Search

As the fastest-growing crowdfunding model, equity crowdfunding (ECF) brings high returns and uncertainty. In this context, it is crucial to understand these crowdfunding projects' actual performance. Since ECF is currently in the early stage of integration, there are still a lot of risk issues, such as the uncertainty of equity structure, capital supervision, or project management. Therefore, this paper develops a new profitability indicator, "return on registered capital," to test its impact on the ECF project's actual return. This paper studies which factors affect the financial performance of ECF projects through the traditional statistical model and a deep neural network (DNN) model. There is evidence that return on registered capital affects the actual return of the project. At the same time, the company's operating time and the number of employees had an unexpected effect on project performance. In addition, the recognition accuracy of the DNN model in this study exceeds 97%, which affirms the applicability of the DNN model in the analysis of ECF success factors. This paper also uses tenfold cross-validation to prove that deep learning has certain advantages in this topic's accuracy and generalization error. This study explores whether company representatives' gender and knowledge level affect project performance. The results will be described in detail in the paper. [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]

Background: Lung cancer is one of the cancers with the highest mortality rate in China. With the rapid development of high-throughput sequencing technology and the research and application of deep learning methods in recent years, deep neural networks based on gene expression have become a hot research direction in lung cancer diagnosis in recent years, which provide an effective way of early diagnosis for lung cancer. Thus, building a deep neural network model is of great significance for the early diagnosis of lung cancer. However, the main challenges in mining gene expression datasets are the curse of dimensionality and imbalanced data. The existing methods proposed by some researchers can't address the problems of high-dimensionality and imbalanced data, because of the overwhelming number of variables measured (genes) versus the small number of samples, which result in poor performance in early diagnosis for lung cancer. Method: Given the disadvantages of gene expression data sets with small datasets, high-dimensionality and imbalanced data, this paper proposes a gene selection method based on KL divergence, which selects some genes with higher KL divergence as model features. Then build a deep neural network model using Focal Loss as loss function, at the same time, we use k-fold cross validation method to verify and select the best model, we set the value of k is five in this paper. Result: The deep learning model method based on KL divergence gene selection proposed in this paper has an AUC of 0.99 on the validation set. The generalization performance of model is high. Conclusion: The deep neural network model based on KL divergence gene selection proposed in this paper is proved to be an accurate and effective method for lung cancer prediction. [ABSTRACT FROM AUTHOR]

In recent years, China's economy has been developing rapidly, and people's life and quality of life have been improving; more importantly, people's habits and living habits have also developed from the previous "unhygienic" and "not very careful" to the current "Healthier, more hygienic, greener and more sophisticated" direction. In the process of this development, due to the rapid development of the economy and the industrialization of cities, the incidence of heart disease is also increasing year by year. According to relevant studies, China's urbanization process has been unprecedented, the number of urbanized people in China has exploded in recent years, and the ratio of urban to rural population has increased from about 20 percent to about 75 percent today. The effects of the population and the urbanization of urban architecture are affecting people's physical and mental health, both consciously and unconsciously, causing both positive and negative physical and mental effects on the psychological and physical levels. In this paper, the concept of deep learning is fully utilized to train the CNN neural network model and apply it to the ECG abnormality recognition and prediction examination. In order to fully validate the application and significance of deep learning in ECG abnormality identification and prediction, the whole project was completed through subjective and objective experiments. The experimental results show that, from the subjective aspect, the ECG examination has been accepted by most people for different age groups, and the analysis results of the ECG examination with the deep learning model in this paper are more satisfactory; from the objective aspect, the CNN-ECG abnormality recognition prediction network model trained in this paper has high. The accuracy of the model for ECG abnormality recognition prediction can reach 86% when the learning rate is set to 0.0001 and the batch size is set to 120, and the model can reduce the burden of medical personnel to a certain extent. [ABSTRACT FROM AUTHOR]

In recent years, with the development of geological prospecting from shallow ore to deep and hidden ore, the difficulty of prospecting is increasing day by day, so the application of computer technology and new methods of geological and mineral exploration is paid more and more attention. The mining and prediction of geological prospecting information based on deep learning have become the frontier field of earth science. However, as a deep artificial intelligence algorithm, deep learning still has many problems to be solved in the big data mining and prediction of geological prospecting, such as the small number of training samples of geological and mineral images, the difficulty of building deep learning network models, and the universal applicability of deep learning models. In this paper, the training samples and convolutional neural network models suitable for geochemical element data mining are constructed to solve the above problems, and the model is successfully applied to the prediction research of gold, silver, lead and zinc polymetallic metallogenic areas in South China. Taking the Pangxidong research area in the west of Guangdong Province as an example, this paper carries out prospecting target prediction research based on a 1:50000 stream sediment survey original data. Firstly, the support vector machine (SVM) model and statistical method were used to determine the ore-related geochemical element assemblage. Secondly, the experimental data of geochemical elements were augmented and a dataset was established. Finally, ResNet-50 neural network model is used for data training and prediction research. The experimental results show that the areas numbered 9, 29, 38, 40, 95, 111, 114, 124, 144 have great metallogenic potential, and this method would be a promising tool for metallogenic prediction. By applying the ResNet-50 neural network in metallogenic prediction, it can provide a new idea for the future exploration of mineral resources. In order to verify the generality of the research method in this paper, we conducted experimental tests on the geochemical dataset of B area, another deposit research area in South China. The results show that 100% of the prediction area obtained by using the proposed method covers the known ore deposit area. This model also provides method support for further delineating the prospecting target area in study area B. [ABSTRACT FROM AUTHOR]

Tennis is an important sporting event. In China, the number of people who study and participate in tennis can be said to be quite large, but there are not many people who insist on playing tennis in the end. Some coaches and tennis groups have come up with a number of different teaching styles in order to keep tennis players going. Its main features are as follows: To cultivate students' interest in learning, the sequence of strokes in tennis is used for teaching. Although tennis has no special requirements for the physical condition of the athlete, tennis is a relatively difficult sport to master. What is lacking at present is more effective and simpler teaching methods, so training should be carried out in a targeted manner in the tennis baseline stroke technique. In order to make the tennis baseline stroke teaching more intuitive and easy to learn, this paper applies the neural network learning algorithm to the research and analysis of the tennis baseline stroke technique and proposes the core technology of the tennis baseline stroke technique. On this basis, the key techniques of tennis baseline stroke technique are discussed. Based on this, a basic model based on deep convolutional neural network is proposed. The experimental results show that the forehand scores of the three throwing methods in the experimental group are 6.47 and 7.28, respectively, which is better than the control group. The three throwing methods are self-throwing, close throwing, and long throwing. It shows that the teaching method in this paper can greatly improve the students' tennis baseline hitting skills. [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]

