scholarly journals Application of Keras neural network in the era of big data

2021 ◽  
Vol 2083 (4) ◽  
pp. 042090
Author(s):  
Junqi Wang
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Missing Values ◽  
Data Filtering ◽  
Id3 Algorithm ◽  
The Neural Network ◽  
Hidden Layer ◽  
High Level ◽  
First Time

Abstract The experiment uses crawler tools to obtain data, and the data is preprocessed to find missing values and eliminate invalid data, meanwhile, the model is constructed by information entropy and ID3 algorithm so as to select the desired amount of features, and then basic modeling and data filtering is performed to train and evaluate the model for the first time, finally, in order to get a more ideal model, this experiment The optimal model is obtained by changing the number of hidden layers and neurons of the neural network to build a high-level neural network API neural network model written by pure python - Keras neural network model. The results show that when the model defines a 2-layer neural network and the number of neurons in the hidden layer is fourteen, the accuracy of the model is the highest, and the accuracy of the test set is as high as ninety-one percent.

scholarly journals Classification and Forecasting of Bollywood Movies by Commercial Success using Back-Propagation Neural Network model

2019 ◽  
Vol 9 (2) ◽  
pp. 3600-3607
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Data Filtering ◽  
Commercial Success ◽  
The Neural Network ◽  
Proposed Model

Forecasting commercial success of motion pictures remained challenging for producers, critics and other industry leaders in this changing world of web and online media. In this study, the author has explored a back-propagation neural network model with 23 numeric input (BPNN-N23) for classification of Bollywood movies released during the years 2014 through 2017. The proposed model classifies movies in three classes namely “HIT”, “AVERAGE” and “FLOP”. Common procedures like data filtering, data cleaning and data normalization have been followed prior to feeding those data to the neural network. After comparing the performance of the proposed model with the benchmark models and works, the results show that the said model shows performance that is comparable to the published ones with respect to the assumed Indian empirical settings. This research reveals the extent of the effects and roles of the considered factors as well as the proposed model in predicting the fate of a Bollywood movie in India.

scholarly journals Various heuristic algorithms to minimise the two-page crossing numbers of graphs

2015 ◽  
Vol 5 (1) ◽  
pp. 22-40 ◽  
Author(s):  
Hongmei He ◽  
Ana Sălăgean ◽  
Erkki Mäkinen ◽  
Imrich Vrt’o
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Heuristic Algorithms ◽  
Crossing Numbers ◽  
The Neural Network ◽  
Graph Families ◽  
First Time ◽  
Planar Drawing ◽  
Test Suites

AbstractWe propose several new heuristics for the twopage book crossing problem, which are based on recent algorithms for the corresponding one-page problem. Especially, the neural network model for edge allocation is combined for the first time with various one-page algorithms. We investigate the performance of the new heuristics by testing them on various benchmark test suites. It is found out that the new heuristics outperform the previously known heuristics and produce good approximations of the planar crossing number for severalwell-known graph families. We conjecture that the optimal two-page drawing of a graph represents the planar drawing of the graph.

Estimating Traffic Volume on Minor Roads at Rural Stop-Controlled Intersections using Deep Learning

2019 ◽  
Vol 2673 (4) ◽  
pp. 108-116 ◽  
Author(s):  
Mostafa H. Tawfeek ◽  
Karim El-Basyouny
Keyword(s):  
Neural Network ◽  
Linear Regression ◽  
Network Model ◽  
Neural Network Model ◽  
Deep Neural Network ◽  
Safety Management ◽  
Estimation Method ◽  
Estimation Model ◽  
The Neural Network ◽  
Annual Average Daily Traffic

Safety Performance Functions (SPFs) are regression models used to predict the expected number of collisions as a function of various traffic and geometric characteristics. One of the integral components in developing SPFs is the availability of accurate exposure factors, that is, annual average daily traffic (AADT). However, AADTs are not often available for minor roads at rural intersections. This study aims to develop a robust AADT estimation model using a deep neural network. A total of 1,350 rural four-legged, stop-controlled intersections from the Province of Alberta, Canada, were used to train the neural network. The results of the deep neural network model were compared with the traditional estimation method, which uses linear regression. The results indicated that the deep neural network model improved the estimation of minor roads’ AADT by 35% when compared with the traditional method. Furthermore, SPFs developed using linear regression resulted in models with statistically insignificant AADTs on minor roads. Conversely, the SPF developed using the neural network provided a better fit to the data with both AADTs on minor and major roads being statistically significant variables. The findings indicated that the proposed model could enhance the predictive power of the SPF and therefore improve the decision-making process since SPFs are used in all parts of the safety management process.

