scholarly journals Activity Recognition using Accelerometer Sensor and Machine Learning Classifiers

2021 ◽  
Vol 9 (VII) ◽  
pp. 1153-1158
Author(s):  
Emin T Sunny
Keyword(s):  
Machine Learning ◽  
Principal Component Analysis ◽  
Activity Recognition ◽  
Human Activities ◽  
Principal Component ◽  
Clinical Consideration ◽  
Repeat Region ◽  
Accelerometer Sensor ◽  
Machine Learning Classifiers ◽  
Information Signal

Development affirmation is considered as a huge endeavor in various applications, particularly in clinical consideration organizations. Among these applications consolidate clinical decisive, seeing of customers' consistently timetable and ID of unusual cases. Here we present an approach for the development affirmation using an accelerometer sensor embedded in a mobile phone. This strategy uses a transparently available accelerometer dataset as the unrefined information signal. The features of the sign are picked subject to the time and repeat space. By then, Principal Component Analysis (PCA) is used to diminish the dimensionality of the features and concentrate the primary ones that can describe human activities. An assessment collaboration is performed between the primary unrefined data and PCA-based features and moreover, time and repeat region features are similarly contemplated using a couple of AI classifiers. The got results show that the PCA-based features get higher affirmation rate while repeat region features have higher exactness, with the speed of 96.11% and 92.10% independently.

Classification of Observations through Combination of the Dimension Reduction and the Cluster Analysis

2017 ◽  
Vol 7 (8) ◽  
pp. 30
Author(s):  
Hyeuk Kim
Keyword(s):  
Machine Learning ◽  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Unsupervised Learning ◽  
Principal Component ◽  
Component Analysis ◽  
Baseball Players ◽  
Partitioning Around Medoids ◽  
Different Characteristics

Unsupervised learning in machine learning divides data into several groups. The observations in the same group have similar characteristics and the observations in the different groups have the different characteristics. In the paper, we classify data by partitioning around medoids which have some advantages over the k-means clustering. We apply it to baseball players in Korea Baseball League. We also apply the principal component analysis to data and draw the graph using two components for axis. We interpret the meaning of the clustering graphically through the procedure. The combination of the partitioning around medoids and the principal component analysis can be used to any other data and the approach makes us to figure out the characteristics easily.

Analysis of the Bath Motion in the MM-SQC Dynamics Using Unsupervised Machine Learning Dimensionality Reduction Approaches: Principal Component Analysis

2020 ◽  
Author(s):  
Jiawei Peng ◽  
Yu Xie ◽  
Deping Hu ◽  
Zhenggang Lan
Keyword(s):  
Machine Learning ◽  
Principal Component Analysis ◽  
Collective Motion ◽  
Principal Component ◽  
Component Analysis ◽  
Nonadiabatic Dynamics ◽  
Trajectory Data ◽  
Unsupervised Machine Learning ◽  
Physical Knowledge ◽  
Vibronic Couplings

The system-plus-bath model is an important tool to understand nonadiabatic dynamics for large molecular systems. The understanding of the collective motion of a huge number of bath modes is essential to reveal their key roles in the overall dynamics. We apply the principal component analysis (PCA) to investigate the bath motion based on the massive data generated from the MM-SQC (symmetrical quasi-classical dynamics method based on the Meyer-Miller mapping Hamiltonian) nonadiabatic dynamics of the excited-state energy transfer dynamics of Frenkel-exciton model. The PCA method clearly clarifies that two types of bath modes, which either display the strong vibronic couplings or have the frequencies close to electronic transition, are very important to the nonadiabatic dynamics. These observations are fully consistent with the physical insights. This conclusion is obtained purely based on the PCA understanding of the trajectory data, without the large involvement of pre-defined physical knowledge. The results show that the PCA approach, one of the simplest unsupervised machine learning methods, is very powerful to analyze the complicated nonadiabatic dynamics in condensed phase involving many degrees of freedom.

scholarly journals Criteria for choosing the number of dimensions in a principal component analysis: An empirical assessment

2020 ◽  
Author(s):  
Renata Silva ◽  
Daniel Oliveira ◽  
Davi Pereira Santos ◽  
Lucio F.D. Santos ◽  
Rodrigo Erthal Wilson ◽  
...  
Keyword(s):  
Machine Learning ◽  
Principal Component Analysis ◽  
Hypothesis Test ◽  
Feature Learning ◽  
Principal Component ◽  
Component Analysis ◽  
Scree Plot ◽  
Open Issue ◽  
Chained Tasks ◽  
High Dimensional Datasets

Principal component analysis (PCA) is an efficient model for the optimization problem of finding d' axes of a subspace Rd' ⊆ Rd so that the mean squared distances from a given set R of points to the axes are minimal. Despite being steadily employed since 1901 in different scenarios, e.g., mechanics, PCA has become an important link in machine learning chained tasks, such as feature learning and AutoML designs. A frequent yet open issue that arises from supervised-based problems is how many PCA axes are required for the performance of machine learning constructs to be tuned. Accordingly, we investigate the behavior of six independent and uncoupled criteria for estimating the number of PCA axes, namely Scree-Plot %, Scree Plot Gap, Kaiser-Guttman, Broken-Stick, p-Score, and 2D. In total, we evaluate the performance of those approaches in 20 high dimensional datasets by using (i) four different classifiers, and (ii) a hypothesis test upon the reported F-Measures. Results indicate Broken-Stick and Scree-Plot % criteria consistently outperformed the competitors regarding supervised-based tasks, whereas estimators Kaiser-Guttman and Scree-Plot Gap delivered poor performances in the same scenarios.

scholarly journals A novel method for total chlorine detection using machine learning with electrode arrays

RSC Advances ◽  
2019 ◽  
Vol 9 (59) ◽  
pp. 34196-34206
Author(s):  
Zhe Li ◽  
Shunhao Huang ◽  
Juan Chen
Keyword(s):  
Machine Learning ◽  
Principal Component Analysis ◽  
Cyclic Voltammetry ◽  
Support Vector Regression ◽  
Principal Component ◽  
Measurement Model ◽  
Support Vector ◽  
Electrode Arrays ◽  
Soft Measurement ◽  
Novel Method

Establish soft measurement model of total chlorine: cyclic voltammetry curves, principal component analysis and support vector regression.

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