scholarly journals Modeling Judges’ Scores in Artistic Gymnastics

2019 ◽  
Vol 12 (1) ◽  
pp. 1-9
Author(s):  
Melanie Mack ◽  
Maximilian Bryan ◽  
Gerhard Heyer ◽  
Thomas Heinen
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Evaluation Process ◽  
Model Calculations ◽  
Kinematic Data ◽  
Complete Dataset ◽  
Exploratory Approach ◽  
Movement Kinematic ◽  
Alternative Approaches ◽  
The Relationship

Background: In artistic gymnastics, performance is observed and evaluated by judges based on criteria defined in the code of points. However, there is a manifold of influences discussed in the literature that could potentially bias the judges’ evaluations in artistic gymnastics. In this context, several authors claim the necessity for alternative approaches to judging gymnastics utilizing biomechanical methods. Objective: The aim of this study was to develop and evaluate a model-based approach to judge gymnastics performance based on quantitative kinematic data of the performed skills. Methods: Four different model variants based on kinematic similarity calculated by a multivariate exploratory approach and the Recurrent Neural Network method were used to evaluate the relationship between the movement kinematics and the judges’ scores. The complete dataset consisted of movement kinematic data and judgment scores of a total of N = 173 trials of three different skills and routines from women’s artistic gymnastics. Results: The results exhibit a significant relationship between the predicted score and the actual score for six of the twelve model calculations. The different model variants yielded a different prediction performance in general across all skills and also in terms of the different skills. In particular, only the Recurrent Neural Network model exhibited significant correlation values between the actual and the predicted scores for all three investigated skills. Conclusion: The results were discussed in terms of the differences of the models as well as the various factors that might play a role in the evaluation process.

scholarly journals Prediction of PCR amplification from primer and template sequences using recurrent neural network

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Kotetsu Kayama ◽  
Miyuki Kanno ◽  
Naoto Chisaki ◽  
Misaki Tanaka ◽  
Reika Yao ◽  
...  
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Pcr Amplification ◽  
Nucleotide Sequences ◽  
Specific Primer ◽  
First Report ◽  
Novel Method ◽  
Primer Sequence ◽  
Dna Template ◽  
The Relationship

AbstractWe have developed a novel method to predict the success of PCR amplification for a specific primer set and DNA template based on the relationship between the primer sequence and the template. To perform the prediction using a recurrent neural network, the usual double-stranded formation between the primer and template nucleotide sequences was herein expressed as a five-lettered word. The set of words (pseudo-sentences) was placed to indicate the success or failure of PCR targeted to learn recurrent neural network (RNN). After learning pseudo-sentences, RNN predicted PCR results from pseudo-sentences which were created by primer and template sequences with 70% accuracy. These results suggest that PCR results could be predicted using learned RNN and the trained RNN could be used as a replacement for preliminary PCR experimentation. This is the first report which utilized the application of neural network for primer design and prediction of PCR results.

scholarly journals RnRTD: Intelligent Approach Based on the Relationship-Driven Neural Network and Restricted Tensor Decomposition for Multiple Accusation Judgment in Legal Cases

2019 ◽  
Vol 2019 ◽  
pp. 1-18 ◽  
Author(s):  
Xiaoding Guo ◽  
Hongli Zhang ◽  
Lin Ye ◽  
Shang Li
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Recurrent Neural Network ◽  
Feature Space ◽  
Tensor Decomposition ◽  
Target Feature ◽  
Legal Cases ◽  
Legal Case ◽  
The Relationship

The use of intelligent judgment technology to assist in judgment is an inevitable trend in the development of judgment in contemporary social legal cases. Using big data and artificial intelligence technology to accurately determine multiple accusations involved in legal cases is an urgent problem to be solved in legal judgment. The key to solving these problems lies in two points, namely, (1) characterization of legal cases and (2) classification and prediction of legal case data. Traditional methods of entity characterization rely on feature extraction, which is often based on vocabulary and syntax information. Thus, traditional entity characterization often requires extensive energy and has poor generality, thus introducing a large amount of computation and limitation to subsequent classification algorithms. This study proposes an intelligent judgment approach called RnRTD, which is based on the relationship-driven recurrent neural network (rdRNN) and restricted tensor decomposition (RTD). We represent legal cases as tensors and propose an innovative RTD method. RTD has low dependence on vocabulary and syntax and extracts the feature structure that is most favorable for improving the accuracy of the subsequent classification algorithm. RTD maps the tensors, which represent legal cases, into a specific feature space and transforms the original tensor into a core tensor and its corresponding factor matrices. This study uses rdRNN to continuously update and optimize the constraints in RTD so that rdRNN can have the best legal case classification effect in the target feature space generated by RTD. Simultaneously, rdRNN sets up a new gate and a similar case list to represent the interaction between legal cases. In comparison with traditional feature extraction methods, our proposed RTD method is less expensive and more universal in the characterization of legal cases. Moreover, rdRNN with an RTD layer has a better effect than the recurrent neural network (RNN) only on the classification and prediction of multiple accusations in legal cases. Experiments show that compared with previous approaches, our method achieves higher accuracy in the classification and prediction of multiple accusations in legal cases, and our algorithm is more interpretable.

scholarly journals Chronic Disease Prediction Using Character-Recurrent Neural Network in The Presence of Missing Information

2019 ◽  
Vol 9 (10) ◽  
pp. 2170 ◽  
Author(s):  
Changgyun Kim ◽  
Youngdoo Son ◽  
Sekyoung Youm
Keyword(s):  
Neural Network ◽  
Chronic Diseases ◽  
Recurrent Neural Network ◽  
Multilayer Perceptron ◽  
Missing Values ◽  
Health And Nutrition ◽  
Partially Observed ◽  
Data Points ◽  
Multilayer Perceptron Network ◽  
The Relationship

