scholarly journals Sports Training Teaching Device Based on Big Data and Cloud Computing

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Gexin Chao ◽  
Wu Gang
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Rapid Development ◽  
Experimental Studies ◽  
Survey Method ◽  
Analysis Method ◽  
Sports Training ◽  
Teaching Device ◽  
Research Theme ◽  
Traditional Mode

With the advent of the era of big data (BD), people have higher requirements for information, knowledge, and technology. Taking the Internet as the carrier, the use of cloud computing technology for distance education has become a trend. Our country’s physical training teaching has also begun to change from traditional mode to modern mode. In order to improve the overall quality of our country’s national sports, this paper studies the teaching device of sports training based on BD and cloud computing. This article mainly uses the questionnaire survey method, the experimental analysis method, the data analysis method, and the data statistics method to have an in-depth understanding of the research theme and uses swimming as an example to design the sports training device. 52% of people think that water in the ears and itching during swimming are more serious problems. After further understanding, an experimental design was carried out. Experimental studies have shown that the combination of BD and cloud computing can effectively solve the problems existing in the traditional teaching model, so as to achieve the goal of efficient and rapid development.

scholarly journals The Construction and Thinking of Cloud Computing and Big Data Technology in Smart Campus

2019 ◽  
Vol 3 (2) ◽  
pp. 152
Author(s):  
Xianglan Wu
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Data Storage ◽  
Information Technologies ◽  
Rapid Development ◽  
Smart Campus ◽  
Huge Data ◽  
New Information ◽  
Big Data Technology

<p>In today's society, the rise of the Internet and rapid development make every day produce a huge amount of data. Therefore, the traditional data processing mode and data storage can not be fully analyzed and mined these data. More and more new information technologies (such as cloud computing, virtualization and big data, etc.) have emerged and been applied, the network has turned from informationization to intelligence, and campus construction has ushered in the stage of smart campus construction.The construction of intelligent campus refers to big data and cloud computing technology, which improves the informatization service quality of colleges and universities by integrating, storing and mining huge data.</p>

scholarly journals Construction of Live Broadcast Training Platform Based on “Cloud Computing” and “Big Data” and “Wireless Communication Technology”

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Zhidong Sun ◽  
Xueqing Li
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Wireless Communication ◽  
Communication Technology ◽  
Security Policy ◽  
High Efficiency ◽  
Rapid Development ◽  
Science And Technology ◽  
Live Broadcast ◽  
The Impact

With the rapid development of information technology, a scientific theory is brought by the rapid progress of science and technology. The advancement of science and technology of the impact on every field, changing the mode of transmission of information, the advent of big data for promotion and dissemination of resources played their part, let more and more people benefit. In the context of cloud computing, big data ushered in another upsurge of development and growth. Given this, the live broadcast training platform, which focuses on enterprise staff training and network education, arises at the right moment. People favor its convenience, real-time performance, and high efficiency. However, the low-value density of big data and cloud computing’s security problem has difficulties constructing a live broadcast training platform. In this paper, the live broadcast training platform’s structure is improved by constructing three modules: the live training module based on cloud computing, the user recommendation module based on big data, and the security policy guarantee module. In addition, to ensure that the trainees can receive training anytime and anywhere, this paper uses wireless communication technology to ensure the quality and speed of all users’ live video sources.

scholarly journals Intelligent Sports Training System Based on Artificial Intelligence and Big Data

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Chunguang Li ◽  
Jianbiao Cui
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Human Body ◽  
Joint Angle ◽  
Rapid Development ◽  
Human Motion ◽  
Training System ◽  
Human Motion Analysis ◽  
Sports Training ◽  
Organizational System

All activities in training fields are for the improvement of athletes’ competitive abilities. A sports training system is an organizational system to achieve common goals. Competitive ability is one of the main manifestations of the evolution of the training system. With the rapid development of computer technology, people have begun to combine virtual reality and other technologies to achieve scientific sports-assisted training to eliminate traditional sports training that relied purely on experience. Pose estimation obtains the position, angle, and additional information about the human body in the image in a two-dimensional plane or three-dimensional space by establishing the mapping relationship between the human body features and the human body posture. This article demonstrates a golf-assisted training system to realize the transformation from an experience-based sports training method to a human motion analysis method, using artificial intelligence and big data. The swing posture parameters of the trainer and the coach are obtained using the posture estimation of a human body. Based on this information, an auxiliary training system is built. The two parameters of the joint angle trajectory and the posture similarity are used as auxiliary indicators to compare the trainers. The joint angle trajectory is analyzed, and the coach is guided based on the similarity of the posture.

scholarly journals Application of Teaching Quality Assessment Based on Parallel Genetic Support Vector Algorithm in the Cloud Computing Teaching System

