machine learning

METHODS OF MACHINE LEARNING IN MODERN METROLOGY

In the modern world of scientific and technological progress, the requirements for the accuracy and reliability of measurements are becoming increasingly stringent. The rapid development of machine learning (ML) methods opens up perspectives for improving metrological processes and enhancing the quality of measurements. This article explores the potential application of ML methods in metrology, outlining the main types of ML models in automatic instrument calibration, analysis, and prediction of data.

Machine learning models selection under uncertainty: application in cancer prediction

Cancer stands as the foremost global cause of mortality, with millions of new cases diagnosed each year.  Many research papers have discussed the potential benefits of Machine Learning (ML) in cancer prediction, including improved early detection and personalized treatment options.  The literature also highlights the challenges facing the field, such as the need for large and diverse datasets as well as interpretable models with high performance.  The aim of this paper is to suggest a new approach in order to select and assess the generalization performance of ML models

Simultaneous surrogate modeling and dimension reduction using unsupervised learning. Application to parametric wing shape optimization

This paper presents a machine-learning-based approach that enables simultaneous surrogate modeling and dimension reduction and applies it to aerodynamic parametric shape optimization.  Aerodynamic shape optimization is a crucial process in various industries, including aerospace, automotive, and renewable energy.  It involves iteratively improving the properties of a system by evaluating an objective function and driving its minimization or maximization using an optimization algorithm.  However, the evaluation of aerodynamic objective functions requires computationally

Machine learning for forecasting some stock market index

In this paper, we evaluate the QMLKF algorithm, designed in the previous paper [Benmoumen M. Numerical optimization of the likelihood function based on Kalman Filter in the GARCH models. Mathematical Modeling and Computing.  9 (3), 599–606 (2022)] for parameter estimation of GARCH models, by transposing it to real data and then present our machine learning for forecasting the returns of some stock indices.

Machine learning and similar image-based techniques based on Nash game theory

The use of computer vision techniques to address the task of image retrieval is known as a Content-Based Image Retrieval (CBIR) system.  It is a system designed to locate and retrieve the appropriate digital image from a large database by utilizing a query image.  Over the last few years, machine learning algorithms have achieved impressive results in image retrieval tasks due to their ability to learn from large amounts of diverse data and improve their accuracy in image recognition and retrieval.  Our team has developed a CBIR system that is reinforced by two machine

Identification of Birds' Voices Using Convolutional Neural Networks Based on Stft and Mel Spectrogram

Threats to the climate and global changes in ecological processes remain an urgent problem throughout the world. Therefore, it is important to constantly monitor these changes, in particular, using non-standard approaches. This task can be implemented on the basis of research on bird migration information. One of the effective methods of studying bird migration is the auditory method, which needs improvement.

Intelligent System for Detecting Plagiarism in Technical Texts

The authors of the article developed a scientific reasoning, designed, and developed an intelligent system for detecting plagiarism in technical texts. The work defines the problem of plagiarism in the modern world and its relevance and analyzes the latest research and publications devoted to the latest methods of using intelligent information technologies to detect plagiarism.

Machine Learning Methods to Increase the Energy Efficiency of Buildings

Predicting a building’s energy consumption plays an important role as it can help assess its energy efficiency, identify and diagnose energy system faults, and reduce costs and improve climate impact. An analysis of current research in the field of ensuring the energy efficiency of buildings, in particular, their energy assessment, considering the types of models under consideration, was carried out.

Data Set Formation Method for Checking the Quality of Learning Language Models of the Transitive Relation in the Logical Conclusion Problem Context

A method for data set formation has been developed to verify the ability of pre-trained models to learn transitivity dependencies. The generated data set was used to test the quality of learning the transitivity dependencies in the task of natural language inference (NLI). Testing of a data set with a size of 10,000 samples (MultiNLI) used to test the RoBerta model.

Information System for Ukrainian Text Voiceover Based on Nlp and Machine Learning Methods

During the research, an information system for voicing Ukrainian-language text was developed based on NLP and machine learning methods. The created information system is implemented in the form of a desktop application, which allows the process of voicing the Ukrainian-language text. The created system included all stages of software development: the design process, the implementation process, and the testing process.