artificial intelligence

This article presents a comparative analysis of automatic Solidity smart contract generation using large language models (LLMs) based on two approaches: natural language textual descriptions and formal algebraic specifications. The study analyzes smart contracts generated by LLMs (ChatGPT-4, Claude 3.7 Sonnet, DeepSeek-V3) as well as by the AI-based tool GitHub Copilot, evaluating their syntactic correctness, compliance with initial requirements, and security.

The integration of artificial intelligence algorithms in agriculture has been studied to support efficient and sustainable agricultural production through the use of machine learning technologies, automated data collection, and analysis of large volumes of geospatial data to improve land resource management using geographic information systems. The article analyzes the prospects of applying artificial intelligence (AI) methods in the QGIS geographic information system for spatial analysis of agricultural land use.

WebAssembly (WASM) has emerged as a compelling and transformative solution for executing high- performance Artificial Intelligence (AI) and Machine Learning (ML) models directly within frontend web applications. Traditionally, AI/ML model deployment has been dominated by backend servers due to significant computational demands, coupled with the performance limitations of JavaScript and the overhead of client-server communication.

This article discusses modern approaches to organizing continuous integration (CI) and continuous delivery (CD) processes in software development using artificial intelligence (AI) technologies. The historical development of CI/CD is analyzed, along with their role in ensuring high-quality software, the main advantages and disadvantages of traditional approaches, and the prospects for integrating AI technologies to automate and optimize these processes.

The problem of high-performance and efficient order scheduling is a common combinatorial optimization problem in various industrial contexts. Creation of a model capable of generating schedules balanced in terms of quality and computational time poses a significant challenge due to the large action space. This study proposes a high-performant environment and a reinforcement learning model for allocating orders to resources using a mechanism of invalid action masking.

In today’s world, where technology is developing at an incredible speed, automated information collection systems have become an integral part of our lives. They affect various aspects of society, including subjective human rights. This impact can be viewed from several perspectives. First of all, it is the significant advantages of automated systems. Such information collection systems significantly increase efficiency and accuracy in many areas. They allow for the rapid processing of large amounts of data, which facilitates informed decision-making.

The article examines the ways artificial intelligence is influencing the penetration testing procedure. As technology advances and cyber threats grow more com- mon, conventional testing methods are insufficient. Artificial intelligence aids in automating processes like vulnerability detection and real-world attack simulation, leading to quicker, more precise results with reduced dependence on human input. Machine learning is a game-changer in identifying hidden security flaws by analyzing past attacks and abnormal patterns.

The article presents possible strategies and approaches to address the growing cybersecurity threat landscape, new trends and innovations, such as artificial intelligence and machine learning for cyber threat detection and automation. The paper presents well-known machine learning classifiers for data classification. The dataset has been taken from a report by the Center for Strategic and International Studies. The presented model accuracy assessment study has been significant variation among algorithms based on different network intrusion detection systems.

The article is dedicated to monitoring and researching the environmental condition of areas that have the status of nature-reserved. The work describes the creation of an information system with elements of artificial intelligence for studying the environmental condition of nature-reserved territories. As a result of the analysis of monitoring data, primary data for work was identified, and a database was formed. Special attention is paid to the description of the information model of the object and the indicators selected for its adequate functioning.