Probability Of Identification Research Articles

A comparison of electron ionization mass spectra obtained at 70 eV, low electron energies, and with cold EI and their NIST library identification probabilities

Electron ionization (EI) mass spectra of 46 compounds from several different compound classes were measured. Their molecular ion abundances were compared as obtained with 70-eV EI, with low eV EI (such as 14 eV), and with EI mass spectra of vibrationally cold molecules in supersonic molecular beams (Cold EI). We further compared these mass spectra in their National Institute of Standards and Technology (NIST) library identification probabilities. We found that Low eV EI is not a soft ionization method, and it has little or no influence on the molecular ion relative abundances for large molecules and those with weak or no molecular ions. Low eV EI for compounds with abundant or dominant molecular ions in their 70 eV mass spectra results in the reduction of low mass fragment ions abundances thereby reducing their NIST library identification probabilities thus rarely justifies its use in real-world applications. Cold EI significantly enhances the relative abundance of the molecular ions particularly for large compounds; yet, it retains the low mass fragment ions; hence, Cold EI mass spectra can be effectively identified by the NIST library. Different standard EI ion sources provide different 70 eV EI mass spectra. Among the Agilent technologies ion sources, the "Extractor" exhibits relatively abundant molecular ions compared with the "Inert" ion source, while the "High efficiency source" (HES) provides mass spectra with depleted molecular ions compared with the "Inert" ion source or NIST library mass spectra. These conclusions are demonstrated and supported by experimental data in nine figures and two tables.

Figures of merit and statistics for detecting faulty species identification with DNA barcodes: A case study in Ramaria and related fungal genera.

DNA barcoding can identify biological species and provides an important tool in diverse applications, such as conserving species and identifying pathogens, among many others. If combined with statistical tests, DNA barcoding can focus taxonomic scrutiny onto anomalous species identifications based on morphological features. Accordingly, we put nonparametric tests into a taxonomic context to answer questions about our sequence dataset of the formal fungal barcode, the nuclear ribosomal internal transcribed spacer (ITS). For example, does DNA barcoding concur with annotated species identifications significantly better if expert taxonomists produced the annotations? Does species assignment improve significantly if sequences are restricted to lengths greater than 500 bp? Both questions require a figure of merit to measure of the accuracy of species identification, typically provided by the probability of correct identification (PCI). Many articles on DNA barcoding use variants of PCI to measure the accuracy of species identification, but do not provide the variants with names, and the absence of explicit names hinders the recognition that the different variants are not comparable from study to study. We provide four variant PCIs with a name and show that for fixed data they follow systematic inequalities. Despite custom, therefore, their comparison is at a minimum problematic. Some popular PCI variants are particularly vulnerable to errors in species annotation, insensitive to improvements in a barcoding pipeline, and unable to predict identification accuracy as a database grows, making them unsuitable for many purposes. Generally, the Fractional PCI has the best properties as a figure of merit for species identification. The fungal genus Ramaria provides unusual taxonomic difficulties. As a case study, it shows that a good taxonomic background can be combined with the pertinent summary statistics of molecular results to improve the identification of doubtful samples, linking both disciplines synergistically.

A search for transiting planets around FGKM dwarfs and subgiants in the TESS full frame images of the Southern ecliptic hemisphere

ABSTRACT In this work, we present the analysis of 976 814 FGKM dwarf and subgiant stars in the Transiting Exoplanet Survey Telescope (TESS) full frame images (FFIs) of the Southern ecliptic hemisphere. We present a new pipeline, DIAmante, developed to extract optimized, multisector photometry from TESS FFIs and a classifier, based on the Random Forest technique, trained to discriminate plausible transiting planetary candidates from common false positives. A new statistical model was developed to provide the probability of correct identification of the source of variability. We restricted the planet search to the stars located in the least crowded regions of the sky and identified 396 transiting planetary candidates among which 252 are new detections. The candidates’ radius distribution ranges between 1 R⊕ and 2.6 RJ with median value of 1 RJ and the period distribution ranges between 0.25 and 105 d with median value of 3.8 d. The sample contains four long period candidates (P > 50 d), one of which is new, and 64 candidates with periods between 10 and 50 d (42 new ones). In the small planet radius domain (4R < R⊕), we found 39 candidates among which 15 are new detections. Additionally, we present 15 single transit events (14 new ones), a new candidate multiplanetary system, and a novel candidate around a known TOI. By using Gaia dynamical constraints, we found that 70 objects show evidence of binarity. We release a catalogue of the objects we analysed and the corresponding light curves and diagnostic figures through the MAST and ExoFOP portals.