Drought is an important meteorological event in China and can cause severe damage to both livelihoods and socio‐ecological systems, but current seasonal prediction skill for drought is far from successful. This study used convolutional neural network (CNN) to build an effective seasonal forecast model for the summer consecutive dry days (CDD) over China. The principal components (PC) of the six leading empirical orthogonal function modes of CDD anomaly were predicted by CNN, using the previous winter precipitation, 2‐m temperature and 500 hPa geopotential height as predictors. These predicted PCs were then projected onto the observed spatial patterns to obtain the final predicted summer CDD anomaly over China. In the independent hindcast period of 2007–2018, the interannual variabilities of first six PCs were significantly predicted by CNN. The spatial patterns of CDD were then skillfully predicted with anomaly correlation coefficient skills generally higher than 0.2. The interannual variability of summer CDD over the middle and lower Yangtze River valley, northwestern China and northern China were significant predicted by our CNN model three months in advance. CNN identified that the previous winter precipitation was the important predictor for PC1–PC3, whereas the previous winter 2‐m temperature and 500 hPa geopotential height were important for the prediction of PC4–PC6. This research provides a new and effective method for the seasonal prediction of summer drought. Plain Language Summary: Drought can cause serious agricultural and ecosystem disasters, so its forecast is valuable for preventing and mitigating related natural disasters and regional socioeconomic sustainability. However, current prediction skill for drought is far from successful since its extreme feature. The gradually emerging deep learning methods offer new possibilities, but how to effectively apply deep learning models in climate prediction with a small sample size remains an open question. In this paper, we build seasonal prediction convolutional neural network model for summer consecutive dry days over China using previous winter predictors. This model achieves significant prediction skill three months in advance. The empirical orthogonal function decomposition is used to reduce the dimensionality of consecutive dry days data in our model. Our research provides a new perspective for drought prediction, and it is expected that such method will be also useful for other seasonal climate prediction problems. Key Points: Convolutional neural network (CNN) skillfully predicts summer consecutive dry days (CDD) over China three months in advanceThe principal components of CDD are predicted by CNN and then projected on the observed spatial patternsPrevious winter 2‐m temperature, geopotential height at 500 hPa and precipitation are the essential predictors in CNN [ABSTRACT FROM AUTHOR]

At present, the process of security issuance in China has changed from examination and approval system to approval system. With the increasing stock that enters the capital market through the IPO, establishing an effective financial risk monitoring and control system of listed companies has great practical significance and application value. In the perspective of big data, this paper constructs a new practical financial early warning integration model. A total of 32,283 financial statements of 3,025 listed companies are collected from 2000 to 2017. First, this paper collected and built a financial crisis prediction database: a total of 32,283 financial statements of 3,025 listed companies occurred from year 2000 to 2017 and corresponding financial indicators including 6 primary indicators and 25 secondary indicators are collected. Next, the financial crisis model based on financial indicators is proposed and trained by the classic machine learning method such as random forest and gradient boosting decision tree. And then, through natural language processing and deep learning technology, this paper also builds a financial crisis prediction model based on financial statement text. Finally, an ensemble model is proposed and its performances are evaluated; the results indicate that the proposed ensemble model performed the best and significantly outperforms other benchmark methods for financial crisis prediction and identification, indicating that it can be employed as an intelligent identification system to enhance identification accuracy for early warning and identification of financial crisis of listed companies in China stock marketing. [ABSTRACT FROM AUTHOR]

With the downward pressure of China's economy and the impact of the epidemic, the accumulated market risk has increased the liquidity pressure of the banking industry, and the mismatch between deposit maturity and loan maturity is the main cause for the increase of liquidity risk. The twenty-first century is the era of rapid and in-depth development of data management technology. The explosive growth of massive financial data makes the information data related to the liquidity risk of commercial banks present the characteristics of complexity, diversity, and heterogeneity. The traditional risk early warning model cannot deal with the influence between a large number of influencing factors and the nonlinear factors of commercial bank liquidity risk. Based on this transformation, the circular neural network model is introduced into the field of liquidity risk early warning of commercial banks from the perspective of the mismatch risk of financing maturity of commercial banks, and the driving factors and risk warning signs of liquidity risk of commercial banks are further analyzed from the institutional level, policy level, industry level, and micro commercial bank level. This paper uses network crawler technology, text analysis, and grounded analysis technology to intelligently identify the liquidity risk of commercial banks and establishes an early warning index system based on the influencing factors of commercial banks and internal liquidity risk. Also, it constructs an intelligent early warning model of commercial bank liquidity risk based on deep learning and uses the data of commercial banks from 2000 to 2020 for early warning. The results show that the constructed model has high accuracy, which can provide support for banks and relevant government departments to formulate and resolve bank liquidity risk. [ABSTRACT FROM AUTHOR]

Background: Peer assessment has played an important role in large‐scale online learning, as it helps promote the effectiveness of learners' online learning. However, with the emergence of numerical grades and textual feedback generated by peers, it is necessary to detect the reliability of the large amount of peer assessment data, and then develop an effective automated grading model to analyse the data and predict learners' learning results. Objectives: The present study aimed to propose an automated grading model with reliability detection. Methods: A total of 109,327 instances of peer assessment from a large‐scale teacher online learning program were tested in the experiments. The reliability detection approach included three steps: recurrent convolutional neural networks (RCNN) was used to detect grade consistency, bidirectional encoder representations from transformers (BERT) was used to detect text originality, and long short‐term memory (LSTM) was used to detect grade‐text consistency. Furthermore, the automated grading was designed with the BERT‐RCNN model. Results and Conclusions: The effectiveness of the automated grading model with reliability detection was shown. For reliability detection, RCNN performed best in detecting grade consistency with an accuracy rate of 0.889, BERT performed best in detecting text originality with an improvement of 4.47% compared to the benchmark model, and LSTM performed best with an accuracy rate of 0.883. Moreover, the automated grading model with reliability detection achieved good performance, with an accuracy rate of 0.89. Compared to the absence of reliability detection, it increased by 12.1%. Implications: The results strongly suggest that the automated grading model with reliability detection for large‐scale peer assessment is effective, with the following implications: (1) The introduction of reliability detection is necessary to help filter out low reliability data in peer assessment, thus promoting effective automated grading results. (2) This solution could assist assessors in adjusting the exclusion threshold of peer assessment reliability, providing a controllable automated grading tool to reducing manual workload with high quality. (3) This solution could shift educational institutions from labour‐intensive grading procedures to a more efficient educational assessment pattern, allowing for more investment in supporting instructors and learners to improve the quality of peer feedback. Lay Description: What is already known about this topic: Peer assessment has played an important role in large‐scale online learning, as it helps promote the effectiveness of learners' online learning.Issues such as disagreement between peer assessors, rough assessment, and plagiarism in large‐scale online learning can decrease peer assessment reliabilityIncorporating extensive data into a training model may result in grading uncertainties. What this paper adds: Detecting the peer assessment reliability before grading is essential in the context of large‐scale online learning.This study aimed to propose and validate an automated grading model with reliability detection for the large‐scale online peer assessment, which will help improve the effectiveness of automated grading, combining the advantages of computer technology and human expertise. Implications for practice and/or policy: The introduction of reliability detection is necessary to help filter out low reliability data in peer assessment, thus promoting effective automated grading results.This solution could assist assessors in adjusting the exclusion threshold of peer assessment reliability, providing a controllable automated grading tool to reducing manual workload with high quality. [ABSTRACT FROM AUTHOR]

