Intelligent Analysis Technology Research Articles

Machine learning algorithms for the prediction of adverse prognosis in patients undergoing peritoneal dialysis

BackgroundAn appropriate prediction model for adverse prognosis before peritoneal dialysis (PD) is lacking. Thus, we retrospectively analysed patients who underwent PD to construct a predictive model for adverse prognoses using machine learning (ML).MethodsA retrospective analysis was conducted on 873 patients who underwent PD from August 2007 to December 2020. A total of 824 patients who met the inclusion criteria were included in the analysis. Five commonly used ML algorithms were used for the initial model training. By using the area under the curve (AUC) and accuracy (ACC), we ranked the indicators with the highest impact and displayed them using the values of Shapley additive explanation (SHAP) version 0.41.0. The top 20 indicators were selected to build a compact model that is conducive to clinical application. All model-building steps were implemented in Python 3.8.3.ResultsAt the end of follow-up, 353 patients withdrew from PD (converted to haemodialysis or died), and 471 patients continued receiving PD. In the complete model, the categorical boosting classifier (CatBoost) model exhibited the strongest performance (AUC = 0.80, 95% confidence interval [CI] = 0.76–0.83; ACC: 0.78, 95% CI = 0.72–0.83) and was selected for subsequent analysis. We reconstructed a compression model by extracting 20 key features ranked by the SHAP values, and the CatBoost model still showed the strongest performance (AUC = 0.79, ACC = 0.74).ConclusionsThe CatBoost model, which was built using the intelligent analysis technology of ML, demonstrated the best predictive performance. Therefore, our developed prediction model has potential value in patient screening before PD and hierarchical management after PD.

Retracted: Analysis on the Effect of Wushu Project Propagation in Nonmaterial Cultural Field of Environmental Protection Based on Artificial Intelligence Analysis Technology.

[This retracts the article DOI: 10.1155/2022/9138516.].

Innovation of teaching methods of international Chinese language education in universities based on artificial intelligence analysis technology

Abstract Exploring the innovative teaching methods of international Chinese language education in colleges and universities under the new media environment is to transmit better and develop Chinese culture. This paper constructs an international Chinese language education platform using graph convolutional neural network, RNN algorithm, and long and short-term memory neural network combined with big data technology under artificial intelligence analysis technology and gives that the platform consists of an infrastructure layer, data resource layer, application service layer, and user business layer. The plurality and interactivity indexes are analyzed for the constructed international Chinese education platform. Regarding plurality, A, B, C, and D ratings account for 40.82%, 35.4%, 14.71%, and 9.07%, respectively. Regarding interactivity, the percentage of overall user evaluation data above grade C is 93.4%. This shows that building an international Chinese language education platform can effectively enhance the diversified development of Chinese language education and meet the learning needs of diverse people. The international Chinese education platform uses strong interactivity to achieve communication between users and realize the development of Chinese culture transmission.

Research and application of intelligent hyperspectral analysis technology for Chinese materia medica

With the development of imaging technology and artificial intelligence, hyperspectral imaging technology provides a fast, non-destructive, intelligent, and precise new method for the analysis of Chinese materia medica(CMM). This paper summarized the methods and applications of hyperspectral imaging technology combined with intelligent analysis technology in the field of CMM in recent years, focusing on the acquisition and preprocessing of hyperspectral data, intelligent analysis methods of hyperspectral data, and practical cases of these technologies in the field of CMM. Hyperspectral data of CMM can provide spectral information with nanometer-level resolution and rich spatial texture information simultaneously. This paper summarized the acquisition process, including black-and-white board calibration and region-of-interest extraction, and preprocessing methods including smoothing, differentiation, scale-space, and scattering correction. The feature extraction methods in terms of spectral, spatial, color, and texture were briefly described, and common modeling methods were summarized. Finally, this paper reviewed the research cases of the application of the above methods to the fields of CMM, such as authenticity identification, origin tracing, variety recognition, year identification, sulfur fumigation degree determination, and quantitative measurement.

