machine learning

The impact of activation functions on LTSM server load prediction accuracy: machine learning approach

The continuously growing number of users and their requests to the server demands substantial resources to ensure fast responses without delays.  However, server load is inherently unevenly distributed throughout the day, week, or month.  Accurately predicting the required resources and dynamically managing their allocation is crucial, as it can lead to significant cost savings in server maintenance without compromising the user experience.  This study investigates the influence of activation function choice on the forecasting accuracy of Long Short-Term Memory (LSTM) n

IDENTIFYING GRAPE DISEASES BY IMAGES USING ARTIFICIAL INTELLIGENCE METHODS

The paper uses modern artificial intelligence methods to investigate models and methods for determining grape disease. The existing methodologies for classification and recognition by images of grape diseases using neural networks are analyzed. Several problems for improving recognition results are highlighted.

CODE SEARCH METHOD IN PUBLIC REPOSITORIES USING WMC METRIC

This study explores the correlation between the popularity of open-source repositories and their quality, as assessed using static code quality metrics. The primary focus is on defining key indicators for two distinct paradigms, namely functional and object-oriented programming, and developing a code search method to systematically process repositories retrieved during the search process.

Artificial intelligence in penetration testing: leveraging AI for advanced vulnerability detection and exploitation

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.

Predicting cyberspace intrusions using machine learning algoritms

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.

Modern approaches to the diagnosis of neurological disorders using artificial neural networks

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.

SYSTEMIZATION OF REQUIREMENTS FOR OPERATIONAL QUALITY CONTROL SYSTEMS OF MEAT PRODUCTS

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.

Development of an Automated Natural Language Text Analysis System Using Transformers

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.

Real-time Anomaly Detection in Distributed Iot Systems:a Comprehensive Review and Comparative Analysis

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.

Use of artificial intelligence methods and tools in the construction of cloud it infrastructures

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.