Intelligent forecasting of economic indexes has been an important demand for sustainable management of smart cities. Existing methods for this purpose were mostly established upon the basis of economic mechanism. Econometric models are the most general technical means in this area. However, in era of digital economy, increasing amount of big data has brought great change to traditional production. It is becoming more difficult for conventional technological forecasting methods to deal with multi-dimensional economic indexes. To deal with such challenge, this paper introduces the artificial intelligence algorithms to implement automatic information processing, and proposes a deep neural network-based intelligent forecasting method for multi-dimensional economic indexes in smart cities. Specifically, a deep neural network with three-layer structure is developed as the backbone methodology. For empirical analysis, the real-world data from "Chengdu–Chongqing Economic Circle" in China from 2012 to 2022 are selected as the main simulation scenario. Four major indexes are selected as the main research object: gross product (GDP), per capita GDP, GDP growth rate and the proportion of tertiary industry in GDP. The experimental results show that the proposal can well deal with such forecasting problem from a data-driven perspective, with a proper forecasting effect on historical data. [ABSTRACT FROM AUTHOR]

In recent years, China's engineering construction management level has been greatly improved, but compared with other industries, the construction industry still has low production efficiency, serious waste, and low level of information problems, and especially in the process of engineering management practice, schedule delay has become the focus of engineering management problems. With the continuous development of science and technology, computer and information technology have been continuously applied in engineering, among which deep neural network (DNN) technology, lean management, information visualization technology, and other technologies have become the hot spot of industry research, and the application of these emerging technologies to improve the level of project schedule control has become an urgent demand of the industry. Therefore, on the basis of deep learning, this paper analyzes the principle and application of object detection and feature extraction constructed by neural network and combines text feature extraction and image feature extraction methods. This application provides a new idea for the development of the construction industry. [ABSTRACT FROM AUTHOR]

The demand for talents extends beyond theoretical knowledge to include students' IPE (Ideological and Political Education). We can only continue to promote the rapid development of university teaching in China by putting in place a solid management system. It is the foundation of college students' IPE in this era of rapid information technology development to accurately adapt to and understand the actual problems and requirements of IPE proposed by the development of the times and innovate and develop accordingly. Deep learning (DL) is a sophisticated machine learning algorithm that outperforms previous technologies in speech and image recognition. In this paper, DLNN (deep learning neural network) technology is applied to IPE evaluation and a set of university-specific IPE evaluation index systems is developed. The BPNN (BP Neural Network) evaluation model is used to train and study a specific amount of teaching quality data in the MATLAB simulation tool. On the prediction of IPE evaluation, the BPNN algorithm has a prediction error of about 0.050.4 according to the findings. The validity and accuracy of using the BPNN algorithm to model IPE quality are demonstrated. [ABSTRACT FROM AUTHOR]

This paper attempts to apply recurrent neural networks (RNN) to price forecasts and financial trading. Compared with previous neural networks models, the recurrent neural network can better use the previous information to infer subsequent events, which is more suitable for price time series analysis. Long Short-Term Memory (LSTM) has made structural changes to the RNN to avoid long-term dependency problems. The empirical research uses the 2010–2017 price panel data of four kinds of soybean futures in China's futures market, and confirms the model's improved predictive ability through statistical tests. The empirical analysis of futures trading verifies the practice of these model strategies in terms of risked return. This paper improves and expands the application of recurrent neural networks model, and provides a new idea for applying artificial neural network algorithm to futures trading. [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]

Landslides, widespread and highly dangerous geological disasters, pose significant risks to humankind and the ecological environment. Consequently, predicting landslides is vital for disaster prevention and mitigation strategies. At present, the predominant methods for predicting landslide susceptibility are evolving from conventional machine learning techniques to deep learning approaches. At present, the predominant methods for predicting landslide susceptibility are evolving from conventional machine learning techniques to deep learning approaches. Prior studies have shown that in the context of landslide susceptibility, these models frequently underperform relative to tree-based machine learning algorithms. This shortcoming has restricted the application of deep learning in this domain. To overcome this challenge, this study presents the TabNet algorithm, which combines the interpretability and selective feature extraction of tree models with the representation learning and comprehensive training capabilities of neural network models. This paper explores the potential of employing the TabNet algorithm for landslide susceptibility analysis in China's WanZhou region and evaluates its performance against traditional machine learning techniques. The experimental data indicate that the TabNet algorithm achieves a recall score of 0.898 and an AUC of 0.915, demonstrating a generalization capability that is comparable to that of classical machine learning algorithms. [ABSTRACT FROM AUTHOR]

Many colleges in China have adopted the policy of recruiting students by academic subject categories in order to optimize the talent training mode. To solve the problems in major selection after enrollment, this paper has designed an intelligent algorithm model for recommending college majors. Compared with existing methods for assigning college majors, the model uses deep neural networks and clustering algorithms to simulate complex calculations in the human brain. It uses historical learning data from senior students or graduates to predict the future grades of freshmen, judge their adaptability to various college majors, reduce human interference in the college major selection process, recommend the most suitable college major to students. [ABSTRACT FROM AUTHOR]

The classification that distinguishes whether machines are driving on roads or working in fields based on their global navigation satellite system (GNSS) trajectories is essential for effective management of cross-regional agricultural machinery services in China. In this paper, a novel field–road classification method utilizing multiple deep neural networks (MultiDNN) is proposed to enhance the accuracy of field and road point classification. The MultiDNN model incorporates a bi-directional long short-term memory network (BiLSTM), a topology adaptive graph convolution network (TAG), and a self-attention network (ATT) to effectively extract spatio-temporal features for field–road classification. The BiLSTM is used to capture temporal relationships along the time axis of a trajectory, providing global contextual information for each point. Then, the TAG network is used to obtain the spatio-temporal relationships between adjacent points in a trajectory, offering local contextual information for each point. Finally, the ATT network assigns varying weights to features to emphasize important characteristics. The performance of the MultiDNN model was evaluated using a wheat harvesting trajectory dataset, and the results showed that it achieved a high degree of accuracy, up to 89.75%, outperforming the best baseline method (GCN) by 2.79%. [ABSTRACT FROM AUTHOR]

Load Forecast (LF) is an important task in the planning, control and application of public power systems. Accurate Short Term Load Forecast (STLF) is the premise of safe and economical operation of a power system. In the research of short-term power load forecasting, machine learning and deep learning are the most popular methods at present, but there still exists a problem that the single and simple structure of power load forecasting model leads to low accuracy of load forecasting. In order to improve the accuracy of STLF, a Gated Cycle Unit (GRU)-Transformer combined neural network model is proposed. Transformer encoder structure is used as feature extractor to mine the complex mapping relationships between the input features and load. The advantage of self-attention mechanism is used to solve the problem of information loss of long sequences in short-term power load forecasting. At the same time, the multivariate time series model of GRU is used for model training. The experimental results on the power load data set of a certain region in southwest China and Panama City show that the proposed combined model prediction method has higher accuracy than those proposed in other literatures, which further proves its feasibility and superiority. [ABSTRACT FROM AUTHOR]

