Information Systems and Technology Applications

A decision-support model using Analytic Hierarchy Process to select optimal application server architectures by evaluating multiple design criteria and architectural trade-offs.

Researchers developed an intelligent software system using network models and vector optimization to manage investment projects adaptively under multiple objectives and uncertainty.

Methodology and automated system for identifying and prioritizing geometric and structural factors in vector image quality assessment using dependency-weighting analysis.

Analysis of how generative artificial intelligence transforms research practices, examining opportunities in data analysis and pattern recognition alongside ethical challenges.

Framework for managing cooperative digital library systems that incorporate artificial intelligence services and intelligent management solutions for government organizations.

Quantization of neural network output tensors reduces storage for speaker recognition databases. Study evaluates bit depth reduction impacts on recognition accuracy across three architectures.

Framework for implementing data management mechanisms to support decision-making in software development companies, integrating technological and organizational perspectives.

Adaptive BPM system for SMEs integrates machine learning and serverless architecture with cybersecurity, reducing operational costs by 25-30% and improving adaptability by 35-40% in developing.

Comparative analysis of income-driven and NAV-driven REIT approaches in developed markets with assessment of organizational frameworks for Ukrainian real estate market development.

Enterprise SharePoint migration framework integrating strategies, governance practices, security protocols, and change management for successful cloud transition and compliance alignment.

Research adapts DBSCAN clustering to automate early identification of configuration items in enterprise information systems, enabling architectural synthesis.

Engineering optimization of MLOps processes for high-load recommendation systems integrating streaming features, parameter servers, and online training for latency and quality under scale.