Acute hyperglycaemia can impair driving skill in young type 1 diabetes mellitus patients

AimDrivers with diabetes are at increased risk of being involved in road accidents. Therefore, this study aimed to evaluate the effects of acute hyperglycaemia (AH) compared with euglycaemia on driving ability in patients with type 1 diabetes mellitus (T1DM). MethodsEighteen drivers with T1DM were asked to navigate twice through nine hazardous scenarios, using a driving simulator, during euglycaemia and then again during AH (mean blood glucose: 138 ± 34 mg/dL and 321 ± 29 mg/dL, respectively) in a counterbalanced crossover study. Driving performance was continually monitored for driving speed, steering wheel angle, acceleration, and location and velocity of other vehicles and obstacles, with drivers wearing a mobile head-mounted eye-tracking system. ResultsThe main findings were that, during AH, participants were less likely to identify a hazard [probability of identification (POI): 0.5725 ± 0.5], glanced fewer times at the hazard (3.24 ± 5.9), maintained shorter headway (between-vehicle) distance (mean: 40.87 ± 20.15 m) and had an increased number of braking events per km driven (6.69 ± 5.20) compared with driving during euglycaemia (POI: 0.733 ± 0.4; number of glances: 3.69 ± 6.99; headway distance: 50.46 ± 26.2 m; number of braking events per km driven: 4.31 ± 3.87; P < 0.05 for all parameters). ConclusionThis study provides evidence that AH impairs driving performance in young T1DM patients by demonstrating the negative effects of AH on both hazard perception and speed management.

Analysis of impurities in pharmaceuticals by LC-MS with cold electron ionization.

Pharmaceuticals require careful and precise determination of their impurities that might harm the user upon consumption. Although today, the most common technique for impurities identification is liquid chromatography-mass spectrometry (LC-MS/MS), it has several downsides due to the nature of the ionization method. Also, the analyses in many cases are targeted thus despite being present, some of the compounds will not be revealed. In this paper, we propose and show a new method for untargeted analysis and identification of impurities in active pharmaceutical ingredients (APIs). The instrument used for these analyses is a novel electron ionization (EI) LC-MS with supersonic molecular beams (SMB). The EI-LC-MS-SMB was implemented for analyses of several drug samples spiked with an impurity. The instrument provides EI mass spectra with enhanced molecular ions, named Cold EI, which increases the identification probabilities when the compound is identified with the aid of an EI library like National Institute of Standards and Technology (NIST). We analyzed ibuprofen and its impurities, and both the API and the expected impurity were identified with names and structures by the NIST library. Moreover, other unexpected impurities were found and identified proving the ability of the EI-LC-MS-SMB system for truly untargeted analysis. The results show a broad dynamic range of four orders of magnitude at the same run with a signal-to-noise ratio of over 10 000 for the API and almost uniform response.

The Gaitprint: Identifying Individuals by Their Running Style.

Recognizing the characteristics of a well-developed running style is a central issue in athletic sub-disciplines. The development of portable micro-electro-mechanical-system (MEMS) sensors within the last decades has made it possible to accurately quantify movements. This paper introduces an analysis method, based on limit-cycle attractors, to identify subjects by their specific running style. The movement data of 30 athletes were collected over 20 min. in three running sessions to create an individual gaitprint. A recognition algorithm was applied to identify each single individual as compared to other participants. The analyses resulted in a detection rate of 99% with a false identification probability of 0.28%, which demonstrates a very sensitive method for the recognition of athletes based solely on their running style. Further, it can be seen that these differentiations can be described as individual modifications of a general running pattern inherent in all participants. These findings open new perspectives for the assessment of running style, motion in general, and a person’s identification, in, for example, the growing e-sports movement.

COVID-19: What does it mean for digital social protection?

