground motions, which may benefit other applications, for example, improving the retrieval of seismic source parameters. Finally, SeismAmp is trained on data from Japan, future studies could explore transfer learning for practical applications in other regions. [ABSTRACT FROM AUTHOR]

With the increasing availability of large datasets and improvements in prediction algorithms, machine-learning-based techniques, particularly deep learning algorithms, are becoming increasingly popular. However, deep-learning algorithms have not been widely applied to predict container freight rates. In this paper, we compare a long short-term memory (LSTM) method and a seasonal autoregressive integrated moving average (SARIMA) method for forecasting the comprehensive and route-based Shanghai Containerized Freight Index (SCFI). The research findings indicate that the LSTM deep learning models outperformed SARIMA models in most of the datasets. For South America and the east coast of the U.S. routes, LSTM could reduce forecasting errors by as much as 85% compared to SARIMA. The SARIMA models performed better than LSTM in predicting freight movements on the west and east Japan routes. The study contributes to the literature in four ways. First, it presents insights for improving forecasting accuracy. Second, it helps relevant parties understand the trends of container freight markets for wiser decision-making. Third, it helps relevant stakeholders understand overall container shipping market trends. Lastly, it can help hedge against the volatility of freight rates. [ABSTRACT FROM AUTHOR]

Index tracking is one of the most popular passive strategy in portfolio management. However, due to some practical constrains, a full replication is difficult to obtain. Many mathematical models have failed to generate good results for partial replicated portfolios, but in the last years a data driven approach began to take shape. This paper proposes three heuristic methods for both selection and allocation of the most informative stocks in an index tracking problem, respectively XGBoost, Random Forest and LASSO with stability selection. Among those, latest deep autoencoders have also been tested. All selected algorithms have outperformed the benchmarks in terms of tracking error. The empirical study has been conducted on one of the biggest financial indices in terms of number of components in three different countries, respectively Russell 1000 for the USA, FTSE 350 for the UK, and Nikkei 225 for Japan. [ABSTRACT FROM AUTHOR]

Credit scoring model (CSM) is a risk management tool that assesses the credit worthiness of a customer borrower by estimating her probability of default based on historical data. Traditionally CSM is built by logit model or decision tree algorithm in financial companies, and in recent studies CSM has been integrated with machine learning algorithms such as random forest and gradient boosting to process a number of complex attributes of customer borrowers. On the other hand, CSM has been facing a critical challenge ‐ the domain adaptation of customer borrowers. For domain adaptation problem, transfer learning techniques are generally utilized, however, it is quite difficult to execute precise predictions for unknown domain datasets in CSM because the distributions of labels could be different depending on the characteristics of domains. Therefore, there is no appropriate transfer learning method to solve domain adaptation problem in credit scoring. In this paper we propose a comprehensive transfer learning framework using Bayesian network to extract useful knowledge based on probability distributions to predict probability of default of customer borrowers more precisely than existing machine learning and transfer learning methods. Experimental results showed the proposed method performed over the existing machine learning and transfer learning methods for accuracy of predictions. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

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Although measuring the ideal points of news media is essential for testing political communication theories based on spatial theory, prior methods of estimating ideal points of media outlets have various shortcomings, including high cost in terms of time and human resources and low applicability to different countries. We propose that unsupervised machine learning techniques for text data, specifically the combination of a text scaling method and latent topic modeling, can be applied to estimate ideal points of media outlets. We applied our proposed methods to editorial texts of ten national and regional newspapers in Japan, where prior approaches are not applicable because newspapers have never officially endorsed particular parties or candidates, and because high-quality training data for supervised learning are not available. Our two studies, one of which analyzed editorials on a single typically ideological topic while the other investigated all editorials published by the target papers in one year, confirmed the popular view of Japanese newspapers' ideological slant, which validates the effectiveness of our proposed approach. We also illustrate that our methods allow scholars to investigate which issues are closely related to the respective ideological positions of media outlets. Furthermore, we use the estimated ideal points of newspapers to show that Japanese people partially tend to read ideologically like-minded newspapers and follow such newspapers' Twitter accounts even though their slant is not explicit. [ABSTRACT FROM AUTHOR]