The aim of this study was to predict chronic diseases in individual patients using a character-recurrent neural network (Char-RNN), which is a deep learning model that treats data in each class as a word when a large portion of its input values is missing. An advantage of Char-RNN is that it does not require any additional imputation method because it implicitly infers missing values considering the relationship with nearby data points. We applied Char-RNN to classify cases in the Korea National Health and Nutrition Examination Survey (KNHANES) VI as normal status and five chronic diseases: hypertension, stroke, angina pectoris, myocardial infarction, and diabetes mellitus. We also employed a multilayer perceptron network for the same task for comparison. The results show higher accuracy for Char-RNN than for the conventional multilayer perceptron model. Char-RNN showed remarkable performance in finding patients with hypertension and stroke. The present study utilized the KNHANES VI data to demonstrate a practical approach to predicting and managing chronic diseases with partially observed information.

A new car-following model considering recurrent neural network

2019 ◽  
Vol 33 (26) ◽  
pp. 1950304 ◽  
Author(s):  
Chen Hua
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Relative Velocity ◽  
Road Traffic ◽  
Safe Distance ◽  
Velocity Difference ◽  
Car Following ◽  
Depth Analysis ◽  
Car Following Model ◽  
The Relationship

A new car-following model is proposed based on recurrent neural network (RNN) to effectively describe the state change and road traffic congestion while the vehicle is moving. The model firstly gives a full velocity difference car-following model according to the driver’s reaction sensitivity and relative velocity, and then takes the vehicle position and velocity as the input parameters to optimize the safe distance between the front and rear vehicles in the car-following model based on RNN model. Finally, the effectiveness of the above model is validated by building a simulation experiment platform, and an in-depth analysis is conducted on the relationship among influencing factors, e.g., relative velocity, reaction sensitivity, headway, etc. The results reveal that, compared with traditional car-following models, the model can quickly analyze the relationship between initial position and velocity of the vehicle in a shorter time and thus obtain a smaller safe distance. In the case of small velocity difference between the front and rear vehicles, the running velocity of the front and rear vehicles is relatively stable, which is conducive to maintaining the headway.

scholarly journals Designing Time Series Crime Prediction Model using Long Short-Term Memory Recurrent Neural Network

2020 ◽  
Vol 9 (4) ◽  
pp. 402-405
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Recurrent Neural Network ◽  
Short Term Memory ◽  
Series Data ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Crime Incidence ◽  
The Relationship

Crime influences people in many ways. Prior studies have shown the relationship between time and crime incidence behavior. This research attempts to determine and examine the relationship between time, crime incidences types and locations by using one of the neural network models for time series data that is, Long Short-Term Memory network. The collected data is pre-processed, analyzed and tested using Long Short-Term Memory recurrent neural network model. R-square score is also used to test the accuracy. The study results show that applying Long Short-Term Memory Recurrent Neural Network (LSTM RNN) enables to come up with more accurate prediction about crime incidence occurrence with respect to time. Predicting crimes accurately helps to improve crime prevention and decision and advance the justice system.

scholarly journals Application of a Recurrent Neural Network and Simplified Semianalytical Method for Continuous Strain Histories Estimation

2019 ◽  
Vol 2019 ◽  
pp. 1-13
Author(s):  
Huan Luo ◽  
Miaohua Huang ◽  
Wei Xiong
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Fatigue Loading ◽  
Operating Conditions ◽  
Multibody Model ◽  
Novel Approach ◽  
Model Training ◽  
Semianalytical Method ◽  
The Relationship ◽  
Nonlinear Autoregressive

The durability and reliability of structural components are usually assessed based on fatigue loading under operating conditions. To obtain accurate fatigue loading in the form of continuous strain histories, a novel approach is proposed based on the combination of a recurrent neural network and simplified semianalytical method. The recurrent neural network named nonlinear autoregressive model with exogenous inputs (NLARX) is applied to determine the relationship between external loads and corresponding fatigue loading. Owing to the generalization ability of NLARX, semianalytical method, which is used to obtain sample database for NLARX model training and testing, is implemented with simplified multibody model. Durability tests of a torsion beam rear suspension are introduced to demonstrate the effectiveness of the proposed approach. The experimental results show that our proposed approach is able to achieve better estimation results, when compared with the conventional semianalytical method.

Research on behavior recognition based on feature fusion of automatic coder and recurrent neural network

2020 ◽  
Vol 39 (6) ◽  
pp. 8927-8935
Author(s):  
Bing Zheng ◽  
Dawei Yun ◽  
Yan Liang
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Behavior Pattern ◽  
Rapid Development ◽  
Video Data ◽  
Support Vector ◽  
Behavior Recognition ◽  
Learning Methods ◽  
The Impact ◽  
Internet Of Things Technology

Under the impact of COVID-19, research on behavior recognition are highly needed. In this paper, we combine the algorithm of self-adaptive coder and recurrent neural network to realize the research of behavior pattern recognition. At present, most of the research of human behavior recognition is focused on the video data, which is based on the video number. At the same time, due to the complexity of video image data, it is easy to violate personal privacy. With the rapid development of Internet of things technology, it has attracted the attention of a large number of experts and scholars. Researchers have tried to use many machine learning methods, such as random forest, support vector machine and other shallow learning methods, which perform well in the laboratory environment, but there is still a long way to go from practical application. In this paper, a recursive neural network algorithm based on long and short term memory (LSTM) is proposed to realize the recognition of behavior patterns, so as to improve the accuracy of human activity behavior recognition.

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