Browsing by Author "Ketonen, Vili"

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Anomaly Detection for Injection Molding Machines Using Probabilistic Deep Learning
(2021-01-25) Ketonen, Vili
Perustieteiden korkeakoulu | Master's thesis
In manufacturing industries, monitoring the complicated devices often necessitates automated methods that can leverage the multivariate time series data produced by the machines. However, analyzing this data can be challenging due to varying noise levels in the data and possible nonlinear relations between the process variables, requiring appropriate tools to deal with such properties. This thesis proposes a deep learning-based approach to detect anomalies and interpret their root causes from multivariate time series data, which can be applied in a near real-time setting. The proposed approach extends an existing model from the literature, which employs a variational autoencoder architecture and recurrent neural networks to capture both stochasticity and temporal relations of the data. The anomaly detection and root cause interpretation performance of the proposed method is compared against five baseline algorithms previously proposed in the literature using real-world data collected from plastic injection molding machines and artificially generated multivariate time series data. The results of this thesis show that the proposed method performs well on the evaluated multivariate time series datasets, mostly outperforming the baseline methods. Additionally, the approach had the best performance among the selected methods on providing root cause interpretation of the detected anomalies. The experiments conducted in this thesis suggest that deep learning-based algorithms are beneficial for anomaly detection in scenarios where the problem is too complicated for traditional methods, and enough training data is available. However, the amount of real-world injection molding machine data used in the experiments is relatively small, and therefore further experiments should be performed with larger datasets to obtain more generalizable results.
Characterizing vaping posts on instagram by using unsupervised machine learning
(2020-09) Ketonen, Vili; Malik, Aqdas
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Electronic cigarettes (e-cigarettes) usage has surged substantially across the globe, particularly among adolescents and young adults. The ever-increasing prevalence of social media makes it highly convenient to access and engage with content on numerous substances, including e-cigarettes. A comprehensive dataset of 560,414 image posts with a mention of #vaping (shared from 1 June 2019 to 31 October 2019) was retrieved by using the Instagram application-programming interface. Deep neural networks were used to extract image features on which unsupervised machine-learning methods were leveraged to cluster and subsequently categorize the images. Descriptive analysis of associated metadata was further conducted to assess the influence of different entities and the use of hashtags within different categories. Seven distinct categories of vaping related images were identified. A majority of the images (40.4 %) depicted e-liquids, followed by e-cigarettes (15.4 %). Around one-tenth (9.9 %) of the dataset consisted of photos with person(s). Considering the number of likes and comments, images portraying person(s) gained the highest engagement. In almost every category, business accounts shared more posts on average compared to the individual accounts. The findings illustrate the high degree of e-cigarettes promotion on a social platform prevalent among youth. Regulatory authorities should enforce policies to restrict product promotion in youth-targeted social media, as well as require measures to prevent underage users' access to this content. Furthermore, a stronger presence of anti-tobacco portrayals on Instagram by public health agencies and anti-tobacco campaigners is needed.
Detecting Fake News in Finland by Exploiting the Social Context on Facebook
(2018-04-25) Ketonen, Vili
Perustieteiden korkeakoulu | Bachelor's thesis