COVID-19 has hit a world in which social protection schemes are increasingly augmented with digital measures. Digital identity schemes are especially being adopted to match citizens’ data with social protection entitlements, enabling authentication through demographic and, increasingly, biometric data at the point of access. In this commentary, I discuss three sets of implications that COVID-19 has yielded on digital social protection, whose central trade-off – increasing the probabilities of accurate user identification, at the cost of greater exclusions – has become even more problematic during the crisis. I argue that three forms of data injustice – legal, informational and design-related, previously identified in datafied social protection schemes, will need to be monitored in the post-pandemic scenario. I finally observe that the crisis exposes the long-term need to place digitality within social protection schemes that expand user entitlements rather than constraining them. Implications of such reflections are drawn for the study of data-based social welfare interventions.

Long-Term Tracking of Group-Housed Livestock Using Keypoint Detection and MAP Estimation for Individual Animal Identification.

Tracking individual animals in a group setting is a exigent task for computer vision and animal science researchers. When the objective is months of uninterrupted tracking and the targeted animals lack discernible differences in their physical characteristics, this task introduces significant challenges. To address these challenges, a probabilistic tracking-by-detection method is proposed. The tracking method uses, as input, visible keypoints of individual animals provided by a fully-convolutional detector. Individual animals are also equipped with ear tags that are used by a classification network to assign unique identification to instances. The fixed cardinality of the targets is leveraged to create a continuous set of tracks and the forward-backward algorithm is used to assign ear-tag identification probabilities to each detected instance. Tracking achieves real-time performance on consumer-grade hardware, in part because it does not rely on complex, costly, graph-based optimizations. A publicly available, human-annotated dataset is introduced to evaluate tracking performance. This dataset contains 15 half-hour long videos of pigs with various ages/sizes, facility environments, and activity levels. Results demonstrate that the proposed method achieves an average precision and recall greater than 95% across the entire dataset. Analysis of the error events reveals environmental conditions and social interactions that are most likely to cause errors in real-world deployments.

Improved gait recognition through gait energy image partitioning

AbstractRecently, human gait pattern has turned into an essential biometric feature to recognize an individual remotely. Gait as a feature becomes challenging owing to variation in appearance under different covariate conditions (eg, shoe, surface, haul, viewpoint and attire). The covariates may alter few fragment of gait while other fragment stay unaltered, leading to lower the probability of correct identification. To overcome such variation, an improved gait recognition strategy is proposed in this article by gait energy image partitioning and selection processing. Our method involves pre‐processing of raw video for silhouette extraction, gait cycle detection, segmentation into different regions, and histogram of gradients feature extraction from selected segments. In this way, the specific features across complete gait cycles are extracted precisely. Finally, recognition is done by using K‐NN. The proposed strategy has been assessed using the CASIA B gait database. Our outcomes shows a particular proposed strategy accomplishes high recognition rate and outperforms the advanced gait recognition mechanism.

Problems, Limitations, and Challenges in Species Identification of Ascomycota Members on the Basis of ITS Regions

The internal transcribed spacer (ITS) region is regarded as a formal fungal primary barcode with a high probability of the correct identification for a broad group of fungi. ITS sequences have been widely used to determine many fungal species and analysis of rDNA ITS is still one of the most popular tools used in mycology. However, this region is not equally variable in all groups of fungi; therefore, identification may be problematic and result in ambiguous data, especially in some species-rich genera of Ascomycota. For these reasons, identification based on rDNA ITS is usually complemented by morphological observations and analysis of additional genes. Reliable species identification of Ascomycota members is essential in diagnosing plant diseases, verifying air quality and the effectiveness of agronomic practices, or analyzing relationships between microorganisms. Therefore, the present study aimed to verify, using specific examples, the extent to which ITS sequence analysis is useful in species identification of pathogens and saprobionts from Ascomycota and demonstrate problems related to such identification in practice. We analyzed 105 ITS sequences of isolates originating from air and plant material. Basic local alignment search tool (BLASTn) significantly contributed to the reliable species identification of nearly 80% of isolates such as &lt;em&gt;Arthrinium arundinis&lt;/em&gt;, &lt;em&gt;Beauveria bassiana&lt;/em&gt;, &lt;em&gt;Boeremia exigua&lt;/em&gt;, &lt;em&gt;Cladosporium cladosporioides&lt;/em&gt;, &lt;em&gt;Epicoccum nigrum&lt;/em&gt;, &lt;em&gt;Nigrospora oryzae&lt;/em&gt;, &lt;em&gt;Sclerotinia sclerotiorum&lt;/em&gt;, or &lt;em&gt;Sordaria fimicola &lt;/em&gt;and members of the genera &lt;em&gt;Alternaria &lt;/em&gt;and &lt;em&gt;Trichoderma&lt;/em&gt;. However, for most isolates, additional morphological observations, information regarding the isolate origin and, where possible, a PCR with species-specific primers were helpful and complementary. Using our practical approach, we determined that ITS-based species identification and comparative analysis with GenBank sequences significantly helps identifying Ascomycota members. However, in many cases, this should be regarded as suggestive of a taxon because the data usually require the use of additional tools to verify the results of such analysis.

