LEXICON-BASED APPROACH IN GENERALIZATION EVALUATION IN RUSSIAN LANGUAGE MEDIA

We consider generalization as a property of human thinking to make general conclusion based on authors’ own experience and observations and one of the techniques of authors use to manipulate the readership and present an algorithm for evaluation of the generalization in texts. The algorithm is based on the lexicon-based approach. To search the generalization we use ready-made dictionary (KEY-dictionary) and RuSentiLex dictionary. KEY-dictionary contains words and phrases (elements) that express the generalization. In RuSentiLex we take the words and phrases that express opinion and fact. The algorithm searches exact matches the elements from text with the elements from the dictionaries, it is also important that the elements from different dictionaries have their weights. New method is developed for automatic detection of generalization in texts from official media. Numerical calculations of generalization were performed using a special software application. To test the proposed approach the expert estimation were used

Automatic speech segmentation using throat-acoustic correlation coefficients

This work considers one of the approaches to the solution of the task of discrete speech signal automatic segmentation. The aim of this work is to construct such an algorithm which should meet the following requirements: segmentation of a signal into acoustically homogeneous segments, high accuracy and segmentation speed, unambiguity and reproducibility of segmentation results, lack of necessity of preliminary training with the use of a special set consisting of manually segmented signals. Development of the algorithm which corresponds to the given requirements was conditioned by the necessity of formation of automatically segmented speech databases that have a large volume. One of the new approaches to the solution of this task is viewed in this article. For this purpose we use the new type of informative features named TAC-coefficients (Throat-Acoustic Correlation coefficients) which provide sufficient segmentation accuracy and effi- ciency

Using machine learning algorithm for diagnosis of stomach disorders

Medicine is one of the rich sources of data, generating and storing massive data, begin from description of clinical symptoms and end by different types of biochemical data and images from devices. Manual search and detecting biomedical patterns is complicated task from massive data. Data mining can improve the process of detecting patterns. Stomach disorders are the most common disorders that affect over 60% of the human population. In this work, the classification performance of four non-linear supervised learning algorithms i.e. Logit, K-Nearest Neighbour, XGBoost and LightGBM for five types of stomach disorders are compared and discussed. The objectives of this research are to find trends of using or improvements of machine learning algorithms for detecting symptoms of stomach disorders, to research problems of using machine learning algorithms for detecting stomach disorders. Bayesian optimization is considered to find optimal hyperparameters in the algorithms, which is faster than the grid search method. Results of the research show algorithms that base on gradient boosting technique (XGBoost and LightGBM) gets better accuracy more 95% on the test dataset. For diagnostic and confirmation of diseases need to improve accuracy, in the article, we propose to use optimization methods for accuracy improvement with using machine learning algorithms