scholarly journals Driver Drowsiness Detection Using Convolutional Neural Networks

2021 ◽  
Vol 9 (VII) ◽  
pp. 2652-2658
Author(s):  
Md Gouse Pasha
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Emotional State ◽  
Steering Wheel ◽  
Road Accidents ◽  
Driver Drowsiness ◽  
The Neural Network ◽  
Segmentation Methods ◽  
Neural Network Technology

Accidents are now increasingly increasing as more cases are caused by driver drowsiness. To reduce these situations we were working on something that could reduce numbers and get accidents early. Seeing a drowsy driver behind the steering wheel once and warning him could reduce road accidents. In this case drowsiness is detected using an automatic camera, where, based on the captured image, the neural network detects whether the driver is awake or tired. Convolutional Neural Network Technology (CNN) has been used as part of a neural network, where each framework is examined separately and the average of the last 20 frames are tested, corresponding for about one second to a set of training and test data. We analyse image segmentation methods, construct a model based on convolutional neural networks. Using a detailed database of more than 2000 image fragments we are training and analysing the segmentation network to extract the emotional state of the driver in images.

Hand gesture recognition by means of region-based convolutional neural networks

2017 ◽  
Vol 10 (27) ◽  
pp. 1329-1342 ◽  
Author(s):  
Javier O. Pinzon Arenas ◽  
Robinson Jimenez Moreno ◽  
Paula C. Useche Murillo
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Convolutional Neural Networks ◽  
Gesture Recognition ◽  
Hand Gesture ◽  
Hand Gestures ◽  
The Neural Network ◽  
Average Accuracy

This paper presents the implementation of a Region-based Convolutional Neural Network focused on the recognition and localization of hand gestures, in this case 2 types of gestures: open and closed hand, in order to achieve the recognition of such gestures in dynamic backgrounds. The neural network is trained and validated, achieving a 99.4% validation accuracy in gesture recognition and a 25% average accuracy in RoI localization, which is then tested in real time, where its operation is verified through times taken for recognition, execution behavior through trained and untrained gestures, and complex backgrounds.

scholarly journals Baby Cry Detection in Domestic Environment using Convolutional Neural Networks

2020 ◽  
Vol 9 (7) ◽  
pp. 793-795
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Network Architecture ◽  
Web Application ◽  
Well Being ◽  
Emotion Detection ◽  
Neural Network Architecture ◽  
Domestic Environment ◽  
The Neural Network

In this paper we will identify a cry signals of infants and the explanation behind the screams below 0-6 months of segment age. Detection of baby cry signals is essential for the pre-processing of various applications involving crial analysis for baby caregivers, such as emotion detection. Since cry signals hold baby well-being information and can be understood to an extent by experienced parents and experts. We train and validate the neural network architecture for baby cry detection and also test the fastAI with the neural network. Trained neural networks will provide a model and this model can predict the reason behind the cry sound. Only the cry sounds are recognized, and alert the user automatically. Created a web application by responding and detecting different emotions including hunger, tired, discomfort, bellypain.

scholarly journals Neural networks application to determine the types and magnitude of aberrations from the pattern of the point spread function out of the focal plane

2021 ◽  
Vol 2086 (1) ◽  
pp. 012148
Author(s):  
P A Khorin ◽  
A P Dzyuba ◽  
P G Serafimovich ◽  
S N Khonina
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Point Spread Function ◽  
Focal Plane ◽  
Prediction Errors ◽  
Point Spread ◽  
The Neural Network ◽  
Spread Function ◽  
The Mean

Abstract Recognition of the types of aberrations corresponding to individual Zernike functions were carried out from the pattern of the intensity of the point spread function (PSF) outside the focal plane using convolutional neural networks. The PSF intensity patterns outside the focal plane are more informative in comparison with the focal plane even for small values/magnitudes of aberrations. The mean prediction errors of the neural network for each type of aberration were obtained for a set of 8 Zernike functions from a dataset of 2 thousand pictures of out-of-focal PSFs. As a result of training, for the considered types of aberrations, the obtained averaged absolute errors do not exceed 0.0053, which corresponds to an almost threefold decrease in the error in comparison with the same result for focal PSFs.