Since its beginning in 1992 in Japan, International Symposium on Semiconductor Manufacturing (ISSM) has provided unique opportunities to share the best practices of semiconductor manufacturing technologies for professionals. At the symposiums, semiconductor manufacturing professionals discussed the technologies developed to meet the worldwide requirements for advanced manufacturing. It is becoming crucial to re-examine semiconductor manufacturing in terms of fundamental principles to improve the performance of semiconductor devices. Moreover, utilizing artificial intelligence and machine learning technologies to improve semiconductor manufacturing have become a new challenge. These manufacturing technology challenges are showing the need for drastic revolutionary concept and stronger collaborative efforts to find solutions to the precompetitive challenges. [ABSTRACT FROM AUTHOR]

This paper presents a novel bed-leaving sensor system for real-time recognition of bed-leaving behavior patterns. The proposed system comprises five pad sensors installed on a bed, a rail sensor inserted in a safety rail, and a behavior pattern recognizer based on machine learning. The linear characteristic between loads and output was obtained from a load test to evaluate sensor output characteristics. Moreover, the output values change linearly concomitantly with speed to attain the sensor with the equivalent load. We obtained benchmark datasets of continuous and discontinuous behavior patterns from ten subjects. Recognition targets using our sensor prototype and their monitoring system comprise five behavior patterns: sleeping, longitudinal sitting, lateral sitting, terminal sitting, and leaving the bed. We compared machine learning algorithms of five types to recognize five behavior patterns. The experimentally obtained results revealed that the proposed sensor system improved recognition accuracy for both datasets. Moreover, we achieved improved recognition accuracy after integration of learning datasets as a general discriminator.

In this paper, we present an ensemble stacked generalization (ESG) approach for better prediction of electric vehicles (EVs) energy consumption. ESG is a weighted combination of multiple base regression models to one meta-regressor, which enhances the model prediction and decreases the model variance over a single regressor model. For the current study, we develop ESG by combining three individual base machine learning algorithms, i.e., Decision Tree (DT), Random Forest (RF), and K-Nearest Neighbor (KNN), to predict the EVs' energy consumption. Tackling the challenge of predicting EVs' energy consumption, the data were collected from Aichi Prefecture, Japan, combining the digital elevation map with long-term GPS tracking data. EVs energy consumption in terms of energy efficiency (kWh/km) was estimated using several important variables such as average trip speed (km/h), trip distance, nighttime lighting, air conditioner (A/C), heater usage ratio, and road gradient. Several statistical evaluation metrics were used to evaluate the performance of the proposed methods. The prediction results show that ESG is more robust in predicting EVs' energy consumption and outperformed other models by yielding more acceptable values for proposed evaluation metrics. The results further demonstrate that the accuracy of predictive models for EVs energy consumption can be reasonably accomplished by adopting stacking techniques. The finding of this study could provide essential guidance to decision-makers and practitioners for planned development and optimal placing of EV charging infrastructures in urban areas. [ABSTRACT FROM AUTHOR]

