Implementation of Evolutionary Methods of Solving the Travelling Salesman Problem in a Robotic Warehouse

Oliinyk, Аndrii; Fedorchenko, Ievgen; Stepanenko, Alexander; Rud, Mykyta; Goncharenko, Dmytro

doi:10.1007/978-3-030-43070-2_13

Cited by 9 publications

(2 citation statements)

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“…Thinning is applied only during model training. In order to compensate for the effect of reducing the number of features during thinning, the features that have not been zeroed are multiplied by 1/(1 -R), where R is the thinning coefficient [13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28].…”

Section: Preventing Overfittingmentioning

confidence: 99%

Computer Model for Detecting Cervical Spine Fractures based on Computed Tomography Images

Fedorchenko,

Oliinyk,

Chornobuk

et al. 2024

IJC

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The paper discusses methods of detecting cervical spine fractures based on computed tomography images using machine learning algorithms. Solving such a problem is important in the context of providing emergency care to patients with suspected spinal fractures, when accurate decision-making based on radiological data can be critical. In this case, such a machine learning model can speed up the work of a radiologist and reduce the importance of the human factor in making important decisions. After a review of analogs from the literature, it has been found that convolutional neural networks appear to be the most promising method. Using a publicly available dataset, a model "Fracture detection 3" based on a convolutional neural network is developed to solve the problem. The model demonstrates a classification accuracy of 98.25%, sensitivity of 99%, and specificity of 97.5%, which is ahead of the literature. For comparison with traditional methods, models based on the support vector method, decision tree, and k-nearest method are developed using a similar dataset. "Fracture detection 3" outperforms all developed models based on traditional methods in terms of classification accuracy.

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Section: Preventing Overfittingmentioning

confidence: 99%

Computer Model for Detecting Cervical Spine Fractures based on Computed Tomography Images

Fedorchenko,

Oliinyk,

Chornobuk

et al. 2024

IJC

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“…The accuracies obtained for these three conditions were 81.25%, 75.00% and 43.75% respectively. A multi-face recognition system is proposed by the researchers in [19] [20] to detect the prisoners in jail and the accuracy was 87%. Next, the researchers proposed a face recognition using a CNN model in [21] [22].…”

Section: Related Workmentioning

confidence: 99%

Face Recognition System Design and Implementation using Neural Networks

Alsyayadeh,

Aziz

et al. 2022

IJACSA

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Face recognition technology is used in biometric security systems to identify a person digitally before granting the access to the system or the data in it. There are many kidnappings or abduction cases happen around us, however, the kidnap suspects will be set free if there is lack of evidence or when the victims are not able to testify in court because they suffer from post-traumatic stress disorder (PTSD). The objectives of this study are, to develop a device that will capture the image of a kidnapper as evidence for future reference and send the captured image to the family of the victim through email, to design a face recognition system to be used in searching kidnap suspects and to determine the best training parameters for the convolution neural network (CNN) layers used by the proposed face recognition system. The accuracy of the proposed system is tested with three different datasets, namely the AT&T database, face database from [23] and a custom face dataset. The results are 87.50%, 92.19% and 95.93% respectively. The overall face recognition accuracy of the proposed system is 98.48%. The best training parameters for the proposed CNN model are kernel size of 5x5, 32 and 64 filters for first and second convolutional layers and learning rate of 0.001.

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