With the orderly advancement of China Energy Development Strategic Action Plan, clean energy has become a major trend in the energy market. As a major industry of clean energy, natural gas industry plans to consume at least 10% of the total primary energy by 2020. The energy structure will be improved in an orderly manner to achieve the goal of energy conservation, consumption reduction, and emission reduction. To achieve energy saving and emission reduction, and using clean energy effectively, accurate prediction of natural gas consumption is of great importance. Because of the many influencing factors affecting natural gas demand, this paper first utilizes STRIPAT to analyze the factors affecting natural gas consumption and then uses a deep learning ensemble approach to analyze and predict China's natural gas consumption. One is an advanced deep neural network model named gated recurrent unit model which is used to model the nonlinear and complex relationships of natural gas consumption with its factors. The other is a powerful ensemble method named bootstrap aggregation which generates multiple data sets for training a set of base models. Our approach combines the advantages of these two technologies to forecast the demand for China's natural gas market. In empirical research, our method has been tested by some competitive methods and has shown superiority. [ABSTRACT FROM AUTHOR]

With the reform of electric power system, major progress has been made in the construction of the electricity market. Electricity prices are a key influencing factor in the electricity market, and each participant trades electricity based on the price of electricity. Therefore, improving the accuracy of electricity price forecasts is important for every player in the electricity market. Prediction using single-layer neural networks has limited accuracy. Due to the high accuracy of machine learning in forecasting, the method of deep belief network is used to predict the price of electricity in the future. Real data from the U.S. PJM electricity market are used for simulation and compared with the prediction models of other neural networks. The results show that the prediction accuracy of the deep belief network model is higher, and the use of the deep belief network can provide an effective method for China's electricity sales companies to predict electricity prices. [ABSTRACT FROM AUTHOR]

Soil salinization has become one of the major environmental problems threatening food security. The identification and knowledge of the spatial distributions of soil salinization are key in addressing this problem. This study assumes that a good saline land identification effect can be obtained with the help of deep learning methods. Therefore, this study used the OLI sensor data from the Landsat-8, based on the U2-Network, and proposes a method to extract saline land from remote sensing images. The study also adds different salinity indices (SI, SI1, and SI2) to explore its impact on classification accuracy. Through our method, accurate saline soil distribution information were obtained, and several verification indicators (the Intersection-over-Union (IoU), recall, precision, and F1-score) were all measured above 0.8. In addition, compared with the multi-spectral training results, the classification accuracy increased after adding a specific salinity index, and most of the accuracy indices increased by about 2% (the IoU increased by 3.70%, recall increased by 1.50%, precision increased by 2.81%, and F1-score increased by 2.13%). In addition, we also included a case study based on our methodology to analyze the distribution characteristics and changes of saline soil in the Zhenlai area of Northeast China from 2016 to 2020. We found that the area of saline land in the Zhenlai area has reduced, which shows that the extraction method proposed in this study is feasible. Overall, this paper indicates that deep learning-based methods can efficiently extract the salinity of soil and enhance the mapping of its spatial distribution. The study has the broad impact of supplementing satellite imagery for salinity modeling and helping to guide agricultural land management practices for northeastern China and other salinized regions. [ABSTRACT FROM AUTHOR]

The problem of global warming is getting more serious, and vehicle emission is the main cause. In recent years, the number of locomotives in China has been increasing at a rate of more than 20% per year, and the problem of automobile pollution is becoming more serious. The transportation industry is the main source of fossil fuel combustion and environmental pollution. Therefore, in this paper, we propose a multi-view deep neural network (MVDNN) to analyze the key factors affecting the fuel consumption of automobiles. The experiments show that the introduction of human input improves the prediction accuracy and the root mean square error (RMSE) achieves 0.993. In addition, this paper also finds that for drivers, driving habits, driving frequency, and safety awareness are the most important factors affecting the fuel consumption of vehicles by combining Lasso regression with MVDNN. Finally, by comparing the prediction accuracy of different experiments, relevant policy suggestions are made. [ABSTRACT FROM AUTHOR]

The unidirectional consolidation theory of soils is widely used in certain conditions and approximate calculations. The multidirectional theory of soil consolidation is more reasonable than the unidirectional theory in practical applications but is much more complicated in terms of index determination and solution. To address the above problem, in this paper, we propose a deep learning method using physics-informed neural networks (PINN) to predict the excess pore water pressure of two-dimensional soil consolidation. In the proposed method, (1) a fully connected neural network is constructed; (2) the computational domain, partial differential equation (PDE), and constraints are defined to generate data for model training; and (3) the PDE of two-dimensional soil consolidation and the model of the neural network are connected to reduce the loss of the model. The effectiveness of the proposed method is verified by comparison with the numerical solution of PDE for two-dimensional consolidation. Moreover, the FEM and the proposed PINN-based method are applied to predict the consolidation of foundation soils in a real case of Sichuan Railway in China, and the results are quite consistent. The proposed deep learning approach can be used to investigate large and complex multidirectional soil consolidation. [ABSTRACT FROM AUTHOR]

Annual monitoring of the spatial distribution of cultivated land is important for maintaining the ecological environment, achieving a status quo of land resource management, and guaranteeing agricultural production. With the gradual development of remote sensing technology, it has become a common practice to obtain cultivated land boundary information on a large scale with the help of satellite Earth observation images. Traditional land use classification methods are affected by multiple types of land cover, which leads to a decrease in the accuracy of cultivated land mapping. In contrast, although the current advanced methods (such as deep learning) can obtain more accurate cultivated land mapping results than traditional methods, such methods often require the use of a massive amount of training samples, large computing power, and highly complex model tuning processes, increasing the cost of mapping and requiring the involvement of more professionals. This has hindered the promotion of related methods in mapping institutions. This paper proposes a method based on time series vector features (MTVF), which uses vector thinking to establish the features. The advantage of this method is that the introduction of vector features enlarges the differences between the different land cover types, which overcomes the loss of mapping accuracy caused by the influences of the spectra of different ground objects and ensures the calculation efficiency. Moreover, the MTVF uses a traditional method (random forest) as the classification core, which makes the MTVF less demanding than advanced methods in terms of the number of training samples. Sentinel-2 satellite images were used to carry out cultivated land mapping for 2020 in northern Henan Province, China. The results show that the MTVF has the potential to accurately identify cultivated land. Furthermore, the overall accuracy, producer accuracy, and user accuracy of the overall study area and four sub-study areas were all greater than 90%. In addition, the cultivated land mapping accuracy of the MTVF is significantly better than that of the maximum likelihood, support vector machine, and artificial neural network methods. [ABSTRACT FROM AUTHOR]

This paper develops a hierarchical deep learning machine (HDLM) to efficiently achieve both quantitative and qualitative online transient stability prediction (TSP). For the sake of improving its online efficiency, multiple generators’ fault-on trajectories as well as the two closest data-points in pre-/post-fault stages are acquired by PMUs to form its raw inputs. An anti-noise graphical transient characterization technique is tactfully designed to transform multiplex trajectories into 2-D images, within which system-wide transients are concisely described. Then, following the divide-and-conquer philosophy, the HDLM trains a two-level convolutional neural network (CNN) based regression model. With stability margin regressions hierarchically refined, it manages to perform reliable and adaptive online TSP almost immediately after fault clearance. Test results on the IEEE 39-bus test system and the real-world Guangdong Power Grid in South China demonstrate the HDLM's superior performances on both stability status and stability margin predictions. [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]

