Explore the vast range of titles available.

This paper proposed a two-level hybrid stacking model for the classification of crops—wheat, soybean, and barley—based on multispectral orthomosaics obtained from uncrewed aerial vehicles. The proposed method unites gradient boosting algorithms (LightGBM, XGBoost, CatBoost) and tree ensembles (RandomForest, ExtraTrees, Attention-MLP deep neural network), whose predictions fuse at the meta-level using ExtraTreesClassifier. Spectral channels, along with a wide range of vegetation indices and their statistical characteristics, are used to construct the feature space. Experiments on an open dataset showed that the proposed model achieves high classification accuracy (Accuracy ≈ 95%, macro-F1 ≈ 0.95) and significantly outperforms individual algorithms across all key metrics. An analysis of the seasonal dynamics of vegetation indices confirmed the feasibility of monitoring phenological phases and early detection of stress factors. Furthermore, spatial segmentation of orthomosaics achieved approximately 99% accuracy in constructing crop maps, making the developed approach a promising tool for precision farming. The study’s results showed the high potential of hybrid ensembles for scaling to other crops and regions, as well as for integrating them into digital agricultural information systems.

This paper addresses the problem of multi-class classification of bacterial microscopic images using a rigorous experimental protocol designed to prevent information leakage and improve performance. The dataset consists of 2034 images representing 33 taxa, organized by class. Data integrity checks confirmed the absence of corrupted or unreadable files. To formalize image characteristics and ensure quality control, indirect geometric and textural features were calculated, including minimum frame size, brightness statistics (mean and standard deviation), Shannon entropy, Laplace variance, and Sobel gradient energy. Quality checks revealed a small proportion of images with extreme brightness (2.5074%), while no samples with critically low sharpness according to the selected criteria were detected. Statistical analysis of interclass differences using the Kruskal–Wallis test with multiple comparison correction demonstrated the high discriminatory power of texture features, specifically gradient energy (ε2 = 0.819987) and Laplace variance (ε2 = 0.709904). Feature correlations were consistent with their physical interpretation, revealing a strong positive relationship between sharpness and gradient energy. Principal component analysis confirmed a strong structural pattern, with the first two components explaining 75.5766% of the total variance. For a unified comparison, classical machine learning, transfer learning, and modern deep architectures were evaluated within a single protocol.

Rare event detection in large tabular data remains a computationally challenging problem due to class imbalance, heterogeneous feature distributions, and unstable thresholds. Traditional machine learning approaches based on individual models and fixed thresholds often exhibit limited robustness and reproducibility in such settings. This paper proposes a hybrid ensemble framework for rare event detection that integrates heterogeneous machine learning models through threshold-aware probabilistic aggregation. The framework combines gradient-boosted decision trees, regularized linear models, and neural networks, leveraging their complementary inductive biases. To ensure reproducibility and robust performance evaluation under severe class imbalance, a leaky-controlled evaluation protocol is employed, including rootwise summation, probability calibration, and validation-based threshold optimization. The proposed approach is evaluated on a large tabular dataset containing approximately 50,000 observations. Experimental results demonstrate improved rare event detection and robust generalization performance compared to individual baseline models. Explainability is achieved through Shapley Additive Explanations (SHAP)-based attribution analysis and clustering in the explanation space, enabling transparent analysis of ensemble decision-making behavior. The proposed framework represents a general-purpose computational solution for rare event detection and can be applied to a wide range of data-driven decision-making and anomaly detection problems.

The article focuses on enhancing stock market price prediction through artificial neural networks and machine learning. It underscores the significance of improving forecast accuracy by incorporating historical stock prices, macroeconomic indicators, news events, and technical indicators. Exploring deep learning principles, it delves into convolutional neural networks (CNN), recurrent neural networks (RNN), including long short-term memory (LSTM), and gated recurrent unit (GRU) modifications. This financial time series processing study covers data preprocessing, creating training/test sets, and selecting evaluation metrics. Results suggest promising applications for the developed forecasting models in stock markets, stressing the importance of considering various factors for precise forecasts in dynamic financial environments. Historical reserve data serves as the model foundation. Integration of macroeconomic, news, and technical indicators offers a holistic approach, aiding trend and anomaly identification for enhanced forecasts. The article recommends suitable deep learning architectures, highlighting LSTM and GRU's effectiveness in adapting to intricate data dependencies. Experimental outcomes showcase these architectures' benefits in predicting stock market prices, offering valuable insights for finance and asset management professionals in financial analysis and machine learning realms.

