Identifying Fruit Plants' Diseases Using Deep Learning Techniques Bolotbek Biibosunov, Begaly Khalmuratov, Zholchubek Kutunaev, Yu Xinqi, Li Jiaohui, et al. International Conference on Artificial Intelligence Computer Data Sciences and Applications Acdsa 2026, 2026
MACHINE LEARNING FOR CROP YIELD FORECASTING Bolotbek Biibosunov, Baratbek Sabitov, Saltanat Biibosunova, Zhamin Sheishenov, Sharshenbek Zhusupkeldiev, et al. Cybernetics and Physics, 2023 Amid the persistent rise in global population, there has been a heightened focus on food security by academia, governmental initiatives, and international endeavors. Food security serves as a critical pillar in the national security framework, contributing to a nation’s sovereignty and self-sufficiency in food supply. To fulfill global requirements for essential food items, there is an imperative need to enhance agricultural efficiency across countries. Concurrently, agricultural practices must align with contemporary quality standards and meet consumer needs, drawing upon an integrated approach to crop cultivation technologies and yield classifications. Methodologies and tools for yield augmentation, grounded in scientific advancements in predictive modeling, are of paramount importance. Investigating the plethora of variables that contribute to optimal crop development, which in turn influences yield, poses significant challenges. Comprehensive inquiries that incorporate cutting-edge scientific and technological methodologies are essential for creating precise yield forecasts. The evolving landscape of yield modeling and prediction has emerged as a technologically sophisticated domain. Advanced methods such as machine learning and deep learning offer robust platforms for addressing crop yield forecasting, particularly when coupled with extensive datasets on environmental variables. A growing body of literature suggests the promising role of computational technologies and machine learning paradigms, inclusive of various forms of remote sensing data, in fine-tuning yield models. Yield prediction models are often characterized by intricate nonlinear equations influenced by a range of factors: seed quality and diversity, soil attributes, climatic variables, fertilizer usage, and other agronomic practices. The impacts of these variables on crop yield are varied, with some exerting greater influence than others. Additionally, crop yield is susceptible to adverse environmental and climatic conditions. While there exists a rich corpus of research on yield forecasting, addressing this issue remains an exigent priority in the agricultural sector.
Information technologies for landslides and mudflows research Bolotbek Biibosunov, Jenish Beksulanov E3s Web of Conferences, 2020 This article presents the results of research using computer technology and mathematical modeling in relation to hydrodynamic processes that determine such natural disasters as landslides and mudflows common in the territory of the Kyrgyz Republic. A specialized website is proposed, which contains the results of scientific research on natural and man-made disasters and exogenous geological processes (EGP). The following systems were used as the main database management systems (DBMS): MS Access, My SQL and PostgreSQL. Thus, the main means of developing computer programs and computational procedures are Delphi, Python, Visual Basic, Java and JavaScript. Web technologies and the following software tools were used to design and create the site: Python, JavaScript, PhP and HTML. Modern level of scientific research presumes and obliges development and using of new information technologies. In this regard there was defined a problem on mathematical modelling and information technologies using for research and forecasting of EGP on the territory of Kyrgyzstan. There are proposed hydrodynamic models and numerical methods of their solution. Information system is developed for landslides, mudflows, and other EGP types, typical for Kyrgyzstan.
Creation of a computer-assisted mathematical model for the raw materials biological processing Periodico Tche Quimica, 2020
Development of Digital Platform for Social Media Creating in the Kyrgyz Republic Bolotbek Biibosunov, Saltanat Biibosunova, Marat Kozhonov ACM International Conference Proceeding Series, 2019 Here in the paper we outline and tell about the Project developed by us, named ELTOR.KG -- multipurpose hardware and software platform, autonomous information network, virtual cloud technology. Our ElTOR.KG system allows implementing all the above-mentioned components to build a modern information society. ELTOR.KG information network consists of two mutually integrated informational web portals, social media and business network. The public network is of a social nature and belongs to the category of Government-to-Public-to-Citizens (G2P2C) - Government-Public-Citizens. This network has been pretested and is ready for launch. The business network is commercial in nature, designed for small and medium-sized businesses, manufacturing, agriculture, trade, services, etc. and belongs to the category of Business-to-business-to-Customer (B2B2C) - Business-Business-Consumer. This network is in the testing phase. ELTOR.KG Project as an information network is designed and developed on its own software platform.