scholarly journals Using the neural network in clinical systems

2021 ◽  
Vol 2131 (4) ◽  
pp. 042008
Author(s):  
Yu S Gusynina ◽  
T A Shornikova
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Local Level ◽  
Probabilistic Method ◽  
Complex Function ◽  
Two Dimensions ◽  
Image Size ◽  
Network Training ◽  
The Neural Network

Abstract The article examines the identification of human bone fractures using convoluted neural networks. The method of recognition of photographs of patients is intended for automated systems of identification and video recording of images. Convolutional neural networks have a number of advantages, such as invariability when reducing or increasing image size, immunity to photo movements and deviations, changes in image perspective, and many other image errors. In addition, convolutional neural networks allow you to combine neurons at a local level in two dimensions, connect photographic elements in any place, and also reduce the total number of weights. The work describes a multi-layer convolutional network. The layers of which it consists are divided into two types: convolutional and sub-selective. Of interest is the use of the principle of weighting in the work. This principle allows you to reduce the number of characteristics of the neural network that can be trained. Network training is based on the rule of minimizing empirical error. This rule is based on the algorithm of inverse error propagation. This algorithm provides an instant calculation of the gradient of a complex function of several variables in case the function itself is predefined. Neural network training is based on probabilistic method. This method leads to more optimal results due to interference in the restructuring of network weights. The work confirms the axiomatics of the applied neural network, its architecture and its learning algorithm.

scholarly journals Identification of Earth’s surface objects using ensembles of convolutional neural networks.

2021 ◽  
pp. 114-123
Author(s):  
Evgenii E. Marushko ◽  
Alexander A. Doudkin ◽  
Xiangtao Zheng
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Support Vector ◽  
Machine Learning Methods ◽  
The Neural Network ◽  
Vector Machines ◽  
Identification Technique ◽  
Optimal Values ◽  
Fold Cross Validation

The paper proposes an identification technique of objects on the Earth’s surface images based on combination of machine learning methods. Different variants of multi-layer convolutional neural networks and support vector machines are considered as original models. A hybrid convolutional neural network that combines features extracted by the neural network and experts is proposed. Optimal values of hyperparameters of the models are calculated by grid search methods using k-fold cross-validation. The possibility of improving the accuracy of identification based on the ensembles of these models is shown. Effectiveness of the proposed technique is demonstrated by the example of images obtained by synthetic aperture radar.

scholarly journals Study of Convolutional Neural Networks applied to Image Stereo Matching

2020 ◽  
Author(s):  
João Pedro Poloni Ponce ◽  
Ricardo Suyama
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Error Rate ◽  
Stereo Matching ◽  
Structural Parameters ◽  
Neural Model ◽  
Temporal Difference ◽  
Training Time ◽  
The Neural Network

Stereo images are images formed from two or more sources that capture the same scene so that it is possible to infer the depth of the scene under analysis. The use of convolutional neural networks to compute these images has been shown to be a viable alternative due to its speed in finding the correspondence between the images. This raises questions related to the influence of structural parameters, such as size of kernel, stride and pooling policy on the performance of the neural network. To this end, this work sought to reproduce an article that deals with the topic and to explore the influence of the parameters mentioned above in function of the results of error rate and losses of the neural model. The results obtained reveal improvements. The influence of the parameters on the training time of the models was also notable, even using the GPU, the temporal difference in the training period between the maximum and minimum limits reached a ratio of six times.

scholarly journals Training algorithms for convolutional neural networks

2019 ◽  
Vol 15 (1) ◽  
pp. 41-54
Author(s):  
Arsentiy Igorevich Bredikhin
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Learning Algorithm ◽  
Signal Propagation ◽  
Training Algorithms ◽  
Classification Problems ◽  
The Neural Network ◽  
Matlab Programming ◽  
Implementation In Matlab

In this article we consider one of the most used classes of neural networks convolutional neural networks (hereinafter CNN). In particular, the areas of their application, algorithms of signal propagation by CNN and CNN training are described and the methods of CNN functioning algorithms implementation in MATLAB programming language are given. The article presents the results of research on the effectiveness of the CNN learning algorithm in solving classification problems with its help. In the course of these studies, such a characteristic of the neural network as the dynamics of the network error values depending on the learning rate is considered, and the correctness of the algorithm of learning convolutional neural network is checked. In this case, the problem of handwritten digits recognition on the MNIST sample is used as a classification task.

