KALMYK FOLK TALE IN A FOREIGN LANGUAGE TRANSLATION (ON THE EXAMPLE OF THE ANALYSIS OF ORIGINAL AND TRANSLATED TEXTS OF FAIRY TALE "AYU CHIKTE AND AVKHA TSETSEN")

Исследование посвящено сравнительному анализу оригинального и переводных текстов калмыцкой народной сказки «Аю Чикт Авха Цецен хойр» («Аю Чикте и Авха Цецен») из репертуара сказителя М. Буринова. В процессе сличения исходного текста сказки на калмыцком языке (1960) и русскоязычного перевода М. Г. Ватагина (1964) отмечается характер разночтений и неточностей, обнаруженных в иноязычном нарративе в передаче смысла отдельных эпизодов сюжета, формульных выражений, словосочетаний, играющих важную роль в сказочном повествовании. Изучение фольклорного текста в его разноязычных воплощениях представляется актуальным в свете проблем, возникающих при взаимодействии текстов дистантных культур. Для передачи национальной специфики сказочной традиции требуется максимальная точность при переводе, имеющим важное значение для понимания исконного смысла оригинального текста. The study is devoted to a comparative analysis of the original and translated texts of the Kalmyk folk tale "Ayu Chikt Avkha Tsetsn khoir" ("Ayu Chikte and Avkha Tsetsen") from the repertoire of the narrator M. Burinov. In the process of comparing the original text of the fairy tale in the Kalmyk language (1960) and the Russian translation by M. G. Vatagina (1964) notes the nature of the discrepancies and inaccuracies found in the foreign language narrative in the transfer of the meaning of individual episodes of the plot, formula expressions, word combinations), which play an important role in the fairy tale narration. The study of a folklore text in its multilingual embodiments is relevant in the light of the problems that arise within the interaction of texts of distant cultures. To convey the national specifics of the fairy - tale tradition, maximum accuracy is required when translating episodes, formulas and some words that are important for understanding the original meaning of an original text.

KALMYK FOLK TALE IN A FOREIGN LANGUAGE TRANSLATION (ON THE EXAMPLE OF THE ANALYSIS OF ORIGINAL AND TRANSLATED TEXTS OF FAIRY TALE "AYU CHIKTE AND AVKHA TSETSEN")

Исследование посвящено сравнительному анализу оригинального и переводных текстов калмыцкой народной сказки «Аю Чикт Авха Цецен хойр» («Аю Чикте и Авха Цецен») из репертуара сказителя М. Буринова. В процессе сличения исходного текста сказки на калмыцком языке (1960) и русскоязычного перевода М. Г. Ватагина (1964) отмечается характер разночтений и неточностей, обнаруженных в иноязычном нарративе в передаче смысла отдельных эпизодов сюжета, формульных выражений, словосочетаний, играющих важную роль в сказочном повествовании. Изучение фольклорного текста в его разноязычных воплощениях представляется актуальным в свете проблем, возникающих при взаимодействии текстов дистантных культур. Для передачи национальной специфики сказочной традиции требуется максимальная точность при переводе, имеющим важное значение для понимания исконного смысла оригинального текста. The study is devoted to a comparative analysis of the original and translated texts of the Kalmyk folk tale "Ayu Chikt Avkha Tsetsn khoir" ("Ayu Chikte and Avkha Tsetsen") from the repertoire of the narrator M. Burinov. In the process of comparing the original text of the fairy tale in the Kalmyk language (1960) and the Russian translation by M. G. Vatagina (1964) notes the nature of the discrepancies and inaccuracies found in the foreign language narrative in the transfer of the meaning of individual episodes of the plot, formula expressions, word combinations), which play an important role in the fairy tale narration. The study of a folklore text in its multilingual embodiments is relevant in the light of the problems that arise within the interaction of texts of distant cultures. To convey the national specifics of the fairy - tale tradition, maximum accuracy is required when translating episodes, formulas and some words that are important for understanding the original meaning of an original text.

