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 provides a systematic and critical review of stock market forecasting of the Moroccan emerging financial market, analyzing a comprehensive corpus of 41 academic studies published between 2012 and 2025. The review maps the field's evolution, revealing a paradigm shift towards machine learning (ML), with 75.6% of studies published since 2020. The qualitative synthesis reveals a "performance paradox": despite the methodological sophistication, traditional econometric models often outperform complex AI models in key tasks like volatility forecasting. The prima
This paper investigates intelligent models and methods for automated knowledge assessment and academic integrity analysis in digital educational platforms. The rapid spread of generative artificial intelligence tools has created new challenges for existing e-learning systems, which are primarily oriented toward result-based evaluation rather than process analysis. An integrated approach combining behavioral, textual, and statistical features of student learning activity has been proposed.
This article is licensed under the Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0)
This article is licensed under the Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0)
This article is licensed under the Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0)
The article considers methods and means of face recognition in a video data stream using elements of machine learning. The main approaches to face identification based on computer vision methods are analyzed, in particular the principal component analysis (PCA) method, the local binary pattern analysis (LBP) method and the linear discriminant analysis (LDA, Fisherfaces) method. The principles of facial feature formation and algorithms for their classification are described.
The formation of project teams, the allocation of resources, the assessment of specialists' competencies, and the forecasting of project results all require analysing a significant number of interrelated factors. Traditional approaches to IT project management often rely on expert assessments and managers' experience, leading to subjectivity in decision-making and reduced team performance.
The article investigates the limitations of modern approaches to time series forecasting in complex dynamic and nonlinear processes whose structure may consist of heterogeneous data types. The relevance of the study is driven by the rapid growth of data volumes in information systems, their diversity, and the need to improve forecasting accuracy under conditions of non-stationarity and multifactor influence.
This paper presents a hybrid quantum logistic regression model for binary data classification that combines classical logistic regression methods with quantum computing to improve efficiency and speed up the learning process.