scholarly journals Differential Diagnosis of Parasitic Diseases of the Gastrointestinal Tract Using Artificial Neural Networks

2021 ◽  
Vol 3 (5) ◽  
pp. 01-05
Author(s):  
U N Musevi
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Differential Diagnosis ◽  
Gastrointestinal Tract ◽  
Parasitic Diseases ◽  
Disease Prediction ◽  
Network Technology ◽  
Test Results ◽  
The Neural Network ◽  
Neural Network Technology

Disorders of the functional state of the gastrointestinal tract associated with the influence of various parasites are considered. The symptoms of diseases caused by parasites and their location in the gastrointestinal tract are given. The possibility of using neural network technology in diagnosing illnesses as a result of the influence of various parasites is estimated. The structure of the neural network is given, indicating the set of inputs and outputs, as well as the result of its training. For the created neural network, test results for the respective symptoms and disease prediction results for these symptoms were obtained.

scholarly journals Avoiding Overfitting: A Survey on Regularization Methods for Convolutional Neural Networks

2022 ◽  
Author(s):  
Claudio Filipi Gonçalves dos Santos ◽  
João Paulo Papa
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Input Data ◽  
Data Augmentation ◽  
Critical Factor ◽  
Regularization Methods ◽  
Feature Maps ◽  
The Neural Network ◽  
Public Repositories

Several image processing tasks, such as image classification and object detection, have been significantly improved using Convolutional Neural Networks (CNN). Like ResNet and EfficientNet, many architectures have achieved outstanding results in at least one dataset by the time of their creation. A critical factor in training concerns the network’s regularization, which prevents the structure from overfitting. This work analyzes several regularization methods developed in the last few years, showing significant improvements for different CNN models. The works are classified into three main areas: the first one is called “data augmentation”, where all the techniques focus on performing changes in the input data. The second, named “internal changes”, which aims to describe procedures to modify the feature maps generated by the neural network or the kernels. The last one, called “label”, concerns transforming the labels of a given input. This work presents two main differences comparing to other available surveys about regularization: (i) the first concerns the papers gathered in the manuscript, which are not older than five years, and (ii) the second distinction is about reproducibility, i.e., all works refered here have their code available in public repositories or they have been directly implemented in some framework, such as TensorFlow or Torch.

scholarly journals Investigation of the Rupture Surface of the Titanium Alloy Using Convolutional Neural Networks

Materials ◽  
2018 ◽  
Vol 11 (12) ◽  
pp. 2467 ◽  
Author(s):  
Ihor Konovalenko ◽  
Pavlo Maruschak ◽  
Olegas Prentkovskis ◽  
Raimundas Junevičius
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Physical And Mechanical Properties ◽  
Previous Method ◽  
The Neural Network ◽  
Image Pixels ◽  
Characteristic Features ◽  
Metallic Specimen ◽  
Classical Image

The research of fractographic images of metals is an important method that allows obtaining valuable information about the physical and mechanical properties of a metallic specimen, determining the causes of its fracture, and developing models for optimizing its properties. One of the main lines of research in this case is studying the characteristics of the dimples of viscous detachment, which are formed on the metal surface in the process of its fracture. This paper proposes a method for detecting dimples of viscous detachment on a fractographic image, which is based on using a convolutional neural network. Compared to classical image processing algorithms, the use of the neural network significantly reduces the number of parameters to be adjusted manually. In addition, when being trained, the neural network can reveal a lot more characteristic features that affect the quality of recognition in a positive way. This makes the method more versatile and accurate. We investigated 17 models of convolutional neural networks with different structures and selected the optimal variant in terms of accuracy and speed. The proposed neural network classifies image pixels into two categories: “dimple” and “edge”. A transition from a probabilistic result at the output of the neural network to an unambiguously clear classification is proposed. The results obtained using the neural network were compared to the results obtained using a previously developed algorithm based on a set of filters. It has been found that the results are very similar (more than 90% similarity), but the neural network reveals the necessary features more accurately than the previous method.

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