Predict A Person's Personality Based On The Shape of Handwriting of The Letters "i", "o", and "t" Using The Levenberg Marquardt Backpropagation Method

Abstract-The literature shows that Graphology is common and relatively useful in our life. For example, as one of the job requirements. Professional organizations hire a professional handwriting analyst called Graphologist to analyze the characteristic traits of the candidates by identified their handwriting. However, the accuracy of handwriting analysis depends on how skilled the graphologist is, two graphologists which predict the same handwriting may give us a different result of the prediction. To improve the accuracy, we develop a system that can automatically predict a person’s personality based on the shape of the handwriting of the letters "i", "o", and "t" using the Levenberg Marquardt Backpropagation method. Based on this research we got the maximum accuracy by using 2 hidden layers. We got 71,42% of accuracy for the letter “i”, 76,92% of accuracy for the letter “o”, and 60% of accuracy for letter the “t”.

Lexicon Based Sentiment Analysis in Indonesia Languages : A Systematic Literature Review

This systematic literature review aims to determine the trend of lexicon based sentiment analysis research in Indonesian Language in the last two years. The focus of the study is on the understanding of preprocessing used in lexicon-based sentiment analysis studies in the last two years, the lexicon used in these studies, and classification accuracy. The main question in this SLR : what techniques of lexicon based sentiment analysis will provide the highest accuracy. The most widely used preprocessing methods in previous research are tokenization, case conversion, stemming, remove punctuation, remove stop word, remove or replace emoji and emoticons, and normalization or slangword conversion. The sentiment labeling process in previous studies calculated based on the comparison of the number of negative sentiment keywords with positive sentiment keywords in one sentence. The maximum accuracy from previous study is 90%. The most widely used lexicon is NRC and Inset which is a lexicon dictionary in Indonesian. Knowledge of this can be used to propose a better model for lexicon based sentiment analysis in Indonesian Languages.

Pro/Antioxidant State as a Potential Biomarker of Schizophrenia

To allow better diagnosis and management of psychiatric illnesses, the use of easily accessible biomarkers are proposed. Therefore, recognition of some diseases by a set of related pathogenesis biomarkers is a promising approach. The study aims to assess the usefulness of examining oxidative stress (OS) in schizophrenia as a potential biomarker of illness using the commonly used data mining decision tree method. The study group was comprised of 147 participants: 98 patients with schizophrenia (SZ group), and the control group (n = 49; HC). The patients with schizophrenia were divided into two groups: first-episode schizophrenia (n = 49; FS) and chronic schizophrenia (n = 49; CS). The assessment included the following biomarkers in sera of patients: catalase (CAT), glutathione peroxidase (GPx), superoxide dismutase-1 (SOD-1), glutathione reductase (GR), reduced glutathione (GSH), total antioxidant capacity (TAC), ferric reducing ability of plasma (FRAP), advanced glycation end products (AGEs), advanced oxidation protein products (AOPP), dityrosine (DITYR), kynurenine (KYN), N-formylkynurenine (NFK), tryptophan (TRY), total oxidant status (TOS), nitric oxide (NO) and total protein. Maximum accuracy (89.36%) for distinguishing SZ from HC was attained with TOS and GPx (cut-off points: 392.70 and 15.33). For differentiating between FS and CS, the most promising were KYN, AOPP, TAC and NO (100%; cut-off points: 721.20, 0.55, 64.76 and 2.59). To distinguish FS from HC, maximum accuracy was found for GSH and TOS (100%; cut-off points: 859.96 and 0.31), and in order to distinguish CS from HC, the most promising were GSH and TOS (100%; cut-off points: 0.26 and 343.28). Using redox biomarkers would be the most promising approach for discriminating patients with schizophrenia from healthy individuals and, in the future, could be used as an add-on marker to diagnose and/or respond to treatment.

Investigation of robustness of hybrid artificial neural network with artificial bee colony and firefly algorithm in predicting COVID-19 new cases: case study of Iran

As an ongoing public health menace, the novel coronavirus pandemic has challenged the world. With several mutations and a high transmission rate, the virus is able to infect individuals in an exponential manner. At the same time, Iran is confronted with multiple wave peaks and the health care system is facing a major challenge. In consequence, developing a robust forecasting methodology can assist health authorities for effective planning. In that regard, with the help of Artificial Neural Network-Artificial Bee Colony (ANN-ABC) and Artificial Neural Network- Firefly Algorithm (ANN-FA) as two robust hybrid artificial intelligence-based models, the current study intends to select the optimal model with the maximum accuracy rate. To do so, first a sample of COVID-19 confirmed cases in Iran ranging from 19 February 2020 to 25 July 2021 is compiled. 75% (25%) of total observation is randomly allocated as training (testing) data. Afterwards, an ANN models is trained with Levenberg Marquardt algorithm. Accordingly, based on R-squared and root-mean-square error criteria, the optimal number of hidden neurons is computed as 17. The proposed ANN model is employed to develop ANN-ABC and ANN-FA models for achieving the maximum accuracy rate. According to ANN-ABC, the R- squared values of the optimal model are 0.9884 and 0.9885 at train and test stages, correspondingly. In respect to ANN-FA, for the selected model, the R-squared ranged from 0.9954 to 0.9940 at the train and test phases, respectively. The results indicated that both hybrid ANN-ABC and ANN-FA are the robust predictor of COVID-19 new cases in Iran. Additionally, with a slight difference, the ANN-FA model outperformed ANN-ABC algorithm.

