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.
In medical image analysis, deep learning and convolutional neural networks (CNN) are widely employed, particularly in tasks such as classification and segmentation. This study specifically addresses their application in healthcare for detecting cardiomegaly, a condition characterized by an enlarged heart, often related to factors such as hypertension or coronary artery diseases. The primary objective is to develop an algorithm to identify cardiomegaly in chest X-ray images, constituting a binary classification problem (whether the image exhibits cardiomegaly or not).
The paper highlights the increasing importance of machine learning (ML) in mobile applications, with mobile devices becoming ubiquitous due to their accessibility and functionality. Various ML models, including Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs), are explored for their applications in real-time classification on mobile devices. The paper identifies key challenges in deploying these models, such as limited computational resources, battery consumption, and the need for real-time performance.
In agriculture, early detection of crop diseases is imperative for sustainability and maximizing yields. Rooted in Agriculture 4.0, our innovative approach combines pre-trained Convolutional Neural Networks (CNNs) models with data-driven solutions to address global challenges related to water scarcity. By integrating the combined $L_{1}/L_{2}$ regularization technique to our model layers, we enhance their flexibility, reducing the risk of the overfitting effect of the model. In the orange dataset used in our experiments, we have 1790 orange images, including a class
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.
The work highlights neural network applications to medical images, namely X-ray images. An overview of neural networks used to analyze medical images was conducted. Such a neural network has been implemented and tested on third-party images.