Linear Static Response of Suspension Arm Based on Artificial Neural Network Technique

2011 ◽  
Vol 213 ◽  
pp. 419-426
Author(s):  
M.M. Rahman ◽  
Hemin M. Mohyaldeen ◽  
M.M. Noor ◽  
K. Kadirgama ◽  
Rosli A. Bakar
Keyword(s):  
Neural Network ◽  
Finite Element Analysis ◽  
Finite Element ◽  
Network Model ◽  
Neural Network Model ◽  
Mesh Size ◽  
Maximum Displacement ◽  
Element Analysis ◽  
The Neural Network ◽  
The Finite Element Analysis

Modeling and simulation are indispensable when dealing with complex engineering systems. This study deals with intelligent techniques modeling for linear response of suspension arm. The finite element analysis and Radial Basis Function Neural Network (RBFNN) technique is used to predict the response of suspension arm. The linear static analysis was performed utilizing the finite element analysis code. The neural network model has 3 inputs representing the load, mesh size and material while 4 output representing the maximum displacement, maximum Principal stress, von Mises and Tresca. Finally, regression analysis between finite element results and values predicted by the neural network model was made. It can be seen that the RBFNN proposed approach was found to be highly effective with least error in identification of stress-displacement of suspension arm. Simulated results show that RBF can be very successively used for reduction of the effort and time required to predict the stress-displacement response of suspension arm as FE methods usually deal with only a single problem for each run.

scholarly journals Contribution to image restoration using a neural network model

2002 ◽  
Vol Volume 1, 2002 ◽  
Author(s):  
Karim Achour ◽  
Nadia Zenati ◽  
Oualid Djekoune
Keyword(s):  
Neural Network ◽  
Image Restoration ◽  
Network Model ◽  
Neural Network Model ◽  
Hopfield Neural Network ◽  
Optimization Method ◽  
Nous Proposons ◽  
International Audience ◽  
High Level

International audience The reduction of the blur and the noise is an important task in image processing. Indeed, these two types of degradation are some undesirable components during some high level treatments. In this paper, we propose an optimization method based on neural network model for the regularized image restoration. We used in this application a modified Hopfield neural network. We propose two algorithms using the modified Hopfield neural network with two updating modes : the algorithm with a sequential updates and the algorithm with the n-simultaneous updates. The quality of the obtained result attests the efficiency of the proposed method when applied on several images degraded with blur and noise. La réduction du bruit et du flou est une tâche très importante en traitement d'images. En effet, ces deux types de dégradations sont des composantes indésirables lors des traitements de haut niveau. Dans cet article, nous proposons une méthode d'optimisation basée sur les réseaux de neurones pour résoudre le problème de restauration d'images floues-bruitées. Le réseau de neurones utilisé est le réseau de « Hopfield ». Nous proposons deux algorithmes utilisant deux modes de mise à jour: Un algorithme avec un mode de mise à jour séquentiel et un algorithme avec un mode de mise à jour n-simultanée. L'efficacité de la méthode mise en œuvre a été testée sur divers types d'images dégradées.

scholarly journals Predicting Wins, Losses and Attributes’ Sensitivities in the Soccer World Cup 2018 Using Neural Network Analysis

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3213 ◽  
Author(s):  
Amr Hassan ◽  
Abdel-Rahman Akl ◽  
Ibrahim Hassan ◽  
Caroline Sunderland
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Average Distance ◽  
Deep Understanding ◽  
The Neural Network ◽  
Testing Error ◽  
Training Error ◽  
Using Data ◽  
The Relationship

Predicting the results of soccer competitions and the contributions of match attributes, in particular, has gained popularity in recent years. Big data processing obtained from different sensors, cameras and analysis systems needs modern tools that can provide a deep understanding of the relationship between this huge amount of data produced by sensors and cameras, both linear and non-linear data. Using data mining tools does not appear sufficient to provide a deep understanding of the relationship between the match attributes and results and how to predict or optimize the results based upon performance variables. This study aimed to suggest a different approach to predict wins, losses and attributes’ sensitivities which enables the prediction of match results based on the most sensitive attributes that affect it as a second step. A radial basis function neural network model has successfully weighted the effectiveness of all match attributes and classified the team results into the target groups as a win or loss. The neural network model’s output demonstrated a correct percentage of win and loss of 83.3% and 72.7% respectively, with a low Root Mean Square training error of 2.9% and testing error of 0.37%. Out of 75 match attributes, 19 were identified as powerful predictors of success. The most powerful respectively were: the Total Team Medium Pass Attempted (MBA) 100%; the Distance Covered Team Average in zone 3 (15–20 km/h; Zone3_TA) 99%; the Team Average ball delivery into the attacking third of the field (TA_DAT) 80.9%; the Total Team Covered Distance without Ball Possession (Not in_Poss_TT) 76.8%; and the Average Distance Covered by Team (Game TA) 75.1%. Therefore, the novel radial based function neural network model can be employed by sports scientists to adapt training, tactics and opposition analysis to improve performance.

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