scholarly journals Research on Collaborative Innovation Platform of Internet of Things Industry Based on Data Mining Technology

2021 ◽  
Vol 1881 (4) ◽  
pp. 042072
Author(s):  
Yetong Wang ◽  
Junhua Ku
Keyword(s):  
Data Mining ◽  
Internet Of Things ◽  
Mining Technology ◽  
Collaborative Innovation ◽  
Innovation Platform

Design of College English Process Evaluation System Based on Data Mining Technology and Internet of Things

2020 ◽  
Vol 16 (2) ◽  
pp. 18-33 ◽  
Author(s):  
Hongli Lou
Keyword(s):  
Data Mining ◽  
Internet Of Things ◽  
Process Evaluation ◽  
Evaluation System ◽  
Current Situation ◽  
The Internet ◽  
Mining Technology ◽  
College English ◽  
Procedural Evaluation ◽  
The Internet Of Things

This article proposes a new idea for the current situation of procedural evaluation of college English based on Internet of Things. The Internet of Things is used to obtain the intelligent data to enhance the teaching flexibility. The data generated during the process of procedural evaluation is carefully analyzed through data mining to infer whether the teacher's procedural evaluation in English teaching can be satisfied.

WIT120 data mining technology based on internet of things

2019 ◽  
Vol 23 (4) ◽  
pp. 680-688
Author(s):  
Qingyuan Zhou ◽  
Zongming Zhang ◽  
Yuancong Wang
Keyword(s):  
Data Mining ◽  
Internet Of Things ◽  
Mining Technology

scholarly journals Constructing Sports Multi-Index Data Analysis Based on 5G IoT Technology

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Hui Wang ◽  
Ben Zhao
Keyword(s):  
Data Mining ◽  
Data Analysis ◽  
Internet Of Things ◽  
Real Data ◽  
Exercise Duration ◽  
Exercise Heart Rate ◽  
Mining Technology ◽  
Multi Index ◽  
Index Data ◽  
Internet Of Things Technology

The arrival of the new era and the development of 5G Internet of Things (IoT) technology have made our lives and work easier and more convenient. The vigorous development of the IoT has been applied in many fields, among which, especially the data mining technology of the IoT ushered in the spring of this era of information explosion. Full application of data mining technology can provide real data well. Application analysis provides value and decision support. In order to apply 5G Internet of Things technology to the sports industry to help study the multi-index data of various sports activities so as to better help modern people have a healthy sports concept, Nemo builds relevant data analysis based on 5G Internet of Things technology. This article analyzes the research on the construction of sports multi-index data based on 5G IoT, makes full use of the IoT to mine sports-related data, and launches a multi-index discussion on it. First, the literature data method is adopted to learn the theoretical knowledge of IoT, artificial neural network, deep learning, etc., and establish a sports multi-index data analysis research model based on machine learning and massive data processing technology. Finally, for modern people, sports hobbies, types, exercise duration, exercise heart rate, and other aspects are analyzed. The results show that modern people prefer aerobic exercise, especially jogging and cycling, accounting for 47% and 41%, and the proportion of people who spend more than 60 minutes in the gym is as high as 48%. This shows that even though most people are busy at work, they still realize the importance of physical exercise and are willing to do sports.

Advanced Statistical Tools for Improving Yield and Reliability

1999 ◽  
Author(s):  
Richard C. Kittler
Keyword(s):  
Data Mining ◽  
Analysis Data ◽  
Neural Nets ◽  
Future Trends ◽  
Statistical Tools ◽  
Mining Technology ◽  
Explanatory Variables ◽  
Data Coverage ◽  
Statistical Approaches ◽  
Adequate Data

Abstract Analysis of manufacturing data as a tool for failure analysts often meets with roadblocks due to the complex non-linear behaviors of the relationships between failure rates and explanatory variables drawn from process history. The current work describes how the use of a comprehensive engineering database and data mining technology over-comes some of these difficulties and enables new classes of problems to be solved. The characteristics of the database design necessary for adequate data coverage and unit traceability are discussed. Data mining technology is explained and contrasted with traditional statistical approaches as well as those of expert systems, neural nets, and signature analysis. Data mining is applied to a number of common problem scenarios. Finally, future trends in data mining technology relevant to failure analysis are discussed.

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