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The complexity in stock index futures markets, influenced by the intricate interplay of human behavior, is characterized as nonlinearity and dynamism, contributing to significant uncertainty in long-term price forecasting. While machine learning models have demonstrated their efficacy in stock price forecasting, they rely solely on historical price data, which, given the inherent volatility and dynamic nature of financial markets, are insufficient to address the complexity and uncertainty in long-term forecasting due to the limited connection between historical and forecasting prices. This paper introduces a pioneering approach that integrates financial theory with advanced deep learning methods to enhance predictive accuracy and risk management in China's stock index futures market. The SF-Transformer model, combining spot-forward parity and the Transformer model, is proposed to improve forecasting accuracy across short and long-term horizons. Formulated upon the arbitrage-free futures pricing model, the spot-forward parity model offers variables such as stock index price, risk-free rate, and stock index dividend yield for forecasting. Our insight is that the mutual information generated by these variables has the potential to significantly reduce uncertainty in long-term forecasting. A case study on predicting major stock index futures prices in China demonstrates the superiority of the SF-Transformer model over models based on LSTM, MLP, and the stock index futures arbitrage-free pricing model, covering both short and long-term forecasting up to 28 days. Unlike existing machine learning models, the Transformer processes entire time series concurrently, leveraging its attention mechanism to discern intricate dependencies and capture long-range relationships, thereby offering a holistic understanding of time series data. An enhancement of mutual information is observed after introducing spot-forward parity in the forecasting. The variation of mutual information and ablation study results highlights the significant contributions of spot-forward parity, particularly to the long-term forecasting. Overall, these findings highlight the SF-Transformer model's efficacy in leveraging spot-forward parity for reducing uncertainty and advancing robust and comprehensive approaches in long-term stock index futures price forecasting. [ABSTRACT FROM AUTHOR]

Accurate rainfall measurement with a precise spatial and temporal resolution is essential for making informed decisions during disasters and conducting scientific studies, particularly in regions characterized by intricate terrain and limited coverage of automated weather stations. Retrieval of precipitation with satellite is currently the most effective means to obtain precipitation over large‐scale areas. The key to enhancing the accuracy of precipitation estimation and forecasting in regions with complex terrain lies in effectively integrating satellite data with topographic information. This paper introduces a deep learning approach called AttUnet_R_SFT that utilizes high temporal, spatial, and spectral resolution data obtained from the Fengyun 4A satellite, and incorporates the Deep Spatial Feature Transform (SFT) layer to incorporate geographical data for estimating half‐hourly precipitation in northeastern China. We assess it by compared to operational near‐real‐time satellite precipitation products demonstrated to be successful in estimating precipitation and baseline deep learning models. According to the experimental findings, the AttUnet_R_SFT model outperforms practical precipitation products and baseline deep learning models in both identifying and estimating precipitation. The main enhancement of the model performance is shown in the windward slope of the Greater Khingan Mountains as a result of the successful incorporation of geographical data. Hence, the suggested framework holds the capability to function as a superior and dependable satellite‐derived precipitation estimation solution in regions characterized by intricate terrain and infrequent rainfall. The findings of this study indicate that the utilization of deep learning algorithms for satellite precipitation estimation shows potential as a fruitful avenue for further research. Plain Language Summary: A deep learning model named AttUnet_R_SFT is proposed, which use high temporal, spatial and spectral resolution data from the Fengyun 4A satellite, and combines with the Deep Spatial Feature Transform (SFT) layer to input geographic information for half‐hourly precipitation estimation in the complex terrain region represented by northeast China. The model can provide a reference for improving the performance of precipitation estimation in areas with complex topography. Key Points: A deep‐learning model is proposed to effectively fuse satellite multispectral data of the Fengyun 4A satellite, with topographic informationHalf‐hourly precipitation is estimated with higher temporal resolution, which is closer to the operational needs of weather forecastingAs precipitation in the study area is a non‐high‐frequency event, data enhancement is attempted to use and obtain effective results [ABSTRACT FROM AUTHOR]

It is of great significance to accurately grasp ground-level ozone concentration on a wide scale in order to cope with the increasingly serious ground-level ozone pollution problem. Currently, satellite technology can be used to monitor ground-level ozone concentration on a wide scale, but there is the problem of insufficient spatial resolution. The use of ground monitoring stations to detect ground-level ozone concentration can obtain more accurate ozone concentration around the station, but there are problems of limited number of stations and sparse distribution. Therefore, it is still a challenge to accurately grasp the ground-level ozone concentration at a large scale and high spatial resolution. To address this issue, this paper proposes a ground-level ozone concentration inversion algorithm based on deep learning methods, incorporating data from remote sensing satellites, ground monitoring stations, meteorological conditions, geography, and land use. Taking Yunnan Province in China as the experimental region and utilizing five-fold cross-validation, the model's Mean Square Error (MSE) reached 340.45 and Root Mean Square Error (RMSE) reached 18.45. Compared to machine learning models, the error decreased by 14.53%, outperforming Random Forest models, Convolution Neural Networks, and Vision Transformers. This study offers a referential approach for accurately capturing ground-level ozone concentrations at a large scale with high spatial resolution. [ABSTRACT FROM AUTHOR]

The accurate estimation of forest aboveground biomass is of great significance for forest management and carbon balance monitoring. Remote sensing instruments have been widely applied in forest parameters inversion with wide coverage and high spatiotemporal resolution. In this paper, the capability of different remote-sensed imagery was investigated, including multispectral images (GaoFen-6, Sentinel-2 and Landsat-8) and various SAR (Synthetic Aperture Radar) data (GaoFen-3, Sentinel-1, ALOS-2), in aboveground forest biomass estimation. In particular, based on the forest inventory data of Hangzhou in China, the Random Forest (RF), Convolutional Neural Network (CNN) and Convolutional Neural Networks Long Short-Term Memory Networks (CNN-LSTM) algorithms were deployed to construct the forest biomass estimation models, respectively. The estimate accuracies were evaluated under the different configurations of images and methods. The results show that for the SAR data, ALOS-2 has a higher biomass estimation accuracy than the GaoFen-3 and Sentinel-1. Moreover, the GaoFen-6 data is slightly worse than Sentinel-2 and Landsat-8 optical data in biomass estimation. In contrast with the single source, integrating multisource data can effectively enhance accuracy, with improvements ranging from 5% to 10%. The CNN-LSTM generally performs better than CNN and RF, regardless of the data used. The combination of CNN-LSTM and multisource data provided the best results in this case and can achieve the maximum R2 value of up to 0.74. It was found that the majority of the biomass values in the study area in 2018 ranged from 60 to 90 Mg/ha, with an average value of 64.20 Mg/ha. [ABSTRACT FROM AUTHOR]