Synthesis and determination of analytical characteristics and differentiation of positional isomers in the series of N-(2-methoxybenzyl)-2-(dimethoxyphenyl)ethanamine using chromatography-mass spectrometry.

N-(2-Methoxybenzyl)-2,5-dimethoxyphenethylamines (NBOMes) are synthetic phenethylamine derivatives emerging on the global drug market and reported to be associated with untoward effects in people who use drugs. Its action involves agonism at serotonin 5-HT2A receptors, affecting cognitive and behavioral processes. However, certain isomers of NBOMes may not show any psychoactive effects. They are not controlled by legislation and can be tested as pharmaceutical drugs. This study deals with the differentiation among positional isomers of 25H-NBOMe differing in the position of the two methoxy groups in the phenylethyl moiety of the molecule, using chromatography-mass spectrometry methods. The gas chromatography analysis showed that the isothermal mode was more efficient than the usually applied temperature-programming mode for the separation of the mentioned isomers. Electron ionization mass spectra of 25H-NBOMe isomers were highly similar, often resulting in a high probability of erroneous identification. However, mass spectra of their trifluoroacetyl or pentafluoropropanoyl derivatives were easily identified as they contained fragments with many significant differences. The proposed analysis using liquid chromatography-tandem mass spectrometry could distinguish the isomers of 25H-NBOMe without the need for any derivatization.

Fourier Transform Infrared Spectroscopy Based Complementary Diagnosis Tool for Autism Spectrum Disorder in Children and Adolescents.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder that begins early in life and continues lifelong with strong personal and societal implications. It affects about 1%–2% of the children population in the world. The absence of auxiliary methods that can complement the clinical evaluation of ASD increases the probability of false identification of the disorder, especially in the case of very young children. In this study, analytical models for auxiliary diagnosis of ASD in children and adolescents, based on the analysis of patients’ blood serum ATR-FTIR (Attenuated Total Reflectance-Fourier Transform Infrared) spectra, were developed. The models use chemometrics (either Principal Component Analysis (PCA) or Partial Least Squares Discriminant Analysis (PLS-DA)) methods, with the infrared spectra being the X-predictor variables. The two developed models exhibit excellent classification performance for samples of ASD individuals vs. healthy controls. Interestingly, the simplest, unsupervised PCA-based model results to have a global performance identical to the more demanding, supervised (PLS-DA)-based model. The developed PCA-based model thus appears as the more economical alternative one for use in the clinical environment. Hierarchical clustering analysis performed on the full set of samples was also successful in discriminating the two groups.

A Monte Carlo-Based Outlier Diagnosis Method for Sensitivity Analysis

An iterative outlier elimination procedure based on hypothesis testing, commonly known as Iterative Data Snooping (IDS) among geodesists, is often used for the quality control of modern measurement systems in geodesy and surveying. The test statistic associated with IDS is the extreme normalised least-squares residual. It is well-known in the literature that critical values (quantile values) of such a test statistic cannot be derived from well-known test distributions but must be computed numerically by means of Monte Carlo. This paper provides the first results on the Monte Carlo-based critical value inserted into different scenarios of correlation between outlier statistics. From the Monte Carlo evaluation, we compute the probabilities of correct identification, missed detection, wrong exclusion, over-identifications and statistical overlap associated with IDS in the presence of a single outlier. On the basis of such probability levels, we obtain the Minimal Detectable Bias (MDB) and Minimal Identifiable Bias (MIB) for cases in which IDS is in play. The MDB and MIB are sensitivity indicators for outlier detection and identification, respectively. The results show that there are circumstances in which the larger the Type I decision error (smaller critical value), the higher the rates of outlier detection but the lower the rates of outlier identification. In such a case, the larger the Type I Error, the larger the ratio between the MIB and MDB. We also highlight that an outlier becomes identifiable when the contributions of the measures to the wrong exclusion rate decline simultaneously. In this case, we verify that the effect of the correlation between outlier statistics on the wrong exclusion rate becomes insignificant for a certain outlier magnitude, which increases the probability of identification.

