Застосування методів градієнтного бустингу на прикладі задач аналізу тональності тексту

Назва кваліфікаційної роботи: " Застосування методів градієнтного бустингу на прикладі задач аналізу тональності тексту". Опис роботи: Робота складається з 42 сторінок, включає 13 рисунків, 4 таблиці та містить 19 джерел. Метою роботи є створення реалізації та дослідження побудованої моделі градієнтного бустингу для класифікації думок у тексті. Об'єктом дослідження тональність тексту та думки які він поширює. У ході дослідження була розроблена модель градієнтного бустингу для класифікації тональності повідомлень. В роботі представлений детальний аналіз передобробки даних, включаючи особливості даних та їх вплив на роботу моделі. Результати роботи представлені у вигляді графіків з аналізом отриманої моделі та її ефективності. Вказані напрямки можливого вдосконалення отриманих результатів.Title of the qualification work: "Application of Gradient Boosting Methods in Sentiment Analysis Tasks" Description of the work: The work consists of 42 pages, including 13 figures, 4 tables, and contains 19 references. The aim of the work is to develop and investigate the implemented model of gradient boosting for sentiment analysis in text. The object of the research is messages from the social media platform Twitter, while the subject of the research is the opinions expressed in these messages. During the research, a gradient boosting model for sentiment classification of messages was developed. The work presents a detailed analysis of data preprocessing, including the characteristics of the data and their impact on the model's performance. The results of the work are presented in the form of graphs with an analysis of the obtained model and its effectiveness. Furthermore, ideas for further improvement and enhancement of the model are provided

Development of a mathematical model to enable optimal efficiency of the indabuko lithium-ion battery.

Masters Degree. University of KwaZulu-Natal, Pietermaritzburg.Cathode materials are the foremost primary challenge for the vast scale application of lithium-ion batteries in electric vehicles and the stockpiles of power. Foreseeing the properties of cathode materials is one of the central issues in energy storage. In the recent past, density functional theory (DFT) calculations aimed at materials property predictions offered the best trade-off between computational cost and accuracy compared to experiments. However, these calculations are still excessive and costly, limiting the acceleration of new materials discovery. Now the results from different computational materials science codes are made available in databases, which permit quick inquiry and screening of various materials by their properties. Such gigantic materials databases allow a dominant data-driven methodology in materials discovery, which should quicken advancements in the field. This study was aimed at applying machine learning algorithms on existing computations to make precise predictions of physical properties. Thus, the dissertation primary goal was build best ML models that are capable of predicting DFT calculated properties such as, formation energy, energy band-gap and classify materials as stable or unstable based on their thermodynamic stability. It was established that the algorithms only require the chemical formula as input when predicting materials properties. The theoretical aspect of this work describes the current machine learning algorithms and presents "descriptors"-representations of materials in a dataset that plays a significant role in prediction accuracy. Also, the dissertation examined how various descriptors and algorithms influence learning model. The Catboost Regressor was found to be the best algorithm for determining all the properties that were selected in this study. Results indicated that with appropriate descriptors and ML algorithms it is feasible to foresee formation energy with coefficient of determination (R2) of 0.95, mean absolute error (MAE) of 0.11 eV and classify materials into stable and unstable with 86% of accuracy and area under the ROC Curve (AUC) of 89%. Lastly, we build a web application that allow users to predict material properties easily