scholarly journals The model of recognition of driving styles and classification of traffic interaction strategies

2017 ◽  
Vol 8 (4) ◽  
pp. 123-133 ◽  
Author(s):  
S.B. Efremov
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Road Traffic ◽  
Future Design ◽  
Traffic Conditions ◽  
Dangerous Driving ◽  
The Neural Network

In this paper, we propose the grounds for constructing a neural network model for recognizing the driving styles with the aim of classifying the types interaction strategies of drivers in road traffic. The purpose of this article is that the possible future design of systems for recognizing the driving styles based on the neural network. Our system is able to identify and classify strategies for interaction of drivers in traffic conditions, as well as to allocate traffic interaction strategies that can be correlated with “types of dangerous driving”.

scholarly journals Neural network model for recognition of driving strategies and interaction of drivers in traffic conditions

2018 ◽  
Vol 9 (4) ◽  
pp. 153-166
Author(s):  
S.B. Efremov
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Traffic Flow ◽  
Network Model ◽  
Neural Network Model ◽  
Self Organizing Map ◽  
Flow Conditions ◽  
Traffic Conditions ◽  
Dangerous Driving ◽  
The Neural Network

The paper presents a neural network model for recognizing driving strategies based on the interaction of drivers in traffic flow conditions. The architecture of the model, based on self-organizing map (SOM), consisting of various neural networks based on RBF (Radial Basis Function). The purpose of this work is to describe the architecture and structure of the neural network model, which allows to recognize the strategic features of driving. Our neural network is able to identify the interaction strategies of cars (drivers) in traffic flow conditions, as well as to identify such behavioral patterns of movement that can be correlated with different types of dangerous driving. From the results of the study, it follows that neural networks of the SOM RBF type are able to recognize and classify the types of interactions in traffic conditions based on modeling the analysis of the trajectories of cars. This neural network showed a high percentage of recognition and clear clustering of similar driving strategies.

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.

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.

scholarly journals Vibration-based classification of road damages: gyroscope data and a simple neural network model

2018 ◽  
Vol 195 ◽  
pp. 012068
Author(s):  
Fergyanto E. Gunawan ◽  
Herriyandi ◽  
Benfano Soewito ◽  
Tuga Mauritsius ◽  
Nico Surantha
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Simple Neural Network

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 Malware Classification with Improved Convolutional Neural Network Model

2020 ◽  
Vol 12 (6) ◽  
pp. 30-43
Author(s):  
Sumit S. Lad ◽  
◽  
Amol C. Adamuthe
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Personal Information ◽  
Support Vector ◽  
Svm Classifier ◽  
Gray Scale ◽  
Malware Classification

Malware is a threat to people in the cyber world. It steals personal information and harms computer systems. Various developers and information security specialists around the globe continuously work on strategies for detecting malware. From the last few years, machine learning has been investigated by many researchers for malware classification. The existing solutions require more computing resources and are not efficient for datasets with large numbers of samples. Using existing feature extractors for extracting features of images consumes more resources. This paper presents a Convolutional Neural Network model with pre-processing and augmentation techniques for the classification of malware gray-scale images. An investigation is conducted on the Malimg dataset, which contains 9339 gray-scale images. The dataset created from binaries of malware belongs to 25 different families. To create a precise approach and considering the success of deep learning techniques for the classification of raising the volume of newly created malware, we proposed CNN and Hybrid CNN+SVM model. The CNN is used as an automatic feature extractor that uses less resource and time as compared to the existing methods. Proposed CNN model shows (98.03%) accuracy which is better than other existing CNN models namely VGG16 (96.96%), ResNet50 (97.11%) InceptionV3 (97.22%), Xception (97.56%). The execution time of the proposed CNN model is significantly reduced than other existing CNN models. The proposed CNN model is hybridized with a support vector machine. Instead of using Softmax as activation function, SVM performs the task of classifying the malware based on features extracted by the CNN model. The proposed fine-tuned model of CNN produces a well-selected features vector of 256 Neurons with the FC layer, which is input to SVM. Linear SVC kernel transforms the binary SVM classifier into multi-class SVM, which classifies the malware samples using the one-against-one method and delivers the accuracy of 99.59%.

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