scholarly journals Way of application of convolutional neural networks for personality recognition and user emotions by keyboard handwriting

2021 ◽  
pp. 9-17
Author(s):  
Yurii Kulakov ◽  
Liudmyla Tereikovska ◽  
Ihor Tereikovskyi
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Convolutional Neural Network ◽  
Structural Parameters ◽  
Computer Experiments ◽  
Training Sample ◽  
Input Field ◽  
The Neural Network ◽  
Important Direction ◽  
Personality Recognition

An important direction of increasing the security and expanding the functionality of modern information systems is the introduction of face recognition tools and user emotions by their keyboard handwriting. The expediency of improving the indicated recognition means by introducing modern neural network solutions into them is shown. A way has been developed for using a convolutional neural network for recognizing a user's face and emotions from keyboard handwriting, the features of which are the procedure for adapting the structural parameters of a convolutional neural network of the VGG type to the expected conditions of use and a procedure for determining the input field, which provides the representation of the parameters of colored channels. After adapting the structural parameters, the VGG network was implemented using the MATLAB R2018b application package, which made it possible to carry out computer experiments aimed at verifying the proposed method. As a result of the conducted computer experiments, it was determined that the use of the proposed method of applying a convolutional neural network makes it possible to achieve a user face recognition accuracy of about 82% with 50 learning epochs. The need for further research in the direction of the formation of a training sample is shown, which will ensure high-quality training of the neural network model.

scholarly journals Deep learning application for box-office evaluation of images

2020 ◽  
Vol 44 (1) ◽  
pp. 127-132
Author(s):  
V.G. Efremtsev ◽  
N.G. Efremtsev ◽  
E.P. Teterin ◽  
P.E. Teterin ◽  
V.V. Gantsovsky
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Sample Preparation ◽  
Convolutional Neural Network ◽  
Preliminary Data ◽  
Classification Accuracy ◽  
Training Sample ◽  
Data Preparation ◽  
Box Office ◽  
The Neural Network

The possibility of application a convolutional neural network to assess the box-office effect of digital images is reviewed. We studied various conditions for sample preparation, optimizer algorithms, the number of pixels in the samples, the size of the training sample, color schemes, compression quality, and other photometric parameters in view of effect on training the neural network. Due to the proposed preliminary data preparation, the optimum of the architecture and hyperparameters of the neural network we achieved a classification accuracy of at least 98%.

scholarly journals MODEL OF FORMATION OF STUDY EXAMPLES OF THE NEURAL NETWORK INTENDED FOR THE ANALYSIS OF THE KEYBOARD HANDWRITING

2020 ◽  
Vol 1 (9) ◽  
pp. 104-114
Author(s):  
Liudmyla Tereikovska
Keyword(s):  
Neural Network ◽  
Network Analysis ◽  
Convolutional Neural Network ◽  
Control Systems ◽  
Emotional State ◽  
Computer Experiments ◽  
Information Control ◽  
Neural Network Analysis ◽  
Input Field ◽  
Biometric Information

The article is devoted to increasing the efficiency of technologies of covert monitoring of operators' activity by information and control systems of various purposes for face recognition and emotional state. It is shown that from the standpoint of the possibility of using standard computer peripherals as a sensor for reading biometric parameters, inalienability from the user, the widespread use of information control systems of symbolic password and technological data, the complexity of forgery of biometric information, and the possibility of covert monitoring prospects have the means of keyboard analysis. The necessity of improving the methodology of neural network analysis of keyboard handwriting for authentication and recognition of the emotional state of information computer system operators is substantiated. The prospects of application of convolutional neural networks are determined, which leads to the need to improve the technology of determining the parameters of educational examples in terms of forming the input field of convolutional neural network and forming many parameters of keyboard handwriting to be analyzed. A model of formation of educational examples has been developed, which due to the application of a reasonable set of input parameters and the use of a rectangular input field of a convolutional neural network reduces the resource consumption of neural network recognition tools and provides accuracy of neural network analysis of keyboard handwriting at 75%. The proposed theoretical solutions were verified by computer experiments. The expediency of correlation of ways of further researches with development of representative databases of keyboard handwriting is shown.

scholarly journals A Detection algorithm based on Convolutional Neural Network

2018 ◽  
Author(s):  
Fei Rong ◽  
Li Shasha ◽  
Xu Qingzheng ◽  
Liu Kun
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Training Model ◽  
Test Sample ◽  
Training Sample ◽  
Detection Algorithm ◽  
Small Scale ◽  
Detection Model ◽  
Sample Data ◽  
Ratio Change

The Station logo is a way for a TV station to claim copyright, which can realize the analysis and understanding of the video by the identification of the station logo, so as to ensure that the broadcasted TV signal will not be illegally interfered. In this paper, we design a station logo detection method based on Convolutional Neural Network by the characteristics of the station, such as small scale-to-height ratio change and relatively fixed position. Firstly, in order to realize the preprocessing and feature extraction of the station data, the video samples are collected, filtered, framed, labeled and processed. Then, the training sample data and the test sample data are divided proportionally to train the station detection model. Finally, the sample is tested to evaluate the effect of the training model in practice. The simulation experiments prove its validity.

scholarly journals APPLICATION OF CONVOLUTIONAL NEURAL NETWORK FOR DETECTION OF MELANOMA USING SKIN LESION IMAGE ON MOBILE DEVICE

2021 ◽  
Vol 3 (1) ◽  
pp. 8-14
Author(s):  
D. V. Fedasyuk ◽  
◽  
T. V. Demianets ◽  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Mobile Device ◽  
Mobile Application ◽  
High Accuracy ◽  
File Size ◽  
Melanoma Detection ◽  
The Neural Network ◽  
Classification Time

A melanoma is the deadliest skin cancer, so early diagnosis can provide a positive prognosis for treatment. Modern methods for early detecting melanoma on the image of the tumor are considered, and their advantages and disadvantages are analyzed. The article demonstrates a prototype of a mobile application for the detection of melanoma on the image of a mole based on a convolutional neural network, which is developed for the Android operating system. The mobile application contains melanoma detection functions, history of the previous examinations and a gallery with images of the previous examinations grouped by the location of the lesion. The HAM10000-based training dataset has been supplemented with the images of melanoma from the archive of The International Skin Imaging Collaboration to eliminate class imbalances and improve network accuracy. The search for existing neural networks that provide high accuracy was conducted, and VGG16, MobileNet, and NASNetMobile neural networks have been selected for research. Transfer learning and fine-tuning has been applied to the given neural networks to adapt the networks for the task of skin lesion classification. It is established that the use of these techniques allows to obtain high accuracy of the neural network for this task. The process of converting a convolutional neural network to an optimized Flatbuffer format using TensorFlow Lite for placement and use on a mobile device is described. The performance characteristics of the selected neural networks on the mobile device are evaluated according to the classification time on the CPU and GPU and the amount of memory occupied by the file of a single network is compared. The neural network file size was compared before and after conversion. It has been shown that the use of the TensorFlow Lite converter significantly reduces the file size of the neural network without affecting its accuracy by using an optimized format. The results of the study indicate a high speed of application and compactness of networks on the device, and the use of graphical acceleration can significantly decrease the image classification time of the tumor. According to the analyzed parameters, NASNetMobile was selected as the optimal neural network to be used in the mobile application of melanoma detection.

Sign in / Sign up

Export Citation Format

Share Document