Arcada University of Applied Sciences

About: Arcada University of Applied Sciences is a education organization based out in Helsinki, Finland. It is known for research contribution in the topics: Extreme learning machine & Artificial neural network. The organization has 107 authors who have published 306 publications receiving 3397 citations.

High-Performance Extreme Learning Machines: A Complete Toolbox for Big Data Applications

Abstract: This paper presents a complete approach to a successful utilization of a high-performance extreme learning machines (ELMs) Toolbox for Big Data. It summarizes recent advantages in algorithmic performance; gives a fresh view on the ELM solution in relation to the traditional linear algebraic performance; and reaps the latest software and hardware performance achievements. The results are applicable to a wide range of machine learning problems and thus provide a solid ground for tackling numerous Big Data challenges. The included toolbox is targeted at enabling the full potential of ELMs to the widest range of users.

Predicting distress in European banks

Abstract: The paper develops an early-warning model for predicting vulnerabilities leading to distress in European banks using both bank and country-level data. As outright bank failures have been rare in Europe, the paper introduces a novel dataset that complements bankruptcies and defaults with state interventions and mergers in distress. The signals of the early-warning model are calibrated not only according to the policymaker’s preferences between type I and II errors, but also to take into account the potential systemic relevance of each individual financial institution. The key findings of the paper are that complementing bank-specific vulnerabilities with indicators for macro-financial imbalances and banking sector vulnerabilities improves model performance and yields useful out-of-sample predictions of bank distress during the current financial crisis.

Applying configurational analysis to IS behavioural research: a methodological alternative for modelling combinatorial complexities

Abstract: An important limitation of regression-based analysis stems from the assumption of symmetric relationships between variables, which is often violated. To overcome this limitation within IS research, we propose the use of the fuzzy-set qualitative comparative analysis FsQCA method. The paper elaborates on the rationale for applying this approach to IS behavioural research and how to tailor FsQCA for this purpose. A systematic interpretation of the technique covering its mathematical properties and advanced features is provided. Drawing from an illustrative study of mobile government services adoption by residents of rural areas, the paper demonstrates FsQCA's potential to supplement regression-based IS behavioural research, by i examining asymmetric relationships between a set of antecedents and the IS phenomenon of interest, ii providing nuanced coverage of necessary and sufficient conditions for emergence of an IS behavioural outcome, and iii identifying various configurations of conditions in association with users' demographic characteristics. © 2015 Blackwell Publishing Ltd

The Natural History of Idiopathic Frozen Shoulder: A 2- to 27-year Followup Study

Abstract: Background The natural history of spontaneous idiopathic frozen shoulder is controversial. Many studies claim that complete resolution is not inevitable. Based on the 40-year clinical experience of the senior author, we believed most patients with idiopathic frozen shoulder might have a higher rate of resolution than earlier thought.