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Breast cancer is a cancer disease that seriously threatens women's health and occupies the first place in female cancer mortality. At present, the incidence rate of breast cancer in China is the first in the world and is on the rise. In view of the serious harm of breast cancer to life and health, researchers and institutions are making unremitting efforts to find a perfect diagnosis and treatment plan. With the improvement of computer performance and machine learning levels, intelligent algorithms have been able to replace human behavior and judgment in some fields. The traditional breast cancer diagnosis process requires medical experts to observe patient data repeatedly. In this case, the algorithm technology is used to quickly feedback a high probability reference result to doctors, which is particularly important to increase the diagnosis efficiency and reduce the burden of doctors. In order to improve the accuracy of existing breast cancer recognition methods, this paper proposes and implements a scheme based on a whale optimization algorithm to iteratively adjust the key parameters of the support vector machine to improve the accuracy of breast cancer recognition. In order to verify the performance of the WOA-SVM algorithm, this paper uses the Wisconsin breast cancer data in the UCI database for performance verification experiments. Experiments show that the WOA-SVM model has higher recognition accuracy than the traditional breast cancer recognition model. [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]

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]

Landslides occur in most countries. As one of the most serious geological hazards, landslides affect infrastructure construction. Thus, it is vital to prepare reliable landslide susceptibility evaluation maps to avoid landslide-prone areas for various construction projects. In recent years, supervised machine learning algorithms have been widely used in landslide susceptibility evaluation, but many flaws remain in the selection of nonlandslide point samples for comparative analysis. It is significant to improve the authenticity of sample data and reduce the impact of noise. China's Funing County was used as a case study in this paper, which first identified 122 landslide incidents based on historical data, fieldwork, and remote sensing images to create a landslide inventory in the research area. In addition, 12 causal factors of landslides were determined, including elevation, slope, aspect, plan curvature, profile curvature, distance to roads, distance to rivers, distance to faults, rainfall, normalized difference vegetation index (NDVI), lithology, and land cover. K-means clustering was used to purify the factor data factors before data-driven certainty factor (CF) and frequency ratio (FR) models, and machine learning models, random forest (RF) and artificial neural network (ANN), were used for a comparative study on landslide susceptibility evaluation in Funing County. The results show that the selection method of nonlandslide sample data will affect the accuracy of different evaluation models. The purified sample data improved the prediction accuracy of the four models, with significant prediction accuracy improvements observed in the ANN model. The purification of nonlandslide sample data by K-means method is of great significance for the drawing of landslide sensitivity map. [ABSTRACT FROM AUTHOR]

Small and micro enterprises play a very important role in economic growth, technological innovation, employment and social stability etc. Due to the lack of credible financial statements and reliable business records of small and micro enterprises, they are facing financing difficulties, which has become an important factor hindering the development of small and micro enterprises. Therefore, a credit risk measurement model based on the integrated algorithm of improved GSO (Glowworm Swarm Optimization) and ELM (Extreme Learning Machine) is proposed in this paper. First of all, according to the growth and development characteristics of small and micro enterprises in the big data environment, the formation mechanism of credit risk of small and micro enterprises is analyzed from the perspective of granularity scaling, cross-border association and global view driven by big data, and the index system of credit comprehensive measurement is established by summarizing and analyzing the factors that affect the credit evaluation index. Secondly, a new algorithm based on the parallel integration of the good point set adaptive glowworm swarm optimization algorithm and the Extreme learning machine is built. Finally, the integrated algorithm based on improved GSO and ELM is applied to the credit risk measurement modeling of small and micro enterprises, and some sample data of small and micro enterprises in China are collected, and simulation experiments are carried out with the help of MATLAB software tools. The experimental results show that the model is effective, feasible, and accurate. The research results of this paper provide a reference for solving the credit risk measurement problem of small and micro enterprises and also lay a solid foundation for the theoretical research of credit risk management. [ABSTRACT FROM AUTHOR]

