Risk early warning safety model for sports events based on back propagation neural network machine learning

2019 ◽  
Vol 118 ◽  
pp. 332-336 ◽  
Author(s):  
Haijun Zhang ◽  
Yuanle Li ◽  
Haili Zhang
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Early Warning ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Sports Events

scholarly journals Safety Warning Model of Coal Face Based on FCM Fuzzy Clustering and GA-BP Neural Network

Symmetry ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 1082
Author(s):  
Fanqiang Meng
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Early Warning ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Coal Face ◽  
Early Warning Model ◽  
Working Face ◽  
The Neural Network ◽  
Warning Model

Risk and security are two symmetric descriptions of the uncertainty of the same system. If the risk early warning is carried out in time, the security capability of the system can be improved. A safety early warning model based on fuzzy c-means clustering (FCM) and back-propagation neural network was established, and a genetic algorithm was introduced to optimize the connection weight and other properties of the neural network, so as to construct the safety early warning system of coal mining face. The system was applied in a coal face in Shandong, China, with 46 groups of data as samples. Firstly, the original data were clustered by FCM, the input space was fuzzy divided, and the samples were clustered into three categories. Then, the clustered data was used as the input of the neural network for training and prediction. The back-propagation neural network and genetic algorithm optimization neural network were trained and verified many times. The results show that the early warning model can realize the prediction and early warning of the safety condition of the working face, and the performance of the neural network model optimized by genetic algorithm is better than the traditional back-propagation artificial neural network model, with higher prediction accuracy and convergence speed. The established early warning model and method can provide reference and basis for the prediction, early warning and risk management of coal mine production safety, so as to discover the hidden danger of working face accident as soon as possible, eliminate the hidden danger in time and reduce the accident probability to the maximum extent.

scholarly journals A Machine Learning Method for the Detection of Brown Core in the Chinese Pear Variety Huangguan Using a MOS-Based E-Nose

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4499 ◽  
Author(s):  
Hao Wei ◽  
Yu Gu
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Learning Algorithms ◽  
Economic Value ◽  
Back Propagation ◽  
Principal Component ◽  
Back Propagation Neural Network ◽  
Machine Learning Algorithms ◽  
Oxide Semiconductor ◽  
Learning Machine

The brown core is an internal disorder that significantly affects the palatability and economic value of Chinese pears. In this study, a framework that includes a back-propagation neural network (BPNN) and extreme learning machine (ELM) (BP-ELMNN) was proposed for the detection of brown core in the Chinese pear variety Huangguan. The odor data of pear were collected using a metal oxide semiconductor (MOS) electronic nose (E-nose). Principal component analysis was used to analyze the complexity of the odor emitted by pears with brown cores. The performances of several machine learning algorithms, i.e., radial basis function neural network (RBFNN), BPNN, and ELM, were compared with that of the BP-ELMNN. The experimental results showed that the proposed framework provided the best results for the test samples, with an accuracy of 0.9683, a macro-precision of 0.9688, a macro-recall of 0.9683, and a macro-F1 score of 0.9685. The results demonstrate that the use of machine learning algorithms for the analysis of E-nose data is a feasible and non-destructive method to detect brown core in pears.

Machine learning for reparameterization of four-site water models: TIP4P-BG and TIP4P-BGT

2021 ◽  
Author(s):  
Hong-fei Ye ◽  
Jian Wang ◽  
Yong-gang Zheng ◽  
Hong-wu Zhang ◽  
Zhen Chen
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Genetic Algorithm ◽  
High Precision ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Site Water ◽  
Water Models

Two high-precision water models are established based on the combination of a back-propagation neural network and genetic algorithm.

scholarly journals Multi-Sensor Data Fusion Algorithm for Indoor Fire Early Warning Based on BP Neural Network

Information ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 59
Author(s):  
Lesong Wu ◽  
Lan Chen ◽  
Xiaoran Hao
Keyword(s):  
Neural Network ◽  
Early Warning ◽  
Back Propagation ◽  
Fire Detection ◽  
Back Propagation Neural Network ◽  
Standard Test ◽  
Sensor Data ◽  
Fusion Algorithm ◽  
Trend Extraction ◽  
Fire Scenarios

Fire early warning is an important way to deal with the faster burning rate of modern home fires and ensure the safety of the residents’ lives and property. To improve real-time fire alarm performance, this paper proposes an indoor fire early warning algorithm based on a back propagation neural network. The early warning algorithm fuses the data of temperature, smoke concentration and carbon monoxide, which are collected by sensors, and outputs the probability of fire occurrence. In this study, non-uniform sampling and trend extraction were used to enhance the ability to distinguish fire signals and environmental interference. Data from six sets of standard test fire scenarios and six sets of no-fire scenarios were used to test the algorithm proposed in this paper. The test results show that the proposed algorithm can correctly alarm six standard test fires from these 12 scenarios, and the fire detection time is shortened by 32%.

scholarly journals Modify the Accuracy of MODIS PWV in China: A Performance Comparison Using Random Forest, Generalized Regression Neural Network and Back−Propagation Neural Network

2021 ◽  
Vol 13 (11) ◽  
pp. 2215
Author(s):  
Zhaohui Xiong ◽  
Xiaogong Sun ◽  
Jizhang Sang ◽  
Xiaomin Wei
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Water Vapor ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Generalized Regression Neural Network ◽  
Learning Methods ◽  
Machine Learning Methods

