Video-based Flame Detection using LBP-based Descriptor: Influences of Classifiers Variety on Detection Efficiency

Abstract

Techniques to detect the flame at an early stage are necessary in order to prevent the fire and minimize the damage. The flame detection technique based on the physical sensor has limited disadvantages in detecting the fire early. This paper presents the results of using local binary patterns for solving flames detecting problem and proposes modifications to improve the quality of detector work. Experimentally found that using support vector machines classifier with a kernel based on Gaussian radial basis functions shows the best results compared to other SVM cores or classifier k-nearest neighbors.

Design of the architecture of an intelligent system for distributing commercial content in the internet space based on SEO-technologies, neural networks, and Machine Learning

“…Thus, the denominator does not depend on a given category. When calculating an estimate, the denominator used is the total amount of content, which was estimated for at least one category [52].…”

Analysis of statistical methods for stable combinations determination of keywords identification

“…Parsing by chunks is a breakdown of sentences into non-intersecting word combinations [47][48][49][50][51], i. e. (flat structure)¹complete parsing, for example, (the boy (with the hat)) ←→ (the boy) with (the hat)). …”

Development of the linguometric method for automatic identification of the author of text content based on statistical analysis of language diversity coefficients

“…The ratio of the number of sentences to the number of words of a certain text It was found [14], that the text of a Ukrainian fairy tale has K z =0,77, and the text of a scientific article in Ukrainian -3.0, that is, the coherence in the second text is 3.9 times stronger than in the first one. There are no official standards for speech diversity coefficients for K l and K s [51], but the reference point for comparison and evaluation of a text in a homogeneous group of texts is the average statistical rate of the value of the coefficient for passages of the equal length [52]. 100 words will be accepted as the minimum size (length) of a passage, we will consider that the coefficients have already stabilized, reflecting the real features of the author's language [53].…”