глибинне навчання

The problem of high-performance and efficient order scheduling is a common combinatorial optimization problem in various industrial contexts. Creation of a model capable of generating schedules balanced in terms of quality and computational time poses a significant challenge due to the large action space. This study proposes a high-performant environment and a reinforcement learning model for allocating orders to resources using a mechanism of invalid action masking.

The article is devoted to the theoretical foundations and practical aspects of facial recognition using neural networks. The paper analyzes various approaches to feature extraction from facial images, as well as methods for training neural networks for recognition. Special attention is paid to problems related to variations in lighting, facial expressions, and other factors affecting recog- nition accuracy. The paper describes the creation and optimization of a neural network for real-time human face recognition.

Ensemble methods can be used for many tasks, some of the most popular being: classification, regression, and image segmentation. Image segmentation is a challenging task, where the use of ensemble machine learning methods provides an opportunity to improve the accuracy of neural network predictions.

The paper highlights the increasing importance of machine learning (ML) in mobile applications, with mobile devices becoming ubiquitous due to their accessibility and functionality. Various ML models, including Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs), are explored for their applications in real-time classification on mobile devices. The paper identifies key challenges in deploying these models, such as limited computational resources, battery consumption, and the need for real-time performance.

The article describes a study of identification of fake news based on natural language processing, big data analysis and deep learning technology. The developed system automatically checks the news for signs of fake news, such as the use of manipulative language, unverified sources and unreliable information. Data visualization is implemented on the basis of a friendly user interface that displays the results of news analysis in a convenient and understandable format.

The study delves into the significant environmental threat posed by cesium-137, a byproduct of nuclear mishaps, industrial activities, and past weapons tests. The persistence of cesium-137 disrupts ecosystems by contaminating soil and water, which subsequently affects human health through the food chain. Traditional monitoring techniques like gamma spectroscopy and soil sampling face challenges such as variability and the intensive use of resources.

This paper explores the use of diarization systems which employ advanced machine learning algorithms for the precise detection and separation of different speakers in audio recordings for the implementation of an intruder detection system. Several state-of-the-art diarization models including Nvidia’s NeMo, Pyannote and SpeechBrain are compared. The performance of these models is evaluated using typical metrics used for the diarization systems, such as diarization error rate (DER) and Jaccard error rate (JER).