This article discusses a large number of textural features and integral transformations for the analysis of texture-type images. It also discusses the description and analysis of the features of applying existing methods for segmenting texture areas in images and determining the advantages and disadvantages of these methods and the problems that arise in the segmentation of texture areas in images. The purpose of the ongoing research is to use methods and determine the effectiveness of methods for the analysis of aerospace images, which are a combination of textural regions of natural origin and artificial objects. Currently, the automation of the processing of aerospace information, in particular images of the earth’s surface, remains an urgent task. The main goal is to develop models and methods for more efficient use of information technologies for the analysis of multispectral texture-type images in the developed algorithms. The article proposes a comprehensive approach to these issues, that is, the consideration of a large number of textural features by integral transformation to eventually create algorithms and programs applicable to solving a wide class of problems in agriculture.  

Image noise reduction is an important task in the field of computer vision and image processing. Traditional noise filtering methods may be limited by their ability to preserve image details. The purpose of this work is to study and apply deep learning methods to reduce noise in images. The main tasks of noise reduction in images are the removal of Gaussian noise, salt and pepper noise, noise of lines and stripes, noise caused by compression, and noise caused by equipment defects. In this paper, such noises as the removal of raindrops, dust, and traces of snow on the images were considered. In the work, complex patterns and high noise density were studied. A deep learning algorithm, such as the decomposition method with and without preprocessing, and their effectiveness in applying noise reduction are considered. It is expected that the results of the study will confirm the effectiveness of deep learning methods in reducing noise in images. This may lead to the development of more accurate and versatile image processing methods capable of preserving details and improving the visual quality of images in various fields, including medicine, photography, and video.

The use of deep learning algorithms for the classification of crop diseases is one of the promising areas in agricultural technology. This is due to the need for rapid and accurate detection of plant diseases, which allows timely measures to be taken to treat them and prevent their spread. One of them is to increase productivity and maintain land quality through the timely detection of diseases and pests in agriculture and their elimination. Traditional classification methods in machine learning and algorithms in deep learning were compared to note the high accuracy in detecting pests and crop diseases. The advantages and disadvantages of each model considered during training were taken into account, and the Inception V3 algorithm was incorporated into the application. They can monitor the condition of crops on a daily basis with the help of new technology-applications on gadgets. Aerial photographs used by research institutes and agricultural grain centers do not show the changes that occur in agricultural grains, that is, diseases and pests. Therefore, the method proposed in this paper determines the types of diseases and pests of cereals through a mobile application and suggests ways to deal with them.

The article is an in-depth analysis of two leading approaches in the field of generative modeling: generative adversarial networks (GANs) and the pixel-to-pixel (Pix2Pix) image translation model. Given the growing interest in automation and improved image processing, the authors focus on the key operating principles of each model, analyzing their unique characteristics and features. The article also explores in detail the various applications of these approaches, highlighting their impact on modern research in computer vision and artificial intelligence. The purpose of the study is to provide readers with a scientific understanding of the effectiveness and potential of each of the models, and to highlight the opportunities and limitations of their application. The authors strive not only to cover the technical aspects of the models, but also to provide a broad overview of their impact on various industries, including medicine, the arts, and solving real-world problems in image processing. In addition, we have identified prospects for the use of these technologies in various fields, such as medicine, design, art, entertainment, and in unmanned aerial vehicle systems. The ability of GANs and Pix2Pix to adapt to a variety of tasks and produce high-quality results opens up broad prospects for industry and research.

In this paper, the properties of solutions are researched in mathematical models of biomass dynamics of microbial communities. The model of internal-flow system is used for research of phytoplankton community in aquatic ecosystem. The properties of solutions coincide with dynamics of nature communities demonstrating dominance of several species in the community.

Crop risk insurance in Kazakhstan is an urgent problem for agricultural producers and insurers, since agriculture is in the zone of constant natural and economic risks, where the main share of risks is associated with weather events affecting the production of agricultural crops. In order to reduce natural risks in agriculture, to ensure the protection of the property interests of farmers in crop production from the consequences of adverse natural phenomena, measures are being taken by the state and business, however, there are problems that both agricultural producers and insurers face. Public-private partnership has established itself as a successful tool for interaction between business and the state, and now in Kazakhstan a lot of work is being done for the qualitative growth and development of the PPP mechanism, which requires more active interaction between the state and business in the field of crop production.