In many buildings with wooden structures, the service life and comfort of the floor will be affected by the walking load of the human body. The vibration effect generated by the human walking on the floor will affect the comfort that the floor brings to people. This article primarily focuses on the study of the correlation between floor vibration and human walking load in wooden buildings. It provides an analysis and summary of research findings from both domestic and international sources. Furthermore, the data collected is utilized to establish a model for estimating human walking load. The advent of information technology has witnessed significant transformative changes spurred by shifts in societal needs. Notably, the establishment of wireless sensor networks stands as a significant milestone within the field of information technology, highlighting advancements in science and technology. The evolution of wireless sensor networks has proven instrumental in various sectors within China, including but not limited to the military, environmental monitoring, biological research and medical systems, disaster monitoring, and energy allocation. This paper uses deep learning algorithms to analyze the node deployment problems of wireless sensor networks, summarizes a reasonable node deployment plan through specific research and practice, and uses collaborative deep learning methods to improve the operating efficiency of the network. In addition, this article also tested the vibration effect of wood structure buildings, analyzed different influencing factors, and provided a good reference point for the formulation of wood structure comfort standards. [ABSTRACT FROM AUTHOR]

The mining of open pit mines is widespread in China, and there are many cases of landslide accidents. Therefore, the problem of slope stability is highlighted. The stability of the slope is a factor that directly affects the mining efficiency and the safety of the entire mining process. According to the statistics, there is a 15 percent chance of finding landslide risk in China's large-scale mines. And due to the expansion of the mining scale of the enterprise, the problem of slope stability has become increasingly obvious, which has become a major subject in the study of open-pit mine engineering. In order to better predict the slope stability coefficient, this study takes a mine in China as a case to deeply discuss the accuracy of different algorithms in the stability calculation, and then uses a deep learning algorithm to study the stability under rainfall conditions. The change of the coefficient and the change of the stability coefficient before and after the slope treatment are experimentally studied with the displacement of the monitoring point. The result shows that the safety coefficient calculated by the algorithm in this paper is about 7% lower than that of the traditional algorithm. In the slope stability analysis before treatment, the safety factor calculated by the algorithm in this paper is 1.086, and the algorithm in this paper is closer to reality. In the stability analysis of the slope after treatment, the safety factor calculated by the algorithm in this paper is 1.227, and the stability factor meets the requirements of the specification. It also shows that the deep learning algorithm effectively improves the efficiency of the slope stability factor prediction and improves security during project development. [ABSTRACT FROM AUTHOR]

Machine learning models have been widely used in landslide susceptibility prediction. However, landslide multidimensional feature extraction, model generalization ability, and prediction quantification of deep learning are still challenging. This paper proposes a new approach, the landslide density-based bidirectional long short-term memory (LD-BiLSTM) model with multichannel input and an optimized sampling strategy to predict and map landslide susceptibility in active seismic mountainous areas of Sichuan Province, China. First, to ensure the generalization ability of the LD-BiLSTM model, other regions in Sichuan were selected as the model training area independent of the prediction area (Luding County). Multichannel landslide datasets were constructed to extract high-dimensional geospatial features of landslides. Subsequently, the landslide density of each grid cell was utilized as the label for the corresponding input sample. The LD-BiLSTM model was improved by using transfer learning and sampling optimization strategies, which makes our method attenuate the impact of historical landslide inventory deviation on the spatial susceptibility mode compared with the existing DL model, which usually uses landslide objects (LO) as input sample labels. Model performance evaluation results show that the LD-BiLSTM model (precision = 0.903, recall = 0.899, F1-score = 0.901, Area under receiver operating characteristic curve (AUC) = 0.940) outperformed the LO-BiLSTM model (precision = 0.812, recall = 0.815. F1-score = 0.813, AUC = 0.910) in the case areas. Meanwhile, the performance of the LD-BiLSTM model (AUC = 0.9407) significantly outperformed both the information value (IV) (AUC = 0.7207) model and the random forest (RF) (AUC = 0.8116) models in the landslide prediction area (Luding), which confirms that the proposed LD-based method is superior to traditional LO-based methods. Significantly, our approach can effectively extract the spatial distribution of landslides and predict potential landslides in complex high-mountain environments. [ABSTRACT FROM AUTHOR]

The ionospheric foF2 prediction model GWO-ALSTM is proposed in this paper, which combines the Gray Wolf Optimization (GWO) algorithm and the attention mechanism based on the long short-term memory (LSTM) model. The datasets used in this model were obtained from the oblique ionosondes of the China Ground-based Seismo-ionospheric Monitoring Network. The data from 2010 to 2012 was used for training, and the data of 2013 was used for verification. The input parameters of the model include local time, day number, sunspot number, F10.7 solar flux, Ap index, Dst index, and foF2 at the previous moment, and the output parameter is foF2 at the current moment. The IRI2016 model, GABP model, LSTM model and GWO-ALSTM model were compared with the observed results for analysis. The results show that the GWO-ALSTM model is superior to the LSTM model, the GABP model and the IRI2016 model. Meanwhile, the comparative analyses of the diurnal variations of foF2 show that the curve of the GWO-ALSTM model fits the observed values more closely than the curve of any other model under the geomagnetic quiet and storm conditions. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) has gained significant popularity through wearable technologies. Several IoT devices are used in educational institutions, smart homes, and security management. Their widespread use in sports training applications using IoT (WS-IoT)-based wearable sensors is noteworthy. Meanwhile, artificial intelligence (AI) has evolved rapidly, and its applications have expanded across many diverse fields. Over the past few years, China has placed greater emphasis on physical education to enhance students' physical fitness and overall capabilities. A significant number of colleges and universities have adopted artificial intelligence to transform traditional single-mode sports training methods. This paper introduces a continuous monitoring system for sports individuals using the WS-IoT. The goal of coaches is to maximize the results of athlete training across various sports events using AI technology and their sports expertise. In this study, we examine the use of AI technology in sports and assess the practical implications of its implementation. To achieve the objective function, we used a Bayesian deep classifier and deep learning algorithms that utilize adaptive optimization. Utilizing artificial intelligence in Chinese educational institutions improves training efficiency and effectiveness. The system was then rigorously tested and analyzed in terms of the quality-of-service parameters, including communication collisions and packet loss rates. It is shown that packet loss rates increase with increasing packet numbers, but collision rates remain unchanged. Furthermore, a practical application of the system in colleges and universities showed that 14.4% of the students were extremely satisfied with the use of teaching materials, 24.5% were satisfied with the materials, 30.1% were satisfied with the training methods, and 17.9% were dissatisfied. [ABSTRACT FROM AUTHOR]

