Search results for "koneoppiminen"

Comparison of feature importance measures as explanations for classification models

2021

AbstractExplainable artificial intelligence is an emerging research direction helping the user or developer of machine learning models understand why models behave the way they do. The most popular explanation technique is feature importance. However, there are several different approaches how feature importances are being measured, most notably global and local. In this study we compare different feature importance measures using both linear (logistic regression with L1 penalization) and non-linear (random forest) methods and local interpretable model-agnostic explanations on top of them. These methods are applied to two datasets from the medical domain, the openly available breast cancer …

The Riverine Organism Drift Imager: A new technology to study organism drift in rivers and streams

2023

1. Drift or downstream dispersal is a fundamental process in the life cycle of many riverine organisms. In the face of rapidly declining freshwater biodiversity, there is a need to enhance our capacity to study the drift of riverine organisms, by overcoming the limitations of traditional labour-intensive sampling methods that result in data of low temporal and spatial resolution. 2. To address this need, we developed a new technology, the Riverine Organism Drift Imager (RODI), which combines in situ imaging with machine-learning classification. This technique expands on the traditional methodology by replacing the collection cup of a drift net with a camera system that continuously images r…

Euclid preparation : XIII. Forecasts for galaxy morphology with the Euclid Survey using deep generative models

2022

We present a machine learning framework to simulate realistic galaxies for the Euclid Survey, producing more complex and realistic galaxies than the analytical simulations currently used in Euclid. The proposed method combines a control on galaxy shape parameters offered by analytic models with realistic surface brightness distributions learned from real Hubble Space Telescope observations by deep generative models. We simulate a galaxy field of 0.4 deg2 as it will be seen by the Euclid visible imager VIS, and we show that galaxy structural parameters are recovered to an accuracy similar to that for pure analytic Sérsic profiles. Based on these simulations, we estimate that the Euclid Wide …

Euclid preparation : XXII. Selection of quiescent galaxies from mock photometry using machine learning

2023

The Euclid Space Telescope will provide deep imaging at optical and near-infrared wavelengths, along with slitless near-infrared spectroscopy, across ~15 000deg2 of the sky. Euclid is expected to detect ~12 billion astronomical sources, facilitating new insights into cosmology, galaxy evolution, and various other topics. In order to optimally exploit the expected very large dataset, appropriate methods and software tools need to be developed. Here we present a novel machine-learning-based methodology for the selection of quiescent galaxies using broadband Euclid IE, YE, JE, and HE photometry, in combination with multi-wavelength photometry from other large surveys (e.g. the Rubin LSST). The…

On data mining applications in mobile networking and network security

2014

Description of movement sensor dataset for dog behavior classification

2022

The description and results of the original investigation are found in: Dog behaviour classification with movement sensors placed on the harness and the collar, Kumpulainen, P., Valldeoriola Cardó, A., Somppi, S., Törnqvist, H., Väätäjä, H., Majaranta, P., Gizatdinova, Y., Antink, C. H., Surakka, V., V. Kujala, M., Vainio, O. & Vehkaoja, A., Aug 2021, In: Applied Animal Behaviour Science. 241, 7 p., 105393. Movement sensor data from seven static and dynamic dog behaviors (sitting, standing, lying down, trotting, walking, playing, and (treat) searching i.e. sniffing) was collected from 45 middle to large sized dogs with six degree-of-freedom movement sensors attached to the collar and the ha…

Automatic social distance estimation for photographic studies: Performance evaluation, test benchmark, and algorithm

2022

The social distancing regulations introduced to slow down the spread of COVID-19 virus directly affect a basic form of non-verbal communication, and there may be longer term impacts on human behavior and culture that remain to be analyzed in proxemics studies. To obtain quantitative results for such studies, large media and/or personal photo collections must be analyzed. Several social distance monitoring methods have been proposed for safety purposes, but they are not directly applicable to general photo collections with large variations in the imaging setup. In such studies, the interest shifts from safety to analyzing subtle differences in social distances. Currently, there is no suitabl…

Zero-shot Semantic Segmentation using Relation Network

2021

Zero-shot learning (ZSL) is widely studied in recent years to solve the problem of lacking annotations. Currently, most studies on ZSL are for image classification and object detection. But, zero-shot semantic segmentation, pixel level classification, is still at its early stage. Therefore, this work proposes to extend a zero-shot image classification model, Relation Network (RN), to semantic segmentation tasks. We modified the structure of RN based on other state-of-the-arts semantic segmentation models (i.e. U-Net and DeepLab) and utilizes word embeddings from Caltech-UCSD Birds 200-2011 attributes and natural language processing models (i.e. word2vec and fastText). Because meta-learning …

Generative adversarial networks with bio-inspired primary visual cortex for Industry 4.0

2022

Biologicalization (biological transformation) is an emerging trend in Industry 4.0 affecting digitization of manufacturing and related processes. It brings up the next generation of manufacturing technology and systems that extensively use biological and bio-inspired principles, materials, functions, structures and resources. This research is a contribution to the further convergence of computer and human vision for more robust and accurate automated object recognition and image generation. We present VOneGANs, a novel class of generative adversarial networks (GANs) with the qualitatively updated discriminative component. The new model incorporates a biologically constrained digital primary…

Algorithmic issues in computational intelligence optimization: from design to implementation, from implementation to design

2016

The vertiginous technological growth of the last decades has generated a variety of powerful and complex systems. By embedding within modern hardware devices sophisticated software, they allow the solution of complicated tasks. As side effect, the availability of these heterogeneous technologies results into new difficult optimization problems to be faced by researchers in the field. In order to overcome the most common algorithmic issues, occurring in such a variety of possible scenarios, this research has gone through cherry-picked case-studies. A first research study moved from implementation to design considerations. Implementation limitations, such as memory constraints and real-time r…