No abstract

The aim of this paper is to determine the extent to which infrared (IR) thermal imaging may be used for skin burn depth evaluation. The analysis can be made on the basis of the development of a thermal model of the burned skin. Different methods such as the traditional clinical visual approach and the IR imaging modalities of static IR thermal imaging, active IR thermal imaging and active-dynamic IR thermal imaging (ADT) are analyzed from the point of view of skin burn depth diagnostics. In ADT, a new approach is proposed on the basis of parametric image synthesis. Calculation software is implemented for single-node and distributed systems. The properties of all the methods are verified in experiments using phantoms and subsequently in vivo with animals with a reference histopathological examination. The results indicate that it is possible to distinguish objectively and quantitatively burns which will heal spontaneously within three weeks of infliction and which should be treated conservatively from those which need surgery because they will not heal within this period.

In recent years, advancements in the field of the artificial intelligence (AI) gained a huge momentum due to the worldwide appliance of this technology by the industry. One of the crucial areas of AI are neural networks (NN), which enable commercial utilization of functionalities previously not accessible by usage of computers. Intrusion detection system (IDS) presents one of the domains in which neural networks are widely tested for improving overall computer network security and data privacy. This article gives a thorough overview of recent literature regarding neural networks usage in intrusion detection system area, including surveys and new method proposals. Short tutorial descriptions of neural network architectures, intrusion detection system types and training datasets are also provided.

No abstract

No abstract