deep learning

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.

INTRACRANIAL HEMORRHAGE SEGMENTATION USING NEURAL NETWORK AND RIESZ FRACTIONAL ORDER DERIVATIVE-BASED TEXTURE ENHANCEMENT

This paper explores the application of the U-Net architecture for intracranial hemorrhage segmentation, with a focus on enhancing segmentation accuracy through the incorporation of texture enhancement techniques based on the Riesz fractional order derivatives. The study begins by conducting a review of related works in the field of computed tomography (CT) scan segmentation. At this stage also a suitable dataset is selected.

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.

SYSTEM FOR DETERMINING THE SOUND SOURCE COORDINATES

The authors investigated the effect of changes in the acoustic signal propagation speed and the accuracy of sensor positioning on the accuracy of sound source localization. The mean absolute error grows with the displacement of the microphones relative to the nominal coordinates (X, Y). The same trend is observed with an increase in the actual acoustic signal velocity deviation from the velocity under normal environmental conditions.

Covid-19 Diagnosis Using Deep Learning From X-Ray and CT Images – Overview

Since the outbreak of the pandemic in 2019, Covid-19 has become one of the most important topics in the field of medicine. This disease, caused by the SARS- CoV-2 virus, can lead to serious respiratory diseases and other complications. They can even lead to death. In recent years, the number of Covid-19 cases around the world has increased significantly, resulting in the need for rapid and effective diagnosis of the disease. Currently, the use of deep learning in medical diagnostics is becoming more and more common.

Implementing quality assurance practices in teaching machine learning in higher education

The development of machine learning and deep learning (ML/DL) change the skills expected by society and the form of ML/DL teaching in higher education.  This article proposes a formal system to improve ML/DL teaching and, subsequently, the graduates' skills.  Our proposed system is based on the quality assurance (QA) system adapted to teaching and learning ML/DL and implemented on the model suggested by Deming to continuously improve the QA processes.

Deep learning for photovoltaic panels segmentation

Due to advanced sensor technology, satellites and unmanned aerial vehicles (UAV) are producing a huge amount of data allowing advancement in all different kinds of earth observation applications.  Thanks to this source of information, and driven by climate change concerns, renewable energy assessment became an increasing necessity among researchers and companies.  Solar power, going from household rooftops to utility-scale farms, is reshaping the energy markets around the globe.  However, the automatic identification of photovoltaic (PV) panels and solar farms' status i

Road users detection for traffic congestion classification

One of the important problems that urban residents suffer from is Traffic Congestion.  It makes their life more stressful, it impacts several sides including the economy: by wasting time, fuel and productivity.  Moreover, the psychological and physical health.  That makes road authorities required to find solutions for reducing traffic congestion and guaranteeing security and safety on roads.  To this end, detecting road users in real-time allows for providing features and information about specific road points.  These last are useful for road managers and also for road users about congeste

Overview of deep learning and mobile edge computing in autonomous driving

In recent years, mobile edge computing and deep learning have attracted strong industry attention in the application scenario of autonomous driving. Mobile edge computing reduces the transmission delay of autonomous driving information by offloading computational tasks to edge servers to reduce the network load; deep learning can effectively improve the accuracy of obstacle detection, thereby enhancing the stability and safety of autonomous driving.