The shear wave velocity is among the key parameters that are responsible for damage caused during an earthquake. Determining shear wave velocity is a costly and time-consuming direct geophysical method. In the present paper, a machine learning model has been developed to obtain the subsurface shear wave velocity profile without using direct methods. The bore log information and the subsurface shear wave velocity profile available at various stations of Japan's Kyoshin network (K-NET) have been utilized for training this machine learning model. The parametric correlation study indicates that simple parameters like rock/soil type, the thickness of the layer in the model, and standard penetration test (SPT-N) value directly correlate with the medium's shear wave velocity. A stacked ensemble machine learning model named VelProfES (an acronym for Velocity Profiler using Ensemble machine learning models) has been developed in this paper and has been utilized for effective prediction of the shear wave velocity profile using basic information from borelog data. The dataset used in the training and testing of the machine learning model consists of borelog and shear wave velocity information from 1101 stations. Of 1101 stations, 657, 283, and 71 stations have been utilized for training, testing, and validating the machine learning model. Training, testing, and validation of the developed machine learning model consist of parameters from 12351, 5279, and 1388 velocity layers. The problem of data imbalance based on soil type has been addressed using an additional 10510 layers of synthetic borelog data generated from conditional generative adversarial networks (CTGANs). A feature and model ablation study was conducted for the VelProfES model to provide substantiation for the model and feature selection choices. The predicted shear wave velocity profiles were compared at specific stations, focusing on average velocities at 5, 10, 15, and 20 depths. Further, the predicted values have been compared with the empirical relation of Sil and Haloi (2017) and a trained polynomial model. The machine learning model demonstrates close alignment between predicted and actual values across a broad spectrum of velocities, a contrast not observed in the empirical relation and polynomial model. The results show that the machine learning models and augmented data generated using CTGANs can efficiently minimize the error between actual and predicted subsurface shear wave velocity values. • Describe detailed machine learning strategy for predicting shear wave velocity. • Generative Adversarial Network used to handle unbalanced data problem. • Describes correlation of shear wave velocity with Rock/soil type, layer, and SPT-N. • Comparison of described model with recently developed methods for all regions. • Performance of the machine learning model is predicted. [ABSTRACT FROM AUTHOR]

This paper proposes a micro‐Doppler radar‐based method to classify common pedestrians and pedestrians texting on a smartphone (called a smartphone zombies). We used a micro‐Doppler radar to collect motion data of the gait of participants texting on smartphones while walking, and then generated time‐frequency distribution images (spectrogram images). They were input into a convolutional neural network (CNN) to classify gait patterns. Results using measured data confirmed that the classification accuracy exceeded 90%, validating the effectiveness of the proposed method. Furthermore, by applying feature visualization by Grad‐Cam, we found that the motion of the leg swinging has essential features for the classification of smartphone zombies. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Japan is one of the world's leading super-aging societies, with the highest average life expectancy in the world. 30.3% of the population will be 65 years old or older by 2025, and 13.0% will be 75 years old or older. In addition, the number of heart failure patients is increasing yearly. The number of heart failure patients is increasing by about 10,000 each year and is estimated to reach 1.2 million by 2020 and 1.3 million by 2030. The reason for the rapid increase in the number of heart failure patients in Japan is the aging of the population. Therefore, machine learning to predict atrial fibrillation is employed in this paper. We conducted a trial using risk assessment of cerebral infarction and other factors. As a result of the analysis, we extracted highly influential evaluation indices for each characteristic of atrial fibrillation. [ABSTRACT FROM AUTHOR]

Traffic census data are essential for investigating traffic volumes and vehicle movements, and count mechanization is currently the most efficient way to obtain and utilize advanced traffic census data. However, efforts to mechanize traffic censuses have not progressed significantly in Japan owing to the price of such systems, the size of the necessary equipment, and privacy issues. In this paper, we propose a novel vehicle-counting sensor system that is inexpensive and easy to set up. Our system is based on a piezoelectric vibration sensor that senses road vibrations from passing vehicles. More specifically, the system consists of (i) a vibration sensor device that we designed and prototyped in-house and (ii) a passing vehicle estimation method that determines the number of passing vehicles from the vibration sensor data. Our system, which achieves high accuracy owing to the use of machine learning (ML), makes it possible to conduct traffic censuses by simply placing the sensor on sidewalks next to the road that is being surveyed. To demonstrate the utility of our system, we conducted an experiment in which the vibration sensor was placed on a sidewalk, and then linear discriminant analysis (LDA) was used to estimate the number of vehicles that were traveling on the adjacent road using only the data collected from the vibration sensor. Our results showed that the number of passing vehicles could be estimated with an accuracy of 98.3%. [ABSTRACT FROM AUTHOR]