Since the limitation of carbon emissions, China's photovoltaic (PV) industry has developed vigorously, while some traditional heavy industries have been violently hit. Therefore, the industrial production data exhibits significant nonlinear and complexity characteristics, which may affect prediction accuracy, thus hindering the corresponding department's decision-making. Consequently, a novel structure-adaptive fractional Bernoulli grey model is presented in this paper to surmount this toughie, and the core innovations can be summarized as follows. Initially, a novel time function term is utilized to depict the accumulative time effect, which can smoothly represent the dynamic variations and significantly strengthen the robustness of the new model. Besides, the fractional-order accumulation technique, which could effectively improve the predicting accuracy, is employed in the proposed model. Furthermore, the adaptability and generalizability of the proposed model can be enhanced by the self-adaptive parameters optimized by the Particle Swarm Optimization. For illustration and verification purposes, experiments on forecasting the annual output of Photovoltaic modules in China and the annual output of steel in Beijing are compared with a range of benchmarks, including the classic GM (1, 1), conventional econometric technology, and machine learning methods. And the results confirmed that the proposed model is superior to all benchmark models, which indicates that the novel model is indeed suitable for industrial production forecasting. [ABSTRACT FROM AUTHOR]

The long-term and short-term volatilities of financial market, combined with the complex influence of linear and nonlinear information, make the prediction of stock price extremely difficult. This paper breaks away from the traditional research framework of increasing the number of explanatory variables to improve the explanatory ability of multifactor model and provides a new financial trading strategy system by introducing Light Gradient Boosting Machine (LightGBM) algorithm into stock price prediction and by constructing the minimum variance portfolio of mean-variance model with Conditional Value at Risk (CVaR) constraint. The new system can capture the nonlinear relationship between pricing factors without specific distributions. The system uses Exclusive Feature Bundling to solve the problem of sparse high-dimensional feature matrix in financial data, so as to improve the ability of predicting stock price, and it can also intuitively screen variables with high impact through the factor importance score. Furthermore, the risk assessment based on CVaR in the system is more sufficient and consistent than the traditional portfolio theory. The experiments on China's stock market from 2008 to 2018 show that the trading strategy system provides a strong logical basis and practical effect for China's financial market decision. [ABSTRACT FROM AUTHOR]

At present, many large-scale engineering equipment can obtain massive in-situ data at runtime. In-depth data mining is conducive to the real-time understanding of equipment operation status or recognition of service environment. This paper proposes a geological type recognition system by the analysis of in-situ data recorded during TBM tunneling to address geological information acquisition during TBM construction. Owing to high dimensionality and nonlinear coupling between parameters of TBM in-situ data, the dimensionality reduction feature engineering and machine learning methods are introduced into TBM in-situ data analysis. The chi-square test is used to screen for sensitive features due to the disobedience to common distributions of TBM parameters. Considering complex relationships, ANN, SVM, KNN, and CART algorithms are used to construct a geology recognition classifier. A case study of a subway tunnel project constructed using an earth pressure balance tunnel boring machine (EPB-TBM) in China is used to verify the effectiveness of the proposed geological recognition method. The result shows that the recognition accuracy gradually increases to a stable level with the increase of input features, and the accuracy of all algorithms is higher than 97%. Seven features are considered as the best selection strategy among SVM, KNN, and ANN, while feature selection is an inherent part of the CART method which shows a good recognition performance. This work provides an intelligent path for obtaining geological information for underground excavation TBM projects and a possibility for solving the problem of engineering recognition of more complex geological conditions. [ABSTRACT FROM AUTHOR]

Artificial upwelling, artificially pumping up nutrient-rich ocean waters from deep to surface, is increasingly applied to stimulating phytoplankton activity. As a proxy for the amount of phytoplankton present in the ocean, the concentration of chlorophyll a (chl-a) may be influenced by water physical factors altered in artificial upwelling processes. However, the accuracy and convenience of measuring chl-a are limited by present technologies and equipment. Our research intends to study the correlations between chl-a concentration and five water physical factors, i.e., salinity, temperature, depth, dissolved oxygen (DO), and pH, possibly affected by artificial upwelling. In this paper, seven models are presented to predict chl-a concentration, respectively. Two of them are based on traditional regression algorithms, i.e., multiple linear regression (MLR) and multivariate quadratic regression (MQR), while five are based on intelligent algorithms, i.e., backpropagation-neural network (BP-NN), extreme learning machine (ELM), genetic algorithm-ELM (GA-ELM), particle swarm optimization-ELM (PSO-ELM), and ant colony optimization-ELM (ACO-ELM). These models provide a quick prediction to study the concentration of chl-a. With the experimental data collected from Xinanjiang Experiment Station in China, the results show that chl-a concentration has a strong correlation with salinity, temperature, DO, and pH in the process of artificial upwelling and PSO-ELM has the best overall prediction ability. [ABSTRACT FROM AUTHOR]

