Su Weiti, Ping Xiaoou, Tseng Yi-Ju, et al. Multiple time series data processing for classification with period merging algorithm [ J ]. Procedia Computer Science,2014,37 ( 8 ) :301-308.Su Weiti; Ping Xiaoou; Ts
The combination of complementary features generated by both time–frequency and time–space analysis methods is therefore promising for enhancing the classification power of the sequential deep learning. Data ECG data ECG signals capture the electrical activity of a human heart over a period of time....
COD, compared to STORM and Abstract-C algorithm, reduces the number of objects in each window and requires less memory space. Another method was developed to process large data volume proposed by Cao et al. (2014b); it optimizes the range queries by not storing the objects in same window...
We recently developed a deep learning-based classification algorithm—EmbryoNet—trained with manually annotated images to detect such defects and link them to one of the main embryonic signaling pathways31. This classification approach used a finite number of predetermined classes. We reasoned that ...
If an algorithm cannot naturally handle multivariate data, the simplest approach to adapt a univariate classifier to MTSC is to ensemble it over the multivariate dimensions. We compare the bespoke algorithms to these dimension independent approaches on the 26 of the 30 MTSC archive problems where ...
The classification is divided into 2 types Sinus Rhythm (heart is beating in a uniform pattern) Atrial fibrillation (form of irregular rhythm, user should probably go see their doctor) If Apple Watch is unable to determine the ECG result, either due to a low or a high heart rate or due...
Propose MTNet, which uses a memory component and attention mechanism to store the long-term historical data and deal with a period of time rather than a single time step 提出MTNet,使用一个记忆模块和注意力机制来存储长期的历史数据,并且可以同时处理一段序列而非单独的时间步 Temporal Pattern Attention...
We use the optimum smoothed result (St) to improve the CNN algorithm performance. CNN with lucas hidden layers CNN is the main algorithm of this research. CNN has the capacity to learn meaningful features automatically from high-dimensional data. The input layer used one feature since it is a...
Experimental results demonstrated that the proposed RTFN achieved decent performance in both supervised classification and unsupervised clustering. Specifically, the proposed RTFN-based supervised algorithm performed the best on 39 out of 85 univariate datasets in the UCR2018 archive CRediT authorship ...
At first, using DT binary classification (C5.0 algorithm) predicts the unstable generators. In this stage, inputs are rotor angles, and the most precise accuracy achieves after 30 cycles of data gathering (0.5 second after fault clearance). In the second stage, the nature of instability ...