Different from traditional algorithms and model, machine learning is a systematic and comprehensive application of computer algorithms and statistical models, and it has been widely used in many fields. In the field of finance, machine learning is mainly used to study the future trend of capital market price. In this paper, to predict the time-series data of stock, we applied the traditional models and machine learning models for forecasting the linear and non-linear problem, respectively. First, stock samples that occurred from year 2010 to 2019 at the New York Stock Exchange are collected. Next, the ARIMA (autoregressive integrated moving average model) model and LSTM (long short-term memory) neural network model are applied to train and predict stock price and stock price subcorrelation. Finally, we evaluate the proposed model by several indicators, and the experiment results show that: (1) Stock price and stock price correlation are accurately predicted by the ARIMA model and LSTM model; (2) compared with ARIMA, the LSTM model performance better in prediction; and (3) the ensemble model of ARIMA-LSTM significantly outperforms other benchmark methods. Therefore, our proposed method provides theoretical support and method reference for investors about stock trading in China stock market. [ABSTRACT FROM AUTHOR]

Optimal operation of hydropower reservoir systems is a classical optimization problem of high dimensionality and stochastic nature. A key challenge lies in improving the interpretability of operation strategies, i.e., the cause–effect relationship between system outputs (or actions) and contributing variables such as states and inputs. This paper reports for the first time a new deep reinforcement learning (DRL) framework for optimal operation of reservoir systems based on deep Q-networks (DQNs), which provides a significant advance in understanding the performance of optimal operations. DQN combines Q-learning and two deep artificial neural networks (ANNs), and acts as the agent to interact with the reservoir system through learning its states and providing actions. Three knowledge forms of learning considering the states, actions, and rewards were constructed to improve the interpretability of operation strategies. The impacts of these knowledge forms and DRL learning parameters on operation performance were analyzed. The DRL framework was tested on the Huanren hydropower system in China, using 400-year synthetic flow data for training and 30-year observed flow data for verification. The discretization levels of reservoir water level and energy output yield contrasting effects: finer discretization of water level improved performance in terms of annual hydropower generated and hydropower production reliability; however, finer discretization of hydropower production can reduce search efficiency, and thus the resulting DRL performance. Compared with benchmark algorithms including dynamic programming, stochastic dynamic programming, and decision tree, the proposed DRL approach can effectively factor in future inflow uncertainties when determining optimal operations and can generate markedly higher hydropower. This study provides new knowledge of the performance of DRL in the context of hydropower system characteristics and data input features, and shows promise for potentially being implemented in practice to derive operation policies that can be updated automatically by learning from new data. [ABSTRACT FROM AUTHOR]

This paper investigates the expected results of the current COVID-19 outbreak to arrivals of Chinese tourists to the USA and Australia. The growing market share of Chinese tourism and the fact that the county was the first to experience the pandemic make China a suitable proxy for predictions on global tourism. We employ data from the 2003 SARS outbreak to train a deep learning artificial neural network named Long Short Term Memory (LSTM). The neural network is calibrated for the particulars of the current pandemic. Our findings, which are cross-validated using backtesting, suggest that recovery of arrivals to pre-crisis levels can take from 6 to 12 months and this can have significant adverse effects not only on the tourism industry but also on other sectors that interact with it. [ABSTRACT FROM AUTHOR]

Urban particulate matter forecasting is regarded as an essential issue for early warning and control management of air pollution, especially fine particulate matter (PM2.5). However, existing methods for PM2.5 concentration prediction neglect the effects of featured states at different times in the past on future PM2.5 concentration, and most fail to effectively simulate the temporal and spatial dependencies of PM2.5 concentration at the same time. With this consideration, we propose a deep learning-based method, AC-LSTM, which comprises a one-dimensional convolutional neural network (CNN), long short-term memory (LSTM) network, and attention-based network, for urban PM2.5 concentration prediction. Instead of only using air pollutant concentrations, we also add meteorological data and the PM2.5 concentrations of adjacent air quality monitoring stations as the input to our AC-LSTM. Hence, the spatiotemporal correlation and interdependence of multivariate air quality-related time-series data are learned by the CNN–LSTM network in AC-LSTM. The attention mechanism is applied to capture the importance degrees of the effects of featured states at different times in the past on future PM2.5 concentration. The attention-based layer can automatically weigh the past feature states to improve prediction accuracy. In addition, we predict the PM2.5 concentrations over the next 24 h by using air quality data in Taiyuan city, China, and compare it with six baseline methods. To compare the overall performance of each method, the mean absolute error (MAE), root-mean-square error (RMSE), and coefficient of determination (R2) are applied to the experiments in this paper. The experimental results indicate that our method is capable of dealing with PM2.5 concentration prediction with the highest performance. [ABSTRACT FROM AUTHOR]

Chinese mitten crab is a very popular food in China. Due to different nutrients between the male crab and the female crab, it is necessary to distinguish crab's gender before selling in the market. Current computer vision methods need complex image preprocessing and cannot distinguish crab's gender through the crab shell. In this paper, a new classification approach for crab's gender using the deep convolutional neural network (CNN) was proposed. Firstly, four different types of data augmentation methods were used to enrich the data set. Then, a batch normalization technique was taken to solve the problem of the gradient disappearance and shorten the training time. In order to suppress over-fitting phenomenon, a dropout technique was also taken into consideration. Experimental results demonstrated the effectiveness of the proposed crab's gender classification method. The proposed method achieved 98.90% classified accuracy which is 3 percentage points higher than two state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Ice clouds are mostly composed of different ice crystal habits. It is of great importance to classify ice crystal habits seeing as they could greatly impact single‐scattering properties of ice crystal particles. The single‐scattering properties play an important role in the study of cloud remote sensing and the Earth's atmospheric radiation budget. However, there are countless ice crystals with different shapes in ice clouds, and the task of empirical classification based on naked‐eye observations is unreliable, time consuming and subjective, which leads to classification results having obvious uncertainties and biases. In this paper, the images of ice crystals observed from airborne Cloud Particle Imager in China are used to establish an ice crystal data set called Ice Crystals Database in China, which consists of 10 habit categories containing over 7,000 images. We propose an automatic classification model of ice crystal habits, called TL‐ResNet152, which is a deep convolutional neural network based on the newly developed method of transfer learning. The results show that the TL‐ResNet152 model could achieve reliable performance in ice crystal habits classification with the accuracy of 96%, which is far more accurate than traditional classification methods. Achieving high‐precision automatic classification of ice crystal habits will help us better understand the radiation characteristics of ice clouds. Plain Language Summary: In recent years, Convolutional Neural Networks (CNNs), as one of the representative algorithms used in deep learning, have been widely applied in the field of image classification and have achieved remarkable results. However, to the best of our knowledge, there are few studies regarding the application of deep CNNs to ice crystal image classification. In this paper, we propose an automatic classification model of ice crystal habits called TL‐ResNet152, which is a deep CNN based on a newly developed method of transfer learning. Using the TL‐ResNet152 model, high precision and automatic classification of ice crystal habits could be achieved, furthering our understanding of radiation characteristics of ice clouds. We have set up an ice crystal data set, called Ice Crystals Database in China, which consists of 10 habit categories with 7,282 images. As far as we know, it is the first publicly available ice crystals data set in China, pertaining to ice crystals observed in natural ice clouds. The publication of this database will promote more and more researches into understanding the physical process of ice clouds based on ice crystal habits classification. Key Points: An ice crystal data set called Ice Crystals Database in China (ICDC) containing over 7,000 images has been first establishedWe propose an automatic classification model of ice crystal habits, TL‐ResNet152, which is a convolutional neural network created using transfer learningThe high‐precision automatic classification of ice crystal habits is achieved by using this classification model [ABSTRACT FROM AUTHOR]

