In the paper, existing methods of data augmentation have been analyzed and their shortcomings associated with limitations when operating with images obtained using unmanned aerial vehicles have been identified. A new specialized method for contextual augmentation of small objects, which supports spatial realism and is capable of providing the required density of small objects at the training stage of neural network models, has been proposed. Given the complexity of implementing the proposed method of contextual augmentation of small objects, it has been decided to divide the development process into several stages. To test empirically the proposed method, the Single Shot Detector MobileNet V2 model located in the TensorFlow 2 Detection Model Zoo repository has been selected as the base detector. This neural network model has been trained on the VisDrone dataset using standard data augmentation methods and the developed method. The obtained detection results of the basic neural network model have been compared with the improved model in accordance with the evaluation protocol of the Common Objects in Context Evaluation metrics library. Experimental studies have shown that context-aware augmentation significantly improves the ability of neural network models to detect small objects. The obtained result is of great practical importance when operating with compact energy-efficient systems with limited performance and memory.
Detecting plant diseases is a challenging problem for farmers, but computer vision and image processing are being offered as solutions. The main problem is the limited information that can be obtained from an image alone. Using computer vision alone does not take into account weather conditions and visual similarities between symptoms of various diseases. These problems can be solved by developing an expert system using an ontology containing knowledge about plant diseases, pathogens, and symptoms. We developed an ontology of plant diseases by integrating existing ontologies and adding disease-causing factors. Our Plants and their Diseases Ontology can improve diagnostic accuracy by including detailed descriptions of symptoms and their association with specific pathogens. Wikidata served as the primary source of taxonomic data, and queries against the data allowed for the extraction of relationships between plants, pathogens, and diseases. Using data from the American Phytopathological Society and the European and Mediterranean Plant Protection Organization, we were able to identify relationships not previously documented in Wikidata. Furthermore, the use of large language models helped to extract various symptoms of pathogens that cause plant diseases. The final ontology consists of 5002 classes and eight properties that relate various entities, including plants, pathogens, and symptoms.
This article continues the development of the Descriptive Theory of Image Analysis proposed by the authors and continues a series of articles on characterizing the image formalization space as a mathematical object—a mathematical space. The image formalization space is defined with the aim of formalizing and standardizing the description of all admissible states of image representations and procedures for bringing images to a recognizable form: a single model corresponding to a certain mathematical space and allowing its implementation in therein. The main result is the definition of the image formalization space as a mathematical space and the hierarchical structure of its organization.
The problems related to the assessment of pronunciation accuracy in speech, identification and classification of linguistic accents, and identification of the speaker’s language have been intensively studied in recent decades. The development of neural network technologies and the emergence of open source large language models have created new opportunities for the successful development of this field. In this paper, the methods for assessing the pronunciation quality of Russian words by Chinese speakers using information from a large, self-supervised multilingual model are considered. Algorithmic assessments of pronunciation quality are proposed and their correlation with expert (human) assessments is evaluated. It is shown that algorithmic assessments of pronunciation quality can be considered as more rigorous and competitive. Using a dataset of speech recordings of Chinese and Russian speakers, experimental values for indicators of Russian word pronunciation quality among Chinese speakers were obtained. The results show, for example, that a significant percentage of regular pronunciation errors can be interpreted as the devoicing of voiced consonants. These data are consistent with expert knowledge. The algorithmic criteria obtained can be useful for the development of pronunciation training methods and software, particularly for providing learners with effective feedback, conveying information about the pronunciation quality of individual words and localizing pronunciation errors.
In this paper, an approach to automated classification of brain magnetic resonance imaging images according to Alzheimer’s disease stages using explainable machine learning methods has been proposed. A comparative analysis of neural network architectures, which resulted in the development of an effective model that achieves 95