Extracting useful information from a large number of policy texts is a challenging and insufficiently discussed topic. Utilizing large sample policy texts and a method of machine learning, this study contributes to the research gap by systematically analyzing the temporal evolution and spatial differentiation of China's land policy from 1998 to 2018. A framework comprising six major themes of land policy, namely, "land development, land acquisition and demolition, cultivated land protection, land planning, land consolidation and utilization, and land confirmation and transfer" is first established, according to the theoretical and institutional background of land management. Based on this framework, the Latent Dirichlet Allocation analysis of more than 20,000 policy documents at different levels of government shows that, (1) temporally, the priority of land policy evolves with the spirit of the central document and the macropolitical and economic conditions and, (2) spatially, there are significant differences in land policies among provinces. Overall, the analysis of land policy documents shows the tradeoff between cultivated land protection and land development and also the emphasize on other topics, with the changes in land policy priorities in different periods and regions. [ABSTRACT FROM AUTHOR]

The development of the financial industry and its integration with information technology have promoted FinTech innovation. China is a major contributor to FinTech innovation, but few studies have systematically summarized FinTech innovation and development in China from the perspective of patents. This lacuna is attributable to the lack of a generally accepted classification of FinTech patents and the unavailability of classified Chinese FinTech patent text data. To fill this research gap, we developed a classification of Chinese FinTech patents and manually annotated a set of patent texts to train machine learning and deep learning models to classify massive Chinese patent application data and identify different types of FinTech innovations. Among the evaluated models, the character-level convolutional neural network (CNN) model and BERT model classified FinTech innovation most accurately. We used the character-level CNN to classify 20,529 Chinese FinTech patent applications from 2013 to 2020. The classified dataset was used to briefly analyze the history of FinTech innovation development in China and its future prospects. [ABSTRACT FROM AUTHOR]

At present, the scientific research and innovation team in colleges and universities has become an important carrier for the development of science and technology and personnel training in China, but in terms of its development status, the role of the scientific research and innovation team has not been really brought into play. Most colleges and universities only have the organizational form of scientific research and innovation teams. Too many scientific research tasks still rely on a small number of backbones. The cohesion of the team is not enough, and the combined force of the team is not manifested. In production practice, more and more high-dimensional data with dimension p approaching or even exceeding the number of samples n are collected and stored. Unlike traditional data analysis, high-dimensional data analysis is more complex and difficult. Layer of scientific research and innovation teams in colleges and universities is often ignored by managers, which is manifested by ignoring the cultural construction of the team. In the eyes of team managers, the time spent on building a team culture is far less than the time it takes to do a few more scientific experiments. University scientific research and innovation team is a multi-level network system. The asymptotic and nonasymptotic theories of random matrices break the framework of classical multivariate statistical analysis and are very suitable for the study of statistical characteristics of high-dimensional data, which can help machine learning algorithms to complete the analysis of high-dimensional data and expand its application range. The research and innovation team in colleges and universities is deeply analyzed from the perspective of nonlinearity. It is believed that the scientific research and innovation team in colleges and universities is a complex nonlinear system in terms of scientific research objects-interdisciplinary research, or from the perspective of personnel composition. Correspondingly, the construction should be considered from a nonlinear perspective in the process, following the systematic, integral, and multi-dimensional characteristics of nonlinear systems. Specifically applied to the construction of the team, first, from a macro perspective, three principles should be paid attention to at the beginning of the creation of the team, and then the specific application should be explained in detail. It is hoped that a new perspective will be put forward in the construction of scientific research innovation teams in colleges and universities to better promote the development of teams. [ABSTRACT FROM AUTHOR]

Tunnel settlement commonly occurs during the tunnel construction processes in large cities. Existing forecasting methods for tunnel settlements include model-based approaches and artificial intelligence (AI) enhanced approaches. Compared with traditional forecasting methods, artificial neural networks can be easily implemented, with high performance efficiency and forecasting accuracy. In this study, an extended machine learning framework is proposed combining particle swarm optimization (PSO) with support vector regression (SVR), back-propagation neural network (BPNN), and extreme learning machine (ELM) to forecast the surface settlement for tunnel construction in two large cities of China P.R. Based on real-world data verification, the PSO-SVR method shows the highest forecasting accuracy among the three proposed forecasting algorithms. [ABSTRACT FROM AUTHOR]