machine learning

Dynamic learning rate adjustment using volatility in LSTM models for KLCI forecasting

The prediction of financial market behaviour constitutes a multifaceted challenge, attributable to the underlying volatility and non-linear characteristics inherent within market data.  Long Short-Term Memory (LSTM) models have demonstrated efficacy in capturing these complexities.  This study proposes a novel approach to enhance LSTM model performance by modulating the learning rate adaptively based on market volatility.  We apply this method to forecast the Kuala Lumpur Composite Index (KLCI), leveraging volatility as a key input to adapt the learning rate during trai

EVALUATION OF MULTIMODAL DATA SYNCHRONIZATION TOOLS

The constant growth of data volumes requires the development of effective methods for managing, processing, and storing information. Additionally, it is advisable to apply multimodal approaches for knowledge aggregation to extract additional knowledge. Usually, the problem of efficient processing of multimodal data is associated with high-quality data preprocessing. One of the most critical preprocessing steps is synchronizing multimodal data streams to analyze complex interactions in different data types.

Front-end Framework for Building Applications With Adaptive User Interfaces Using Machine Learning Methods

The article examines approaches to developing a front-end framework for creating web applications with an adaptive graphical interface that dynamically adjusts to the individual needs of users through machine learning algorithms. The relevance of the problem lies in the need to develop interfaces capable of simultaneously meeting the needs of different demographic groups, which requires flexibility in customizing the user experience (UX) and user interface (UI) of modern websites.

DECISION SUPPORT SYSTEM FOR DISINFORMATION, FAKES AND PROPAGANDA DETECTION BASED ON MACHINE LEARNING

Due to the simplification of the processes of creating and distributing news via the Internet, as well as due to the physical impossibility of checking large volumes of information circulating in the network, the volume of disinformation and fake news distribution has increased significantly. A decision support system for identifying disinformation, fakes and propaganda based on machine learning has been built. The method of news text analysis for identifying fakes and predicting the detection of disinformation in news texts has been studied.

ARTIFICIAL NEURAL NETWORKS IMPLEMENTATION IN MOBILE ROBOTIC PLATFORM CONTROL SYSTEM

In the era of rapid technological advancement, when robotics and intelligent systems are becoming an integral part of everyday life, the importance of developing control systems for mobile robotic platforms using artificial neural networks becomes extremely high and relevant. This field not only has significant practical needs but also holds considerable potential for innovative development. The evolution of modern robotics and computational intelligence has necessitated the creation of more efficient and adaptive mobile robotic systems.

Neuro-symbolic models for ensuring cybersecurity in critical cyber-physical systems

This paper presents the results of a comprehensive study on the application of the neuro-symbolic approach for detecting and preventing cyber threats in railway systems, a critical component of cyber-physical infrastructures. The increasing complexity and integration of physical systems with digital technologies have made such infrastructures vulnerable to cyberattacks, where breaches can result in severe consequences, including system failures, financial losses, and threats to public safety and the environment.

Application of the Bayesian approach to modeling credit risks

A computer model for analyzing, evaluating, and forecasting bank credit risks has been developed.  Utilizing a Bayesian network (BN) and established parameter estimation methods, this model was implemented in the Python programming language.  It predicts the probability that a borrower may fail to meet financial obligations, such as repaying a loan.

Intelligent Fake News Prediction System Based on NLP and Machine Learning Technologies

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.

Analysis of the Use of HS and HTS Codes in Customs Classification Systems: Challenges and Opportunities of Integration of IT Technologies

The peculiarities of the use of the harmonized system of description and coding of goods, the harmonized tariff system of codes in modern customs classification systems are analyzed. Special attention is paid to the challenges that arise when applying these codes, in particular due to the complexity of the product nomenclature, as well as the variety of product descriptions. In addition, the possibilities of integrating IT technologies, machine learning and artificial intelligence methods to automate and optimize customs classification procedures are being explored.