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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 explores the application of neuro- symbolic approaches utilizing artificial neural networks for diagnosing neurological disorders among individuals with autism spectrum conditions. It demonstrates how these networks can identify and enhance distinctive strengths, such as advanced pattern recognition and systematic reasoning, facilitating their integration into professional environments.

This paper presents a study on organizing requirements for automated meat quality control systems. It identifies key quality indicators–color, texture, marbling, and gloss–and analyzes the technical and functional parameters essential for practical assessment. The research highlights integrating computer vision, image processing, and machine learning algorithms to enhance objectivity, accuracy, and evaluation speed. The proposed approach aims to reduce human influence, enable real-time monitoring, and offer scalable solutions suitable for large-scale producers and small enterprises.

The article is dedicated to the study of the development of an automated medical text analysis system using modern artificial intelligence technologies and natural language processing. The current state and prospects for the development of automated medical text analysis are analyzed. The main methods and technologies used in this field, including machine learning, deep learning, and natural language processing, are examined.

The rapid expansion of the Internet of Things (IoT) has resulted in a substantial increase of diverse data from distributed devices. This extensive data stream makes it increasingly important to implement robust and efficient real-time anomaly detection techniques that can promptly alert about issues before they could escalate into critical system failures.

The paper examines the explores the use of artificial intelligence (AI) methods and tools for the efficient construction, management, and optimization of cloud IT infrastructures. The main challenges related to the automation of deployment, scaling, monitoring, and resource optimization in the cloud environment are analyzed, along with the role of AI in addressing these issues. Approaches to integrating AI to improve productivity, reduce operational costs, and enhance the security of cloud platforms are discussed.

Detection and identification of inclusions in the modeling of stationary processes is a crucial task in many technical fields, including materials science, electronics, and non-destructive testing. The presence of inclusions can affect the mechanical, thermal, and electrical properties of a material, making the accurate determination of their geometric and physical characteristics essential. The use of modern numerical methods and deep learning techniques opens new opportunities for improving the efficiency and accuracy of prediction results.

The rapid accumulation of information assets requires new approaches to their storage and protection. The article is devoted to the analysis of modern approaches to storing large volumes of data, taking into account their efficiency, reliability and security. Key technologies such as cloud platforms, local solutions and distributed storage systems are considered, as well as the features of their application.

The paper analyzes the role of pseudorandom number generators in ensuring the efficiency and stability of machine learning algorithms. The key areas and forms of such application are identified.