Going beyond previous work, this paper presents a systematic literature review that explores the deployment of satellites, drones, and ground-based sensors for yield prediction in agriculture. It covers multiple aspects of the topic, including crop types, key sensor platforms, data analysis techniques, and performance in estimating yield. To this end, datasets from Scopus and Web of Science were analyzed, resulting in the full review of 269 out of 1429 retrieved publications. Our study revealed that China (93 articles, >1800 citations) and the USA (58 articles, >1600 citations) are prominent contributors in this field; while satellites were the primary remote sensing platform (62%), followed by airborne (30%) and proximal sensors (27%). Additionally, statistical methods were used in 157 articles, and model-based approaches were utilized in 60 articles, while machine learning and deep learning were employed in 142 articles and 62 articles, respectively. When comparing methods, machine learning and deep learning methods exhibited high accuracy in crop yield prediction, while other techniques also demonstrated success, contingent on the specific crop platform and method employed. The findings of this study serve as a comprehensive roadmap for researchers and farmers, enabling them to make data-driven decisions and optimize agricultural practices, paving the way towards a fully digitized yield prediction. [ABSTRACT FROM AUTHOR]

Buiding data-driven models using machine learning methods has gradually become a common approach for studying reservoir parameters. Among these methods, deep learning methods are highly effective. From the perspective of multi-task learning, this paper uses six types of logging data—acoustic logging (AC), gamma ray (GR), compensated neutron porosity (CNL), density (DEN), deep and shallow lateral resistivity (LLD) and shallow lateral resistivity (LLS) —that are inputs and three reservoir parameters that are outputs to build a porosity saturation permeability network (PSP-Net) that can predict porosity, saturation, and permeability values simultaneously. These logging data are obtained from 108 training wells in a medium-low permeability oilfield block in the western district of China. PSP-Net method adopts a serial structure to realize transfer learning of reservoir-parameter characteristics. Compared with other existing methods at the stage of academic exploration to simulating industrial applications, the proposed method overcomes the disadvantages inherent in single-task learning reservoir-parameter prediction models, including easily overfitting and heavy model-training workload. Additionally, the proposed method demonstrates good anti-overfitting and generalization capabilities, integrating professional knowledge and experience. In 37 test wells, compared with the existing method, the proposed method exhibited an average error reduction of 10.44%, 27.79%, and 28.83% from porosity, saturation, permeability calculation. The prediction and actual permeabilities are within one order of magnitude. The training on PSP-Net are simpler and more convenient than other single-task learning methods discussed in this paper. Furthermore, the findings of this paper can help in the re-examination of old oilfield wells and the completion of logging data. [ABSTRACT FROM AUTHOR]

Semantic image segmentation in computer networks is designed to determine the category to which each pixel in an image belongs. It is a basic computer vision task and has a very wide range of applications in practice. In recent years, semantic image segmentation algorithms in computer networks based on deep learning have attracted widespread attention due to their fast speed and high accuracy. However, due to the large number of downsampling layers in a deep learning model, the segmentation results are usually poor at the edge of an object, and there is currently no universal quantitative evaluation index to measure the performance of segmentation at the edge of an object. Solving these two problems is of great significance to semantic image segmentation algorithms in China. Based on traditional evaluation indicators, this paper proposes a region-based evaluation index to quantitatively measure the performance of segmentation at the edge of an object and proposes an improved loss function to improve model performance. The existing semantic image segmentation methods are summarized. This paper proposes regional-based evaluation indicators. Taking advantage of the particularity of semantic image segmentation tasks, this paper presents an efficient and accurate method for extracting the edges of objects. By defining the distance from pixels to the edges of objects, this paper proposes a fast algorithm for calculating the edge area. Based on this, three methods are proposed as well as an area-based evaluation indicator. The experimental results show that the accuracy of the loss function proposed in this paper, compared with that of the current mainstream cross-entropy loss function, is improved by 1% on the DeepLab model. For area-based evaluation indicators, a 4% accuracy improvement can be achieved, and on other segmentation models, there is also a significant improvement. [ABSTRACT FROM AUTHOR]

Bearing in mind European Green Deal assumptions regarding a significant reduction of green house emissions, electricity generation from Renewable Energy Sources (RES) is more and more important nowadays. Besides this, accurate and reliable electricity generation forecasts from RES are needed for capacity planning, scheduling, managing inertia and frequency response during contingency events. The recent three years have proved that Machine Learning (ML) models are a promising solution for forecasting electricity generation from RES. In this review, the 8-step methodology was used to find and analyze 262 relevant research articles from the Scopus database. Statistic analysis based on eight criteria (ML method used, renewable energy source involved, affiliation location, hybrid model proposed, short term prediction, author name, number of citations, and journal title) was shown. The results indicate that (1) Extreme Learning Machine and ensemble methods were the most popular methods used for electricity generation forecasting from RES in the last three years (2020–2022), (2) most of the research was carried out for wind systems, (3) the hybrid models accounted for about a third of the analyzed works, (4) most of the articles concerned short-term models, (5) the most researchers came from China, (6) and the journal which published the most papers in the analyzed field was Energies. Moreover, strengths, weaknesses, opportunities, and threats for the analyzed ML forecasting models were identified and presented in this paper. [ABSTRACT FROM AUTHOR]

Previous studies have investigated the determinants of urban tourism development from the various attributes of neighborhood quality. However, traditional methods to assess neighborhood quality are often subjective, costly, and only on a small scale. To fill this research gap, this study applies the recent development in big data of street view images, deep learning algorithms, and image processing technology to assess quantitatively four attributes of neighborhood quality, namely street facilities, architectural landscape, green or ecological environment, and scene visibility. The paper collects more than 7.8 million Baidu SVPs of 232 prefecture-level cities in China and applies deep learning techniques to recognize these images. This paper then tries to examine the influence of neighborhood quality on regional tourism development. Empirical results show that both levels of street facilities and greenery environment promote tourism. However, the construction intensity of the landscape has an inhibitory influence on the development of tourism. The threshold test shows that the intensity of the influence varies with the city's overall economic level. These conclusions are of great significance for the development of China's urban construction and tourism economy, and also provide a useful reference for policymakers. The methodological procedure is reduplicative and can be applied to other challenging cases. [ABSTRACT FROM AUTHOR]

In this paper, 1,538 papers retrieved with the keywords "sports artificial intelligence (AI)" on the Web of Science database since 2007 were taken as the data source, and the Cite Space V software was used to visualize and analyze them. A visual knowledge graph was used to streamline the countries, institutions and authors conducting sports AI research, discipline distribution, research hotspots and development trends in the past 15 years. Subsequently, its development direction and research progress were discussed. Sports AI was widely distributed, with the US, China and the UK leading the way. The most prolific authors and teams in research on sports AI were concentrated in American universities. Their main research direction is to develop and improve smart wearable devices based on machine learning and deep learning technologies for different groups of people. Research on sports AI involved multiple disciplines, which mainly applied and referred to research methodologies and theories on engineering, computer science and sports science. It could be seen from the frequency and centrality of keywords that in the current field of sports AI, machine learning is the main direction, artificial neural networks is the main algorithm, and practical and empirical research based on data mining is the focus. The research hotspots were divided into three major clusters: physical health promotion, sports injury prevention and control, and athletic performance enhancement. How to introduce intelligent technology into sports for a perfect integration still has an arduous and long way to go. Future development requires joint efforts and participation of scientific researchers, professionals and common people. [ABSTRACT FROM AUTHOR]

