Deep neural network based missing data prediction of electrocardiogram signal using multiagent reinforcement learning

Carbon monoxide (CO) is one of the most important pollutants since it is selected for API calculation. Therefore, it is paramount to ensure that there is no missing data of CO during the analysis. There are numbers of occurrences that may contribute to the missing data problems such as inability of the instrument to record certain parameters. In view of this fact, a CO prediction model needs to be developed to address this problem. A dataset of meteorological and air pollutants value was obtained from the Air Quality Division, Department of Environment Malaysia (DOE). A total of 113112 datasets were used to develop the model using sensitivity analysis (SA) through artificial neural network (ANN). SA showed particulate matter (PM10) and ozone (O3) were the most significant input variables for missing data prediction model of CO. Three hidden nodes were the optimum number to develop the ANN model with the value of R2 equal to 0.5311. Both models (artificial neural network-carbon monoxide-all parameters (ANN-CO-AP) and artificial neural network-carbon monoxide-leave out (ANN-CO-LO)) showed high value of R2 (0.7639 and 0.5311) and low value of RMSE (0.2482 and 0.3506), respectively. These values indicated that the models might only employ the most significant input variables to represent the CO rather than using all input variables.

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Optimization of sand casting performance parameters and missing data prediction

Royal Society Open Science ◽

10.1098/rsos.181860 ◽

2019 ◽

Vol 6 (8) ◽

pp. 181860 ◽

Cited By ~ 4

Author(s):

Qingwei Xu ◽

Kaili Xu ◽

Li Li ◽

Xiwen Yao

Keyword(s):

Neural Network ◽

Missing Data ◽

Bp Neural Network ◽

Grey Relational Analysis ◽

Sand Casting ◽

Performance Parameters ◽

Relational Analysis ◽

Data Prediction ◽

Missing Data Prediction ◽

Grey Relational

Due to a wide range of applications, sand casting occupies an important position in modern casting practice. The main purpose of this study was to optimize the performance parameters of sand casting based on grey relational analysis and predict the missing data using back propagation (BP) neural network. First, the influence of human factors was eliminated by adopting the objective entropy weight method, which also saved manpower. The larger variation degree in the evaluation indicators, indicating that the evaluated projects had good discrimination in this regard, the larger weight should be given to these evaluation indicators. Second, the performance parameters of sand casting were optimized based on grey relational analysis, providing a reference for sand milling. The larger the grey relational degree, the closer the evaluated project was to the ideal project. Third, this paper provided a new method for determining the number of hidden neurons in a network according to the mean square error of training samples, and venting quality was predicted based on BP neural network. The relevant theory was deduced before predicting missing data, such that there will be a general understanding regarding the prediction principle of BP neural network. Fourth, to demonstrate the validity of BP neural network adopted in the process of missing data prediction, grey system theory was applied to compare the result of missing data prediction.

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Fully distributed actor-critic architecture for multitask deep reinforcement learning

The Knowledge Engineering Review ◽

10.1017/s0269888921000023 ◽

2021 ◽

Vol 36 ◽

Author(s):

Sergio Valcarcel Macua ◽

Ian Davies ◽

Aleksi Tukiainen ◽

Enrique Munoz de Cote

Keyword(s):

Neural Network ◽

Reinforcement Learning ◽

Duality Theory ◽

Deep Neural Network ◽

Original Problem ◽

Almost Sure Convergence ◽

Continuous Control ◽

Access Data ◽

Central Station ◽

Common Policy

Abstract We propose a fully distributed actor-critic architecture, named diffusion-distributed-actor-critic Diff-DAC, with application to multitask reinforcement learning (MRL). During the learning process, agents communicate their value and policy parameters to their neighbours, diffusing the information across a network of agents with no need for a central station. Each agent can only access data from its local task, but aims to learn a common policy that performs well for the whole set of tasks. The architecture is scalable, since the computational and communication cost per agent depends on the number of neighbours rather than the overall number of agents. We derive Diff-DAC from duality theory and provide novel insights into the actor-critic framework, showing that it is actually an instance of the dual-ascent method. We prove almost sure convergence of Diff-DAC to a common policy under general assumptions that hold even for deep neural network approximations. For more restrictive assumptions, we also prove that this common policy is a stationary point of an approximation of the original problem. Numerical results on multitask extensions of common continuous control benchmarks demonstrate that Diff-DAC stabilises learning and has a regularising effect that induces higher performance and better generalisation properties than previous architectures.

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Optimising Performance for NB-IoT UE Devices through Data Driven Models

Journal of Sensor and Actuator Networks ◽

10.3390/jsan10010021 ◽

2021 ◽

Vol 10 (1) ◽

pp. 21

Author(s):

Omar Nassef ◽

Toktam Mahmoodi ◽

Foivos Michelinakis ◽

Kashif Mahmood ◽

Ahmed Elmokashfi

Keyword(s):

Neural Network ◽

Reinforcement Learning ◽

Gradient Descent ◽

Deep Neural Network ◽

Narrow Band ◽

Learning Algorithm ◽

Base Station ◽

User Equipment ◽

Data Driven ◽

Superior Performance

This paper presents a data driven framework for performance optimisation of Narrow-Band IoT user equipment. The proposed framework is an edge micro-service that suggests one-time configurations to user equipment communicating with a base station. Suggested configurations are delivered from a Configuration Advocate, to improve energy consumption, delay, throughput or a combination of those metrics, depending on the user-end device and the application. Reinforcement learning utilising gradient descent and genetic algorithm is adopted synchronously with machine and deep learning algorithms to predict the environmental states and suggest an optimal configuration. The results highlight the adaptability of the Deep Neural Network in the prediction of intermediary environmental states, additionally the results present superior performance of the genetic reinforcement learning algorithm regarding its performance optimisation.

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Atrial Fibrillation Detection by the Combination of Recurrence Complex Network and Convolution Neural Network

Journal of Probability and Statistics ◽

10.1155/2019/8057820 ◽

2019 ◽

Vol 2019 ◽

pp. 1-9 ◽

Cited By ~ 5

Author(s):

Xiaoling Wei ◽

Jimin Li ◽

Chenghao Zhang ◽

Ming Liu ◽

Peng Xiong ◽

...

Keyword(s):

Neural Network ◽

Atrial Fibrillation ◽

Complex Network ◽

Individual Variation ◽

Deep Neural Network ◽

Detection Algorithm ◽

Convolution Neural Network ◽

Electrocardiogram Signal ◽

Sensitivity Specificity ◽

Atrial Fibrillation Detection

In this paper, R wave peak interval independent atrial fibrillation detection algorithm is proposed based on the analysis of the synchronization feature of the electrocardiogram signal by a deep neural network. Firstly, the synchronization feature of each heartbeat of the electrocardiogram signal is constructed by a Recurrence Complex Network. Then, a convolution neural network is used to detect atrial fibrillation by analyzing the eigenvalues of the Recurrence Complex Network. Finally, a voting algorithm is developed to improve the performance of the beat-wise atrial fibrillation detection. The MIT-BIH atrial fibrillation database is used to evaluate the performance of the proposed method. Experimental results show that the sensitivity, specificity, and accuracy of the algorithm can achieve 94.28%, 94.91%, and 94.59%, respectively. Remarkably, the proposed method was more effective than the traditional algorithms to the problem of individual variation in the atrial fibrillation detection.

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