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There are two general methods to reduce data rates: extraction of the user's image shape and the use of virtual humans in video conferencing.
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Therefore it is essential to devise a method to reduce data traffic.
In Section 2, we introduce a method to reduce data traffic in video conferencing.
The methods are either applied to reduce data immediately after its acquisition or customized to address some specific problems.
Therefore, the proposed method could be used to reduce data redundancy because it extracts the inherent pattern structure of different fault classes as it is nonlinear by nature.
Conversely, some methods to reduce big data during the data collection process are also proposed in the recent literature [19, 20, 21].
The work in [19] proposed a text feature selection method based on "TongYiCi Cilin" to reduce data's feature dimensions while ensuring data integrity and classification accuracy.
Applying functional knowledge to genomic data is one method that has been used to reduce data complexity and establish biologically plausible arguments.
Although Genetic method has a solution at crossroads to reduce data loss, we should consider that Genetic method has not any solution for traffic lights and, therefore, dissemination may occur in clusters because of traffic lights.
Since modern multiscale, multi-physics simulations can generate petabytes of data, they will require the development of new techniques and methods to reduce the data to familiar quantities of interest (e.g., pin powers, temperatures) with a more reasonable resolution and size.
To prevent overhead of existing flooding methods, to reduce redundancy of data dissemination high communication overhead, unnecessary computation Our proposed model Mobile User-preference-based Data Dissemination method Data dissemination of high accuracy, high data throughput, low computation Size of training matrix.
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