Chest Diseases Detection based on Deep Learning

The paper is on our project that is all about detecting 14 different types of chest diseases using x-ray images of chest. It also neglects the apparels and jewelry’s present on human body while performing the x-ray test thus giving us maximum accuracy of diseases detection. The key goal of project is to know the percentage of diseases detection of all the 14 different tests performed on human chest with maximum accuracy. Chest x-ray imaging is a vital screening and medicine tool for many life threating diseases, however because of shortage of radiologists, the screening tool cannot be wont to treat all patients. Deep learning based mostly medical image classifiers are one potential answer. This project runs, uploads, method and generates reports at any given purpose of your time with accuracy.

Data Operations and an Application for Translating Russian Speech to French Text

In 2006, the French government discretely asked for an assessment of the highest accuracy means available at the time to translate Russian speech into French text. One of us was working with the Grenoble HP site at the time, and so promptly assessed the possibilities using existing speech-to-text and translation software (Nuance and Speechworks). This article describes the surprisingly circuitous route to maximum accuracy (90.3%), and in so doing provides an unexpected insight into discerning the native language of software designed for speech-to-text and translation applications.

Creating Ensemble Classifiers with Information Entropy Diversity Measure

Ensemble classifiers improve the classification accuracy by incorporating the decisions made by its component classifiers. Basically, there are two steps to create an ensemble classifier: one is to generate base classifiers and the other is to align the base classifiers to achieve maximum accuracy integrally. One of the major problems in creating ensemble classifiers is the classification accuracy and diversity of the component classifiers. In this paper, we propose an ensemble classifier generating algorithm to improve the accuracy of an ensemble classification and to maximize the diversity of its component classifiers. In this algorithm, information entropy is introduced to measure the diversity of component classifiers, and a cyclic iterative optimization selection tactic is applied to select component classifiers from base classifiers, in which the number of component classifiers is dynamically adjusted to minimize system cost. It is demonstrated that our method has an obvious lower memory cost with higher classification accuracy compared with existing classifier methods.

Machine Learning Assisted Prediction of Prognostic Biomarkers Associated With COVID-19, Using Clinical and Proteomics Data

With the availability of COVID-19-related clinical data, healthcare researchers can now explore the potential of computational technologies such as artificial intelligence (AI) and machine learning (ML) to discover biomarkers for accurate detection, early diagnosis, and prognosis for the management of COVID-19. However, the identification of biomarkers associated with survival and deaths remains a major challenge for early prognosis. In the present study, we have evaluated and developed AI-based prediction algorithms for predicting a COVID-19 patient’s survival or death based on a publicly available dataset consisting of clinical parameters and protein profile data of hospital-admitted COVID-19 patients. The best classification model based on clinical parameters achieved a maximum accuracy of 89.47% for predicting survival or death of COVID-19 patients, with a sensitivity and specificity of 85.71 and 92.45%, respectively. The classification model based on normalized protein expression values of 45 proteins achieved a maximum accuracy of 89.01% for predicting the survival or death, with a sensitivity and specificity of 92.68 and 86%, respectively. Interestingly, we identified 9 clinical and 45 protein-based putative biomarkers associated with the survival/death of COVID-19 patients. Based on our findings, few clinical features and proteins correlate significantly with the literature and reaffirm their role in the COVID-19 disease progression at the molecular level. The machine learning–based models developed in the present study have the potential to predict the survival chances of COVID-19 positive patients in the early stages of the disease or at the time of hospitalization. However, this has to be verified on a larger cohort of patients before it can be put to actual clinical practice. We have also developed a webserver CovidPrognosis, where clinical information can be uploaded to predict the survival chances of a COVID-19 patient. The webserver is available at http://14.139.62.220/covidprognosis/.