According to statistics released by the WHO, China has the highest prevalence of myopia in the world, with a frequency that is 1.5 times higher than the global average. Asians have the highest prevalence of myopia worldwide. The Ministry of Education and the State General Administration of Sports "2010 National Student Physical Fitness and Health Research Results" show that the incidence of poor vision among primary and secondary school students in China is 67.3%, and elementary school students' vision has decreased by 40.9%. Low vision among youth has become a major cause of affecting the quality of the population and improving national physical fitness; therefore, how to improve and enhance the vision level of youth has become a major issue for the government, sports, and educators face as a major issue. In order to address this issue, this research suggests a deep learning-based vision monitoring and risk prediction model for high myopia eyes and develops a deep artificial neural network that unsupervised learns essential characteristics of physiological time-series data. [ABSTRACT FROM AUTHOR]

Effective prediction of air pollution concentrations is of great importance to both the physical and mental health of citizens and urban pollution control. As one of the main components of air pollutants, accurate prediction of PM2.5 can provide a reference for air pollution control and pollution warning. This study proposes an air pollutant prediction and early warning framework, which innovatively combines feature extraction techniques, feature selection methods and intelligent optimization algorithms. First, the PM2.5 sequence is decomposed into several subsequences using the complete ensemble empirical mode decomposition with adaptive noise, and then the new components of the subsequences with different complexity are reconstructed using fuzzy entropy. Then, the Max-Relevance and Min-Redundancy method is used to select the influencing factors of the different reconstructed components. Then, a two-stage deep learning hybrid framework is constructed to model the prediction and nonlinear integration of the reconstructed components using a long short-term memory artificial neural network optimized by the gray wolf optimization algorithm. Finally, based on the proposed hybrid prediction framework, effective prediction and early warning of air pollutants are achieved. In an empirical study in three cities in China, the prediction accuracy, warning accuracy and prediction stability of the proposed hybrid framework outperformed the other comparative models. The analysis results indicate that the developed hybrid framework can be used as an effective tool for air pollutant prediction and early warning. [ABSTRACT FROM AUTHOR]

The air traffic control sector (ATCS) is the basic unit of the airspace system. If we can identify the congestion of an ATCS, it will help provide decision support for planning and daily operations. However, current methods mainly characterize congestion from the static structure and the dynamic operational features, resulting in poor generalization and operability. To this end, we propose a deep learning method from the perspective of complex networks. It takes aircraft as nodes to construct an aircraft network and utilizes the complexity indices to characterize it. So, the problem of identifying congestion becomes the complexity of the aircraft network. Inspired by active learning methods, we construct a deep active learning (DAL) model for congestion recognition. It adopts an iterative semi-supervised approach to reduce the number of labeled samples while ensuring recognition performance. To make full use of a large number of unlabeled samples, the sparse autoencoder is employed to characterize all labeled samples and unlabeled samples. The hidden layer of the deep neural network is constructed by stacking. In the process of active learning iteration, minimum confidence, marginal sampling, and information entropy are introduced as measures to select samples from the unlabeled sample set with significantly different features from the labeled sample set. The model is applied to three representative sectors in China's airspace as cases. Results suggest that DAL can reduce the labeled sample set's redundancy and achieve the desired performance with the smallest number of samples. Additionally, DAL is superior to the existing mainstream methods in the four objective evaluation indices. [ABSTRACT FROM AUTHOR]

Background: Ameloblastoma, a common benign tumor found in the jaw bone, necessitates accurate localization and segmentation for effective diagnosis and treatment. However, the traditional manual segmentation method is plagued with inefficiencies and drawbacks. Hence, the implementation of an AI-based automatic segmentation approach is crucial to enhance clinical diagnosis and treatment procedures. Methods: We collected CT images from 79 patients diagnosed with ameloblastoma and employed a deep learning neural network model for training and testing purposes. Specifically, we utilized the Mask R-CNN neural network structure and implemented image preprocessing and enhancement techniques. During the testing phase, cross-validation methods were employed for evaluation, and the experimental results were verified using an external validation set. Finally, we obtained an additional dataset comprising 200 CT images of ameloblastoma from a different dental center to evaluate the model's generalization performance. Results: During extensive testing and evaluation, our model successfully demonstrated the capability to automatically segment ameloblastoma. The DICE index achieved an impressive value of 0.874. Moreover, when the IoU threshold ranged from 0.5 to 0.95, the model's AP was 0.741. For a specific IoU threshold of 0.5, the model achieved an AP of 0.914, and for another IoU threshold of 0.75, the AP was 0.826. Our validation using external data confirms the model's strong generalization performance. Conclusion: In this study, we successfully applied a neural network model based on deep learning that effectively performs automatic segmentation of ameloblastoma. The proposed method offers notable advantages in terms of efficiency, accuracy, and speed, rendering it a promising tool for clinical diagnosis and treatment. [ABSTRACT FROM AUTHOR]

With the increasing global population and escalating ecological and farmland degradation, challenges to the environment and livelihoods have become prominent. Coordinating urban development, food security, and ecological conservation is crucial for fostering sustainable development. This study focuses on assessing the "Ecology-Agriculture-Urban" (E-A-U) space in Yulin City, China, as a representative case. Following the framework proposed by Chinese named "environmental capacity and national space development suitability evaluation" (hereinafter referred to as "Double Evaluation"), we developed a Self-Attention Residual Neural Network (SARes-NET) model to assess the E-U-A space. Spatially, the northwest region is dominated by agriculture, while the southeast is characterized by urban and ecological areas, aligning with regional development patterns. Comparative validations with five other models, including Logistic Regression (LR), Naive Bayes (NB), Gradient Boosting Decision Trees (GBDT), Random Forest (RF) and Artificial Neural Network (ANN), reveal that the SARes-NET model exhibits superior simulation performance, highlighting it's ability to capture intricate non-linear relationships and reduce human errors in data processing. This study establishes deep learning-guided E-A-U spatial evaluation as an innovative approach for national spatial planning, holding broader implications for national-level territorial assessments. [ABSTRACT FROM AUTHOR]

