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Application of traditional cultural elements in landscape design

APPLIED MATHEMATICS AND NONLINEAR SCIENCES 2023年

作者： Liu, Tong Zhengzhou Univ Light Ind Eastern Int Art Coll Environm Design Dept Zhengzhou 451450 Peoples R China

Traditional Chinese cultural elements have diversified the framework of contemporary cultural studies and given new life to modern landscape design. Therefore, the use of traditional Chinese cultural elements in modern landscape design is the main task faced by landscape design designers. This paper establishes the svm-design model. An in-depth study on the use of traditional cultural elements in landscape design is launched. The calculation analysis shows that the color application of traditional cultural elements is the most widely used in the creation of landscape space, and its percentage is as high as 60.22%. The use of regional characteristics of traditional cultural elements accounted for the second highest percentage, at 24.73%. The lowest percentage of pattern features of traditional cultural elements is used in the creation of landscape space, only 15.05%. The percentage of the use of traditional cultural elements of the deformation of the exaggerated clothing method is 43%. The reason for its wide application in landscape design is that it is a deformation and exaggeration treatment of the external form of traditional cultural elements to achieve the aesthetic effect of highlighting and amplifying the traditional cultural elements.

关键词： traditional culture landscape design svm algorithm color application elemental characteristics

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Smart Machine Health Prediction Based on Machine Learning in Industry Environment

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INFORMATION 2023年第3期14卷 181页

作者： Yeruva, Sagar Gunuganti, Jeshmitha Kalva, Sravani Salkuti, Surender Reddy Kim, Seong-Cheol VNR Vignana Jyothi Inst Engn & Technol Dept CSE AIML & IoT Hyderabad 500090 India VNR Vignana Jyothi Inst Engn & Technol Dept CSE Hyderabad 500090 India Woosong Univ Dept Railroad & Elect Engn Daejeon 34606 South Korea

In an industrial setting, consistent production and machine maintenance might help any company become successful. Machine health checking is a method of observing the status of a machine to predict mechanical mileage and predict the machine's disappointment. The most often utilized traditional approaches are reactive and preventive maintenance. These approaches are unreliable and wasteful in terms of time and resource utilization. The use of system health management in conjunction with a predictive maintenance strategy allows for the scheduling of maintenance times in such a way that device malfunction is avoided, and thus the repercussions are avoided. IoT can help monitor equipment health and provide the best outcomes, especially in an industrial setting. Internet of Things (IoT) and machine learning models are quite successful in providing ongoing knowledge and comprehensive study on infrastructure performance. Our suggested technique uses a mobile application that seeks to anticipate the machine's health status using a classification method utilizing IoT and machine learning technologies, which might benefit the industry environment by alerting the appropriate maintenance team before inflicting significant harm to the system and disrupting normal operations. A comparison of decision tree, XGBoost, svm, and KNN performance has been carried out. According to our findings, XGBoost achieves higher classification accuracy compared to the other algorithms. As a result, this model is selected for creating a user-based application that allows the user to easily check the state of the machine's health.

关键词： Decision Tree algorithm IoT KNN algorithm machine health prediction predictive maintenance svm algorithm XGBoost algorithm

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Quantitative Analysis of Data Mining Application and Sports Industry Financing Mechanism based on Cloud Computing

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INTERNATIONAL JOURNAL OF GRID AND DISTRIBUTED COMPUTING 2016年第12期9卷 233-244页

作者： Wang, Hao Sun, Jinhai Qufu Normal Univ Sch Phys Educ & Sports Sci Qufu 273100 Peoples R China Shandong Sport Univ Sch Sport Social Sci Shandong Peoples R China Shandong Univ Coll Phys Educ Shandong Peoples R China

With the development of the Internet, enterprise database has accumulated a huge amount of business data;the previous data mining technology is difficult to meet the current technical ability. In cloud computing environment, resources are stored in a distributed form, which is more efficient. In this paper, the authors analyze the sports industry financing mechanism by using data mining technology. Different from the developed countries, China's sports industry is still in its infancy, and sports industry investment and financing is the key to achieve the rapid development. Based on the analysis, we find that China sports industry investment and financing market degree is low, Capital market is difficult to achieve effective allocation of resources, and the improper allocation of resources tends to delay the development of sports industry. On this basis, we put forward relevant policy recommendations.