False Trajectory Privacy Protection Scheme Based on Location Service

When the user applies for a location continuous query service, the user location privacy may be revealed due to different types of trajectory identification. A false trajectory privacy protection scheme is proposed, which uses the real-time generation of similar trajectories to protect the user’s personal location privacy. The false location set is established by initial anonymous location selection, similar trajectory location calculation and generated location selection. The false location is used to form a false trajectory similar to the real location trajectory, which reduces the probability of attacker identification and achieves real-time trajectory anonymity based on location service. Finally, the effectiveness of the scheme is verified by performance analysis and experimental results.

Artificial Intelligence Assisted Mid-Infrared Laser Spectroscopy In Situ Detection of Petroleum in Soils

A simple, remote-sensed method of detection of traces of petroleum in soil combining artificial intelligence (AI) with mid-infrared (MIR) laser spectroscopy is presented. A portable MIR quantum cascade laser (QCL) was used as an excitation source, making the technique amenable to field applications. The MIR spectral region is more informative and useful than the near IR region for the detection of pollutants in soil. Remote sensing, coupled with a support vector machine (SVM) algorithm, was used to accurately identify the presence/absence of traces of petroleum in soil mixtures. Chemometrics tools such as principal component analysis (PCA), partial least square-discriminant analysis (PLS-DA), and SVM demonstrated the effectiveness of rapidly differentiating between different soil types and detecting the presence of petroleum traces in different soil matrices such as sea sand, red soil, and brown soil. Comparisons between results of PLS-DA and SVM were based on sensitivity, selectivity, and areas under receiver-operator curves (ROC). An innovative statistical analysis method of calculating limits of detection (LOD) and limits of decision (LD) from fits of the probability of detection was developed. Results for QCL/PLS-DA models achieved LOD and LD of 0.2% and 0.01% for petroleum/soil, respectively. The superior performance of QCL/SVM models improved these values to 0.04% and 0.003%, respectively, providing better identification probability of soils contaminated with petroleum.

Mobile Social Service User Identification Framework Based on Action-Characteristic Data Retention

Mobile social services are an indispensable part of our daily lives. These services are also favored by criminals because it is difficult to retrieve communication data from them. In the past, communication data provided by telecommunication carriers usually indicated when, from where, and with whom the communication occurred. Presently, it is difficult for law enforcement agencies and public security departments to obtain information regarding mobile social services. For this reason, these departments have requested Internet access service providers to store data that can be used to identify the user of a mobile social service at any given time. However, many non-government and civil society organizations claim that these practices violate privacy rights; hence, they strongly oppose the retention of the subscribers’ data by the government. Currently, the European Union law does not allow “general and indiscriminate retention of traffic data and location data,” except for “targeted” use against “serious crimes.” Under this premise, ensuring the necessary data retention, while reducing the privacy violations and maintaining public security is a challenging task. In this study, a novel identification framework based on different types and action characteristics of mobile social services is proposed. Based on this framework, government agencies do not need to retain general and indiscriminate traffic data, but only data that aid in identification. Thus, this framework substantially reduces the volume of potential targets and improves the probability of correct target identification, ensuring a balance between privacy and public security.

