Search results for "luokitus"
showing 10 items of 48 documents
Separation of track- and shower-like energy deposits in ProtoDUNE-SP using a convolutional neural network
2022
Liquid argon time projection chamber detector technology provides high spatial and calorimetric resolutions on the charged particles traversing liquid argon. As a result, the technology has been used in a number of recent neutrino experiments, and is the technology of choice for the Deep Underground Neutrino Experiment (DUNE). In order to perform high precision measurements of neutrinos in the detector, final state particles need to be effectively identified, and their energy accurately reconstructed. This article proposes an algorithm based on a convolutional neural network to perform the classification of energy deposits and reconstructed particles as track-like or arising from electromag…
Neutrino interaction classification with a convolutional neural network in the DUNE far detector
2020
The Deep Underground Neutrino Experiment is a next-generation neutrino oscillation experiment that aims to measure CP-violation in the neutrino sector as part of a wider physics program. A deep learning approach based on a convolutional neural network has been developed to provide highly efficient and pure selections of electron neutrino and muon neutrino charged-current interactions. The electron neutrino (antineutrino) selection efficiency peaks at 90% (94%) and exceeds 85% (90%) for reconstructed neutrino energies between 2–5 GeV. The muon neutrino (antineutrino) event selection is found to have a maximum efficiency of 96% (97%) and exceeds 90% (95%) efficiency for reconstructed neutrino…
Semantics of Voids within Data: Ignorance-Aware Machine Learning
2021
Operating with ignorance is an important concern of geographical information science when the objective is to discover knowledge from the imperfect spatial data. Data mining (driven by knowledge discovery tools) is about processing available (observed, known, and understood) samples of data aiming to build a model (e.g., a classifier) to handle data samples that are not yet observed, known, or understood. These tools traditionally take semantically labeled samples of the available data (known facts) as an input for learning. We want to challenge the indispensability of this approach, and we suggest considering the things the other way around. What if the task would be as follows: how to buil…
Classifying economics for the common good : a note on the links between sustainable development goals and JEL codes
2021
Purpose This brief note sheds light on the links between Journal of Economic Literature (JEL) codes and the Sustainable Development Goals (SDGs) of the United Nations. Design/methodology/approach Three alternative methods based on keyword overlap to establish links between SDGs and JEL codes are presented. Findings These simple linkages illustrate that the themes of SDGs have corresponding closely related JEL classification codes. Research limitations/implications The mappings presented in this note are based on simple keyword overlap and are therefore necessarily imperfect and incomplete. There is plenty of room for further development. Practical implications Despite the demonstrated possi…
Valtion luottokelpoisuusarvion heikentymisen laukaisevat tekijät
2015
Valtioiden luottoluokitukset ovat nousseet kiinnostusta herättäväksi puheenaiheeksi 2000-luvun finanssikriisin myötä. Aiempaa tutkimusta siitä, mitkä tekijät saavat aikaan luokituksen muutoksen, on olemassa hyvin niukalti. Tässä Pro gradu -työssä pyritään löytämään vastaus kysymykseen: Mikä saa valtion luottoluokituksen heikkenemään? Empiirisessä osiossa analysoidaan probit regressio -mallin avulla, mitkä tekijät selittävät luottoluokituksen heikkenemistä. Aineistona on käytetty euro-alueen maiden kansantaloudellisia muuttujia vuosilta 2002 – 2013. Selittäviksi muuttujiksi löydettiin bruttokansantuotteen ja valtion budjetin alijäämän muutos. Empiiristen tulosten perusteella valtiontalouden …
Multilayer perceptron training with multiobjective memetic optimization
2016
Machine learning tasks usually come with several mutually conflicting objectives. One example is the simplicity of the learning device contrasted with the accuracy of its performance after learning. Another common example is the trade-off that must often be made between the rate of false positive and false negative predictions in diagnostic applications. For computer programs that learn from data, these objectives are formulated as mathematical functions, each of which describes one facet of the desired learning outcome. Even functions that intend to optimize the same facet may behave in a subtly different and mutually conflicting way, depending on the task and the dataset being examined. Mul…
Testing a spectral-based feature set for audio genre classification
2011
Automatic musical genre classification is an important information retrieval task since it can be applied for practical purposes such as the organization of data collections in the digital music industry. However, this task remains an open question because the current state of the art shows far from satisfactory outcomes in terms of classification performance. Moreover, the most common algorithms that are used for this task are not designed for modelling music perception. This study suggests a framework for testing different musical features for use in music genre classification and evaluates the performance of this task based on two musical descriptors. The focus of this study is on automa…
Convolutional Neural Network Based Sleep Stage Classification with Class Imbalance
2022
Accurate sleep stage classification is vital to assess sleep quality and diagnose sleep disorders. Numerous deep learning based models have been designed for accomplishing this labor automatically. However, the class imbalance problem existing in polysomnography (PSG) datasets has been barely investigated in previous studies, which is one of the most challenging obstacles for the real-world sleep staging application. To address this issue, this paper proposes novel methods with signal-driven and image-driven ways of noise addition to balance the imbalanced relationship in the training dataset samples. We evaluate the effectiveness of the proposed methods which are integrated into a convolut…