73 research outputs found
Prediction of Electrical Energy Consumption Using LSTM Algorithm with Teacher Forcing Technique
Electrical energy is an important foundation in world economic growth, therefore it requires an accurate prediction in predicting energy consumption in the future. The methods that are often used in previous research are the Time Series and Machine Learning methods, but recently there has been a new method that can predict energy consumption using the Deep Learning Method which can process data quickly for training and testing. In this research, the researcher proposes a model and algorithm which contained in Deep Learning, that is Multivariate Time Series Model with LSTM Algorithm and using Teacher Forcing Technique for predicting electrical energy consumption in the future. Because Multivariate Time Series Model and LSTM Algorithm can receive input with various conditions or seasons of electrical energy consumption. Teacher Forcing Technique is able lighten up the computation so that it can training and testing data quickly. The method used in this study is to compare Teacher Forcing LSTM with Non-Teacher Forcing LSTM in Multivariate Time Series model using several activation functions that produce significant differences. TF value of RMSE 0.006, MAE 0.070 and Non-TF has RMSE and MAE values of 0.117 and 0.246. The value of the two models is obtained from Sigmoid Activation and the worst value of the two models is in the Softmax activation function, with TF values is RMSE 0.423, MAE 0.485 and Non-TF RMSE 0.520, MAE 0.519.
Protecting the Future: Neonatal Seizure Detection with Spatial-Temporal Modeling
A timely detection of seizures for newborn infants with electroencephalogram
(EEG) has been a common yet life-saving practice in the Neonatal Intensive Care
Unit (NICU). However, it requires great human efforts for real-time monitoring,
which calls for automated solutions to neonatal seizure detection. Moreover,
the current automated methods focusing on adult epilepsy monitoring often fail
due to (i) dynamic seizure onset location in human brains; (ii) different
montages on neonates and (iii) huge distribution shift among different
subjects. In this paper, we propose a deep learning framework, namely STATENet,
to address the exclusive challenges with exquisite designs at the temporal,
spatial and model levels. The experiments over the real-world large-scale
neonatal EEG dataset illustrate that our framework achieves significantly
better seizure detection performance.Comment: Accepted in IEEE International Conference on Systems, Man, and
Cybernetics (SMC) 202
MedLens: Improve mortality prediction via medical signs selecting and regression interpolation
Monitoring the health status of patients and predicting mortality in advance
is vital for providing patients with timely care and treatment. Massive medical
signs in electronic health records (EHR) are fitted into advanced machine
learning models to make predictions. However, the data-quality problem of
original clinical signs is less discussed in the literature. Based on an
in-depth measurement of the missing rate and correlation score across various
medical signs and a large amount of patient hospital admission records, we
discovered the comprehensive missing rate is extremely high, and a large number
of useless signs could hurt the performance of prediction models. Then we
concluded that only improving data-quality could improve the baseline accuracy
of different prediction algorithms. We designed MEDLENS, with an automatic
vital medical signs selection approach via statistics and a flexible
interpolation approach for high missing rate time series. After augmenting the
data-quality of original medical signs, MEDLENS applies ensemble classifiers to
boost the accuracy and reduce the computation overhead at the same time. It
achieves a very high accuracy performance of 0.96% AUC-ROC and 0.81% AUC-PR,
which exceeds the previous benchmark
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