Personalized points-of-interest (POI) recommendation is very important for improving the service quality of location-based social network applications. It has become one of the most popular research directions in the industry and academia. However, the realization of high-quality personalized POI recommendation faces three major challenges: (i) the interest drift issue caused by the spatiotemporal dynamics of user check-in behavior, (ii) how to integrate as much heterogeneous information as possible to alleviate data sparseness and cold start issues, and (iii) how to use implicit feedback to model complex high-order nonlinear user-POI interactions. To jointly address all these challenges, we propose the TGVx recommendation framework and establish a dynamic personalized POI deep recommendation model, where T and G respectively represent time and geographic factors, V represents out-of-town visitors, and x represents time slot number. TGVx is composed of x parallel TGV models where the TG module mines high-order nonlinear user-POI interaction relationships and integrates multisource heterogeneous information, and the V module transfers the check-in records of out-of-town visitors in hometowns and generates pseudo check-in records in the target city. Technically, we design a new unsupervised deep learning network T-SemiDAE for the TG module. We built a POI-word heterogeneous network for the V module and used graph embedding technology to match the most similar POIs across cities and transfer check-in records. The experimental results on the actual datasets show that the TGVx model is always better than other advanced models in terms of accuracy and diversity for local and out-of-town recommendation scenarios. Compared with the best baseline model semi-deep auto-encoder with a conditional layer the average improvement rates of accuracy and diversity of TGVx are 17.1% to 58.6% and 2.25% to 28.86%, respectively. In theory, our research effectively uses data science and analysis methods to design a recommender system. In practice, our research is motivated by practical problems, and the research results have high practical promotion value. History: Accepted by Ram Ramesh, Area Editor for Data Science & Machine Learning. Funding: This work was supported by the National Natural Science Foundation of China [Grants 71831003, 72293563, and 72172025], the Dalian Science and Technology Talent Innovation Support Policy Project [The High-level Talent Innovation Team Project 2022RG17], and the Liaoning Provincial Department of Education [Basic Scientific Research Project LJKMZ20221582]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information (https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.1286) as well as from the IJOC GitHub software repository (https://github.com/INFORMSJoC/2021.0010) at (http://dx.doi.org/10.5281/zenodo.7407123). [ABSTRACT FROM AUTHOR]

Rice is an important food crop in China, and diseases and pests are the main factors threatening its safety, ecology, and efficient production. The development of remote sensing technology provides an important means for non-destructive and rapid monitoring of diseases and pests that threaten rice crops. This paper aims to provide insights into current and future trends in remote sensing for rice crop monitoring. First, we expound the mechanism of remote sensing monitoring of rice diseases and pests and introduce the applications of different commonly data sources (hyperspectral data, multispectral data, thermal infrared data, fluorescence, and multi-source data fusion) in remote sensing monitoring of rice diseases and pests. Secondly, we summarize current methods for monitoring rice diseases and pests, including statistical discriminant type, machine learning, and deep learning algorithm. Finally, we provide a general framework to facilitate the monitoring of rice diseases or pests, which provides ideas and technical guidance for remote sensing monitoring of unknown diseases and pests, and we point out the challenges and future development directions of rice disease and pest remote sensing monitoring. This work provides new ideas and references for the subsequent monitoring of rice diseases and pests using remote sensing. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) is an interdisciplinary field that encompasses a wide range of computer science disciplines, including image recognition, machine learning, human−computer interaction, robotics and so on. Recently, AI, especially deep learning algorithms, has shown excellent performance in the field of image recognition, being able to automatically perform quantitative evaluation of complex medical image features to improve diagnostic accuracy and efficiency. AI has a wider and deeper application in the medical field of diagnosis, treatment and prognosis. Nasopharyngeal carcinoma (NPC) occurs frequently in southern China and Southeast Asian countries and is the most common head and neck cancer in the region. Detecting and treating NPC early is crucial for a good prognosis. This paper describes the basic concepts of AI, including traditional machine learning and deep learning algorithms, and their clinical applications of detecting and assessing NPC lesions, facilitating treatment and predicting prognosis. The main limitations of current AI technologies are briefly described, including interpretability issues, privacy and security and the need for large amounts of annotated data. Finally, we discuss the remaining challenges and the promising future of using AI to diagnose and treat NPC. [ABSTRACT FROM AUTHOR]

Extracting high-accuracy landslide areas using deep learning methods from high spatial resolution remote sensing images is a hot topic in current research. However, the existing deep learning algorithms are affected by background noise and landslide scale effects during the extraction process, leading to poor feature extraction effects. To address this issue, this paper proposes an improved mask regions-based convolutional neural network (Mask R-CNN) model to identify the landslide distribution in unmanned aerial vehicles (UAV) images. The improvement of the model mainly includes three aspects: (1) an attention mechanism of the convolutional block attention module (CBAM) is added to the backbone residual neural network (ResNet). (2) A bottom-up channel is added to the feature pyramidal network (FPN) module. (3) The region proposal network (RPN) is replaced by guided anchoring (GA-RPN). Sanming City, China was selected as the study area for the experiments. The experimental results show that the improved model has a recall of 91.4% and an accuracy of 92.6%, which is 12.9% and 10.9% higher than the original Mask R-CNN model, respectively, indicating that the improved model is more effective in landslide extraction. [ABSTRACT FROM AUTHOR]

As China's economic level and industrial volume continue to develop and expand, the traditional methods of business management and resource scheduling demonstrate significant limitations and misalignments with China's daily economic activities. Traditional industrial and commercial management resource scheduling is highly reliant on manual labor. When confronted with the requirements of large-scale project management, the pure manual mode cannot track the real-time progress of the project flexibly, effectively, and in a timely manner, resulting in the inability to complete the corresponding resource-scheduling work efficiently, which will affect the overall operation progress of the project. Deep neural networks have a high-application potential for solving extremely complex and highly nonlinear optimization problems. Artificial intelligence and deep learning research have advanced at a rapid pace over the past few decades, thanks to the efforts of numerous researchers. Combined with GPU technology, the deep learning framework can provide an extremely complex optimization problem with a practical and feasible optimization scheme and corresponding solution path in a very short amount of time. Therefore, this paper explores the potential application of deep learning technology to industrial and commercial resource-scheduling management. By analyzing the benefits of deep learning technology and the bottleneck issues of existing industrial and commercial resource scheduling, a real-time optimized industrial and commercial resource-scheduling model based on deep learning technology is developed. The model is evaluated using the respective data set. The test results demonstrate that the resource-scheduling model proposed in this paper has strong real-time and high-operation efficiency and can assist engineers in completing the corresponding resource-scheduling tasks. [ABSTRACT FROM AUTHOR]

