A Two-Stage Machine Learning Approach for 5g Mobile Network Augmentation Through Dynamic Selection and Activation of Ue-Vbss

2022 ◽  
Author(s):  
Jansi Rani Sella Veluswami ◽  
Iacovos Ioannou ◽  
Prabagarane Nagaradjane ◽  
Christophoros Christophorou ◽  
Vasos Vassiliou ◽  
...  
Keyword(s):  
Machine Learning ◽  
Mobile Network ◽  
Learning Approach ◽  
Two Stage ◽  
Dynamic Selection ◽  
Machine Learning Approach ◽  
5G Mobile Network

scholarly journals TRM: A Powerful Two-Stage Machine Learning Approach for Identifying SNP-SNP Interactions

2011 ◽  
Vol 76 (1) ◽  
pp. 53-62 ◽  
Author(s):  
Hui-Yi Lin ◽  
Y. Ann Chen ◽  
Ya-Yu Tsai ◽  
Xiaotao Qu ◽  
Tung-Sung Tseng ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Approach ◽  
Two Stage ◽  
Machine Learning Approach

A Two-Stage Machine learning approach for temporally-robust text classification

2017 ◽  
Vol 69 ◽  
pp. 40-58 ◽  
Author(s):  
Thiago Salles ◽  
Leonardo Rocha ◽  
Fernando Mourão ◽  
Marcos Gonçalves ◽  
Felipe Viegas ◽  
...  
Keyword(s):  
Machine Learning ◽  
Text Classification ◽  
Learning Approach ◽  
Two Stage ◽  
Machine Learning Approach

Adaptive characterization of microstructure dataset using a two stage machine learning approach

2020 ◽  
Vol 177 ◽  
pp. 109593 ◽  
Author(s):  
Arun Baskaran ◽  
Genevieve Kane ◽  
Krista Biggs ◽  
Robert Hull ◽  
Daniel Lewis
Keyword(s):  
Machine Learning ◽  
Learning Approach ◽  
Two Stage ◽  
Machine Learning Approach

scholarly journals Two-Stage Latent Dynamics Modeling and Filtering for Characterizing Individual Walking and Running Patterns with Smartphone Sensors

Sensors ◽  
2019 ◽  
Vol 19 (12) ◽  
pp. 2712 ◽  
Author(s):  
Jaein Kim ◽  
Juwon Lee ◽  
Woongjin Jang ◽  
Seri Lee ◽  
Hongjoong Kim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Variables ◽  
High Dimensional ◽  
Learning Approach ◽  
Dynamic Walking ◽  
Dynamics Modeling ◽  
Two Stage ◽  
Human Movements ◽  
Latent Space ◽  
Machine Learning Approach

Recently, data from built-in sensors in smartphones have been readily available, and analyzing data for various types of health information from smartphone users has become a popular health care application area. Among relevant issues in the area, one of the most prominent topics is analyzing the characteristics of human movements. In this paper, we focus on characterizing the human movements of walking and running based on a novel machine learning approach. Since walking and running are human fundamental activities, analyzing their characteristics promptly and automatically during daily smartphone use is particularly valuable. In this paper, we propose a machine learning approach, referred to as ’two-stage latent dynamics modeling and filtering’ (TS-LDMF) method, where we combine a latent space modeling stage with a nonlinear filtering stage, for characterizing individual dynamic walking and running patterns by analyzing smartphone sensor data. For the task of characterizing movements, the proposed method makes use of encoding the high-dimensional sequential data from movements into random variables in a low-dimensional latent space. The use of random variables in the latent space, often called latent variables, is particularly useful, because it is capable of conveying compressed information concerning movements and efficiently handling the uncertainty originating from high-dimensional sequential observation. Our experimental results show that the proposed use of two-stage latent dynamics modeling and filtering yields promising results for characterizing individual dynamic walking and running patterns.

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