Calculation of the Exact Value of the Fractal Dimension in the Time Series for the Box-Counting Method

Kaminsky, R.; Mochurad, Lesia; Shakhovska, Nataliya; Melnykova, Nataliia

doi:10.1109/acitt.2019.8780028

Cited by 6 publications

(2 citation statements)

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“…(5) Biological research: Te classifcation of birds in images may be important for biological research aimed at understanding the evolutionary and genetic aspects of diferent bird species. (6) Environmental pollution monitoring: Changes in bird populations can serve as indicators of environmental pollution. Classifcation of birds in images can help in assessing the impact of pollutants on the bird world.…”

Section: Introductionmentioning

confidence: 99%

“…Computer vision is a feld of science and technology that studies and develops systems that provide computers with the ability to "see" and interpret images and videos in the same way that human vision does [6,7]. Te main purpose of computer vision is to give computers the ability to recognize objects, determine their characteristics, and interact with the environment based on visual data.…”

Section: Introductionmentioning

confidence: 99%

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A New Efficient Classifier for Bird Classification Based on Transfer Learning

Mochurad,

Svystovych

2024

Journal of Engineering

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The problem of classification of different species of birds in the images is relevant in the modern world. The essence is to automatically determine the species of birds depicted in the photo using artificial intelligence. It is important for several areas of human life, in particular for nature conservation, environmental research, education, and ecotourism. The development of a classifier, based on deep learning methods, can help to effectively solve the problem of classifying different species of birds, ensuring high accuracy. The work developed a new effective algorithm for the classification of different species of birds in the images. An optimal model architecture was built using the transfer learning approach. A dataset of 525 bird species was analyzed and preprocessed in detail. The model training process was carried out, which includes two phases: only the upper layers are deactivated and the last 92 layers of the pretrained EfficientNetB5 model (not including BatchNormalization layers) and the top layers are activated. As a result, efficacy indicators were obtained: accuracy = 98.86%, precision = 0.99, recall = 0.99, and F1 score = 0.99, which showed improvement in comparison with modern research. The developed classifier is best suited for such areas of human life as education and ecotourism because for them the number of different species of birds that the classifier can determine is very important.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%