China has proposed two major measures to address the "three rural issues": the first is to abolish the agricultural tax, which has been in place for over 2000 years; the second is to propose the construction of a new socialist countryside, which would mark the end of the old era and the beginning of the new. As a result, this paper employs in-depth learning technology to enhance rural tourism development and the creation of a new socialist countryside. This paper investigates deep-learning-based rural tourism and the creation of a new socialist countryside. Because MSE and MAE reflect the prediction error score, the lower the value, the better the recommendation accuracy. The MSE value of the machine learning algorithm is 2.456, the MSE value of the data mining algorithm is 2.324, and the MSE value of the convolution neural network algorithm is 2.102, when the number reaches 80. It can be concluded that the convolution neural network algorithm proposed in this paper has the lowest MSE and MAE values of the three methods, implying that the convolution neural network algorithm is the best of the three. The use of the convolution neural network algorithm to implement the scientific concept of development and the construction of a new socialist countryside is an important part of creating a harmonious society that fully meets the central government's objectives and requirements for the construction of a new socialist countryside. [ABSTRACT FROM AUTHOR]

Competitive basketball is one of the most popular sports in the world. With the development of China's sports power strategy, the national movement has strengthened the status of basketball in sports. However, China's competitive basketball ranking is not high in the world, and the analysis of the reasons should start with the management system. Therefore, this paper aims to explore the development status and influencing factors of China's competitive basketball management system under the background of deep learning. For the background of deep learning, this paper describes the application of deep learning algorithms in basketball strategy. It adopts the expert interview method for the competitive basketball management system and elaborates in detail on five aspects: target mechanism, competition mechanism, selection mechanism, market mechanism, and incentive mechanism. The experimental results of the article believe that, based on the suggestions of 10 experts, the current Chinese competitive basketball selection mechanism is the most influential factor, with a weight of 16.1%, and the smallest impact is the level of athletes, accounting for 11.4%. [ABSTRACT FROM AUTHOR]

China's cultural classics have high artistic and ideological values in the world, which implies China's historical heritage and the inheritance of the cultural situation of the Chinese nation for thousands of years. At present, with the rapid development of China's economy, especially in the development environment of cultural globalization, China's traditional cultural classics have attracted worldwide attention along with historical and cultural treasures. In fact, other countries need to translate cultural classics out of their love for China's cultural classics or academic research. However, there are a large number of cultural classics in China. According to relevant data, there are approximately 35,000 kinds of cultural classics, out of which only 0.2% are translated into foreign languages. In order to make China's excellent cultural classics known to the world and let more people in other countries understand Chinese culture, this paper studies the translation of cultural classics through in-depth learning. Firstly, this paper introduces the basic concept of deep learning and proposes an algorithm that deeply studies the convolution layer and pool layer. Secondly, the algorithm establishes a deep learning model that calculates and counts the text information of ancient books through explicit intertextuality. Thirdly, the model carries out automatic text translation and lists the analysis process of cultural classics translation based on intertextuality, so as to study cultural classics translation. The obtained results can promote the development of cultural classics translation in China. For the translation of complex cultural classics, the effects of the three models are tested experimentally, of which the sequence model (seq) is the best. This model is fast and simple to extract the text of literary classics. [ABSTRACT FROM AUTHOR]

With the popularization of higher education, China's higher education has moved from the stage of elite education to the stage of universal education, and the number of graduates is also increasing. At present, college students are facing tremendous pressure on employment. One is the huge number of jobs, the other is the difference in professional needs, and the third is the spread of job information, which makes it difficult for college students to find a job that suits them. In order to solve this dilemma, this paper analyzes the related technologies and in-depth basic theories of data mining. After introducing several traditional recommendation algorithms, the traditional convolutional network method is improved from three aspects: activation function, pooling strategy, and loss function. Finally, using the hybrid convolutional neural network, a career recommendation model for college students based on deep learning and machine learning is proposed, and simulation experiments are carried out on it. The main research work is as follows: (1) a hybrid convolutional neural network is proposed, which uses convolution operation to learn high-level features to achieve personalized employment recommendation; (2) the training optimization strategy of the hybrid convolutional neural network is studied, aiming at the activation function, pooling processing, and loss function, and the feasibility of the optimization method is verified through simulation experiments; (3) finally, according to the evaluation index of the recommendation algorithm (recall rate and F1-Score), the recall rate of the algorithm in this paper is nearly 15% higher than that of the DNN model. The experiment is compared with the traditional commonly used recommendation algorithm, and the comparative analysis of the experimental results proves the effectiveness of the algorithm for the employment recommendation of college students. [ABSTRACT FROM AUTHOR]

The rise of FinTech has been meteoric in China. Investing in mutual funds through robo-advisor has become a new innovation in the wealth management industry. In recent years, machine learning, especially deep learning, has been widely used in the financial industry to solve financial problems. This paper aims to improve the accuracy and timeliness of fund classification through the use of machine learning algorithms, that is, Gaussian hybrid clustering algorithm. At the same time, a deep learning-based prediction model is implemented to predict the price movement of fund classes based on the classification results. Fund classification carried out using 3,625 Chinese mutual funds shows both accurate and efficient results. The cluster-based spatiotemporal ensemble deep learning module shows better prediction accuracy than baseline models with only access to limited data samples. The main contribution of this paper is to provide a new approach to fund classification and price movement prediction to support the decision-making of the next generation robo-advisor assisted by artificial intelligence. [ABSTRACT FROM AUTHOR]

This paper focuses on the scientific problem of quantitative mineralization prediction at large depth in the Zaozigou gold deposit, west Qinling, China. Five geological and geochemical indicators are used to establish geological and geochemical quantitative prediction model. Machine learning and Deep learning algorithms are employed for 3D Mineral Prospectivity Mapping (MPM). Especially, the Student Teacher Ore-induced Anomaly Detection (STOAD) model is proposed based on the knowledge distillation (KD) idea combined with Deep Auto-encoder (DAE) network model. Compared to DAE, STOAD uses three outputs for anomaly detection and can make full use of information from multiple levels of data for greater overall robustness. The results show that the quantitative mineral resources prediction by applying the STOAD model has a good performance, where the value of Area Under Curve (AUC) is 0.97. Finally, three main mineral exploration targets are delineated for further investigation. [ABSTRACT FROM AUTHOR]

The complex and varied urban road conditions have always been a difficult and a pivotal component in the study of driverless technology especially at intersections. In China, it is necessary for driverless cars to understand the gestures of traffic police. To identify the traffic police gesture at the intersection, the key step is to detect the traffic police at the intersection. At present, the research on traffic police detection is still in its infancy, there exists common problems such as slow detection speed and other real time problems in this method, and there is not a standardized traffic police data set ether. For the real time problems, this paper introduces the attention mechanism, and proposes a new real-time detection method of traffic police based on attention mechanism. The method proposed in this paper has strong robustness and can quickly complete the target detection task. For the data set problem, this paper analyzes and discloses similar data sets published through research. In the meanwhile, this paper collects 24,530 video data on the actual road, and it extracts and saves 12,000 pictures containing traffic police at frame rate of 30FPS as traffic police detection training and validated data set. [ABSTRACT FROM AUTHOR]

