scholarly journals Detection of Distracted Car Drivers using Convolutional Neural Network

2020 ◽  
Vol 8 (6) ◽  
pp. 4175-4176
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Data Collection ◽  
Road Accident ◽  
Panic Attacks ◽  
The Internet ◽  
The Road ◽  
Vehicle System ◽  
Survey Report ◽  
Self Driving Cars

According to survey report on the internet, the road accident cases are increasing exponentially. Most of the accident cases are due to distraction of drivers, over speed, panic attacks of drivers. We are proposing a system that will take control of the vehicle system if the driver is distracted. The existing system uses a sensor based indication or Recurrent Neural Network (RNN) installed semi self driving cars. The proposed system uses Convolutional Neural Networks to understand the behaviour of the driver and the environment. Naturalistic data collection of ten drivers is being collected and are treated as a qualifying dataset.

scholarly journals DESIGN INFORMATION SYSTEM OF EMPLOYEES TARGET WITH NEURAL NETWORK BACKPROPAGATION

2018 ◽  
Vol 2 (1) ◽  
pp. 5
Author(s):  
Agus Sifaunajah ◽  
Kusworo Adi ◽  
Faikhin .
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Information System ◽  
Information Systems ◽  
Data Collection ◽  
Data Classification ◽  
Civil Servants ◽  
Web Based ◽  
Design Information

Assessment of the performance of civil servants (PNS) is still considered less objective and subjective tended to by some, so we need a solution to improve the objectivity of assessment. The target of employee work (SKP) is one solution to improve objectivity in the assessment of civil servants. Backpropagation is one of the methods in neural networks which is implemented in the information systems of SKP for used classification of data performance. Observation and literature became the method of data collection in this study. Web-based information systems of skp are facilitated for employees in the preparation of assessments. Backpropagation can be implemented to perform data classification of performance. Keyword: Neural network; Backpropagation, Classification, SKP Received: 2 February, 2017; Accepter: 15 March, 2017

scholarly journals PREDETERMINED MOVEMENT OF MOBILE ROBOT USING NEURAL NETWORKS

2014 ◽  
pp. 64-68
Author(s):  
Oleh Adamiv ◽  
Vasyl Koval ◽  
Iryna Turchenko
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Mobile Robot ◽  
Formation Process ◽  
Robot Control ◽  
Experimental Results ◽  
The Road ◽  
Mobile Robot Control ◽  
Different Parts ◽  
Training Sets

This paper describes the experimental results of neural networks application for mobile robot control on predetermined trajectory of the road. There is considered the formation process of training sets for neural network, their structure and simulating features. Researches have showed robust mobile robot movement on different parts of the road.

Study of Highway Crack Diagnosis Based on Cellular Neural Network

2013 ◽  
Vol 427-429 ◽  
pp. 2013-2017
Author(s):  
Sheng Zhuo Yao ◽  
Guo Dong Li ◽  
Fu Xin Zhang ◽  
Lin Ge
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Image Processing ◽  
Cellular Neural Network ◽  
Cellular Neural Networks ◽  
Quality Information ◽  
Image Detection ◽  
The Road ◽  
Standard Level ◽  
On The Road

Road quality information detect system is an important component in architecture quality detect system, also is the basement of successfully working of other related project for the whole country. The study of detecting the road crack is the key to insure the security of accurately detect the road quality in transportation system. In this paper, we come up with a fixed way of road undersized rift image detection by using cellular neural networks. By image processing, building rift networks and details networks and adding the model of similarity undersized rift networks. It can avoid the problem that can not accurately detect undersized crack by only taking the crack feature value. The experiment proved that fixed crack detect computing is easy to do, more accurate to detect the undersized cracks on the road and can reach the standard level of current detect technique.

scholarly journals Avaliação de Modelos de Redes Neurais Recorrentes para Anonimização de Textos em Português

2019 ◽  
Author(s):  
Antônio Franco ◽  
Leonardo Oliveira
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Natural Language Processing ◽  
Language Processing ◽  
Recurrent Neural Networks ◽  
Automatic Generation ◽  
The Other ◽  
The Internet ◽  
Writing Style ◽  
Trade Off

Currently, there are several approaches to provide anonymity on the Internet. However, one can still identify anonymous users through their writing style. With the advances in neural network and natural language processing research, the success of a classifier when accurately identify the author of a text is growing. On the other hand, new approaches that use recurrent neural networks for automatic generation of obfuscated texts have also arisen to fight anonymity adversaries. In this work, we evaluate two approaches that use neural networks to generate obfuscated texts. In our experiments, we compared the efficiency of both techniques when removing the stylistic attributes of a text and preserving its original semantics. Our results show a trade-off between the obfuscation level and the text semantics.

scholarly journals Drink and Drive Prohibitor System With Biometric Authentication And Accident Alerting System Using IOT

2021 ◽  
pp. 1154-1161
Author(s):  
Dhananjay H. Koli ◽  
Prasad S. Dhorje ◽  
Nikita V. Alawane ◽  
Prof. P. A. Upadhye
Keyword(s):  
Public Health Problem ◽  
Road Accident ◽  
Gas Content ◽  
Biometric Authentication ◽  
Drinking And Driving ◽  
Alcohol Content ◽  
The Road ◽  
Vehicle System ◽  
Alerting System ◽  
Near Future

