scholarly journals Density Based Spatial Clustering Application with Noise by Varying Densities

2019 ◽  
Vol 8 (4) ◽  
pp. 5886-5891
Keyword(s):  
Machine Learning ◽  
Spatial Clustering ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Cluster Algorithms ◽  
Model Based ◽  
Dbscan Algorithm ◽  
Density Values ◽  
Original Size

Cluster algorithms are used for grouping up of similar points to form a cluster. It has seen mostly in Machine Learning algorithms. The most popular density-based algorithm is DBSCAN. DBSCAN can find the clusters, irrespective of its shapes and sizes of a cluster. DBSCAN algorithm can easily detect the noise in a clustering dataset. In the proposed algorithm we developed a model based on the existing dbscan algorithm. In the developed algorithm we focus mainly on the epsilon parameter value. Whenever the dbscan algorithm fails to form a cluster we increase the epsilon value by half of its original size. We repeat this step until a cluster is formed. Whenever a cluster is newly formed we change existing epsilon parameter value by adding the 10 percent of the previous used epsilon parameter value. We use epsilon for varying the density of a cluster. So, we can use the dbscan algorithm with the varying density values for developing a cluster. We applied this algorithm on the various datasets.

scholarly journals Deep-ACTINet: End-to-End Deep Learning Architecture for Automatic Sleep-Wake Detection Using Wrist Actigraphy

Electronics ◽  
2019 ◽  
Vol 8 (12) ◽  
pp. 1461 ◽  
Author(s):  
Taeheum Cho ◽  
Unang Sunarya ◽  
Minsoo Yeo ◽  
Bosun Hwang ◽  
Yong Seo Koo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Sleep Disorders ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Feature Engineering ◽  
Model Based ◽  
Wrist Actigraphy ◽  
End To End ◽  
Wake Detection

Sleep scoring is the first step for diagnosing sleep disorders. A variety of chronic diseases related to sleep disorders could be identified using sleep-state estimation. This paper presents an end-to-end deep learning architecture using wrist actigraphy, called Deep-ACTINet, for automatic sleep-wake detection using only noise canceled raw activity signals recorded during sleep and without a feature engineering method. As a benchmark test, the proposed Deep-ACTINet is compared with two conventional fixed model based sleep-wake scoring algorithms and four feature engineering based machine learning algorithms. The datasets were recorded from 10 subjects using three-axis accelerometer wristband sensors for eight hours in bed. The sleep recordings were analyzed using Deep-ACTINet and conventional approaches, and the suggested end-to-end deep learning model gained the highest accuracy of 89.65%, recall of 92.99%, and precision of 92.09% on average. These values were approximately 4.74% and 4.05% higher than those for the traditional model based and feature based machine learning algorithms, respectively. In addition, the neuron outputs of Deep-ACTINet contained the most significant information for separating the asleep and awake states, which was demonstrated by their high correlations with conventional significant features. Deep-ACTINet was designed to be a general model and thus has the potential to replace current actigraphy algorithms equipped in wristband wearable devices.

scholarly journals Machine-learning algorithms for helicopter hydraulic faults detection: model based research

2019 ◽  
Vol 1368 ◽  
pp. 052027
Author(s):  
A M Gareev ◽  
E Yu Minaev ◽  
D M Stadnik ◽  
N S Davydov ◽  
V I Protsenko ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Detection Model ◽  
Model Based
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