With the continuous development of engineering construction in China, more and more large-section highway tunnels have emerged. Different geological engineering environments determine the diversity of construction plans. The determination of construction plans and the prediction of tunnel deformations have always been the key points of engineering construction. In this paper, we use numerical simulations to determine specific construction parameters in the context of actual highway tunnel projects, and then use deep learning methods to predict deformation during tunnel construction, thus providing guidance for construction. We have found that: (i) Different excavation sequences and excavation depths have different effects on the surrounding rock deformation around the tunnel. The optimal excavation sequence through numerical simulation in this study is symmetrical excavation, and the excavation depth is 2 m. (ii) Numerical simulation based on Long Short-Term Memory (LSTM) algorithm is used to predict the tunnel deformation. It is found that the prediction results of the LSTM algorithm are more consistent with the actual monitoring data. (iii) Multi-step prediction is more important for engineering guidance, and three-step prediction can be considered during the process of engineering construction. Therefore, the machine learning algorithm provides a new method for engineering prediction. [ABSTRACT FROM AUTHOR]

Accurately predicting wind and photovoltaic power is one of the keys to improving the economy of wind-solar complementary power generation system, reducing scheduling costs and no-load losses, and ensuring grid stability. However, the natural properties of energy result in complex fluctuations in their corresponding power sequences, making accurate predictions difficult. Therefore, this paper proposes an intelligent prediction system that combines decomposition algorithms and deep learning for ultra-short-term prediction of wind and photovoltaic power. First, an improved decomposition algorithm is proposed, based on fuzzy entropy's property that its value increases with the increase of sequence uncertainty, particle swarm optimization (PSO) is used to search for the optimal parameter combinations of variational modal decomposition (VMD), so that it can automatically adjust the parameters for energy data with different characteristics to reduce the human error. Then, a convolutional neural network (CNN) architecture that balances operational efficiency and prediction performance is constructed, and the hyperparameters of the CNN are optimized using the wild horse optimization algorithm (WHO) to improve the stability and accuracy of the prediction model. In this paper, real data from wind power plants and photovoltaic power plants in China are used as experimental objects, and experiments are carried out in three aspects, namely, benchmark model selection, decomposition algorithm comparison and combined model comparison. The results show that selecting CNN as the benchmark model is a good choice; the improved VMD has better decomposition performance than other state-of-the-art decomposition algorithms. The system proposed in this paper is highly generalizable and adaptive, and its prediction performance and accuracy greatly outperform that of other comparative models, with prediction accuracies improved by 72% and 79%, respectively, compared to a single CNN model. • The hyperparameters of VMD are determined by using PSO based on fuzzy entropy. • Optimize convolutional neural network using the wild horse optimization algorithm. • The intelligent prediction system can accurately predict wind power and PV power. • Experiments based on power data from actual wind farms and PV plants. • A deep learning prediction method applied to wind and solar complementary systems. [ABSTRACT FROM AUTHOR]

Landslide is a devastating natural disaster, causing loss of life and property. It is likely to occur more frequently due to increasing urbanization, deforestation, and climate change. Landslide susceptibility mapping is vital to safeguard life and property. This article surveys machine learning (ML) models used for landslide susceptibility mapping to understand the current trend by analyzing published articles based on the ML models, landslide causative factors (LCFs), study location, datasets, evaluation methods, and model performance. Existing literature considered in this comprehensive survey is systematically selected using the ROSES protocol. The trend indicates a growing interest in the field. The choice of LCFs depends on data availability and case study location; China is the most studied location, and area under the receiver operating characteristic curve (AUC) is considered the best evaluation metric. Many ML models have achieved an AUC value > 0.90, indicating high reliability of the susceptibility map generated. This paper also discusses the recently developed hybrid, ensemble, and deep learning (DL) models in landslide susceptibility mapping. Generally, hybrid, ensemble, and DL models outperform conventional ML models. Based on the survey, a few recommendations and future works which may help the new researchers in the field are also presented. [ABSTRACT FROM AUTHOR]

Timely and accurate survey of construction and demolition waste (C&DW) distribution is of significance for managing C&DW and enhancing the quality of the urban environment, especially in rapidly urbanizing country of China. Automatic C&DW recognition using high-resolution remote sensing images is an extremely important surveying method. Existing C&DW recognition methods can only obtain a rough spatial distribution of C&DW. Mapping precise C&DW distribution on large-scale remote sensing images is still challenging. In this paper, to solve the problem of precise C&DW recognition, a novel deep learning algorithm based on high-resolution remote sensing images is proposed, which we refer to as the cross-channel multi-scale gated fusion network (CCMGNet). CCMGNet extracts depth features of C&DW from RGB and NIR bands by using two independent encoding streams. A gated fusion layer and a multi-scale attention module are designed to effectively fuse the features from two encoding streams. We manually labelled the sample set used for training and testing on 26 remote sensing images captured by the Gaofen-2 (GF-2) satellite. These images were taken in nine typical cities in China over the time period from 2019 to 2021. The results showed that the proposed method is effective in recognizing C&DW, realizing 89.42% and 84.52% in precision and IoU, and is superior to other state-of-the-art deep learning algorithms and existing C&DW recognition methods. The effectiveness of all significant components in CCMGNet was confirmed by ablation experiments. The proposed method was applied to extract C&DW regions in large-scale remote sensing images, with an example of a GF-2 image of Beijing. The high extraction efficiency and satisfactory visual effect demonstrate the potential of the proposed method for surveying the spatial distribution of C&DW. The novel method will play a crucial role in automatically surveying the spatial distribution and size of C&DW across the country. [ABSTRACT FROM AUTHOR]

Short-term load forecasting plays a significant role in the operation of power systems. Recently, deep learning has been generally employed in short-term load forecasting, primarily in the extraction of the characteristics of digital information in a single dimension without taking into account of the impact of external variables, particularly non-digital elements on load characteristics. In this paper, we propose a joint MFF-SAM-GCN to realize short-term load forecasting. First, we utilize a Bi-directional Long Short-Term Memory (Bi-LSTM) network and One-Dimensional Convolutional Neural Network (1D-CNN) in parallel connection to form a multi-feature fusion (MFF) framework, which can extract spatiotemporal correlation features of the load data. In addition, we introduce a Self-Attention Mechanism (SAM) to further enhance the feature extraction capability of the 1D-CNN network. Then with the deployment of a Graph Convolutional Network (GCN), the external non-digital features such as weather, strength, and direction of wind, etc., are extracted. Moreover, the generated weight matrices are incorporated into the load features to enhance feature recognition ability. Finally, we exploit Bayesian Optimization (BO) to find the optimal hyperparameters of the model to further improve the prediction accuracy. The simulation is taken from our proposed model and six benchmark schemes by using the bus load dataset of the Shandong Open Data Network, China. The results show that the RMSE of our proposed MFF-SAM-GCN model is 0.0284, while the SMAPE is 9.453%,the MBE is 0.025, and R-squared is 0.989, which is better than the selected three traditional machine learning methods and the three deep learning models. [ABSTRACT FROM AUTHOR]