2016 ◽  
Vol 11 (08) ◽  
pp. 16
Author(s):  
Haifeng Hu ◽  
Junhui Zheng
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Quality Assessment ◽  
New Technology ◽  
Rapid Development ◽  
Teaching Quality ◽  
University Teaching ◽  
Support Vector ◽  
Teaching Management ◽  
Network Teaching

With the rapid development of China's economy in recent years, the scale of students has expanded gradually, which has led to many new problems, including the problems of the quality and the quantity of teachers, and the teaching facilities being insufficient. The assessment of teaching quality is one of the most important aspects of teaching management, which come to the attention of every university. Therefore, it has become the current focus in the research of university teaching. At the same time, the traditional method of teaching quality assessment has not been able to deal with the phenomenon of big data in the field of education. As a new technology, cloud computing provides a broad space for the development of a new model in the aspects of hardware environment construction, software resource development, network teaching implementation and personal knowledge management. In order to effectively deal with the challenges of big data processing in the field of education, this paper proposes a GA-SVM teaching quality assessment algorithm which is based on MapReduce. Through the design of a map function and reduce function, this paper realizes the parallelization of the GA-SVM algorithm and the selection of the main parameters. Secondly, this paper uses a genetic algorithm to optimize the penalty coefficient and kernel parameters of SVM, and then solves the problem of difficulty in determining the parameters of support vectors. In addition, we improve the sensitivity of the search through the method of logarithmic transformation, and speed up the convergence rate of the GA model. Finally, we compare the parallel algorithm and the serial algorithm on the Hadoop platform. The results of experiments show that the GA-SVM based on MapReduce is suitable for teaching quality assessment under the environment of big data.

Big Data Security Framework for Distributed Cloud Data Centers

2017 ◽  
pp. 288-310 ◽  
Author(s):  
Chandu Thota ◽  
Gunasekaran Manogaran ◽  
Daphne Lopez ◽  
Vijayakumar V.
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Data Storage ◽  
High Efficiency ◽  
Rapid Development ◽  
Data Generation ◽  
Privacy And Security ◽  
Cloud Data ◽  
Efficiency And Effectiveness ◽  
Cloud Data Storage

The rapid development of data generation sources such as digital sensors, networks, and smart devices along with their extensive use is leading to create huge database and coins the term Big Data. Cloud Computing enables computing resources such as hardware, storage space and computing tools to be provided as IT services in a pay-as-you-go fashion with high efficiency and effectiveness. Cloud-based technologies with advantages over traditional platforms are rapidly utilized as potential hosts for big data. However, privacy and security is one of major issue in cloud computing due to its availability with very limited user-side control. This chapter proposes security architecture to prevent and secure the data and application being deployed in cloud environment with big data technology. This chapter discuss the security issues for big data in cloud computing and proposes Meta Cloud Data Storage architecture to protect big data in cloud computing environment.

Big Data Security Framework for Distributed Cloud Data Centers

Web Services ◽  
2019 ◽  
pp. 240-257
Author(s):  
Chandu Thota ◽  
Gunasekaran Manogaran ◽  
Daphne Lopez ◽  
Vijayakumar V.
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Data Storage ◽  
High Efficiency ◽  
Rapid Development ◽  
Data Generation ◽  
Privacy And Security ◽  
Cloud Data ◽  
Efficiency And Effectiveness ◽  
Cloud Data Storage

The rapid development of data generation sources such as digital sensors, networks, and smart devices along with their extensive use is leading to create huge database and coins the term Big Data. Cloud Computing enables computing resources such as hardware, storage space and computing tools to be provided as IT services in a pay-as-you-go fashion with high efficiency and effectiveness. Cloud-based technologies with advantages over traditional platforms are rapidly utilized as potential hosts for big data. However, privacy and security is one of major issue in cloud computing due to its availability with very limited user-side control. This chapter proposes security architecture to prevent and secure the data and application being deployed in cloud environment with big data technology. This chapter discuss the security issues for big data in cloud computing and proposes Meta Cloud Data Storage architecture to protect big data in cloud computing environment.

Big Data Security Framework for Distributed Cloud Data Centers

2018 ◽  
pp. 589-607 ◽  
Author(s):  
Chandu Thota ◽  
Gunasekaran Manogaran ◽  
Daphne Lopez ◽  
Vijayakumar V.
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Data Storage ◽  
High Efficiency ◽  
Rapid Development ◽  
Data Generation ◽  
Privacy And Security ◽  
Cloud Data ◽  
Efficiency And Effectiveness ◽  
Cloud Data Storage

The rapid development of data generation sources such as digital sensors, networks, and smart devices along with their extensive use is leading to create huge database and coins the term Big Data. Cloud Computing enables computing resources such as hardware, storage space and computing tools to be provided as IT services in a pay-as-you-go fashion with high efficiency and effectiveness. Cloud-based technologies with advantages over traditional platforms are rapidly utilized as potential hosts for big data. However, privacy and security is one of major issue in cloud computing due to its availability with very limited user-side control. This chapter proposes security architecture to prevent and secure the data and application being deployed in cloud environment with big data technology. This chapter discuss the security issues for big data in cloud computing and proposes Meta Cloud Data Storage architecture to protect big data in cloud computing environment.

