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Reinforcement Learning for Financial Portfolio Management: A study of Neural Networks for Reinforcement Learning on currency exchange market
(2021)
Portfolio management is the process of continually reallocating funds into financial instruments, aiming to maximize the return. This paper presents a Reinforcement Learning framework where an agent interacts with the ...
Data-Driven Modelling of Gas Turbine Engines
(2021)
This study investigates and compares linear and nonlinear data-driven models of a gas turbine engine. These linear models consist of Ridge, Lasso, and Multi-Task Elastic-Net models, which are set up based on linear ...
Predict patient deterioration in hospital's general ward
(2020)
Increasing amount of patient monitoring data is available in hospitals in an electronic format. Patient data is mostly available from hospital departments which provide intensive treatment, but wireless and wearable sensors ...
Topic Modeling of StormFront Forum Posts
(2021)
The research of radical communities is crucial for preventing violent actions and affecting the community to avoid further radicalisation. In this thesis, we propose a way to analyse semantic topics which were assessed on ...
Scheduling of preventive maintenance using prognostic models - A case study on elevator doors
(2020)
Aim of this thesis is to research how maintenance process of elevator doors can be optimised. To this end, a business goal is set. It is to decrease the amount of unplanned maintenance visits caused by door malfunctions. ...
Bid Shading In First-Price Real-Time Bidding Auctions
(2019)
Online advertisements can be bought through a mechanism called real-time bidding (RTB). In RTB the ads are auctioned in real time on every page load. The ad auctions can be second-price or first-price auctions. In ...
Click-through Rate Prediction In Practice: A study of a click-through rate prediction system
(2019)
Digital advertising is a huge business with tough competition. One of the ways to be more effective in the business is to serve better chosen ads to each user. One way to improve the ad selection is to predict the ...
Large-scale Deep Learning by Distributed Training
(2019)
This thesis is done as part of a service development task of distributed deep learning on the CSC provided infrastructure. The aim is to improve the readiness to provide a service for AI researchers who wish to scale out ...
Predicting fine particulate matter levels in Finnish buildings
(2019)
Fine particulate matter (PM 2.5 ) is considered one of the most harmful air pollutants. While
a large proportion of the particles is originating from outdoor sources, people are mostly
exposed while indoors. Predicting ...
Unsupervised Machine Learning Anomaly Detection for Multivariate Time-Series Data in Wind Turbine Converters
(2020)
Because wind power is one the main clean energy sources, the demand for wind generated energy has been rapidly increasing all over the world. As wind turbine converter is one of the key components in wind turbine, it is ...