Introduction and Objective: Despite the importance of machine learning methods application in traditional medicine there is a no systematic literature review and a classification for this field. This is the first comprehensive literature review of the application of data mining methods in traditional medicine., Method: We reviewed 5 database between 2000 to 2017 based on the Kitchenham systematic review methodology. 502 articles were identified and reviewed for their relevance to application of machine learning methods in traditional medicine, 42 selected papers were classified and categorized on four dimension; 1) application domain of data mining techniques in traditional medicine; 2) the data mining methods most frequently used in traditional medicine; 3) main strength and limitation of data mining techniques in traditional medicine; 4) the performance evaluation methods in data mining methods in traditional medicine., Result: The result obtained showed that main application domain of data mining techniques in traditional medicine was related to syndrome differentiation. Bayesian Networks (BNs), Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs) were recognized as being the methods most frequently applied in traditional medicine. Furthermore, each data mining techniques has its own strength and limitations when applied in traditional medicine. Single scaler methods were frequently used for performance evaluation of data mining methods., Conclusion: Machine learning methods have become an important research field in traditional medicine. Our research provides information about this methods by examining the related articles., (Copyright © 2018 Elsevier B.V. All rights reserved.)

Background: Landslide-affecting factors are uncorrelated or non-linearly correlated, limiting the predictive performance of traditional machine learning methods for landslide susceptibility assessment. Deep learning methods can take advantage of the high-level representation and reconstruction of information from landslide-affecting factors. In this paper, a novel deep learning-based algorithm that combine classifiers of both deep learning and machine learning is proposed for landslide susceptibility assessment. A stacked autoencoder (StAE) and a sparse autoencoder (SpAE) both consist of an input layer for raw data, hidden layer for feature extraction, and output layer for classification and prediction. As a study case, Oda City and Gotsu City in Shimane Prefecture, southwestern Japan, were used for susceptibility assessment and prediction of landslides triggered by extreme rainfall. Results: The prediction performance was compared by analyzing real landslide and non-landslide data. The prediction performance of random forest (RF) was evaluated as better than that of a support vector machine (SVM) in traditional machine learning, so RF was combined with both StAE and SpAE. The results show that the prediction ratio of the combined classifiers was 93.2% for StAE combined with RF model and 92.5% for SpAE combined with RF model, which were higher than those of the SVM (87.4%), RF (89.7%), StAE (84.2%), and SpAE (88.2%). Conclusions: This study provides an example of combined classifiers giving a better predictive ratio than a single classifier. The asymmetric and unsupervised autoencoder combined with RF can exploit optimal non-linear features from landslide-affecting factors successfully, outperforms some conventional machine learning methods, and is promising for landslide susceptibility assessment. [ABSTRACT FROM AUTHOR]

SUMMARY: To rapidly and accurately provide alerts at target sites near the epicentre, we develop an on-site alert-level earthquake early warning (EEW) strategy involving P -wave signals and machine-learning-based prediction equations. These prediction equations are established for magnitude estimation and peak ground velocity (PGV) prediction accounting for multiple feature inputs and the support vector machine (SVM). These prediction equations are called SVM-M model for estimating magnitude and SVM-PGV model for predicting PGV, respectively. According to comparison between the predicted magnitude and PGV values with the predicted threshold values (M = 5.7 and PGV = 9.12 cm s–1, respectively), different alert level (0, 1, 2, 3) is issued at the different recording site when the predicted magnitude or PGV values exceed the given threshold values. Alert level 3 means that both the predicted magnitude and the predicted PGV exceed a given threshold, and there may be serious damage in this recording site. We apply the method to three destructive earthquake events (M ≥ 6.5) occurred in Japan, and our results indicate that with regard to the performance of SVM-PGV model for predicting PGV, at 3 s after P -wave arrival, the percentage of successful alarms (SAs) for these three events is higher than 95, 73 and 94 per cent, respectively, and the percentage of false alarms approaches 0. Additionally, with regard to the performance of SVM-M model for estimating magnitude, at 3 s after P -wave arrival, the percentage of SAs for these three events exceeds 95 per cent, and the percentage of missed alarms approaches 0. Moreover, almost all stations in the areas PGV ≥ 16 cm s–1 (IMM ≥ VII) near the epicentre issue alert level 3. The proposed method provides potential applications in EEW system. [ABSTRACT FROM AUTHOR]