scholarly journals Data Analysis Method of Intelligent Analysis Platform for Big Data of Film and Television

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Youwen Ma ◽  
Yi Wan
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Association Analysis ◽  
Programming Model ◽  
Analysis Method ◽  
Television Industry ◽  
Film And Television ◽  
Box Office ◽  
The Impact ◽  
Audience Rating

Based on cloud computing and statistics theory, this paper proposes a reasonable analysis method for big data of film and television. The method selects Hadoop open source cloud platform as the basis, combines the MapReduce distributed programming model and HDFS distributed file storage system and other key cloud computing technologies. In order to cope with different data processing needs of film and television industry, association analysis, cluster analysis, factor analysis, and K-mean + association analysis algorithm training model were applied to model, process, and analyze the full data of film and TV series. According to the film type, producer, production region, investment, box office, audience rating, network score, audience group, and other factors, the film and television data in recent years are analyzed and studied. Based on the study of the impact of each attribute of film and television drama on film box office and TV audience rating, it is committed to the prediction of film and television industry and constantly verifies and improves the algorithm model.

scholarly journals Fuzzy Method and Neural Network Model Parallel Implementation of Multi-Layer Neural Network Based on Cloud Computing for Real Time Data Transmission in Large Offshore Platform

2017 ◽  
Vol 24 (s2) ◽  
pp. 39-44 ◽  
Author(s):  
Zhang Hu ◽  
Wei Qin
Keyword(s):  
Neural Network ◽  
Cloud Computing ◽  
Big Data ◽  
Parallel Implementation ◽  
Rapid Development ◽  
Optimal Number ◽  
Bp Algorithm ◽  
Map Reduce ◽  
Time Data ◽  
Speed Up

Abstract With the rapid development of electronic technology, network technology and cloud computing technology, the current data is increasing in the way of mass, has entered the era of big data. Based on cloud computing clusters, this paper proposes a novel method of parallel implementation of multilayered neural networks based on Map-Reduce. Namely in order to meet the requirements of big data processing, this paper presents an efficient mapping scheme for a fully connected multi-layered neural network, which is trained by using error back propagation (BP) algorithm based on Map-Reduce on cloud computing clusters (MRBP). The batch-training (or epoch-training) regimes are used by effective segmentation of samples on the clusters, and are adopted in the separated training method, weight summary to achieve convergence by iterating. For a parallel BP algorithm on the clusters and a serial BP algorithm on uniprocessor, the required time for implementing the algorithms is derived. The performance parameters, such as speed-up, optimal number and minimum of data nodes are evaluated for the parallel BP algorithm on the clusters. Experiment results demonstrate that the proposed parallel BP algorithm in this paper has better speed-up, faster convergence rate, less iterations than that of the existed algorithms.

scholarly journals Efficient Attribute-Based Encryption with Privacy-Preserving Key Generation and Its Application in Industrial Cloud

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Yujiao Song ◽  
Hao Wang ◽  
Xiaochao Wei ◽  
Lei Wu
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Rapid Development ◽  
Mobile Internet ◽  
Security And Privacy ◽  
Key Generation ◽  
Cloud Technology ◽  
Industrial Enterprises ◽  
Attribute Based Encryption ◽  
Industrial Big Data

Due to the rapid development of new technologies such as cloud computing, Internet of Things (IoT), and mobile Internet, the data volumes are exploding. Particularly, in the industrial field, a large amount of data is generated every day. How to manage and use industrial Big Data primely is a thorny challenge for every industrial enterprise manager. As an emerging form of service, cloud computing technology provides a good solution. It receives more and more attention and support due to its flexible configuration, on-demand purchase, and easy maintenance. Using cloud technology, enterprises get rid of the heavy data management work and concentrate on their main business. Although cloud technology has many advantages, there are still many problems in terms of security and privacy. To protect the confidentiality of the data, the mainstream solution is encrypting data before uploading. In order to achieve flexible access control to encrypted data, attribute-based encryption (ABE) is an outstanding candidate. At present, more and more applications are using ABE to ensure data security. However, the privacy protection issues during the key generation phase are not considered in the current ABE systems. That is to say, the key generation center (KGC) knows both of attributes and corresponding keys of each user. This problem is especially serious in the industrial big data scenario, because it will cause great damage to the business secrets of industrial enterprises. In this paper, we design a new ABE scheme that protects user’s privacy during key issuing. In our new scheme, we separate the functionality of attribute auditing and key generating to ensure that the KGC cannot know user’s attributes and that the attribute auditing center (AAC) cannot obtain the user’s secret key. This is ideal for many privacy-sensitive scenarios, such as industrial big data scenario.

Sign in / Sign up

Export Citation Format

Share Document