KEYSTROKE DYNAMICS ANALYSIS USING MACHINE LEARNING METHODS

The primary objective of the paper was to determine the user based on its keystroke dynamics using the methods of machine learning. Such kind of a problem can be formulated as a classification task. To solve this task, four methods of supervised machine learning were employed, namely, logistic regression, support vector machines, random forest, and neural network. Each of three users typed the same word that had 7 symbols 600 times. The row of the dataset consists of 7 values that are the time period during which the particular key was pressed. The ground truth values are the user id. Before the application of machine learning classification methods, the features were transformed to z-score. The classification metrics were obtained for each applied method. The following parameters were determined: precision, recall, f1-score, support, prediction, and area under the receiver operating characteristic curve (AUC). The obtained AUC score was quite high. The lowest AUC score equal to 0.928 was achieved in the case of linear regression classifier. The highest AUC score was in the case of neural network classifier. The method of support vector machines and random forest showed slightly lower results as compared with neural network method. The same pattern is true for precision, recall and F1-score. Nevertheless, the obtained classification metrics are quite high in every case. Therefore, the methods of machine learning can be efficiently used to classify the user based on keystroke patterns. The most recommended method to solve such kind of a problem is neural network

The content of acoustic signals and biological effects of noise in conditions of high level of work intensity

BACKGROUND. Biological effects of noise depend on its physical parameters, combination with other hazards, the content of acoustic signals. This article aimed to analyze the difference in biological effects caused by the selection of nonverbal and verbal signals in conditions of a high level of work intensity. METHODS. Work conditions, physical characteristics of noise, levels of work intensity were studied among 75 telephone operators and 96 geophone operators. Levels of permanent hearing thresholds, evaluated by pure-tone audiometry, and results of self-estimation of operators' health were compared. The contribution of the content of acoustic signals in the shifting of hearing thresholds was evaluated by the one-way analysis of variance. RESULTS. Selection of acoustic signals in the noise background (<65 dB), in conditions of high work intensity, causes a significant increase of permanent hearing thresholds in both studied groups comparing to the non-noise exposed population. Combination of the high level of work intensity and distinguishing of nonverbal acoustic messages leads to significant deterioration of health resulting in decreasing of hearing sensitivity and number of complaints on the state of health (p<0.05). The content of acoustic signals significantly contributes to the biological effects of the nose. CONCLUSION. Obtained results testify necessity to revise safe criteria of noise levels for workers, engaged in selection, recognition and distinguishing of acoustic messages in the noise background combined with a high level of work intensity. In case when the energy of the acoustic field cannot be reduced, occupational safety measures should focus on decreasing of work intensity

Sensorineural Hearing Loss in the Structure of Occupational Morbidity in Ukraine: the Problem of Disease Detection

The objective of the research was to compare Ukrainian statistics in occupational morbidity with data of other countries, to analyze the trend of the occupational hearing loss formation in Ukraine over a six-year period (2011 – 2016), to consider a modern state of sensorineural hearing loss detection and prophylaxis.Materials and methods. A comparative analysis of occupational morbidity in Ukraine and other counties within 2011-2016 years was based on the data obtained from the reports of the Social Insurance Fund of Ukraine, Statistical Collector, Eurostat, the International Labour Office, the Bureau of Labor Statistic, etc.Results. The difference in Ukrainian and international statistics in occupational morbidity can be explained by the diversity in the surveillance systems. The sharp decline in occupational morbidity in Ukraine within 2014-2016 is connected neither with the improvement of prophylactic measures nor with creating better work conditions. Sensorineural hearing loss has been ranked fourth in occupational morbidity accounting for 2.5%-4% of professional pathology and is underestimated.Conclusions. The underestimation of occupational hearing loss in Ukraine is determined by economic and organizational reasons, scarce diagnostics during medical examinations, peculiarities of the national surveillance system. A possible solution to this problem includes but is not limited to the reduction in countless pathologies caused by a high level of unreported employment, the establishment of unified sensorineural hearing loss classification, the increase in an accuracy of noise zone determination (noise-map construction), the performance of pure-tone audiometry in extended range (9 – 16 kHz)