In this paper, we propose a computable method to evaluate the aesthetic value of fusion images of folk martial arts and dance based on human visual and aesthetic habits, extract features and use them as evaluation indexes from three aspects: technical features, perceptual features, and social features, establish an aesthetic evaluation model by fusing each index, and determine the influence factors of each index by using online research. The aesthetic evaluation of fusion images of folk martial arts and dance is carried out automatically. The results of the experimental tests on the evaluation dataset of the aesthetic quality of fusion images of folk martial arts and dance at the Communication University of China show that the accuracy of the proposed deep learning algorithm-based method for analyzing the aesthetic index of fusion of folk martial arts and dance can reach 98.08% with certain validity, and the evaluation results of each index are clear and intuitive, which can play a guiding role in improving the aesthetic quality of fusion of folk martial arts and dance from various angles. It can be used as a guide to improving the aesthetics of the fusion of folk martial arts and dance from various perspectives. By integrating the model into the mobile application, it is possible to evaluate and score the aesthetics of multiple portrait photos uploaded by users and select photos to be kept or deleted based on the scoring results, thus simplifying user operations. [ABSTRACT FROM AUTHOR]

As a result of the fast rise of globalization, people in China are learning English at a rapid pace. However, there is a severe shortage of English teachers in the region, which is a major hindrance. To address these concerns, a deep learning-based algorithm is proposed that can not only check English pronunciation but also help learners distinguish between phonemic and quality phonemic while listening and differentiating, as well as correct phonemic errors, thereby increasing their language learning capacity. In order to study the application of nonlinear network identification technology in English learning, this paper evaluates the English pronunciation quality through the deep learning algorithm of deep learning combined with the related contents of neural network data model, and the experimental results of speech recognition structure are analyzed and discussed in detail. The concordance between machine and manual intonation evaluation is 80%, the concordance rate of adjacent intonation evaluation is 98.33%, and the Pearson correlation coefficient is 0.627 that shows the technique is reliable. The method of English pronunciation and speech identification model is sensible and dependable, which can give beginners a punctual, exact and impartial judgment and response guidance, assist learners to get on the differences between their phonemic and standard phonemic, and correct phonemic mistakes, in order to enhance the ability of oral English learning. [ABSTRACT FROM AUTHOR]

Before 2008, the number of surface observation stations in China was small. Thus, the surface observation data were too sparse to effectively support the High-resolution China Meteorological Administration's Land Assimilation System (HRCLDAS) which ultimately inhibited the output of high-resolution and high-quality gridded products. This paper proposes a statistical downscaling model based on a deep learning algorithm in super-resolution to research the above problem. Specifically, we take temperature as an example. The model is used to downscale the 0.0625° × 0.0625°, 2-m temperature data from the China Meteorological Administration's Land Data Assimilation System (CLDAS) to 0.01° × 0.01°, named CLDASSD. We performed quality control on the paired data from CLDAS and HRCLDAS, using data from 2018 and 2019. CLDASSD was trained on the data from 31 March 2018 to 28 February 2019, and then tested with the remaining data. Finally, extensive experiments were conducted in the Beijing-Tianjin-Hebei region which features complex and diverse geomorphology. Taking the HRCLDAS product and surface observation data as the "true values" and comparing them with the results of bilinear interpolation, especially in complex terrain such as mountains, the root mean square error (RMSE) of the CLDASSD output can be reduced by approximately 0.1°C, and its structural similarity (SSIM) was approximately 0.2 higher. CLDASSD can estimate detailed textures, in terms of spatial distribution, with greater accuracy than bilinear interpolation and other sub-models and can perform the expected downscaling tasks. [ABSTRACT FROM AUTHOR]

In practical power systems, it is still very challenging to figure out a cost-effective undervoltage load shedding (UVLS) scheme that can reliably and adaptively react to the short-term voltage stability (SVS) problem. Faced with this challenge, this paper develops an intelligent data-driven predictive UVLS scheme for online SVS enhancement. Inspired by valuable ideas in model predictive control and supplementary excitation control in power systems, a novel deep feedback learning machine (DFLM) is designed to precisely predict future voltage violations after UVLS execution. With the help of the DFLM, the UVLS scheme is aware of potential effects of various candidate UVLS actions. Owing to this desirable nature, it can adaptively respond to diverse SVS conditions and optimize UVLS decisions in a non-iterative way. Further, two well-designed strategies, i.e., stepwise constraint relaxation and incremental DFLM adaptation, are introduced to enhance the scheme's reliability and adaptability during online application. Numerical test results on the Nordic test system and the realistic North GZ Power Grid in China showcase the excellent performances of the proposed scheme. [ABSTRACT FROM AUTHOR]

Accurate estimation of ride-hailing travel mobility is significant for demand management, and transportation planning. Although existing intervening opportunities models based on individual destination selection behavior can estimate travel mobility patterns (e.g., commuter flow, and migration flow), they usually ignore the substitutability of candidate destinations. In the context of ride-hailing travel, people tend to have strong destination preferences, and candidate destinations should be related to individual travel needs. Meanwhile, artificial intelligence offers powerful tools to extract complex nonlinear dependencies of mobility data, which are difficult to capture by traditional intervention opportunities models. This paper proposes a novel ensemble model with conditional intervening opportunities to improve the accuracy of ride-hailing travel mobility estimation by considering the substitutability of candidate destinations, that is, only the location related to people's trip purpose will likely affect people's travel behavior. The proposed ensemble model employs a stacking strategy to integrate six advanced machine learning and deep learning algorithms to extract complex nonlinear dependencies from ride-hailing travel mobility data, and achieve accurate mobility estimation. Furthermore, datasets from two major cities in China with more than 25 million ride-hailing trips are used for model training and experimental comparison. The results indicate that the proposed model outperforms other baseline models in ride-hailing travel mobility estimation tasks. It accurately predicts trip flows and the trip distance distribution, and can capture mobility patterns with strong interpretability. The proposed model can be applied to analyze the travel behavior of ride-hailing passengers, as well as the mobility patterns between different regions. • Propose a novel ensemble model for ride-hailing travel mobility estimation. • Develop conditional intervening opportunities model with substitutability of candidate locations. • Extract nonlinear dependencies from ride-hailing data using artificial intelligence algorithm. • Verify the proposed prediction model using over 25-million ride-hailing trips. • Interpret ride-hailing travel mobility patterns using Shapley additive explanations method. [ABSTRACT FROM AUTHOR]

