Machine learning from coronas using parametrization of images

We were interested to develop an algorithm for detection of coronas of people in altered states of consciousness (two-classes problem). Such coronas are known to have rings (double coronas), special branch-like structure of streamers and/or curious spots. We used several approaches to parametrization of images and various machine learning algorithms. We compared results of computer algorithms with the human expert’s accuracy. Results show that computer algorithms can achieve the same or even better accuracy than that of human experts

GDV technique and machine learning: Current research and results

We use machine learning to analyze GDV images of leaves of apple trees and human fingers. We are interested in two hypotheses: 1. GDV images of plant leaves contain information about plant condition, 2. Human bioelectromagnetic field can be influenced by outside factors, such as vitalized water from special glasses. We performed four independent studies: (a) analyzing coronas of apple leaves, (b) detecting the effect of K2000 glasses on human BEM field, (c) detecting the effect of mobile phones on human BEM field and (d) detecting the effect of energized orbs on human BEM field

DNA barcoding insufficiently identifies European wild bees (Hymenoptera, Anthophila) due to undefined species diversity, genus-specific barcoding gaps and database errors

Recent declines in insect abundances, especially populations of wild pollinators, pose a threat to many natural and agricultural ecosystems. Traditional species monitoring relies on morphological character identification and is inadequate for efficient and standardized surveys. DNA barcoding has become a standard approach for molecular identification of organisms, aiming to overcome the shortcomings of traditional biodiversity monitoring. However, its efficacy depends on the completeness of reference databases. Large DNA barcoding efforts are (almost entirely) lacking in many European countries and such patchy data limit Europe-wide analyses of precisely how to apply DNA barcoding in wild bee identification. Here, we advance towards an effective molecular identification of European wild bees. We conducted a high-effort survey of wild bees at the junction of central and southern Europe and DNA barcoded all collected morphospecies. For global analyses, we complemented our DNA barcode dataset with all relevant European species and conducted global analyses of species delimitation, general and genus-specific barcoding gaps and examined the error rate in DNA data repositories. We found that (i) a sixth of all specimens from Slovenia could not be reliably identified, (ii) species delimitation methods show numerous systematic discrepancies, (iii) there is no general barcoding gap across all bees and (iv) the barcoding gap is genus specific, but only after curating for errors in DNA data repositories. Intense sampling and barcoding efforts in underrepresented regions and strict curation of DNA barcode repositories are needed to enhance the use of DNA barcoding for the identification of wild bees