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Research on the problems and reform strategies of government statistics based on big data technology

APPLIED MATHEMATICS AND NONLINEAR SCIENCES 2023年第1期9卷

作者： Zhang, Xianwei Zhang, Yueyan Shenzhen Polytech Coll Econ Shenzhen Guangdong Peoples R China Second Peoples Hosp Longgang Dist Shenzhen Dept Pharm Shenzhen Guangdong Peoples R China

In the past two years, with the development of big data technology and the revolutionary innovations it has caused within various industries, there has been a boom in the use of big data to transform and optimize government workflow within the field of public administration. In this paper, we build a theoretical framework through government statistics, focusing on the four elements of government statistics. The decision tree algorithm is used to analyze the indicators of the government statistical work problem, mainly from three aspects: grassroots statistical institutions, government revenue, and government forestry construction statistics. The accuracy of the decision tree algorithm is controlled between 83% and 91%, with a variation range value of 8%, which is more accurate than the other two algorithms. The decision tree algorithm helps improve the efficiency and quality of government statistics, thus contributing to the processing of statistical work problems and reform strategies. This study is important for deepening the reform of the Chinese government administration system, improving the construction of a service-oriented government, ensuring the quality of statistical information, and guiding the benign development of statistical work.

关键词： Big data technology decision tree algorithm Information gain Government statistics Reform strategy

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SENTIMENT ANALYSIS OF ONLINE PRODUCT REVIEW USING DEEP LEARNING IN DISTRIBUTED SENSOR NETWORKS

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SCALABLE COMPUTING-PRACTICE AND EXPERIENCE 2023年第4期24卷 1117-1126页

作者： Yao, Jun Tongling Univ Sch Math & Comp Sci Tongling 244061 Peoples R China

Recently, sentiment analysis has been a major business practice for an organization that helps analyze online reviews of different products. An organization can understand the customer's perception of their organizational products by analyzing reviews. On the other hand, this analysis process also helps to understand the customer's emotions about the organizational products. In this process, deep learning and distributed sensor networks play crucial roles in analyzing emotions. Five different steps of this analysis will provide accurate information about the customers' emotions on products. This analysis will help increase the product's value by understanding the customer's perception of where negative feedback improves the products. Through the help of this analysis, an organization will get several benefits that enrich its organizational image. On the other hand, this analysis process will face polarity issues, tone issues, comparative sentence analysis issues, and understanding idioms and emojis issues. The implementation of live API and proper sentiment analysis tools help to increase the effectiveness of this analysis.

关键词： Distributed Sensor Networks Machine Learning decision tree algorithm Natural Language Processing (NLP) Computational Linguistics Support Vector Mechanics

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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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Traces of the Iron Curtain: A multivariate analysis of regional cohesion in Europe

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SOCIO-ECONOMIC PLANNING SCIENCES 2021年 78卷

作者： Borsekova, Kamila Korony, Samuel Nijkamp, Peter Matej Bel Univ Fac Econ Res & Innovat Ctr Tajovskeho 10 Banska Bystrica 97590 Slovakia Alexandru Ioan Cuza Univ Bulevardul Carol I Nr 11 Iasi Romania Open Univ Valkenburgerweg 177 NL-6419 AT Heerlen Netherlands

The present study aims to test relative welfare differences among regions in Europe, so as to examine whether the post-communist era has led to more socio-economic cohesion in Europe. The performance of European regions is analysed, compared, and assessed by using the Regional Competitiveness Index (RCI) and stylised fixed nominal categories. The current status of regional cohesion is tested on the basis of detailed data on 268 NUTS 2 European regions by using a robust methodology oriented towards univariate comparison of location parameters, multivariate classification by the decision tree and CHAID algorithm, and comparison of nominal variables with four values based on density plots. Multivariate classification appears to offer statistically excellent results with an overall correct prediction rate for post-socialist and capitalist regions in Europe of 99.6%. The research results from the Higher education and Innovation pillars, reveal a convergence of capitalist and post-socialist regions with capital cities and a divergence of regions with administrative capitals and other regions. Relatively, the two groups which perform best are both groups with capitals, while the group of capitalist regions with a capital city is significantly better in almost all pillars. The key message is that the transition of post-socialist regions is not yet over. Capitalist regions in Europe perform better than post-socialist regions in eight of the nine pillars of regional competitiveness. Our research results also reveal that the group of post-socialist regions without capital cities are significantly lagging behind the rest of the regions in Europe, and thus form the most vulnerable group of European regions. As there is data continuity in the official RCI classification and measurement, policy makers will be able to compare the performance of their own regions over time and to design appropriate concerted strategies accordingly. From this perspective, our study draws several inte

关键词： Cohesion Region Multivariate analysis Stylised fixed nominal categories decision tree algorithm

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Personal and social predictors of use and non-use of fitness/diet app: Application of Random Forest algorithm

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TELEMATICS AND INFORMATICS 2020年 55卷 101301-101301页

作者： Cho, Jaehee Kim, Sehwan Sogang Univ Sch Commun 811 Bldg MatthewBaekbeom Ro 35 Seoul South Korea Dankook Univ Sch Med Dept Biomed Engn Yongin South Korea

This study investigated the various groups of factors that predict individuals' use and non-use of fitness and diet apps on smartphones. Unlike previous research on fitness and diet apps which have mainly studied individuals' intentions to use the apps, this study focused on the prediction accuracy of various factors that lead people to use fitness and diet apps through analysis of data collected from users as well as non-users of these apps. To examine prediction accuracy, this study applied the Random Forest algorithm. According to the findings, prediction accuracy higher than that of 70 percent was observed for nine factors: age, annual income, education, perceived obesity, dieting efforts, number of smartphone apps currently used, daily time spent with smartphone apps, perceived benefits from exercise, and social influence. A major contribution of this study is its detection of those factors predicting actual behavioral decisions regarding use of fitness and diet apps, as opposed to future intentions.