关键词： Data mining Sports industry svm algorithm Financing mechanism

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Comparative Analysis of Supervised Machine Learning Techniques for Sales Forecasting

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2021年第11期12卷 102-110页

作者： Raizada, Stuti Saini, Jatinderkumar R. Symbiosis Int Deemed Univ Symbiosis Inst Comp Studies & Res Pune Maharashtra India

This study talks about how data mining can be used for sales forecasting in retail sales and demand prediction. Prediction of sales is a crucial task which determines the success of any organization in the long run. There are various techniques available for predicting the sales of a supermarket such as Time Series algorithm, Regression Techniques, Association rule etc. In this paper, a comparative analysis of some of the Supervised Machine Learning Techniques have been done such as Multiple Linear Regression algorithm, Random Forest Regression algorithm, K-NN algorithm, Support Vector Machine (svm) algorithm and Extra Tree Regression to build a prediction model and precisely estimate possible sales of 45 retail outlets of Walmart store which are at different geographical locations. Walmart is one of the foremost stores across the world and thus authors would like to predict the sales accurately. Certain events and holidays affect the sales periodically, which sometimes can also be on a daily basis. The forecast of probable sales is based on a combination of features such as previous sales data, promotional events, holiday week, temperature, fuel price, CPI i.e., Consumer Price Index and Unemployment rate in the state. The data is collected from 45 outlets of Walmart and the prediction about the sales of Walmart was done using various Supervised Machine Learning Techniques. The contribution of this paper is to help the business owners decide which approach to follow while trying to predict the sales of their Supermarket taken into account different scenarios including temperature, holidays, fuel price, etc. This will help them in deciding the promotional and marketing strategy for their products.

关键词： Sales forecasting linear regression random forest regression KNN regression algorithm svm algorithm supervised machine learning techniques

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Research on the innovation of collaborative education model of scientific research and teaching in colleges and universities based on the theory of process reengineering

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APPLIED MATHEMATICS AND NONLINEAR SCIENCES 2023年第2期8卷 1017-1028页

作者： Shi, Chen Ningbo Univ Finance & Econ Ningbo 315175 Zhejiang Peoples R China

Colleges and universities are the major sources of scientific research. They reveal how the effectiveness is related to the development of the whole society and the living standards of all employees. This article starts from the education model, based on relevant theoretical knowledge. It helps to explore and innovate the path to realize the 'college scientific research and teaching collaborative education model' within the university and construct the svm-process reengineering model to study its feasibility. According to the calculation results, the teaching results of the enterprise teaching mode are the lowest, with an average proportion of about 32.74%, the average proportion of the teaching results of the traditional teaching mode is only 42.56%, and the average proportion of the teaching results of the collaborative education is as high as 97.86%. Therefore, measures should be taken to enhance the flexibility of training content and to carry out various forms of school research, teaching and collaborative education models.

关键词： scientific research and teaching collaborative education model process reengineering theory svm algorithm teaching results

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Aquatic plant functional type spectral characteristics analysis and comparison using multi-temporal and multi-source remote sensing over the Poyang Lake wetland,China

Aquatic plant functional type spectral characteristics analy...

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The 18th International Conference on Geoinformatics(第18届国际地理信息科学与技术大会 Geoinformatics 2010)

作者： Peng Gong Lin Wang Iryna Dronova State Key Laboratory of Remote Sensing Science jointly sponsored by the Institute of Remote Sensing Department of Environmental Science Policy and ManagementDivision of Ecosystem Science University o

ISBN: (纸本)9781424473014

In systems with strong seasonal difference in vegetation structure and appearance, multi-temporal imagery can be particularly useful for community-and species-level discrimination. And, since the availability of past data for one source of time series images may be limited, so we need to develop multi-temporal and multi-source method for wetland ecosystem monitoring. To perform this type of analysis, the image spectral characteristics comparison between different aquatic macrophytes and different sensors should be studied firstly. We used TM images, Beijing-1 images and HJ-1 images for this analysis and based on the determination of aquatic plant functional types (PFTs). The objectives of this study were: ⑴ single-sensor single-date aquatic PFT analysis;⑵ multi-source single-date diagnostic spectral characteristics analysis and comparison for different aquatic PFTs;⑶ multi-source multitemporal diagnostic spectral characteristics analysis for different aquatic PFTs. From this analysis we found that: ⑴ For the single-date TM data, the diagnostic spectral band and indexes are Band 2, 4, 5, NDVI, and MNDWI;the best temporal for discriminating different Nonpersistent Emergent Wetland PFTs are in low water level periods, and water infilling and subsiding periods for seasonal submerged and floating aquatic macrophyte. Multispectral Decision Tree classification method lead the more good results for most of PFTs;⑵ the same type of aquatic PFTs have similar and comparable reflectance characteristics between multi-sensor optical data which could satisfy the time series analysis by compensating more available past images;⑶ phenological curves and relative canopy moisture curves extracted from time series remote sensing images provide important information for distinguish different PFTs.

