computer simulation fluid dynamics, fluid and gas mechanics, information technologies, computer added engeeniring
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Scopus Publications
Scopus Publications
Modeling of an intelligent management system for smart poultry farms A.Dzh. Kartanova, Zh.R. Sarypbekova, Z.A. Sydykova, Zh.B. Mamadalieva Bio Web of Conferences, 2025 During the era of digital transformation in Kyrgyzstan, the implementation of innovative solutions based on artificial intelligence technologies and the development of information systems for smart poultry farms can become a key element of sustainable agricultural development and is a pressing issue. This study presents an approach to the conceptual modeling of an intelligent control system for smart poultry farms. This work presents the development of a digital model of the intelligent system of the smart-bird farm, based on which an approach to the integration of Internet of Things (IoT), computer vision and big data processing technologies is implemented. An object-oriented development of an intellectual system using the universal modeling language UML was carried out. The structure of the digital model is presented, the functional components of the system are selected, the objects of the system are considered and the relationship between them, the information flows between the objects and components of the system are described.
Deep learning-based modeling and recognition of poultry diseases Baratbek Sabitov, Nurzat Asanbekova, Nazgul Seitkazieva, Asel Kartanova, Kyzylgul Jentaeva Proceedings of SPIE the International Society for Optical Engineering, 2025 In this paper, various architectures of neural networks of deep learning technologies were created to model viral diseases of poultry. Models for recognizing coccidiosis, salmonellosis, and Newcastle disease, which are common diseases in poultry farming, were built. The constructed models for detecting poultry diseases are aimed at diagnosing sick birds at an early stage. Convolutional neural networks and deep learning models were created, which are based on predicting poultry diseases by classifying healthy and unhealthy feces images of four types. Unhealthy feces images that can be symptoms of coccidiosis, salmonellosis, and Newcastle disease were identified. A model was built using the base model of convolutional neural networks of various architectures. The models were trained using feces images labeled by the farm and the laboratory, and then their hyperparameters of the neural network were fine-tuned. The test set used images labeled with our own data taken from poultry farms. The test accuracy results without fine-tuning are obtained, which amounted to 85.06% for the base CNN, 87.85% for the updated CNN. Fine-tuning while freezing the batch normalization layer improved the accuracy of the model to 95.01% with F1 scores for all classifiers above 83% in all four classes. Considering the smaller weight of the model trained with the CNN and its better generalization ability, we recommend using this model for early detection of poultry diseases at the farm level. The main result of the work is the implementation of models based on transfer learning of VGG 16, ResNet 18 and Efficient Net models. The efficiency of the transfer approach to building models and their comparative analysis are shown. Error matrices and reports on the classification of poultry diseases based on transfer learning up to 99% accuracy are built.
Modeling and forecasting tasks of agriculture based on machine learning Baratbek Sabitov, Asel Kartanova, Talant Kurmanbek uulu, Nazgul Seitkazieva, Ainura Dyikanova, Aida Orozobekova E3s Web of Conferences, 2023 Continuous advances in computer technology have provided good support for the expansion of agricultural research using machine learning. This article considered the current problem of yield forecasting using methods and algorithms of machine learning to support management decision-making in the agricultural sector. For a set of data collected from five districts of the Issyk-Kul region, such as weather conditions, soil characteristics and pre-processing of the sowing area, a study of the yield of various crops using advanced machine learning algorithms, such as the support vector method, k-nearest neighbors, variants of gradient boosting and random forest, etc., is demonstrated. To assess the accuracy of the models, a comparative analysis with the results of multiple regression was carried out. It is shown that powerful regression machine learning algorithms like k-nearest neighbors (KNN), random forest (RF), support vector method (SVR) and gradient boosting (GBR) give tangible results in prediction compared to other machine learning methods (MAPE=10%). The calculation results showed the effectiveness of using algorithms with ensemble methods to solve the problems of yield forecasting, and that environmental factors (weather conditions) have a greater impact on yield than soil genotype.
Teaching methodology in the study of bioethics ZhV Chashina, , AD Kartanova, and Integration of Education, 2016 Introduction: the article discusses significance of use of new technologies in the learning process for realisation of goals of cognitive and affective domain of knowledge. The paper explores the methods of development of educational knowledge, which is achieved by information, reproductive and research means. Based on example of bioethics the paper demonstrates the use of visuals technology (charts, graphs, tables, illustrations, specification, etc.), which performs the following tasks: memorising, analysis and synthesis, comparison and differentiation, categorisation and classification, identification of relationships between facts, and for the revision of the material studied, acquisition of the new knowledge, memo risation of educational material. Materials and Methods: on the basis of the dialectical approach the object of research is new technologies in the learning process, in particular the study of bioethics. By using methods of observation, survey, analysis and synthesis in the educational process the authors prove the efficiency of such technologies as the use of visualisation (diagrams, illustrations), problem-based learning (issues, tasks and situations) and research tasks (case study method). Results: visual method complements the learning process. It allows a deeper understanding of the subject. This method deals with feelings, emotions and consciousness of students. It encourages creativity. In addition this method of material presentation allows reducing the amount of material of an ordinary lecture. It is underscored that in the study of bioethics it is recommended to use a technology of a problem-based learning, which is able to implement the intellectual activity of students by means of questions¸ case-studies, tasks and situations. The most vivid form of such technology is a case method. The basis for the emergence of technology of problembased learning is a certain contradiction between knowledge and practice. This method can be attributed to the simulation model of learning, the benefits of which are the development of skills of practical experience of future specialists. Discussion and Conclusions: the authors conclude that the use of technologies facilitates the solution of the following tasks: development of creative potential of students, analytical skills. It allows to classify and analyse information as well as to master the studied material and to ap ply it in practice.