Drinking and driving is already a serious public health problem ,which is likely to emerge as one of the most significant problems in near future .the system implemented by us aims at reducing the road accident in the near future due to drunk and drive . This project present the progress in using the alcohol detector ,a device that senses a change in the alcoholic gas content of the surrounding air these device is more commonly referred to as a breath analysis, as it analysis the alcohol content from person’s breath. The system detects the presence of alcohol in the vehicle and immediately locks the engine of the vehicle. System starts on fingerprint authentication and also track vehicle with the help of GSM/GPS module in case of accident occurs.

scholarly journals Social Group Behavior Analysis Model Integrating Multitask Learning and Convolutional Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Chuan Zhou ◽  
Suying Gui ◽  
Gaoming Zhang ◽  
Qirui Zhang ◽  
Xudong Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Behavior Analysis ◽  
Social Group ◽  
Group Behavior ◽  
Multitask Learning ◽  
The Internet ◽  
Analysis Model ◽  
The Internet Of Things

Social group behavior analysis has always been the key research direction of sociologists and psychologists. With the rapid development of the Internet of Things and the proposal of deep learning theory, convolutional neural networks are also used in social group research. In the process of social development, group incidents continue to increase, and there are more and more studies on social group behavior analysis. Although the research content and research methods are also richer, the research that combines the Internet of Things, convolutional neural network, and group behavior is more. This article will specifically propose a social group behavior analysis model that combines multitask learning and convolutional neural networks. This paper deeply learns the research of convolutional neural network and group behavior-related theories, makes full use of the advantages of convolutional neural network algorithm and multitask learning mode, and builds a social group behavior analysis model based on multitask learning and convolutional neural network. The experimental results on different data sets are analyzed. The results show that the accuracy rate of the experimental algorithm of convolutional neural network is as high as 95.10%, and it is better than other algorithms in time complexity, which is very suitable for social group behavior analysis.

scholarly journals Complexity Neural Networks for Estimating Flood Process in Internet-of-Things Empowered Smart City

2020 ◽  
Vol 10 (6) ◽  
pp. 118-129
Author(s):  
Mustafa Ahmed Othman Abo Mhara
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Internet Of Things ◽  
Water Conservation ◽  
Forecasting Model ◽  
The Internet ◽  
Hydrological Data ◽  
Complex Features ◽  
The Internet Of Things

With the advancement of the Internet of Things (IoT)-based water conservation computerization, hydrological data is increasingly enriched. Considering the ability of deep learning on complex features extraction, we proposed a flood process forecasting model based on Convolution Neural Network(CNN) with two-dimension(2D) convolutional operation. At first, we imported the spatial-temporal rainfall features of the Xixian basin. Subsequently, extensive experiments were carried out to determine the optimal hyper parameters of the proposed CNN flood forecasting model.

scholarly journals DESIGN INFORMATION SYSTEM OF EMPLOYEES TARGET WITH NEURAL NETWORK BACKPROPAGATION

2017 ◽  
Vol 2 (1) ◽  
pp. 5
Author(s):  
Agus Sifaunajah ◽  
Kusworo Adi ◽  
Faikhin .
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Information System ◽  
Information Systems ◽  
Data Collection ◽  
Data Classification ◽  
Civil Servants ◽  
Web Based ◽  
Design Information

Assessment of the performance of civil servants (PNS) is still considered less objective and subjective tended to by some, so we need a solution to improve the objectivity of assessment. The target of employee work (SKP) is one solution to improve objectivity in the assessment of civil servants. Backpropagation is one of the methods in neural networks which is implemented in the information systems of SKP for used classification of data performance. Observation and literature became the method of data collection in this study. Web-based information systems of skp are facilitated for employees in the preparation of assessments. Backpropagation can be implemented to perform data classification of performance. Keyword: Neural network; Backpropagation, Classification, SKP Received: 2 February, 2017; Accepter: 15 March, 2017

scholarly journals Application of Adaptive Neural Networks for the Filtration of Spam

10.29007/t3rl ◽  
2018 ◽  
Author(s):  
Gela Besiashvili ◽  
Tamar Bliadze ◽  
Zurab Kochladze
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Activation Function ◽  
Learning System ◽  
The Internet ◽  
Individual Consumer ◽  
Filtration System ◽  
Communication Method ◽  
Mail Service ◽  
E Mail

The application of e-mail is the most widespread communication method. The increased email number includes permanently increased amount of unwanted notifications (spam). The statistics claims that 80% of the traffic is spam that lags the internet traffic, consumes space on hard disc, and reduces the capacity of the network, not to mention time spent for email’s classification. The advanced E-mail service packs contain spam filtration program. Nevertheless, to classify the e-mails as spam fully depends on the consumers, as information acceptable (or vital) for one consumer, may be a spam for other. Hence, it is crucial to have automatic spam filtration system for every individual consumer.The paper discuss the solution, a learning system that filters emails through consumers’ parameters and gradually learn how to filter them accordingly. The system based on multi-layer neural network, which uses logistic activation function, learns via tutor and counter-spread algorithm of mistakes

scholarly journals Intelligent Transportation using Deep Learning

2020 ◽  
Vol 9 (3) ◽  
pp. 1619-1625
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Data Collection ◽  
Convolutional Neural Network ◽  
Intelligent Transportation ◽  
Collection System ◽  
Data Collection System ◽  
The Road ◽  
System A ◽  
Transportation Program

The goal of this paper is to advance intelligent transportation program through the creation of a data collection system, a Convolutional Neural Network (CNN) model for intelligent transportation, and a simulator to test the trained CNN model. The data collection system collects data from a vehiclesteering wheel angle, speed, and images of the road from three separate angles at the time of the data collection. A CNN model is then trained with the collected data. The trained CNN model is then tested on a simulator to evaluate its effectiveness.

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