Big Data Intelligent Analysis Technology for the Study of Spatial and Time Trends in the Development of Large Cities

The purpose of this research is to study modern problems and prospects for solving the processing of big data containing information about real estate, as well as the possibility of practical implementation of the methodology for processing such data arrays by designing and filling a special graphic abstraction «metahouse» on a practical example.Materials and methods. The study includes a review of bibliographic sources on the problems of big data analysis and their application in the modern field of construction of large cities. During the study, a technique for presenting data in a graphical form – abstraction was used. The mathematical basis of the technique is the use of multidimensional spaces, where measurements are the characteristics of individual objects. Computer simulation of a practical problem was applied using the C# programming language. Big data storage is based on the MongoDB server. To visualize data, a Web interface based on HTML and CSS is used.Results. In the course of the work, the main characteristics of big data were identified, and the specifics of data arrays consisting of information about real estate objects in a large city were described. When processing data consisting of information about real estate objects of a large city, certain difficulties arise. Thereby, methods for effectively solving the set practical task of processing and searching for patterns in a large data array were proposed: «metahouse» abstraction, data aggregator.Tabular data were obtained for a large city by analyzing three million records containing more than 10 data groups, with a basic set of parameters: floor, number of floors, price, area, living area, kitchen area, type, operation. A MongoDB cluster was created on several computers, each of which was working with its own data set without intermediate results.The results of the computational experiment showed that when using the graphical form (vector) of big data representation, the costs and time for interpreting mining data were reduced.Combining big data processing methods and their presentation through graphical abstraction allows getting new results from existing data sets.Conclusion. During the study, it was found that the presentation of groups of the received data in a graphic image has a number of advantages over a tabular presentation of data (a vector image is easy to scale, the ability to compare without plotting).The proposed way for visualizing big data by constructing abstract vector images is an alternative to traditional tables, allowing you to take a different look at data arrays and the results of their processing. The results obtained can be used both for the primary study of big data processing technologies and as a basis for the development of real applications in the following areas: analysis of changes in the area of houses over time, analysis of changes in the number of floors of urban development, dynamics and distribution of supply and demand, etc.

Retracted: The Application of Motion Trajectory Acquisition and Intelligent Analysis Technology in Physical Education Teaching in Colleges and Universities

Retracted: The Application of Motion Trajectory Acquisition and Intelligent Analysis Technology in Physical Education Teaching in Colleges and Universities

Research on rapid construction technology of three-dimensional safety control area for the complex maintenance work environment of substation

According to the actual needs of the substation operation and maintenance team, this paper studies the defect video intelligent analysis technology in realizing three-dimensional cross-border early warning, analyzes the principle and early warning application of narrowband millimeter-wave technology and develops a set of 3D cross-border early warning system for substation maintenance operation. The system adopts wideband network technology to realize the remote operation and stable data transmission between the system and the warning and analysis service software of mobile terminal. In this early warning analysis system, this study uses the combination of intelligent video analysis and millimeter-wave radar dynamic target capture to solve many deficiencies in early warning accuracy by relying solely on intelligent video analysis and early warning. Through the on-site verification of the test system, the system’s overall function is consistent with the substation maintenance and maintenance requirements. It is simple to use, and the accuracy of the crossover warning reaches more than 85%.

Application of Intelligent Analysis Technology of Football Video Based on Online Target Tracking Algorithm of Motion Characteristics in Football Training.

Because the scene of football video is fixed and simple, the events in football video, such as shooting and offside , also have clear semantics. At the same time, they have sufficient domain knowledge and have broad application prospects. The research framework of sports video intelligence analysis is usually regarded as a three-level framework, namely, the low-level feature layer, the middle-level key primitive generation layer, and the high-level event analysis layer. A MF_O2T (Moving Feature Online Target Tracking) algorithm is proposed. First, based on the marked first frame image, this algorithm extracts a set of standardized local images from the target areas of visible and infrared images as target convolution filters, removes the main color of the stadium by using HSV color space nonuniform quantization algorithm, and extracts the histogram of the main color of players in upper and lower blocks. Experimental results show that the algorithm designed in this paper has strong robustness, can better adapt to player tracking in different scenes of football video, and meets the real-time requirements of football training.

Animation character recognition and character intelligence analysis based on semantic ontology and Poisson equation

Abstract In order to make a deeper research on the existing animation character recognition technology and improve the existing role intelligent analysis technology, semantic ontology and Poisson equation are combined to apply to the animation character recognition and role intelligent analysis technology. For the three-dimensional model, the mapping relationship between semantic tags and local geometric features is extracted to form an intelligent recognition ontology. In the recognition process, support vector machine (SVM) and local geometric features are used to identify semantic tags, and the recognition analysis is carried out according to the semantic tag driving level. Ensure the consistency of animation character model recognition level. In view of the equal perimeter of the recognition boundary under attitude change, the isoline is defined by Poisson equation. This optimization method makes the recognition boundary smooth and consistent under the change of attitude. In the experimental part, various animation character models under different postures are verified and analyzed, and the consistent hierarchical recognition effect is obtained. Compared with the existing methods, the proposed recognition ontology can solve the problem of adaptive selection of optimization parameters of different models and improve the recognition quality.