关键词： TSNDVI image:TSNDWI image:Beijing-1 small satellite data:HJ-1 satellite data:TM data:Decision Tree model svm algorithm

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Obstacle Avoidance algorithm for Indoor Mobility Robot Based on Improved Support Vector Machine 2019

Obstacle Avoidance Algorithm for Indoor Mobility Robot Based...

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Proceedings of the 2019 The 2nd International Conference on Robotics, Control and Automation Engineering

作者： Jie Hao Northwest Minzu University School of Electrical Engineering Lanzhou China

ISBN: (纸本)9781450376228

With the development of artificial intelligence, robot will be applied to every aspect of our life. In the domain of robotics, especially indoor robotics, the ability of obstacle avoidance is one of the most important questions. In this paper, first of all, some classical machine learning algorithms are applied for indoor robot obstacle avoidance which is in the unknown environment based on multiple ultrasonic sensors. During the move, obstacles will be detected by sensors on the robot, through the machine learning algorithm, the robot will know how to do when the sensor detect an obstacle. Then, proposes an improved Support Vector Machine(svm) algorithm based on the Grid Search algorithm. This algorithm can accurately help robot find the trajectory for moving. Experimental results also presented to show the algorithm improved the generalization ability and real-time performance

关键词： svm algorithm ultrasonic sensor obstacle avoidance Indoor mobile robot machine learning algorithm Grid Search

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Research on Multi-factor Stock Selection Strategy based on Improved Particle Swarm Support Vector Machine

Research on Multi-factor Stock Selection Strategy based on I...

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International Symposium on Economic Development and Management Innovation

作者： Canran Xiao Liwei Hou Jun Huang College of Business Administration Hunan University College of Mechanical and Electrical Central South University

ISBN: (纸本)9781510892316

In recent years,the application of machine learning in quantitative trading has attracted more and more *** this paper,a new support vector machine algorithm based on nearest neighbor method and improved discrete particle swarm optimization is *** Hu-Shen 300 stocks as the research object,the improved support vector machine and the original support vector machine are used to construct multi-factor stock selection strategies respectively,and the two strategies are compared in the back-test *** results show that the multi-factor stock selection strategy of support vector machine based on nearest neighbor method and improved discrete particle swarm optimization has higher annual return than the original svm algorithm,and has good execution effect.

关键词： svm algorithm nearest neighbor method improved discrete particle swarm optimization

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Study on the Credibility Based on the Features of Agricultural Information

Study on the Credibility Based on the Features of Agricultur...

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2018 International Conference on Network, Communication, Computer Engineering (NCCE 2018)

作者： Yuyi Zhang Yayao Zuo Xiaobang Chen Faculty of Computer Guangdong University of Technology

ISBN: (纸本)9781510871076

This paper based on agricultural information with timeliness,regional and other complex factors,proposed a method based on the features of agricultural webpage information to classify the credibility of agricultural webpage *** constructing the model of the relationship between the features of agricultural information and the credibility of information,and combining with the svm algorithm of machine learning,the credibility classifier is trained to predict and assess the credibility of agricultural webpage *** show that this method is feasible,as well as to evaluate the credibility based on information sources,has some advantages.

关键词： agricultural webpage svm algorithm credibility classifier

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Research on Android Malicious URL Detection Based on Machine Learning

Research on Android Malicious URL Detection Based on Machine...

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作者： Zijian Ma Zhuoyue Wang Zhiqiang Wang Yuheng Lin Yingying Du School of Computer and Information Technology Beijing Jiaotong University Beijing Electronic Science and Technology Institute State Information Center

In recent years, malicious URL attacks against mobile users are increasing, the detection of malicious URLs is of great significance to defend against network attacks. The traditional research on mobile malicious URL detection has the defects of high false positive rate and false positive rate. In response to these questions, this paper mainly constructs a cross-platform applications to classify malicious URLs based on blacklist technology and support vector machines. Moreover, a malicious URL detection system is designed and developed on Android to achieve accurate detection of malicious domain names such as Trojans and viruses. Finally, lots of experiments are done by testing the data of9174 domain names. The accuracy rate is 98.25%, which improves the security of mobile terminals and web application as a whole.

关键词： URL detection svm algorithm Machine learning Feature extraction Model training

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