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Structure of the Information System for Predicting and Interpreting Changes in the State of the Service User

The paper investigates the problem of predicting changes in user states (including churn) based on session data using deep neural networks. The paper considers the use of long short-term memory models and convolutional neural networks, as well as the use of byte pair coding for data pre-processing. The functionality of the developed information system for forecasting changes in the state of users and interpreting forecasting models, which combines methods of data analysis, building forecasting models and explaining the results, is analysed.

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.

Evaluation of Deep Learning-based Super-resolution Methods for Enhanced Facial Identification Accuracy

This paper presents a comparative analysis of modern super-resolution (SR) methods for improving the accuracy of face recognition in video surveillance systems. The low quality of images obtained from surveillance cameras is a significant obstacle to effective person identification, making the use of SR methods particularly relevant.

ADAPTIVE OBJECT RECOGNITION THROUGH A META-LEARNING APPROACH FOR DYNAMIC ENVIRONMENTS

Object recognition systems often struggle to maintain accuracy in dynamic environments due to challenges such as lighting variations, occlusions, and limited training data. Traditional convolutional neural networks (CNNs) require extensive labeled datasets and lack adaptability when exposed to new conditions. This study aims to develop an adaptive object recognition framework that enhances model generalization and rapid adaptation in changing environments.

UNDERSTANDING LARGE LANGUAGE MODELS: THE FUTURE OF ARTIFICIAL INTELLIGENCE

The article examines the newest direction in artificial intelligence - Large Language Models, which open a new era in natural language processing, providing the opportunity to create more flexible and adaptive systems. With their help, a high level of understanding of the context is achieved, which enriches the user experience and expands the fields of application of artificial intelligence. Large language models have enormous potential to redefine human interaction with technology and change the way we think about machine learning.

Review of disease identification methods based on computed tomography imagery

Methods and approaches to computational diagnosis of various pulmonary diseases via automated analysis of chest images performed with computed tomography were reviewed. Google Scholar database was searched with several queries focused on deep learning and machine learning chest computed tomography imagery analysis studies published during or after 2017. A collection of 39 papers was collected after screening the search results. The collection was split by publication date into two separate sets based on the date being prior to or after the start of the COVID-19 pandemic.

RESEARCH OF PLANT DISEASE DIAGNOSTIC METHODS USING DEEP LEARNING

The article explores the use of convolutional neural networks (CNNs) in the diagnosis and identification of plant diseases and pests. Various methods of plant disease diagnosis, features of datasets, and challenges in this research direction are considered. The article discusses a five-step methodology for determining plant diseases, including data collection, preprocessing, segmentation, feature extraction, and classification. Different deep learning architectures enabling fast and efficient plant disease diagnosis are investigated.

Research of the models for sign gesture recognition using 3D convolutional neural networks and visual transformers

The work primarily focuses on addressing the contemporary challenge of hand gesture recognition, driven by the overarching objectives of revolutionizing military training methodologies, enhancing human-machine interactions, and facilitating improved communication between individuals with disabilities and machines. In-depth scrutiny of the methods for hand gesture recognition involves a comprehensive analysis, encompassing both established historical computer vision approaches and the latest deep learning trends available in the present day.