The Application of Motion Trajectory Acquisition and Intelligent Analysis Technology in Physical Education Teaching in Colleges and Universities

Traditional classroom teaching analysis is mainly based on manual coding of time sampling, which has problems such as overreliance on experts, low analysis efficiency, and difficulty in large-scale services. Classroom teaching analysis based on artificial intelligence technology breaks through the predicament. With the continuous development of intelligent analysis technology, the research on physical education in colleges and universities becomes more and more meaningful. The task of physical education is to impart physical knowledge, technology, and skills to students, enhance their physical fitness, and cultivate their morality, will, and quality. It is one of the ways to implement sports goals. This article proposes how to change traditional teaching based on intelligent analysis technology. This article aims to study how to apply the emerging intelligent analysis technology to the physical education teaching process in colleges and universities; this enables students to be more personalized in the learning process, stimulates their enthusiasm for learning, and meets their basic requirements, breaking through traditional teaching methods, and making it possible to teach students in accordance with their aptitude. In the experimental analysis of this article, from the overall trend of the student evaluation data for 7 consecutive semesters, it can be found that the minimum value of the mean, median, and mode appears in the first semester of the 2016–2017 school year, and the maximum appears in the first semester of the 2019–2020 school year. The average score (mean) of students' evaluation of teaching fluctuates from 4.71 to 4.72, and the fluctuating range of the students' evaluation score (median) of the middlemost students is from 4.56 to 4.66. The results show that the intelligent analysis technology has played a stimulating effect on teachers' teaching and effectively mobilized teachers' teaching initiative. Therefore, the application of intelligent analysis technology is very necessary. Combine health education with moral education, intellectual education, physical education, aesthetic education, and labor education, and integrate it into the whole process of education, teaching, management and services, and build a health education system that is oriented to and responsible for everyone.

Design and Implementation of Intelligent Analysis Technology in Sports Video Target and Trajectory Tracking Algorithm

The object tracking is an important task to future generations to find accurate information from frame (sports or any cctv). The available models like RFO, DT, Xboosting, Gradient boosting, and CNN are outdated for sports video tracking using video. The target and trajectory finding are very important for accurate tracking. Therefore, for crossover, the above limitation’s intelligence analysis is performed with ANN. The video tracking can be very simple and efficient with proposed model. As a result, this ANN offers a sports camera target detection system based on an upgraded grey neural network. This research uses the automatic threshold technique to divide the target object of sports camera and derive the designated target of sports video based on the pixel grey difference of the target image in sports video. The moving objects are tracked by the object tracking algorithm, and features are extracted to obtain the results. The performance measures like accuracy of 95.23%, sensitivity of 94.45%, and recall of 93.34% had been attained.

Research on artificial intelligence safety prediction and intervention model based on ship driving habits

Based on the analysis of the causes of ship accidents, the development prospect and development direction of ship intelligent safe driving, the artificial intelligence safety prediction and intervention model is put forward. This model solves the problem of ship intelligent safety prediction by using intelligent analysis technology and network technology, and promotes the development of ship intelligence and ship safety navigation technology. Additionally, it expands the channels of obtaining information, connects the ship's mechanical and electrical equipment, collects, stores and analyzes the data reasonably, and constructs the intelligent analysis and processing platform of ship small-world data processing to implement intelligent intervention. What is impressive is that it makes ship navigation safer, more economical, more reasonable and optimized, and accelerates the development of ship artificial intelligence safe navigation.

Analysis on the Effect of Wushu Project Propagation in Nonmaterial Cultural Field of Environmental Protection Based on Artificial Intelligence Analysis Technology.

Since the beginning of the new century, the party and the government have always attached great importance to youth sports work and cared about the healthy growth of youth. The CPC Central Committee and the State Council have issued the opinions on strengthening youth sports and strengthening youth physique, which has initially formed a social consensus on promoting the healthy growth of young students. With the process of school education reform and the promulgation and implementation of the Ministry of Education's opinions on implementing and ensuring the time of daily sports activities for primary and middle school students, especially the official launch of the “national sunshine sports for hundreds of millions of students” in 2021, the school sports work in China has entered a new historical stage. Under this premise, the status and importance of Wushu in the intangible cultural field of schools have been gradually improved. As an important part of school sports, Wushu in the field of intangible culture plays an important role in strengthening students' physique, improving students' health, and promoting students' intellectual development. It is also one of the basic ways to realize school sports. How to better play its role has attracted the attention of many experts and scholars. This study focuses on the current situation of Wushu projects in the field of intangible culture in Weixian Middle School of Xingtai City, trying to understand its advantages and existing problems through the current situation, and put forward targeted solutions, so as to enable Wushu projects in the field of intangible culture to play their educational and fitness functions in school sports and provide help for the comprehensive development of middle school students.

