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.
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.
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.
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.
The problem of building a face recognition pipeline faces numerous challenges such as changes in lighting, pose, and facial expressions. The main stages of the pipeline include detection, alignment, feature extraction, and face representation. Each of these stages is critically important for achieving accurate recognition.
A modified neural network model based on Yolo V5 was developed and the quality metrics of object classification on video images built on the basis of existing known basic neural network architectures were compared. The application of convolutional neural networks for processing images from video surveillance cameras is considered in order to develop an optimized algorithm for detecting and classifying objects on video images. The existing models and architectures of neural networks for image analysis were analyzed and compared.
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.