machine learning

Application of Machine Learning Models to Optimize the Quality of Produced Parts in the Automotive Industry

Integrating machine learning techniques into automotive quality control improves responsiveness, accuracy, and efficiency, thereby reducing costs and increasing customer satisfaction.  The study focuses on the cutting process of a mechanical transmission shaft manufactured in the Moroccan automotive industry.  Two models, Decision Tree and Random Forest, are used to analyze the impact of parameters on the conformity of the parts, including length and diameter parameters.  The results show that diameter is the key factor influencing quality, and the Random Forest model,

Future Leader Prediction: AI-Driven Data Mining Approaches

This paper presents a new data-driven approach to the identification of leadership potential, overcoming limitations of traditional methods that are often subjective and biased.  Given that effective leadership is at the core of any organizational innovation and development, we propose a machine learning framework for objective leadership assessment.  We applied data mining techniques to systematically analyzing employee psychological and behavioral indicator-level data to train and evaluate predictive models.  The Random Forest algorithm showed the best performance amo

MATHEMATICAL MODEL OF ERRORS IDENTIFICATION IN TEXTS OF UKRAINIAN CONTENT

The problem of automated error detection in Ukrainian texts is becoming particularly relevant in the context of the growth of digital content. A mathematical model of a decision support system for detecting errors in Ukrainian-language texts has been developed. The process of error identification has been studied as a multi-class classification task at the token level, considering the context of the text. The use of probabilistic models has been proposed to determine the type of error depending on the environment of tokens in the text.

PREDICTION OF INDUSTRIAL EQUIPMENT CONDITION USING COST-SENSITIVE APPROACHES AND CLASSIFICATION THRESHOLD OPTIMIZATION

This paper presents a comprehensive study on the application of modern machine learning methods for predictive maintenance based on the open AI4I Predictive Maintenance dataset. The primary goal of the research is to develop and compare both binary and multiclass classification models that enable not only the prediction of machine failures but also the identification of specific failure types.

Information Theory in Multi-Label Feature Selection: An Analytical Review

In the context of multi-label learning, feature selection (MLFS) is a key process for handling high-dimensional datasets, aiming to retain the most informative features while preserving inter-label relationships.  This study presents an extensive overview of state-of-the-art MLFS approaches founded on principles from information theory.  The paper first introduces the fundamental concepts of information theory, then provides a detailed review of representative MLFS methods along with their theoretical background.  Performance assessments are carried out on real-world mu

Hybrid Behavioural Analysis Method for Early Detection of Anomalous Activity in Web Applications

The research introduces a hybrid behavioural analysis technique for early detection of anomalous user behavior observed on web applications. This strategy involves statistical probability modeling and sequence- based deep learning to design interpretable and robust anomaly detection. A probability baseline has been obtained as a probabilistic basis using KDE (Kernel Density Estimation) and longitudinal time series patterns are sampled using an LSTM network. The hybrid anomaly score combines these two models to dynamically analyze behavioural deviations.

Analysis and Improvement of Information Security Technologies in Distributed and Asymmetric Systems

The article discusses modern information security technologies in distributed and asymmetric systems, as well as problems arising from their implementation in the context of growing cyber threats. An analysis of cryptographic methods, authentication systems, access control, and intrusion detection has been provided. Particular attention has been paid to the limitations of existing technologies and promising areas for their improvement, in particular the use of machine learning methods, block chain technologies, and the Zero Trust concept.

Anomalies Detection and Traffic Monitoring System in Computer Networks

The paper addresses the problem of anomaly detection in network traffic and proposes a comprehensive solution to enhance the level of cybersecurity for organizations of various scales. A comparative analysis of existing monitoring and anomaly detection systems has been carried out, including both open-source solutions and commercial products.

Intellectual Re-Engineering Technologies in Digital Transformation of Public Services

Digital transformation of public services in the context of modern information systems and technologies is a relevant area of research, which is due to the growing demands of society for the quality and speed of public services. In the digital economy, artificial intelligence, machine learning and big data processing technologies play an important role, which can significantly increase the efficiency of public administration.

Forecasting the Development Trends of the IT Market Using Machine Learning Methods

The article explores approaches to forecasting the development trends of the IT market using machine learning methods. The relevance of the research is driven by the high dynamics of the digital economy, rapid technological changes, and the need for scientifically grounded analytical tools in the IT domain. The purpose of the study is to develop a forecasting model capable of identifying patterns in socio-economic, technological, and behavioral indicators that determine the state and prospects of IT market development.