Objectives: This study aimed to analyze publications on artificial intelligence (AI) for falls in older people from a bibliometric perspective. Methods: The Web of Science database was searched for titles of English‐language articles containing the words "artificial intelligence," "deep learning," "machine learning," "natural language processing,", "neural artificial network," "fall," "geriatric," "elderly," "aging," "older," and "old age." An R‐based application (Biblioshiny for bibliometrics) and VOSviewer software were used for analysis. Results: Thirty‐seven English articles published between 2018 and 2024 were included. The year 2023 is the year with the most publications with 16 articles. The most productive research field was "Engineering Electrical Electronic" with seven articles. The most productive country was the United States, followed by China. The most common words were "injuries," "people," and "risk factors." Conclusion: Publications on AI and falls in the elderly are both few in number and the number of publications has increased in recent years. Future research should include relevant analyses in scientific databases, such as Scopus and PubMed. [ABSTRACT FROM AUTHOR]

• We construct a fine-grained classification dataset of wet Age-related macular degeneration (AMD). • A knowledge-driven deep learning model, KFWC, is proposed to improve accuracy in fine-grained disease classification with insufficient data. • KFWC can detect 10 signs of lesions on medical images and can diagnose two subtypes of wet-AMD. • Multiple experiments demonstrate the effectiveness and interpretability of the proposed KFWC. Automated diagnosis using deep neural networks can help ophthalmologists detect the blinding eye disease wet Age-related Macular Degeneration (AMD). Wet-AMD has two similar subtypes, Neovascular AMD and Polypoidal Choroidal Vasculopathy (PCV). However, due to the difficulty in data collection and the similarity between images, most studies have only achieved the coarse-grained classification of wet-AMD rather than a fine-grained one of wet-AMD subtypes. Therefore, designing and building a deep learning model to diagnose neovascular AMD and PCV is a great challenge. To solve this issue, in this paper, we propose a Knowledge-driven Fine-grained Wet-AMD Classification Model (KFWC) to enhance the model's accuracy in the fine-grained disease classification with insufficient data. We innovatively introduced a two-stage method. In the first stage, we present prior knowledge of 10 lesion signs through pre-training; in the second stage, the model implements the classification task with the help of human knowledge. With the pre-training of priori knowledge of 10 lesion signs from input images, KFWC locates the powerful image features in the fine-grained disease classification task and therefore achieves better classification. To demonstrate the effectiveness of KFWC, we conduct a series of experiments on a clinical dataset collected in cooperation with a Grade III Level A ophthalmology hospital in China. The AUC score of KFWC reaches 99.71%, with 6.69% over the best baseline and 4.14% over ophthalmologists. KFWC can also provide good interpretability and effectively alleviate the pressure of data collection and annotation in the field of fine-grained disease classification for wet-AMD. The model proposed in this paper effectively solves the difficulties of small data volume and high image similarity in the wet-AMD fine-grained classification task through a knowledge-driven approach. Besides, this method effectively relieves the pressure of data collection and annotation in the field of fine-grained classification. In the diagnosis of wet-AMD, KFWC is superior to previous work and human ophthalmologists. [ABSTRACT FROM AUTHOR]

The identification of overpass structures in road networks has great significance for multi-scale modeling of roads, congestion analysis, and vehicle navigation. The traditional vector-based methods identify overpasses by the methodologies coming from computational geometry and graph theory, and they overly rely on the artificially designed features and have poor adaptability to complex scenes. This paper presents a novel method of identifying overpasses based on a target detection model (Faster-RCNN). This method utilizes raster representation of vector data and convolutional neural networks (CNNs) to learn task adaptive features from raster data, then identifies the location of an overpass by a Region Proposal network (RPN). The contribution of this paper is: (1) An overpass labelling geodatabase (OLGDB) for the OpenStreetMap (OSM) road network data of six typical cities in China is established; (2) Three different CNNs (ZF-net, VGG-16, Inception-ResNet V2) are integrated into Faster-RCNN and evaluated by accuracy performance; (3) The optimal combination of learning rate and batchsize is determined by fine-tuning; and (4) Five geometric metrics (perimeter, area, squareness, circularity, and W/L) are synthetized into image bands to enhance the training data, and their contribution to the overpass identification task is determined. The experimental results have shown that the proposed method has good accuracy performance (around 90%), and could be improved with the expansion of OLGDB and switching to more sophisticated target detection models. The deep learning target detection model has great application potential in large-scale road network pattern recognition, it can task-adaptively learn road structure features and easily extend to other road network patterns. [ABSTRACT FROM AUTHOR]

The accurate measurement of soil moisture content emerges as a critical parameter within the ambit of agricultural irrigation management, wherein the precise prediction of this variable plays an instrumental role in enhancing the efficiency and conservation of agricultural water resources. This study introduces an innovative, cutting-edge hybrid model that ingeniously integrates Gated Recirculation Unit (GRU) and Transformer technologies, meticulously crafted to amplify the precision and reliability of soil moisture content forecasts. Leveraging meteorological and soil moisture datasets amassed from eight monitoring stations in Hebei Province, China, over the period from 2011 to 2018, this investigation thoroughly assesses the model's efficacy against a diverse array of input variables and forecast durations. This assessment is concurrently contrasted with a range of conventional machine learning and deep learning frameworks. The results demonstrate that (1) the GRU–Transformer model exhibits remarkable superiority across various aspects, particularly in short-term projections (1- to 2-day latency). The model's mean square error (MSE) for a 1-day forecast is notably low at 5.22%, reducing further to a significant 2.71%, while the mean coefficient of determination (R2) reaches a high of 89.92%. Despite a gradual increase in predictive error over extended forecast periods, the model consistently maintains robust performance. Moreover, the model shows exceptional versatility in managing different soil depths, notably excelling in predicting moisture levels at greater depths, thereby surpassing its performance in shallower soils. (2) The model's predictive error inversely correlates with the reduction in parameters. Remarkably, with a streamlined set of just six soil moisture content parameters, the model predicts an average MSE of 0.59% and an R2 of 98.86% for a three-day forecast, highlighting its resilience to varied parameter configurations. (3) In juxtaposition with prevalent models such as Support Vector Regression (SVR), K-Nearest Neighbors (KNN), Gradient Boosting Decision Tree (GBDT), XGBoost, Random Forest, and deep learning models like Deep Neural Network (DNN), Convolutional Neural Network (CNN), and standalone GRU-branch and Transformer-branch models, the GRU–Transformer framework demonstrates a significant advantage in predicting soil moisture content with enhanced precision for a five-day forecast. This underscores its exceptional capacity to navigate the intricacies of soil moisture data. This research not only provides a potent decision-support tool for agricultural irrigation planning but also makes a substantial contribution to the field of water resource conservation and optimization in agriculture, while concurrently imparting novel insights into the application of deep learning techniques in the spheres of agricultural and environmental sciences. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

