The thesis two-folded problem aim was to identify and evaluate candidate Machine Learning (ML) methods and performance methods, for predicting the weekly number of covid-19 infections. The two-folded problem aim was created from studying public health studies where several challenges were identified. One challenge identified was the lack of using sophisticated and hybrid ML methods in the public health research area. In this thesis a comparison of ML methods for predicting the number of covid-19 weekly infections has been performed. A dataset taken from the Public Health Agency in Sweden consisting of 101weeks divided into a 60 % training set and a 40% testing set was used in the evaluation. Five candidate ML methods have been investigated in this thesis called Support Vector Regressor (SVR), Long Short Term Memory (LSTM), Gated Recurrent Network (GRU), Bidirectional-LSTM (BI-LSTM) and LSTM-Convolutional Neural Network (LSTM-CNN). These methods have been evaluated based on three performance measurements called Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and R2. The evaluation of these candidate ML resulted in the LSTM-CNN model performing the best on RMSE, MAE and R2.

В работе была произведена разработка модели машинного обучения на языке программирования Python, а также проведена ее валидация на этапах жизненного цикла. Целью создания модели машинного обучения является прогнозирование магнитных свойств нанокристаллических сплавов на основе железа по химическому составу и условиям обработки. Процесс валидации модели машинного обучения позволяет не только произвести контроль за соблюдением требований, предъявляемых при разработке и эксплуатации модели, к результатам, полученных с помощью моделирования, но и способствует внедрению модели в процесс производства. Процесс валидации включал в себя валидацию данных, в ходе которой были оценены типы, пропуски данных, соответствие цели исследования, распределения признаков и целевых характеристик, изучены корреляции признаков и целевых характеристик; валидацию алгоритмов, применяемых в модели: были проанализированы параметры алгоритмов с целью соблюдения требования о корректной обобщающей способности модели (отсутствие недо- и переобучения); оценку работы модели, благодаря которой был произведен анализ полученных результатов с помощью тестовых данных; верификацию результатов с помощью актуальных данных, полученных из статей, опубликованных с 2010 по 2022 год. В результате валидации модели было показано высокое качество разработанной модели, позволяющее получить оценки качества R2 0,65 и выше. / In this work machine learning model was developed by Python programming language, and also was validated at stages of model’s life cycle. The purpose of creating the machine learning model is to predict the magnetic properties of Fe-based nanocrystalline alloys by chemical composition and processing conditions. The validation of machine learning models allows not only to control the requirements for development and operation of the models, for the results obtained by modeling, but also contrib¬utes to the introduction of the model into production process. The validation process included: data validation: data types and omissions, compliance with the purpose of the study, dis¬tribution of features and target characteristics were evaluated, correlations of features and target characteristics were studied; flgorithms validation: the parameters of the algorithms were analyzed in order to comply with the requirement for the correct generalizing ability of the model (without under- and overfit¬ting); evaluation of the model work: the analysis of the obtained results was carried out using test data; verification of results using actual data obtained from articles published since 2010 to 2022. As a result of the model validation, the high quality of the developed model was shown, which makes it possible to obtain quality metric R2 0.65 and higher.