Search results for "hinta"
showing 10 items of 114 documents
Forecasting the volatility of biofuel feedstock prices: the US evidence
2019
Given that, nowadays, 40% of the US corn crop is used for biofuel production, there is a growing concern that the rise in biofuel production might lead to an increase in food prices. However, it is also obvious that significant growth in biofuel use has minimized the demand for fossil fuel and has hence reduced the volume of carbon emissions. It is therefore crucial to model corn market volatility precisely because such an estimate could play a vital role in stabilizing food and biofuel market prices. For this purpose, we consider using the information content of the corn implied volatility (CIV) index to predict the corn futures market return volatility. Using symmetric and asymmetric GARC…
Automatic Profiling of Open-Ended Survey Data on Medical Workplace Teaching
2019
On-the-job medical training is known to be challenging due to the fast-paced environment and strong vocational profile. It relies on on-site supervisors, mainly doctors and nurses with long practical experience, who coach and teach their less experienced colleagues, such as residents and healthcare students. These supervisors receive pedagogical training to ensure that their guidance and teaching skills are constantly improved. The aim of such training is to develop participants’ patient, collegiate and student guidance skills in a multiprofessional environment, and to expand their understanding of guidance as part of their work as supervisors of healthcare professionals. In this paper, we …
Extracting locations from sport and exercise-related social media messages using a neural network-based bilingual toponym recognition model
2022
Funding: This study is a part of the “Equality in suburban physical activity environments, YLLI” research project (in Finnish: Yhdenvertainen liikunnallinen lähiö, YLLI). The project is being financed by the research program about suburban in Finland “Lähiöohjelma 2020-2022” coordinated by the Ministry of Environment (grant recipient: Dr. Petteri Muukkonen). Sport and exercise contribute to health and well-being in cities. While previous research has mainly focused on activities at specific locations such as sport facilities, “informal sport” that occur at arbitrary locations across the city have been largely neglected. Such activities are more challenging to observe, but this challenge may…
3D Matrix-Based Visualization System of Association Rules
2017
With the growing number of mining datasets, it becomes increasingly difficult to explore interesting rules because of the large number of resultant and its nature complexity. Studies on human perception and intuition show that graphical representation could be a better illustration of how to seek information from the data using the capabilities of human visual system. In this work, we present and implement a 3D matrix-based approach visualization system of association rules. The main visual representation applies the extended matrix-based approach with rule-to-items mapping to general transaction data set. A novel method merging rules and assigning weight is proposed in order to reduce the …
Predicting hospital associated disability from imbalanced data using supervised learning.
2019
Hospitalization of elderly patients can lead to serious adverse effects on their functional capability. Identifying the underlying factors leading to such adverse effects is an active area of medical research. The purpose of the current paper is to show the potential of artificial intelligence in the form of machine learning to complement the existing medical research. This is accomplished by studying the outcome of hospitalization of elderly patients as a supervised learning task. A rich set of features characterizing the medical and social situation of elderly patients is leveraged and using confusion matrices, association rule mining, and two different classes of supervised learning algo…
The Datafication of Hate: Expectations and Challenges in Automated Hate Speech Monitoring.
2020
Laaksonen, S-M.; Haapoja, J.; Kinnunen, T., Nelimarkka, M. & Pöyhtäri, R. (2020, accepted). . Frontiers in Big Data: Data Mining and Management / Critical Data and Algorithm Studies. doi:10.3389/fdata.2020.00003 Hate speech has been identified as a pressing problem in society and several automated approaches have been designed to detect and prevent it. This paper reports and reflects upon an action research setting consisting of multi-organizational collaboration conducted during Finnish municipal elections in 2017, wherein a technical infrastructure was designed to automatically monitor candidates' social media updates for hate speech. The setting allowed us to engage in a 2-fold investiga…
Combining conjunctive rule extraction with diffusion maps for network intrusion detection
2013
Network security and intrusion detection are important in the modern world where communication happens via information networks. Traditional signature-based intrusion detection methods cannot find previously unknown attacks. On the other hand, algorithms used for anomaly detection often have black box qualities that are difficult to understand for people who are not algorithm experts. Rule extraction methods create interpretable rule sets that act as classifiers. They have mostly been combined with already labeled data sets. This paper aims to combine unsupervised anomaly detection with rule extraction techniques to create an online anomaly detection framework. Unsupervised anomaly detectio…
Establishing Video Game Genres Using Data-Driven Modeling and Product Databases
2015
Establishing genres is the first step toward analyzing games and how the genre landscape evolves over the years. We use data-driven modeling that distils genres from textual descriptions of a large collection of games. We analyze the evolution of game genres from 1979 till 2010. Our results indicate that until 1990, there have been many genres competing for dominance, but thereafter sport-racing, strategy, and action have become the most prevalent genres. Moreover, we find that games vary to a great extent as to whether they belong mostly to one genre or to a combination of several genres. We also compare the results of our data-driven model with two product databases, Metacritic and Mobyga…
Data-driven decision support to reduce "driving-under the influence of alcohol" offenses
2018
Extracting valuable knowledge from data to support decision making is a widely practiced trend. Data-driven decision support (DDDS) provides insight for decision makers by exploring and extracting underlying patterns within a dataset. This thesis covers the process of DDDS in reducing driving under the influence of alcohol (DUI) offenses by introducing proposed prison sentences. In this thesis, DDDS is applied to a DUI dataset by analyzing patterns in the dataset and by introducing proposed prison sentences for offenders to reduce the number of DUI cases. Background theories in data mining, machine learning, optimization and decision science that are related to the thesis project are also c…
Do commodity assets hedge uncertainties? What we learn from the recent turbulence period?
2022
AbstractThis study analyses the impact of different uncertainties on commodity markets to assess commodity markets' hedging or safe-haven properties. Using time-varying dynamic conditional correlation and wavelet-based Quantile-on-Quantile regression models, our findings show that, both before and during the COVID-19 crisis, soybeans and clean energy stocks offer strong safe-haven opportunities against cryptocurrency price uncertainty and geopolitical risks (GPR). Soybean markets weakly hedge cryptocurrency policy uncertainty, US economic policy uncertainty, and crude oil volatility. In addition, GSCI commodity and crude oil also offer a weak safe-haven property against cryptocurrency uncer…