[Display omitted] • In the case of a small number of known deposits and mineral occurrences, the prospecting prediction model based on the Siamese network can extract effective features without relying on prior knowledge to realize supervised prospecting prediction. • On the basis of sliding window data enhancement, this paper generates paired sample data sets by randomly combining deposit samples and non-deposit samples, which can further enhance the data and improve the generalization ability and prediction accuracy of the model to a certain extent. • Compared with the prediction results of the weight of evidence method and the convolutional neural network, the prediction results of the Siamese network are better, and 7 new gold prospecting areas have been discovered in Zhangjiakou, Hebei Province, China. Supervised neural networks constitute an important research area for the intelligent prediction of locations for prospecting mineral deposits. Accurate training of a supervised neural network requires many training samples, something that is often difficult to obtain. This paper reports the use of geological, geochemical, and geophysical data by a Siamese network to overcome the problem of insufficient training samples, and implements a supervised deep learning prospecting prediction method based on the Siamese network. Intelligent prediction for gold deposits prospecting is carried out for the Chongli mineral concentrate area in Zhangjiakou, Hebei Province, China, and compared with the weight of evidence method and convolutional neural network model. The results show that:(a) the performance of the Siamese network model is no less than that of the convolutional neural network (CNN) model and better than that of the weight of evidence (WOE) method; (b) the gold prospective areas differentiated by the established models are strongly consistent with geological and metallogenic characteristics in the study area. This study suggests Siamese network model as an effective mineral prospectivity modeling tool. This method is also suitable for prospecting prediction using geoscience data in other areas. [ABSTRACT FROM AUTHOR]

Accurate and timely landslide susceptibility mapping (LSM) is essential to effectively reduce the risk of landslide. In recent years, deep learning has been successfully applied to landslide susceptibility assessment due to the strong ability of fitting. However, in actual applications, the number of labeled samples is usually not sufficient for the training component. In this paper, a deep neural network model based on semi-supervised learning (SSL-DNN) for landslide susceptibility is proposed, which makes full use of a large number of spatial information (unlabeled data) with limited labeled data in the region to train the mode. Taking Jiaohe County in Jilin Province, China as an example, the landslide inventory from 2000 to 2017 was collected and 12 metrological, geographical, and human explanatory factors were compiled. Meanwhile, supervised models such as deep neural network (DNN), support vector machine (SVM), and logistic regression (LR) were implemented for comparison. Then, the landslide susceptibility was plotted and a series of evaluation tools such as class accuracy, predictive rate curves (AUC), and information gain ratio (IGR) were calculated to compare the prediction of models and factors. Experimental results indicate that the proposed SSL-DNN model (AUC = 0.898) outperformed all the comparison models. Therefore, semi-supervised deep learning could be considered as a potential approach for LSM. [ABSTRACT FROM AUTHOR]

With the rapid development of Internet technology, live broadcast industry has also flourished. However, in the public network live broadcast platform, live broadcast security issues have become increasingly prominent. The detection of suspected pornographic videos in live broadcast platforms is still in the manual detection stage, that is, through the supervision of administrators and user reports. At present, there are many online live broadcast platforms in China. In mainstream live streaming platforms, the number of live broadcasters at the same time can reach more than 100,000 people/times. Only through manual detection, there are a series of problems such as low efficiency, poor pertinence, and slow progress. This approach is obviously not up to the task requirements of real-time network supervision. For the identification of whether live broadcasts on the Internet contain pornographic content, a deep neural network model based on residual networks (ResNet-50) is proposed to detect pictures and videos in live broadcast platforms. The core idea of detection is to classify each image in the video into two categories: (1) pass and (2) violation. The experiments verify that the network proposed can heighten the efficiency of pornographic detection in webcasts. The detection method proposed in this article can improve the accuracy of detection on the one hand and can standardize the detection indicators in the detection process on the other. These detection indicators have a certain promotion effect on the classification of pornographic videos. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Image & Graphics is the property of Editorial Office of Journal of Image & Graphics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Cotton (Gossypium hirsutum) is one of the most important cash crops in China, The timely and effective removal of weeds in cotton seedling stage is an important measure to ensure high and stable yield of cotton. Nowadays, weed recognition based on machine vision is widely used. The fast and effective recognition of crop and weed in the field under natural illumination is one of the key technologies for the development of intelligent mechanization weeding pattern. In the one hand, cotton and weeds have similar color feature in the field. Feature presentation of the natural property of target is difficult to be obtained by the hand-engineered feature extractor. The spatial consistency of the obtained features is not good, and the real-time performance of recognition system is reduced for the complex feature extraction algorithm. On the other hand, the effect of image preprocessing has important influence on recognition results. In order to solve the main problems in the current research, we explored the way to improve the recognition accuracy, stability and real-time performance, and a recognition method of crop and weed based on Faster R-CNN．In this paper, cotton seedling at 2-5 leaves stages and weeding during the same stage were used as research objects under natural illumination. Weed identification from digital images taken under natural illumination at field level is still challenging in agricultural image processing applications, though a lot of research has been conducted related to this topic. To address this problem, images including cottons and weeds were taken vertically from top to bottom. A method based on Faster R-CNN convolutional neural network was proposed to identify weeds from cotton plants more accurately and quickly. The residual network was used to extract image features, with ReLU as the activation function and Max-pooling as the down-sampling method. In the region of proposal network, feature pyramid network was introduced to generate target candidate frame, and Softmax regression classifier was utilized to optimize the CNN network. The proposed methodology was implemented on 200 digital images taken under natural illumination. The experimental results demonstrated that, the average accuracy of weed identification reached 95.5%, and the average time for individual weed plant identification was 1.51 s, which was reduced to 0.09 s by using GPU. To test the efficiency of the proposed methodology, YOLOv3 method was also carried out on the same training and test datasets. The weed identification results were assessed by mean average precision and average precision. The experimental results showed that better performance was achieved by using our proposed methodology, and better identification accuracy was reached as well. This indicated that the proposed method had a good effect on weed detection under natural illumination, and it will greatly promote the development of precise weed control. [ABSTRACT FROM AUTHOR]

Cloud recognition is a basic task in ground meteorological observation. It is of great significance to accurately identify cloud types from long-time-series satellite cloud images for improving the reliability and accuracy of weather forecasting. However, different from ground-based cloud images with a small observation range and easy operation, satellite cloud images have a wider cloud coverage area and contain more surface features. Hence, it is difficult to effectively extract the structural shape, area size, contour shape, hue, shadow and texture of clouds through traditional deep learning methods. In order to analyze the regional cloud type characteristics effectively, we construct a China region meteorological satellite cloud image dataset named CRMSCD, which consists of nine cloud types and the clear sky (cloudless). In this paper, we propose a novel neural network model, UATNet, which can realize the pixel-level classification of meteorological satellite cloud images. Our model efficiently integrates the spatial and multi-channel information of clouds. Specifically, several transformer blocks with modified self-attention computation (swin transformer blocks) and patch merging operations are used to build a hierarchical transformer, and spatial displacement is introduced to construct long-distance cross-window connections. In addition, we introduce a Channel Cross fusion with Transformer (CCT) to guide the multi-scale channel fusion, and design an Attention-based Squeeze and Excitation (ASE) to effectively connect the fused multi-scale channel information to the decoder features. The experimental results demonstrate that the proposed model achieved 82.33% PA, 67.79% MPA, 54.51% MIoU and 70.96% FWIoU on CRMSCD. Compared with the existing models, our method produces more precise segmentation performance, which demonstrates its superiority on meteorological satellite cloud recognition tasks. [ABSTRACT FROM AUTHOR]