Water vapor plays an important role in climate change and water cycling, but there are few water vapor products with both high spatial resolution and high accuracy that effectively monitor the change of water vapor. The high precision Global Navigation Satellite System (GNSS) Precipitable Water Vapor (PWV) is often used to calibrate the high spatial resolution Moderate−resolution Imaging Spectroradiometer (MODIS) PWV to produce new PWV product with high accuracy and high spatial resolution. In addition, the machine learning method has a good performance in modifying the accuracy of MODIS PWV. However, the accuracy improvement of different machine learning methods and different modeling timescale is different. In this article, we use three machine learning methods, namely, the Random Forest (RF), Generalized Regression Neural Network (GRNN), and Back−propagation Neural Network (BPNN) to calibrate MODIS PWV in 2019, at annual and monthly timescales. We also use the Multiple Linear Regression (MLR) method for comparison. The root mean squares (RMSs) at the annual timescale with the three machine learning methods are 4.1 mm (BPNN), 3.3 mm (RF), and 3.9 mm (GRNN), and the average RMSs become 2.9 mm (BPNN), 2.8 mm (RF), and 2.5 mm (GRNN) at the monthly timescale. Those results are all better than the MLR method (5.0 mm at the annual timescale and 4.6 mm at the monthly timescale). When there is an obvious variation pattern in the training sample, the RF method can capture the pattern to achieve the best results since the RF achieves the best performance at the annual timescale. Dividing such samples into several sub−samples each having higher internal consistency could further improve the performance of machine learning methods, especially for the GRNN, since GRNN achieves the best performance at the monthly timescale, and the performance of those three machine learning methods at the monthly timescale is better than that of annual timescale. The spatial and temporal variation patterns of the RMS values are significantly weakened after the modeling by machine learning methods for both three methods.

scholarly journals Big Data Analysis of Facebook Users Personality Reconition using Map Reduce Back Propagation Neural Networks

2021 ◽  
Vol 6 (2) ◽  
Author(s):  
Solomon Akinboro ◽  
Isaac K. Ogundoyin ◽  
Ayobami T. Olusesi
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Back Propagation ◽  
Threshold Value ◽  
Back Propagation Neural Network ◽  
Classification Model ◽  
Map Reduce ◽  
Support Vector ◽  
Computationally Efficient ◽  
Personality Recognition

Machine learning has been an effective tool to connect networks of enormous information for predicting personality. Identification of personality-related indicators encrypted in Facebook profiles and activities are of special concern in most research efforts. This research modeled user personality based on set of features extracted from the Facebook data using Map-Reduce Back Propagation Neural Network (MRBPNN). The performance of the MRBPNN classification model was evaluated in terms of five basic personality dimensions: Extraversion (EXT), Agreeableness (AGR), Conscientiousness (CON), Neuroticism (NEU), and Openness to Experience (OPN) using True positive, False Positive, accuracy, precision and F-measure as metrics at the threshold value of 0.32. The experimental results reveal that MRBPNN model has accuracy of 91.40%, 93.89%, 91.33%, 90.43% and 89.13% CON, OPN, EXT, NEU and AGR respectively for personality recognition which is more computationally efficient than Back Propagation Neural Network (BPNN) and Support Vector Machine (SVM). Therefore, personality recognition based on MRBPNN would produce a reliable prediction system for various personality traits with data having a very large instance.  Keywords— Machine learning, Facebook, MRBPNN, Personality Recognition, Neuroticism, Agreeableness.

scholarly journals Incremental Approach of Neural Network in Back Propagation Algorithms for Web Data Mining

2017 ◽  
Vol 6 (2) ◽  
pp. 74
Author(s):  
A. P. Tawdar ◽  
M. S. Bewoor ◽  
S. H. Patil
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Text Categorization ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Classification Task ◽  
Text Documents ◽  
Training Time ◽  
Text Document ◽  
Content Mining

Text Classification is also called as Text Categorization (TC), is the task of classifying a set of text documents automatically into different categories from a predefined set. If a text document relates to exactly one of the categories, then it is called as single-label classification task; otherwise, it is called as multi-label classification task. For Information Retrieval (IR) and Machine Learning (ML), TC uses several tools and has received much attention in the last decades. In this paper, first classifies the text documents using MLP based machine learning approach (BPP) and then return the most relevant documents. And also describes a proposed back propagation neural network classifier that performs cross validation for original Neural Network. In order to optimize the classification accuracy, training time. Proposed web content mining methodology in the exploration with the aid of BPP. The main objective of this investigation is web document extraction and utilizing different grouping algorithm. This work extricates the data from the web URL.

scholarly journals Machine-Learning Prediction of Underwater Shock Loading on Structures

Computation ◽  
2019 ◽  
Vol 7 (4) ◽  
pp. 58
Author(s):  
Zhang ◽  
Drikakis ◽  
Li ◽  
Yan
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Laplace Transform ◽  
Back Propagation ◽  
Underwater Explosion ◽  
Back Propagation Neural Network ◽  
Inverse Laplace Transform ◽  
Structural Stiffness ◽  
Parallel Plates ◽  
Stiffness And Damping

Due to the complex physics of underwater explosion problems, it is difficult to derive analytical solutions with accurate results. In this study, a machine-learning method to train a back-propagation neural network for parameter prediction is presented for the first time in literature. The specific problem is the response of a structure submerged in water subjected to shock loads produced by an underwater explosion, with the detonation point being far away from the structure so that the loading wave can be regarded as a planar shock wave. Two rigid parallel plates connected by a linear spring and a linear dashpot that simulate structural stiffness and damping respectively, represent the structure. Taking the Laplace transform of the governing equations, solving the resulting equations, and then taking the inverse Laplace transform, the simplified problem is analyzed theoretically. The coupled ordinary differential equations governing the motion of the system are also solved numerically by the fourth order Runge–Kutta method and then verified by a finite element method using Ansys/LSDYNA. The parametric training with the back-propagation neural network algorithm was conducted to delineate the effects of structural stiffness and damping on the attenuation of shock waves, the cavitation time, and the time of maximum momentum transfer. The prediction results agree well with the validation and test sample results.

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