MobileNetV2

A Hybrid Deep Learning Model with Bayesian Optimization Technique for Leaf Disease Classification

Detecting plant diseases on time is imperative to improve agricultural productivity and mitigate economic losses.  This study presents a novel artificial intelligence framework for classifying plant diseases, combining advanced deep learning technologies with a hyperparameter optimization strategy.  Specifically, we employed a hybrid architecture that concatenates two pre-trained models, MobileNetV2 and DenseNet201, enriched with custom layers developed by the researchers.  Bayesian Optimization was employed to enhance model performance, focusing on four critical hyperp

SOFTWARE TOOL FOR IMPROVING THE INFORMATIONAL CONTENT OF VISUAL PARAMETERS OF IMAGES OF IR-RADIATION

Night vision devices (NVD) and thermal imaging cameras are widely used in the fields of surveillance, security and monitoring, search and rescue, emergency response, industrial control and maintenance, ground operations and unmanned aerial vehicles. Night vision devices operate on the principle of amplifying residual light (starlight, moonlight, city lighting) in the visible and near-infrared ranges (0.4–0.9 μm). Thermal imaging devices record thermal radiation of objects in the medium (3–5 μm) or far (8–14 μm) infrared range.

Software Implementation of the Algorithm for Recognizing Protective Elements on The Face

The quarantine restrictions introduced during COVID-19 are necessary to minimize the spread of coronavirus disease. These measures include a fixed number of people in the room, social distance, wearing protective equipment. These restrictions are achieved by the work of technological control workers and the police. However, people are not ideal creatures, quite often the human factor makes its adjustments.