Access Data Analysis Technology and Implementation of Electric Power Big Data Achievement Sharing Platform through Artificial Intelligence

With the rapid development of the information age, the data generated by all walks of life is showing an increasing trend of “blowout”. According to statistics, the total amount of data generated by mankind in the past 40,000 years is less than the total amount of data generated from 2010 to 2013 alone. The global big data reserves reached 8.61ZB in 2015 alone, and the growth of data in the future will reach an unpredictable value, entering the data age in an all-round way. Traditional power data computing technology and intelligent analysis technology are undergoing profound changes, and emerging big data intelligent analysis platforms are gradually emerging. With the in-depth development of power informatization and the concept of smart grid, the power industry data has grown exponentially, and the business demand for intelligent analysis of large amounts of power data is increasing day by day. Therefore, the access data analysis technology and realization of the power big data achievement sharing platform based on AI is of great significance. The AI-based power big data achievement sharing platform constructed in this article is a research on previous data analysis, and it aims to use the current cutting-edge artificial intelligence technology to build a scalable and highly available power big data analysis and processing platform to provide fast and reliable smart data services for the power industry, smart grid and other grid businesses. Research has shown that the overall availability of the ultrasonic partial discharge recognition system of the artificial intelligence-based electric power big data achievement sharing platform is 99.9967%, which meets the high availability index requirements, and verified that the artificial intelligence-based power big data achievement sharing platform provides highly available computing, storage and other services for its applications.

Intelligent analysis technology of bamboo structure. Part II: The variability of radial distribution of fiber volume fraction

Scientific and reasonable utilization of bamboo is strongly related to the radial distribution of the fiber volume fraction. In this paper, the K-means clustering algorithm was applied to set separate thresholds for individual vascular bundles, to solve the problem that the bamboo ring cannot be binarized well. We introduced a layered model based on the WEB coordinate system, programmed via JavaScript, to layer the cross-section within the entire bamboo conveniently and accurately. It allowed uniform layering of sample areas of any thickness, any width, and any number of layers. Furthermore, this research systematically studied the bamboo rings at the breast height of Moso bamboo [Phyllostachys edulis (Carr)H. de Lebaie] grown in 12 major producing areas of China and the 50-internode sections from base to top of one bamboo with the method mentioned above. The results indicated that the thickness of a single layer should not be higher than 0.4 mm while examining the gradient structure of bamboo. The radial distribution of fiber volume fraction from 12 areas all followed the ExpDec1 function, and the mean goodness of fit (R2) was 0.988. The radial distribution of fiber volume fraction in the first 45 sections from the base to the top corresponded to the ExpDec1 function. It provided a theoretical basis for the functional improvement of bamboo and the development of new bamboo-based smart materials.

Intelligent analysis technology of bamboo structure. Part I: The variability of vascular bundles and fiber sheath area

The vascular bundle is the most important structural unit of bamboo that determines the growth of bamboo and the properties of bamboo-based materials. A systematic understanding of the characteristics of vascular bundle is the key to clarify the structure-property relationship of bamboo. In this paper, the YOLO (You Only Look Once) algorithm was applied to the field of bamboo structure and a detection model was proposed to solve the key technical problems regarding automatic detection, positioning, counting, and automatic measurement of relevant parameters of vascular bundles. The precision of the detection model was up to 99.59 %, and able to achieve the work in minutes that cannot be done manually. Furthermore, we studied the variation of inter-plant and the variation of axial direction of Moso bamboo [Phyllostachys edulis (Carr)H. de Lebaie]. The results indicated that the total number of vascular bundles of Moso bamboo from 12 different areas was 7196 ± 698, the distribution density of vascular bundles was 2.49 ± 0.58 per mm2, the fiber volume fraction was 23.68 ± 1.89 %. The apparent differences of vascular bundles in different areas result from the growing environment. There were approximately 8000 vascular bundles in the base of Moso bamboo while the number decreased to around 300 at the top, and the decreased vascular bundles were mainly semi-open type and open type. The total area of fiber sheath decreased linearly from base to top of internodes, while the fiber volume fraction was 20–30 % constantly.

