data analysis

Analysis of Approaches to the Development of Intelligent Consulting Systems for Forming Project Teams in the It Industry

The formation of project teams, the allocation of resources, the assessment of specialists' competencies, and the forecasting of project results all require analysing a significant number of interrelated factors. Traditional approaches to IT project management often rely on expert assessments and managers' experience, leading to subjectivity in decision-making and reduced team performance.

Analysis and Prediction of Hydrogen Yield Using Artificial Neural Networks

This study aims to design a reliable prediction model for H$_2$ production by steam methane reforming using artificial neural networks (ANN).  To achieve this, experimental data related to this process were first analyzed and then used to develop an ANN model implemented in Python.  Model construction was carried out in three stages: network architecture design, training, and performance evaluation.  Data analysis revealed that temperature has the dominant influence on the H$_2$ yield, followed by space time.  The resulting ANN model, with an optimal (2–5–1) architectur

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.

Research of Existing Osint Tools and Approaches in the Context of Personal and State Information Security

The article examines modern tools and approaches to conducting OSINT — the analysis of open sources of information. The key role of OSINT, along with other intelligence methods such as HUMINT, IMINT, SIGINT, MASINT and GEOINT, lies in creating a holistic information field that combines open, technical, human and geospatial sources. The constant development of methodologies and improvement of automation tools allows to increase the efficiency and accuracy of the analysis of the received information, which makes OSINT one of the most important elements of modern intelligence.

METHODOLOGY FOR IMPLEMENTING SELF-LEARNING FEEDBACK MODELS IN CRM SYSTEMS: COMPARATIVE ANALYSIS OF EFFECTIVENESS

The article proposes a methodology for implementing self-learning feedback models in Customer Relationship Management (CRM) systems. It examines the key issues of existing CRM systems, including insufficient adaptability to changes in customer behavior and limited capabilities for automatic data analysis.

The role of functional activation in neural networks in the context of financial time series analysis

Nowadays, neural networks are among the most popular analysis tools.  They are effective in solving classification, pattern recognition, and clustering problems.  This paper provides a detailed description and analysis of the operational principles of two neural networks, namely a Siamese network and a multilayer perceptron.  A model for using these neural networks in time series forecasting is proposed.  As an example, a web application was created in which the described neural networks were used to analyze the correlation between pairs of financial assets and assess t

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.

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.

Computer Modelling of Logistic Regression for Binary Classification

This article discusses the practical aspects of applying logistic regression for binary data classification. Logistic regression determines the probability of an object belonging to one of two classes. This probability is calculated with the help of a sigmoid function, the argument of which is a linear convolution of the feature vector of the object with the weighting coefficients obtained during the minimization of the logarithmic loss function. Predicted class labels are determined by comparing the calculated probability with a given threshold value.

Big Data Technology Usage in Electric Transportation Industry

In the context of critical challenges related to global warming and the necessity of reducing carbon footprint, the electric car sector is experiencing significant growth. This progress inevitably leads to the need for expansion and modernization of the charging station infrastructure. This article conducts a detailed analysis of how big data processing technologies can contribute to the optimization of this infrastructure’s use, the efficiency of charging stations, and the development of personalized services for electric vehicle users.