Keyboard Dynamic Analysis by Alexnet Type Neural Network

Tolіupa, Serhii; Tereikovskyi, Ihor; Tereikovskyi, Oleh; Tereikovska, Liudmyla; Nakonechnyi, Volodymyr; Kulakov, Yurii

doi:10.1109/tcset49122.2020.235466

Cited by 9 publications

(5 citation statements)

References 13 publications

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“…Non-image (but in matrix form) training-data was used within CNN model [28]. Another work used pretrained CNN model to implement experimental analysis of KDBRS [29]. It got 80% accuracy with only 100 training samples of each user.…”

Section: Convolutional Neural Network (Cnn)mentioning

confidence: 99%

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Keystroke Dynamics based Recognition Systems using Deep Learning: A Survey

Tewari¹

2022

Preprint

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Section: Convolutional Neural Network (Cnn)mentioning

confidence: 99%

“…To enhance size of training data, augmentation techniques (image processing based) were applied. Transfer learning with pretrained neural network models, were also utilized for the same [29].…”

Section: Performance Metrics For Deep Kdbrsmentioning

confidence: 99%

Keystroke Dynamics based Recognition Systems using Deep Learning: A Survey

Tewari¹

2022

Preprint

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“…As a starting point of the development, the approach given in [20,21] for determining the most effective neural network model was used, which is generally described as follows:…”

Section: Development and Research Of A Neural Network Modelmentioning

confidence: 99%

“…The database used to form the training, validation, and the test sample is specified. According to the recommendations [21,24] The graphs of the accuracy indicators of the segmentation of nerve plexuses built on the basis of the results of experiments conducted using the U-Net, PSPNet and YOLOv4 neural network models are shown in fig. 3-5.…”

Section: Development and Research Of A Neural Network Modelmentioning

confidence: 99%

A Neural Network Model for Object Mask Detection in Medical Images

Tereikovskyi,

Korchenko,

Bushuyev

et al. 2022

International Journal of Electronics and Telecommunications

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In modern conditions in the field of medicine, raster image analysis systems are becoming more widespread, which allow automating the process of establishing a diagnosis based on the results of instrumental monitoring of a patient. One of the most important stages of such an analysis is the detection of the mask of the object to be recognized on the image. It is shown that under the conditions of a multivariate and multifactorial task of analyzing medical images, the most promising are neural network tools for extracting masks. It has also been determined that the known detection tools are highly specialized and not sufficiently adapted to the variability of the conditions of use, which necessitates the construction of an effective neural network model adapted to the definition of a mask on medical images. An approach is proposed to determine the most effective type of neural network model, which provides for expert evaluation of the effectiveness of acceptable types of models and conducting computer experiments to make a final decision. It is shown that to evaluate the effectiveness of a neural network model, it is possible to use the Intersection over Union and Dice Loss metrics. The proposed solutions were verified by isolating the brachial plexus of nerve fibers on grayscale images presented in the public Ultrasound Nerve Segmentation database. The expediency of using neural network models U-Net, YOLOv4 and PSPNet was determined by expert evaluation, and with the help of computer experiments, it was proved that U-Net is the most effective in terms of Intersection over Union and Dice Loss, which provides a detection accuracy of about 0.89. Also, the analysis of the results of the experiments showed the need to improve the mathematical apparatus, which is used to calculate the mask detection indicators.

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“…AlexNet was selected because it is one of the most well-known and widely used convolutional neural network architectures for image classification. Besides, a model of AlexNet is provided in MatLab that is trained on more than one million images and can classify objects in up to 1000 categories [24].…”

Section: Convolutional Neural Network-cnnmentioning

confidence: 99%

Classification of the Microstructural Elements of the Vegetal Tissue of the Pumpkin (Cucurbita pepo L.) Using Convolutional Neural Networks

et al. 2021

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Although knowledge of the microstructure of food of vegetal origin helps us to understand the behavior of food materials, the variability in the microstructural elements complicates this analysis. In this regard, the construction of learning models that represent the actual microstructures of the tissue is important to extract relevant information and advance in the comprehension of such behavior. Consequently, the objective of this research is to compare two machine learning techniques—Convolutional Neural Networks (CNN) and Radial Basis Neural Networks (RBNN)—when used to enhance its microstructural analysis. Two main contributions can be highlighted from this research. First, a method is proposed to automatically analyze the microstructural elements of vegetal tissue; and second, a comparison was conducted to select a classifier to discriminate between tissue structures. For the comparison, a database of microstructural elements images was obtained from pumpkin (Cucurbita pepo L.) micrographs. Two classifiers were implemented using CNN and RBNN, and statistical performance metrics were computed using a 5-fold cross-validation scheme. This process was repeated one hundred times with a random selection of images in each repetition. The comparison showed that the classifiers based on CNN produced a better fit, obtaining F1–score average of 89.42% in front of 83.83% for RBNN. In this study, the performance of classifiers based on CNN was significantly higher compared to those based on RBNN in the discrimination of microstructural elements of vegetable foods.

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Keyboard Dynamic Analysis by Alexnet Type Neural Network

Cited by 9 publications

References 13 publications

Keystroke Dynamics based Recognition Systems using Deep Learning: A Survey

Keystroke Dynamics based Recognition Systems using Deep Learning: A Survey

A Neural Network Model for Object Mask Detection in Medical Images

Classification of the Microstructural Elements of the Vegetal Tissue of the Pumpkin (Cucurbita pepo L.) Using Convolutional Neural Networks

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