machine learning

Today, Artificial Intelligence is a daily routine, becoming deeply entrenched in our lives. One of the most popular and rapidly advancing technologies is speech recognition, which forms an integral part of the broader concept of multimodal data handling. Multimodal data encompasses voice, audio, and text data, constituting a multifaceted approach to understanding and processing information. This paper presents the development of a multimodal handling interface leveraging Google API technologies.

Problem statement. This research addresses the challenge of accurately determining the fullness of the hopper within a screw press for optimal oil extraction efficiency and quality. Existing weight or volume-based measurement methods can often struggle with determining the feed hopper fullness due to variable oil weights during extraction stages, material heterogeneity, environmental influences and imprecise instrument calibration. Purpose.

In a constantly changingbusiness environment, the integration of artificial intelligence (AI) is becoming a fundamental direction in achieving increased revenues and sales volumes for companies. AI and its various applications contribute to identifying patterns in consumer choices, which at the same time contributes to the more effective formation of marketing and sales strategies of companies.

Machine learning is a logical extension of automation using computer systems. While a large number of different areas of human activity have been improved by algorithmic software, a large number of other problems remain unsolved because creating an algorithm for them is almost impossible. One of these fields is science. The empirical approach is still main approach in achieving results, because for many studies there is still no clear mathematical apparatus.

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

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

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.

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