Spatial Analysis of Urban Residential Sensitivity to Heatwave Events: Case Studies in Five Megacities in China

Urban heatwaves increase residential health risks. Identifying urban residential sensitivity to heatwave risks is an important prerequisite for mitigating the risks through urban planning practices. This research proposes a new paradigm for urban residential sensitivity to heatwave risks based on social media Big Data, and describes empirical research in five megacities in China, namely, Beijing, Nanjing, Wuhan, Xi’an and Guangzhou, which explores the application of this paradigm to real-world environments. Specifically, a method to identify urban residential sensitive to heatwave risks was developed by using natural language processing (NLP) technology. Then, based on remote sensing images and Weibo data, from the perspective of the relationship between people (group perception) and the ground (meteorological temperature), the relationship between high temperature and crowd sensitivity in geographic space was studied. Spatial patterns of the residential sensitivity to heatwaves over the study area were characterized at fine scales, using the information extracted from remote sensing information, spatial analysis, and time series analysis. The results showed that the observed residential sensitivity to urban heatwave events (HWEs), extracted from Weibo data (Chinese Twitter), best matched the temporal trends of HWEs in geographic space. At the same time, the spatial distribution of observed residential sensitivity to HWEs in the cities had similar characteristics, with low sensitivity in the urban center but higher sensitivity in the countryside. This research illustrates the benefits of applying multi-source Big Data and intelligent analysis technologies to the understand of impacts of heatwave events on residential life, and provide decision-making data for urban planning and management.

Research on multi feature fusion perception technology of mine fire based on inspection robot

In order to improve the real-time and accuracy of mine fire identification, based on the summary of the existing fire monitoring methods, a mine fire early warning model based on patrol robot and multi-sensor and image recognition is proposed. The video image is obtained by using the high-definition camera of inspection robot, and the flame suspected area of video image is obtained by combining frame difference and background difference after filtering. The improved three scale analytic hierarchy process is used to fuse the circularity feature, sharp angle feature, centroid movement feature, color feature and area growth feature of the flame suspected area image to obtain the flame result. The three-frame difference method is used to binarize the smoke video image, and the smoke texture feature, smoke motion feature and smoke color feature are extracted to recognize the smoke image. The improved three-scale analytic hierarchy process (AHP) is used to fuse and judge the smoke in the combination of infrared thermal imager temperature measurement and smoke sensor. Inspection robot uses unified detection standards and intelligent analysis technology to sense mine smoke and flame. This method can improve the real-time, accuracy and robustness of mine fire perception.

Research on Intelligent Analysis Technology of Network Security Risk Based on Big Data

Aiming at the severity and distribution of abnormal behaviour, attack behaviour, compliance, and vulnerability of equipment in the power information system, this article applies big data technology to analyse and evaluate abnormal behaviour in the network, and uses machine learning to improve the network, the ability to identify security risks and the accuracy of assessments provide strong guarantees and technical support for improving the security and reliability of power information systems.

Volleyball track acquisition and intelligent analysis technology

Volleyball has been developing rapidly since 1996. It has been widely used both in competitive events and among ordinary people. However, with the continuous improvement of the sports level, the traditional manual training methods cannot meet the existing technical requirements. It is a mainstream method to analyze the track of volleyball by the computer in volleyball training. However, there are still some technical problems such as low precision and incomplete analysis. Therefore, this paper puts forward the research of volleyball track acquisition and intelligent analysis technology. In this paper, the shortcomings of the existing technology are systematically analyzed, and on this basis, the optimization and improvement scheme is proposed. The core technology of this project is to improve the original image preprocessing technology and strengthen the system’s feature extraction ability. Finally, combined with the CAMSHAFT moving object tracking algorithm, the technical scheme of this paper is formed. Through a series of technical improvements, the system effectively improves the ability to extract and analyzing the track of volleyball. In order to further verify the practical effectiveness of this scheme, a number of comparative experiments including algorithm accuracy comparison experiment, trajectory recognition detection, and algorithm signal-to-noise ratio verification are carried out. The object of comparison is the current mainstream common filtering algorithm. Through the analysis of experimental data, this method is more accurate than the common filtering algorithm in the extraction of volleyball trajectory, which effectively improves the comprehensive performance and robustness of the traditional method.