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Shekhar Boers

Bio: Shekhar Boers is an academic researcher. The author has contributed to research in topics: Computer science & Motion (physics). The author has an hindex of 1, co-authored 2 publications receiving 2 citations.

Papers
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Journal ArticleDOI
TL;DR: This paper summarizes the research status ofbig data analysis at home and abroad from three aspects: analysis as a service, big data analysis methods, and big data-driven science germination through literature review.
Abstract: With the development of message technology, big data is connected with business management. The research content and present situation of intelligent make policy support systemic in big data are introduced. Based on the knowledge-based systemic and its relationship with make policy-making process, the knowledge and knowledge processing systemic in IDSS are discussed. With the development of computer and its network, many enterprises have established their own message management systemics. How to observe the enterprise itself and the outside world in a flexible, real-time, vertical and horizontal, dynamic and slicing way, so as to capture relevant message for the development needs of the enterprise. The rapid development of big data has aroused widespread concern and attention at home and abroad. Scientific and effective analysis and processing of big data are the core issues in the field of big data. This paper summarizes the research status of big data analysis at home and abroad from three aspects: analysis as a service, big data analysis methods, and big data-driven science germination through literature review. By analyzing the statistical and semantic features of the existing data, we can find out the rules and then summarize them into an abstract data analysis model, thus providing the basis for data analysis, and the establishment of data graph and model can better reflect the specific content of the study. In the research of big data, it is found that both traditional data and big data have packet loss rates. According to the research, the packet loss rate of traditional data is as high as 70.9%, but that of big data is only 34.5%. Making a formula in the calculation of big data can help us to observe more. Through this research, it is found that big data has a more direct relationship with business management, and it controls various ways of enterprises. The era of big data has come, and all walks of life are faced with unprecedented data volume and data analysis requirements.

2 citations

Journal ArticleDOI
TL;DR: Zhang et al. as discussed by the authors studied human motion recognition in dance video images based on an attitude estimation algorithm, which can be used not only for professional dancers' movement correction, dance self-help teaching, and other application scenarios but also for athletes' movement analysis.
Abstract: With the deep integration of science and technology and culture, the estimation of human movements in dance video images will become an important application field of computer vision technology, which can be used not only for professional dancers’ movement correction, dance self-help teaching, and other application scenarios but also for athletes’ movement analysis. Therefore, it will greatly promote the implementation of teaching students in accordance with their aptitude by applying information technology to estimate dancers’ movements and postures in real time and obtaining information of classroom dance teaching status in time. In this paper, human motion recognition in dance video images is studied based on an attitude estimation algorithm. When the number of experiments reaches 20, the average value of deep learning algorithm and particle swarm optimization algorithm is 76.23 and 75.23, respectively, while the average value of attitude estimation algorithm in this paper is 77.95. Therefore, the average results of attitude estimation algorithm in this paper are slightly higher than those of other algorithms.

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Peer Review
TL;DR: In this article , the authors summarized the research status of big data analysis at home and abroad from three aspects: analysis as a service, big-data analysis methods, and big data-driven science germination through literature review.
Abstract: With the development of message technology, big data is connected with business management. The research content and present situation of intelligent make policy support systemic in big data are introduced. Based on the knowledge-based systemic and its relationship with make policy-making process, the knowledge and knowledge processing systemic in IDSS are discussed. With the development of computer and its network, many enterprises have established their own message management systemics. How to observe the enterprise itself and the outside world in a fl exible, real-time, vertical and horizontal, dynamic and slicing way, so as to capture relevant message for the development needs of the enterprise. The rapid development of big data has aroused widespread concern and attention at home and abroad. Scienti fi c and e ff ective analysis and processing of big data are the core issues in the fi eld of big data. This paper summarizes the research status of big data analysis at home and abroad from three aspects: analysis as a service, big data analysis methods, and big data-driven science germination through literature review. By analyzing the statistical and semantic features of the existing data, we can fi nd out the rules and then summarize them into an abstract data analysis model, thus providing the basis for data analysis, and the establishment of data graph and model can better re fl ect the speci fi c content of the study. In the research of big data, it is found that both traditional data and big data have packet loss rates. According to the research, the packet loss rate of traditional data is as high as 70.9%, but that of big data is only 34.5%. Making a formula in the calculation of big data can help us to observe more. Through this research, it is found that big data has a more direct relationship with business management, and it controls various ways of enterprises. The era of big data has come, and all walks of life are faced with unprecedented data volume and data analysis requirements.
Journal ArticleDOI
TL;DR: In this paper , the authors proposed a method to solve the problem of the problem: the one-dimensional graph. .>

Abstract: