Dynamic Neural Network for Business and Market Analysis

Rilo, Javier de Arquer; Hussain, Abir Jaafar; AlTaei, May; Baker, Thar; Al‐Jumeily, Dhiya

doi:10.1007/978-3-030-26763-6_8

Cited by 3 publications

(1 citation statement)

References 7 publications

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“…Moreover, since the body's COM and COP are determined by the motions of all the body segments, the relationship between the single IMU and IA/RCIA can be highly non-linear and time-varying. Predicting the dynamic balance variables using a single IMU can thus be challenging, as the complicated nonlinear dynamic nature of the input-output relationship may affect the accuracy of predictions [49][50][51].…”

Section: Introductionmentioning

confidence: 99%

Recurrent Neural Network Methods for Extracting Dynamic Balance Variables during Gait from a Single Inertial Measurement Unit

Yu,

Yeh,

et al. 2023

Sensors

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Monitoring dynamic balance during gait is critical for fall prevention in the elderly. The current study aimed to develop recurrent neural network models for extracting balance variables from a single inertial measurement unit (IMU) placed on the sacrum during walking. Thirteen healthy young and thirteen healthy older adults wore the IMU during walking and the ground truth of the inclination angles (IA) of the center of pressure to the center of mass vector and their rates of changes (RCIA) were measured simultaneously. The IA, RCIA, and IMU data were used to train four models (uni-LSTM, bi-LSTM, uni-GRU, and bi-GRU), with 10% of the data reserved to evaluate the model errors in terms of the root-mean-squared errors (RMSEs) and percentage relative RMSEs (rRMSEs). Independent t-tests were used for between-group comparisons. The sensitivity, specificity, and Pearson’s r for the effect sizes between the model-predicted data and experimental ground truth were also obtained. The bi-GRU with the weighted MSE model was found to have the highest prediction accuracy, computational efficiency, and the best ability in identifying statistical between-group differences when compared with the ground truth, which would be the best choice for the prolonged real-life monitoring of gait balance for fall risk management in the elderly.

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Section: Introductionmentioning

confidence: 99%

Recurrent Neural Network Methods for Extracting Dynamic Balance Variables during Gait from a Single Inertial Measurement Unit

Yu,

Yeh,

et al. 2023

Sensors

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Multiscale deep network based multistep prediction of high‐dimensional time series from power transmission systems

Zhu

Wang

et al. 2020

Trans Emerging Tel Tech

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Internet of energy makes the future power and energy network a more complicated and intelligent system. With the development of energy industry, the sample data of such system is high dimensional, dynamic, correlative, and complex. In order to meet people's needs and reduce the power redundancy, predicting the future energy demand and production is an essential approach. It is necessary for us to predict the later hours' or days' data, which means multistep prediction. However, the common one‐step prediction model cannot forecast the power demand or production to make adequate preparation and the data have thousands of dimensions, which makes the problem challenging. In addition, the changeable pattern makes the common prediction algorithm do not perform good enough. In this article, we propose a sequence to sequence model to make multistep prediction with a baseline mean squared error (MSE) of 1.49×10−5. In addition, we improve the model to be a multiscale deep network and decrease the MSE to 1.23×10−5 through adding extra information to match different patterns. Furthermore, the multitask learning trick makes the MSE decrease to 1.18×10−5.

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A Systematic Review of Artificial Neural Networks in Medical Science and Applications

Al-Salman

Mustafina

Shahoodh

2020

2020 13th International Conference on Developments in eSystems Engineering (DeSE)

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Dynamic Neural Network for Business and Market Analysis

Cited by 3 publications

References 7 publications

Recurrent Neural Network Methods for Extracting Dynamic Balance Variables during Gait from a Single Inertial Measurement Unit

Recurrent Neural Network Methods for Extracting Dynamic Balance Variables during Gait from a Single Inertial Measurement Unit

Multiscale deep network based multistep prediction of high‐dimensional time series from power transmission systems

A Systematic Review of Artificial Neural Networks in Medical Science and Applications

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