The integration of machine learning technologies into print quality control systems represents a significant advancement in modern printing production. This article examines the application of artificial intelligence methods and computer vision algorithms for automated defect detection, colour consistency monitoring, and real-time quality assessment in printing processes.
This paper considers the problem of adaptive optimization of the training dataset for neural networks in image classification tasks. It has been shown that using the full training dataset is not the best solution. Different training samples have different value for the model. Some samples are representative, some are important for class boundaries, some preserve diversity, and some may be noisy or suspicious. A method for adaptive formation of the active training subset has been proposed. Unlike traditional methods, the proposed approach evaluates the role of each sample during training.
This review paper provides a comprehensive comparative analysis of abandoned object detection algorithms, specifically contrasting rule-based approaches with artificial intelligence (AI-based) models. The manuscript synthesizes existing research, technical documentation, and publicly available benchmark data to evaluate the applicability of these approaches in dynamic video surveillance environments.
This paper presents a method for optimizing deep learning models used in real-time military equipment recognition systems. A key contribution of the work is the application of exponential data augmentation, which significantly increases dataset diversity without requiring additional real-world data. The augmented dataset has been used to train a YOLO-based object detection model capable of recognizing various types of military vehicles, including tanks, armored personnel carriers, and infantry fighting vehicles.
The article addresses the urgent scientific and practical problem of overcoming the "cold start" effect in the development and deployment of Natural Language Processing (NLP) systems aimed at monitoring public opinion and sentiment analysis of Ukrainian-language content. The critical shortage of representative, balanced, and pre-labeled datasets that accurately reflect the specifics of social, economic, and political processes in modern Ukrainian society is identified as the main barrier to integrating advanced neural network solutions.
Summary. Automation of database indexing is a crucial component of modern database management systems that enhances search performance, scalability, and relevance in large-scale data environments. This paper explores the application of deep learning algorithms for building and optimizing vector indexes capable of automatic adaptation to changes in data structure and query patterns. An experimental comparison was conducted between traditional indexing methods (B-Tree, GIN in PostgreSQL) and vector-based indexing using Sentence-BERT embeddings implemented in FAISS and Milvus systems.
The goal of the article is to provide a methodology of improving images quality in low-light conditions trough fusion of data received from telecamera and thermal camera. Data from thermal camera uses for compensation of significant illumination reduction in poor lighting conditions and allow keep required level of information. Proposed method establishes dynamic regulation of fusion coefficients depending on brightness level to minimize artifacts, increase edge sharpness, and improve object detectability.
This article addresses the problem of low accuracy in continuous authentication systems caused by the natural variability of user behavior. An analysis of existing biometric approaches is conducted, justifying the selection of an adaptive two-stage model as an effective method for accounting for the user's psycho-emotional state. The authors designed the AURA (Automatic User Recognition Agent) system using a component-based approach, which allowed for a clear separation of the state identification and authentication tasks.
This study presents the formalization of mathematical models of hardware-optical distortions in digital images captured during aerial photography from onboard systems of Unmanned Aerial Vehicles (UAVs). These distortions significantly affect the accuracy and reliability of automated object detection and classification algorithms in complex outdoor environments.
The paper uses modern artificial intelligence methods to investigate models and methods for determining grape disease. The existing methodologies for classification and recognition by images of grape diseases using neural networks are analyzed. Several problems for improving recognition results are highlighted.