Sirvi Autor "Koljal, Kaire" järgi

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Apache MiNiFi efektiivsus servandmetöötluse raamistikuna
(Tartu Ülikool, 2020) Koljal, Kaire; Jakovits, Pelle, juhendaja; Tartu Ülikool. Loodus- ja täppisteaduste valdkond; Tartu Ülikool. Arvutiteaduse instituut
With the development of IoT and the growth of data collected by IoT devices, sending all the collected data straight to cloud is not always the best option. Unfortunately, not all IoT devices have enough resources to process all the data. The goal of this thesis is to evaluate if using Apache MiNiFi on resource constrained devices like Raspberry Pi has any benefits compared to sending all the data to cloud and if using such frameworks on edge devices is useful. The goal of the comparison is to assess the amount of Raspberry’s resources used and the amount of data processed in both scenarios. In addition, the difference in latency from edge device to Apache Spark will be compared.
Predicting Depression Symptoms Based on Reddit Posts
(Tartu Ülikool, 2022) Koljal, Kaire; Sirts, Kairit, juhendaja; Tartu Ülikool. Loodus- ja täppisteaduste valdkond; Tartu Ülikool. Arvutiteaduse instituut
Using social media posts to predict mental health problems has become a popular topic in Natural Language Processing (NLP). Machine learning has been used for detecting a diagnosis or single symptoms associated with depression. As the clinical picture of depression can differ for people, it is better to detect symptoms instead of diagnosis from the social media posts. In this work, depression symptoms are predicted based on posts from Reddit page r/depression using NLP methods and multi-label classification. This work focuses on evaluating the quality of the annotations and analysing if such data can be used to train a predictive model. Each post is annotated by three annotators and the labels are aggregated in three ways to create three datasets that are used to train Transformers models. The results of this work reveal that on a small dataset with a lower annotation agreement, a majority vote over annotations gives the most reliable dataset and results. RoBERTa model shows the best learning and generalization ability in this work.