Event Prediction Technology Based on Graph Neural Network

Increased utilization of nonlinear loads and fault event on the power system have resulted in a decline in the quality of power provided to the customers. It is fundamental to recognize and distinguish the power quality disturbances in the distribution system. To recognize and distinguish the power quality disturbances, the development of high protection schemes is required. This paper presents an optimal protection scheme for power quality event prediction and classification in the distribution system. The proposed protection scheme combines the performance of both the salp swarm optimization and artificial neural network. Here, artificial neural network is utilized in two phases with the objective function of prediction and classification of the power quality events. The first phase is utilized for recognizing the healthy or unhealthy condition of the system under various situations. Artificial neural network is utilized to perceive the system signal’s healthy or unhealthy condition under different circumstances. In the second phase, artificial neural network performs the classification of the unhealthy signals to recognize the right power quality event for assurance. In this phase, the artificial neural network learning method is enhanced by utilizing salp swarm optimization based on the minimum error objective function. The proposed method performs an assessment procedure to secure the system and classify the optimal power quality event which occurs in the distribution system. At that point, the proposed work is executed in the MATLAB/Simulink platform and the performance of the proposed system is compared with different existing techniques like Multiple Signal Classification-Artificial Neural Network (MUSIC-ANN), and Genetic Algorithm - Artificial Neural Network (GA-ANN). The comparison results demonstrate the superiority of the SSO-ANN technique and confirm its potential to power quality event prediction and classification.

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Event Prediction in Social Graphs Using 1-Dimensional Convolutional Neural Network

Advances in Artificial Intelligence - Lecture Notes in Computer Science ◽

10.1007/978-3-030-18305-9_64 ◽

2019 ◽

pp. 588-592 ◽

Cited By ~ 2

Author(s):

Bonaventure C. Molokwu

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Event Prediction ◽

Social Graphs

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STEP-OP: Short-term Event Prediction in the Operating Room using Hybrid Deep Learning to Forecast Five-Minute Intraoperative Hypotension (Preprint)

10.2196/preprints.31311 ◽

2021 ◽

Author(s):

Sooho Choe ◽

Eunjeong Park ◽

Wooseok Shin ◽

Bonah Koo ◽

Dongjin Shin ◽

...

Keyword(s):

Neural Network ◽

Artificial Intelligence ◽

Deep Learning ◽

Weighted Average ◽

University Hospital ◽

Short Term ◽

Event Prediction ◽

Intraoperative Hypotension ◽

National University ◽

Seoul National University

BACKGROUND Intraoperative hypotension has an adverse impact on postoperative outcomes, However, it is difficult to predict and treat intraoperative hypotension with individual clinical parameter in advance. OBJECTIVE To develop a prediction model to forecast five-minute intraoperative hypotension based on the weighted average ensemble of individual neural networks, which utilize the biosignals recorded during non-cardiac surgery. METHODS In this retrospective observational study, arterial wave form was recorded during non-cardiac operation held between August 2016 and December 2019, at Seoul National University Hospital, Seoul, South Korea. We analyzed the arterial waveforms from the big data in VitalDB repository of electronic health records. We defined 2 s hypotension as the moving average of arterial pressure under 65 mm Hg for 2 s, and intraoperative hypotensive events as the case in which 2 s hypotension lasts for at least 60 s. We developed an artificial intelligence-enabled process called short-term event prediction in the operating room (STEP-OP) for predicting short-term intraoperative hypotension. RESULTS The study was performed on 18,813 subjects undergoing non-cardiac surgeries. Deep-learning algorithms (convolutional neural network [CNN] and recurrent neural network [RNN]) using raw waveforms as input showed a greater area under the precision-recall curve (AUPRC) scores than the logistic regression algorithm (0.698 [95% confidence interval {CI}, 0.690–0.705], 0.706 [95% CI, 0.698–0.715]), compared with 0.673 (95% CI, 0.665–0.682), respectively. STEP-OP performed better and had greater AUPRC values than RNN and CNN algorithms (0.716 [95% CI, 0.708–0.723]). CONCLUSIONS We developed STEP-OP, the weighted average of deep-learning models. It predicted intraoperative hypotension more accurately than the CNN, RNN, and logistic regression models. CLINICALTRIAL The study was approved by the institutional review board of Seoul National University Hospital (H-2008-175-1152). (Trial Registration: ClinicalTrials.gov NCT02914444). Arterial Pressure; artificial intelligence; biosignals; deep learning; hypotension; machine learning

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