[Display omitted] • This paper shows how the problem of CM&FD of OOR wheels can be transformed into an ML problem. • A vehicle-track MBS model considering track irregularities and worn wheel profiles is built. • A DL model, OORNet, is proposed to realize CM&FD of our-of-round wheels. • The feasibility and superiority of OORNet are demonstrated by three other DL models. The problem of train wheel out-of-roundness (OOR) negatively affects both humans and the vehicle-track system, incl. reduced passenger comfort, rapid aging of vehicle/track components, increase in derailment risk, etc. It is therefore of interest to develop an on-board condition monitoring and fault diagnosis (CM&FD) technique for wheel OOR, which contributes not only to the maintenance decision-making of wheelsets but also to clarifying its triggering and evolution mechanisms. This paper first shows how to express the problem of CM&FD of our-of-round wheels as a machine learning problem. A deep learning model, OORNet, is then developed for CM&FD of out-of-round wheels. A vehicle-track multi-body dynamics model of a China railway high-speed (CRH) trailer is meanwhile built to produce a database consisting of vertical axlebox vibration accelerations caused by 2000 different wheel OOR curves. The simulated database is finally used to test the performance of OORNet, and its feasibility and superiority are verified. [ABSTRACT FROM AUTHOR]

With the rapid development of economy, air pollution occurs frequently, which has a huge negative impact on human health and urban ecosystem. Air quality index (AQI) can directly reflect the degree of air pollution. Accurate AQI trend prediction can provide reliable information for the prevention and control of air pollution, but traditional forecasting methods have limited performance. To this end, a dual-scale ensemble learning framework is proposed for the complex AQI time series prediction. First, complete ensemble empirical mode decomposition adaptive noise (CEEMDAN) and sample entropy (SE) are used to decompose and reconstruct AQI series to reduce the difficulty of direct modeling. Then, according to the characteristics of high and low frequencies, the high-frequency components are predicted by the long short-term memory neural network (LSTM), and the low-frequency items are predicted by the regularized extreme learning machine (RELM). At the same time, the improved whale optimization algorithm (WOA) is used to optimize the hyper-parameters of RELM and LSTM models. Finally, the hybrid prediction model proposed in this paper predicts the AQI of four cities in China. This work effectively improves the prediction accuracy of AQI, which is of great significance to the sustainable development of the cities. • A dual-scale ensemble framework is proposed for air quality index prediction. • CEEMDAN technology is adopted to obtain multiple intrinsic mode functions. • An improved WOA algorithm based on dimension learning-based hunting is proposed. • Long short-term memory network is used to predict high-frequency components. • Regularized extremum learning machine is used to predict low-frequency components. [ABSTRACT FROM AUTHOR]

The occurrence of rockburst can cause significant disasters in underground rock engineering. It is crucial to predict and prevent rockburst in deep tunnels and mines. In this paper, the deficiencies of ensemble learning algorithms in rockburst prediction were investigated. Aiming at these shortages, a novel machine learning model, deep forest, was proposed to predict rockburst risk. The deep forest combines the characteristics of deep learning and ensemble models, which can solve complex problems. To develop the deep forest model for rockburst prediction, 329 real rockburst cases were collected to build a comprehensive database for intelligent analysis. Bayesian optimization was proposed to tune the hyperparameters of the deep forest. As a result, the deep forest model achieved 100% training accuracy and 92.4% testing accuracy, and it has more outstanding capability to forecast rockburst disasters compared to other widely used models (i.e., random forest, boosting tree models, neural network, support vector machine, etc.). The results of sensitivity analysis revealed the impact of variables on rockburst levels and the applicability of deep forest with a few input parameters. Eventually, real cases of rockburst in two gold mines, China, were used for validation purposes while the needed data sets were prepared by field observations and laboratory tests. The promoting results of the developed model during the validation phase confirm that it can be used with a high level of accuracy by practicing engineers for predicting rockburst occurrences. [ABSTRACT FROM AUTHOR]

• Dictionary learning is used to model gold prospectivity for gold exploration targeting. • The case study shows that the dictionary learning model is no inferior to the logistic regression model and superior to the one-class SVM model. Machine learning and deep learning anomaly detectors have been successfully used to map mineral prospectivity. However, the establishment of machine learning or deep learning anomaly detection models for mineral prospectivity mapping often requires the determination of a set of initialization parameters in the absence of ground truth data. Improper initialization parameters will degrade the performance of these mineral prospectivity mapping models. Different from most machine learning and deep learning algorithms, dictionary learning is a "white-box" algorithm involving only linear transformations, and building a dictionary learning model requires only an empirical definition of the total number of atoms. Therefore, in this paper, two dictionary learning anomaly detectors were established for mineral prospectivity modeling based on the least angle regression-Lasso (LARS-Lasso) algorithm and the iterative shrinkage-thresholding algorithm (ISTA). The following five-step procedure was proposed for mineral prospectivity modeling using the dictionary learning techniques: (a) an overcomplete dictionary is constructed based on the input data; (b) each data point is transformed into sparse coefficients according to the overcomplete dictionary; (c) the sparse representation of each data point is calculated based on the overcomplete dictionary and the sparse coefficients; and (d) the Euclidean norm of the difference between each data point and its sparse representation is calculated and used as the mineral potential of the data point. The dictionary learning models were established to model gold prospectivity in the Jinchanggouliang district, Inner Mongolia, China, and compared with the logistic regression model and one-class support vector machine model in gold exploration targeting. The result shows that (a) the performances of the dictionary learning models are no less than that of the logistic regression (LGR) model and better than that of the one-class support vector machine (OCSVM) model, and (b) the gold prospective areas differentiated by the established models are strongly consistent with geological and metallogenic characteristics in the study area. Therefore, the dictionary learning algorithms are high-performance mineral prospectivity modeling techniques. It is worth to further test the effectiveness of the dictionary learning algorithms for different types of mineral deposits in different areas in mineral exploration targeting. [ABSTRACT FROM AUTHOR]

• European option datasets were time-sequenced by maturity to extract time information. • Two deep learning models were built for the option pricing tasks. • Our models can price option accurately without volatility calibration and simulation. • An interpretable method was used to find how models improved the pricing results. • Accumulate local effect range method was proposed to get feature emphasis of models. In this paper, we investigated the feasibility of pricing European options with time-sequencing data processing method and deep learning models, based on two European options, the ETF50 options of China and the S&P 500 options of America. Four competing models were built to verify the improvement of the 1D-CNN and LSTM models on the option pricing task. Methods like cross-validations and statistical tests were also used to make our experiments more robust. Besides, in order to increase the stability and the interpretability of our pricing models, we selected the ALE method to interpret and analyze the behavior of the deep learning models. The empirical results indicate that, in both ETF50 option and S&P500 option pricing tasks, the 1D-CNN and LSTM models had significant advantages in forecasting accuracy and robustness under moneyness, trading date or maturity dimension irrespectively. Especially for the LSTM model, which has robust performance using different kinds of cross-validation methods. With the help of ALE method, we proved that the improved performance brought by the 1D-CNN and LSTM models could be attributed to their capability of capturing time-series information and their different emphasis on input features and lags. [ABSTRACT FROM AUTHOR]