Extracranial predictors of recurrent paroxysmal vertigo

The cause of recurrent vertigo is vascular compression of the vestibular cochlear nerve (VC PUN) often. The pathogenesis of recurrent vertigo caused by VC PUN in the adult population did not specified. The aim: to assess the condition of the brachiocephalic vessels in patients with vertigo caused by vascular compression of the vestibular nerve. Materials and methods: We examined 80 patients with recurrent vertigo caused by VC VN according to neuroimaging, average age (43,09±13,47 years) and 71 healthy subjects, average age (45,85 ± 12,98 years). There were performed clinical and neurological examination, magnetic resonance imaging of the brain, ultrasound duplex scanning of the carotid and vertebral arteries, vestibulometry. We compared the diameter of the right and left, as well as the peak systolic blood flow rate (Vps) for the extracranial segments of the right and left vertebral artery (VA), the intima-media complex (IMC), the presence of extravasal compression, stenotic (&gt;20,0%) and non-stenotic (&lt;20,0%) atherosclerotic lesions of the brachiocephalic arteries. Results: In patients with VC VN (on MRI) and signs of latent vestibular dysfunction (VD) in the interictal period is significantly dominated by the tortuosity of VA (χ2 =22,16, p &lt;0,001), atherosclerotic lesion of the arteries (χ2=2,77, p=0,091), extravascular compression of VA (χ2=6,04, p &lt;0,014), VA small diameter, hypoplasia of VA (χ2=5,86, p &lt;0,016) compared to the control group. Statistically significant correlation between IMC-right and right VA diameter, rs=0,42, p=0,0007, between Vps and Vps from right to left, rs=0,39, p=0,001, between IMC on the left and right PA diameter, rs=0,25, p=0,04. Results of the ROC analysis had established: the Vps VA right &gt;40 cm/s probability of identification due to VC VN is 73,1%, the Vps VA left &gt;44 cm/s the probability of detecting VD is 75,0%, indicating high diagnostic value of this indicator in the differential diagnosis of VD, due to the VC VN.

Synergetic interpretation of bifurcations of open systems

Introduction . In order to establish the causes of unexpected bifurcations of open systems, this work is presented. It is based on the corporate interaction of unidirectional open systems, an assumption is made about the reasons for their self-improvement. Main part. The identification of bifurcations of closed and open systems is considered. The prerequisites for the occurrence of incorrect bifurcations of open systems are given. In studies of closed systems, it is imperative that the conditions of structural stability to relatively small perturbations, which cannot be satisfied when identifying open systems, are met. Self-improvement of open systems occurs due to the exchange of information with the external environment. Based on the assumption that the long-term memory of an open system can be considered as an external environment, the exchange of information can be considered through the cooperative interaction of an open single focus the most open system and its long-term memory. The latter leads to the formation of targeted actions that contribute to the self-improvement of an open system. An admissible regularization of a problem identified by an incorrect bifurcation of an open system is shown. The task is regularized in two stages. At the first stage, an operator is defined that regularizes the problem in the format of thinking corresponding to incorrect bifurcation. At the second stage, the solution obtained at the first stage is applied to the problem in the original format. Conclusions. It has been suggested that the occurrence of incorrect bifurcations of open systems is a result of the cooperation of the previously existing long-term memory with its present memory. These reasons indicate the incorrectness of such tasks. It is shown that the use of the logical operator allows partial regularization of such problems by identifying incorrect bifurcations. Due to the wide variety of open systems, the probability of an adequate identification of their bifurcations is obvious.

Analysis of Techniques for Verification of Coating Thickness Gauges

An analysis of the structure and content of verification technique MP 002.D4-14, extends to coating thickness gauges TM-2, TM-3, TM-4, and TM-4T, technique MP 159-261-2016 for verification of SaluTrjn coating thickness gauges, and a technique for verification of coating thickness gauges regulated by GOST 8.502–84 is performed. It is shown that the first two techniques do not take into account the random component of the error, while the third technique does take into account the random component but not correctly. These factors all lead to a high probability of a wrong identification of a model of a thickness gauge that is, in reality, defective to be, instead, a working model or, on the other hand working devices may be incorrectly identified as being defective. To increase the reliability of the results of a verification of coating thickness gauges, recommendations that suggest incorporating them into the content of a verification technique regulated by GOST 8.502–84 are proposed.

Statistical approach to determining the cut-off value in the algorithms of substance identification in Fourier transform infrared spectroscopy

The purpose of the paper was to find the approach to determining the cut-off value in the spectral library matching identification algorithm. We used Pearson correlation coefficient as a similarity measure between the spectra, and according to its value between the reference spectra we divided the spectral library into classes. We considered a situation when one of the investigated spectra had an additive narrowband white noise component with a Gaussian distribution. We examined the impact of the cut-off value on the possibility of calculating the probability of right identification in a single identification experiment by means of probability distribution function, when the signal-to-noise ratio is known a priori. The signal-to-noise ratio was taken from the value area where analytical expressions were unknown. In the research we applied numerical methods. The algorithm of determining the cut-off value based on the fundamental principles of the ROC-curves analysis was proposed. The cut-off value was obtained where possible, and the situation for every class was analysed. Maximal probabilities of right identification were obtained.