关键词： Fitness and diet apps mHealth Technology adoption decision tree algorithm Random Forest algorithm

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A Method for Evaluating the Performance of Unmanned Aerial Navigation Systems Based on Fuzzy decision trees

A Method for Evaluating the Performance of Unmanned Aerial N...

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第十三届中国卫星导航年会

作者： Changyuan Chen Chunlin Shi Teng Liu Haojie Li Qiyang Li Li Ma Information Engineering University Northwestern Polytechnical University Changchun University of Science and Technology

In the process of application, unmanned aerial vehicles(UAVs) are faced with the threat of a complex electromagnetic environment, causing the airborne navigation system to fail to achieve ideal working conditions, and seriously restricting the overall effectiveness of the UAV system. Therefore, accurate assessment of UAV navigation system performance is an important prerequisite for scientific decision-making on the use of UAVs in complex environments in the future. In system design and experimentation, the more realistic electromagnetic interference environments that UAVs may be exposed to are difficult to build, resulting in insufficient data being collected to verify UAV navigation system performance. However, as real data from UAVs is continuously accumulated in various application scenarios at a later stage, it is necessary to explore a data mining-based method for UAV navigation system performance evaluation. In this study, a decision tree algorithm was used to construct a UAV navigation performance level classification assessment model. UAV performance was analysed through UAV navigation capability and survivability indicators. Finally, simulation experiments were conducted to further develop the construction and testing of the decision tree model. The experimental results showed that the model accuracy is above 90%, validating the feasibility of the method.

关键词： UAV Navigation Electromagnetic Environment Performance Evaluation Data Mining decision tree algorithm

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RETRACTED: Application of deep learning algorithm in college English teaching process evaluation (Retracted Article)

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BEHAVIOUR & INFORMATION TECHNOLOGY 2021年第9期40卷 I-X页

作者： Zhao, Xing Baoji Univ Arts & Sci Coll Foreign Languages Baoji 721013 Peoples R China

Data mining can carry out in-depth mining analysis of data information, and its application in college English teaching process is beneficial to explore the potential internal relationship between evaluation results and various factors. Data mining discovers useful information from historical data and can be displayed in the form of classification rules to make better use of data accumulation, which will be beneficial to the improvement and improvement of English teaching quality. This paper proposes a new research program, which applies decision tree technology in the English teaching evaluation system and mines valuable information behind it. This paper chooses GBDT and ID3 decision tree algorithm and draws an application model of English teaching evaluation based on decision tree technology. It finds useful information from massive data, summarises rules and information, and enables teachers to better complete teaching work and improve students' English scores.

关键词： Data mining GBDT ID3 decision tree algorithm deep learning

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Research on Data Mining algorithm Based on Hadoop Cloud Platform

Research on Data Mining Algorithm Based on Hadoop Cloud Plat...

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2019 2nd International Conference on Computer Science and Advanced Materials(CSAM 2019)

作者： Wang Cuiping Lu Jiasheng Rizhao Polytechnic

In the information age, the increase of information content provides convenience for our life. However, the increasing amount of information makes the data scale even larger. Therefore, people have higher and higher requirements on data mining technology. Data mining has become more complex and difficult. This paper first explains the concept of data mining technology and cloud platform. Then it analyzes the Hadoop cloud platform data mining mode. Finally, this paper studies and analyzes how to deal with SPRINT decision tree algorithm of big data.

关键词： Cloud platform Data mining decision tree algorithm

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An overview of big data mining and data privacy protection technologies

An overview of big data mining and data privacy protection t...

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作者： Jinyang Liu Wuhan City College

With the advent of the era of big data, data mining techniques have significantly improved their ability to extract valuable information from data. However, privacy dangers are growing. Consequently, securing the protection of personal privacy during the mining of massive amounts of data has become a significant challenge. This paper examines the relationship between data mining techniques and privacy protection measures through a review of the pertinent literature. It provides a concise analysis of the benefits and drawbacks of commonly utilized classification algorithms in data mining. In addition, it examines the interplay between data mining techniques and privacy protection and summarizes important privacy protection *** privacy protection methods. These techniques include data anonymization,association rule concealing, data perturbation, etc. By comprehending these privacy protection techniques, appropriate privacy safeguards can be selected to ensure the privacy and security of the data when conducting data mining.

关键词： big data mining privacy protection big data processing workflow decision tree algorithm k-means algorithm

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A Critical Study of Classification algorithms Using Diabetes Diagnosis

A Critical Study of Classification Algorithms Using Diabetes...

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IEEE International Advance Computing Conference

作者： Panigrahi Srikanth Dharmaiah Deverapalli Department of Information Technology VNR VJIET Department of Information Technology Shri Vishnu Engineering College for Woman

ISBN: (纸本)9781467382878

This paper predicts the Diabetes Disease based on Data Mining Techniques of Classification algorithms. Classification algorithm and tools may reduce heavy work on Doctors. In this paper Evaluated as Classification algorithms for the Classify of some Diabetes Disease Patient Datasets. Data Mining is one of the main algorithm is Classification. Classification algorithm Examine of the decision tree algorithm, Byes algorithm and Rule based algorithm. These algorithms are evaluate Error Rates and identify of the patients based evolution Function of the measure the accurate results.

关键词： Pima Diabetes Disease Data set Classification algorithm decision tree algorithm Byes algorithms and Rules

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