Search results for "koneoppiminen"
showing 10 items of 218 documents
Päätöspuiden käyttö koneoppimisessa
2017
Päätöspuihin perustuvia menetelmiä ollaan käytetty laajasti koneoppimisen eri tehtävissä. Tämä tutkielma kokoaa tietoa siitä miten ja mihin näitä menetelmiä ollaan käytetty. Käydään läpi yksittäisen päätöspuun luominen datasta ja tämän pohjalta tutustutaan kokoonpanomenetelmiin, joissa kootaan useita päätöspuita. Lopulta tutustutaan vielä joihinkin esiteltyjen menetelmien sovelluksiin. Methods based on decision trees have been utilized widely in different tasks of machine learning. This study compiles information on how and for what purposes have these methods been used. Construction of a decision tree based on data is explained and with that as a basis ensemble methods, which comprise many…
Secrecy analysis and learning-based optimization of cooperative NOMA SWIPT systems
2019
Non-orthogonal multiple access (NOMA) is considered to be one of the best candidates for future networks due to its ability to serve multiple users using the same resource block. Although early studies have focused on transmission reliability and energy efficiency, recent works are considering cooperation among the nodes. The cooperative NOMA techniques allow the user with a better channel (near user) to act as a relay between the source and the user experiencing poor channel (far user). This paper considers the link security aspect of energy harvesting cooperative NOMA users. In particular, the near user applies the decode-and-forward (DF) protocol for relaying the message of the source no…
Adapting to Dynamic LEO-B5G Systems : Meta-Critic Learning Based Efficient Resource Scheduling
2022
Low earth orbit (LEO) satellite-assisted communications have been considered as one of key elements in beyond 5G systems to provide wide coverage and cost-efficient data services. Such dynamic space-terrestrial topologies impose exponential increase in the degrees of freedom in network management. In this paper, we address two practical issues for an over-loaded LEO-terrestrial system. The first challenge is how to efficiently schedule resources to serve the massive number of connected users, such that more data and users can be delivered/served. The second challenge is how to make the algorithmic solution more resilient in adapting to dynamic wireless environments.To address them, we first…
Deep learning approach for prediction of impact peak appearance at ground reaction force signal of running activity
2020
Protruding impact peak is one of the features of vertical ground reaction force (GRF) that is related to injury risk while running. The present research is dedicated to predicting GRF impact peak appearance by setting a binary classification problem. Kinematic data, namely a number of raw signals in the sagittal plane, collected by the Vicon motion capture system (Oxford Metrics Group, UK) were employed as predictors. Therefore, the input data for the predictive model are presented as a multi-channel time series. Deep learning techniques, namely five convolutional neural network (CNN) models were applied to the binary classification analysis, based on a Multi-Layer Perceptron (MLP) classifi…
PIECEWISE ANOMALY DETECTION USING MINIMAL LEARNING MACHINE FOR HYPERSPECTRAL IMAGES
2021
Abstract. Hyperspectral imaging, with its applications, offers promising tools for remote sensing and Earth observation. Recent development has increased the quality of the sensors. At the same time, the prices of the sensors are lowering. Anomaly detection is one of the popular remote sensing applications, which benefits from real-time solutions. A real-time solution has its limitations, for example, due to a large amount of hyperspectral data, platform’s (drones or a cube satellite) constraints on payload and processing capability. Other examples are the limitations of available energy and the complexity of the machine learning models. When anomalies are detected in real-time from the hyp…
Sähköä ja alkemiaa: Tekoälydiskurssit Yleisradion verkkoartikkeleissa
2021
Tässä artikkelissa tarkastelemme sitä, millaisena ja miten tekoäly esitetään suomalaisessa julkisessa keskustelussa, ja ketkä tekoälystä suurelle yleisölle kertovat. Aineistona olemme käyttäneet Yleisradion verkkosivujen tekoälyä käsitteleviä artikkeleja. Tulosten perusteella tekoälystä pääsevät kertomaan useimmin talouden ja teollisuuden aloilla työskentelevät miehet. Aineistossa esiintyneet tekoälytulevaisuuskuvaukset pitäytyvät pitkälti nykyisen länsikeskeisen kapitalistisen maailmankuvan sisällä. Toisin sanoen, ne eivät haastaneet tai ylittäneet vallitsevaa status quoa, vaan näkivät tulevaisuuden pikemminkin lineaarisena kehityksenä nykytilasta. Aiemman tutkimuksen perusteella yleinen k…
Big data ja analytiikka terveydenhuollossa
2016
Elämme datakeskeisessä maailmassa, jossa uutta digitaalista sisältöä syntyy jo muutamissa sekunneissa uskomattomia määriä. Yritykset ja organisaatiot pyrkivät analysoimaan tätä massiivista, monimuotoista ja nopeasti kasvavaa Big Dataa toimiakseen paremmin ja tehokkaammin sekä saadakseen parempaa ymmärrystä omasta toiminnastaan. Big Data ja analytiikka ovat aihe, jota on tutkittu laajasti myös terveydenhuollon näkökulmasta. Terveydenhuolto on yksi toimialoista, joka voi hyötyä Big Datasta ja siihen kohdistuvasta analytiikasta huomattavasti. Tässä tutkielmassa kysytään kuinka analytiikka on kehittynyt ja mihin suuntaan se on kehittymässä. Kysymykseen vastataan tutustumalla liiketoimintatiedon…
Anomaly Detection from Network Logs Using Diffusion Maps
2011
The goal of this study is to detect anomalous queries from network logs using a dimensionality reduction framework. The fequencies of 2-grams in queries are extracted to a feature matrix. Dimensionality reduction is done by applying diffusion maps. The method is adaptive and thus does not need training before analysis. We tested the method with data that includes normal and intrusive traffic to a web server. This approach finds all intrusions in the dataset. peerReviewed
A Deep Learning Model for Automatic Sleep Scoring using Multimodality Time Series
2021
Sleep scoring is a fundamental but time-consuming process in any sleep laboratory. Automatic sleep scoring is crucial and urgent to help address the increasing unmet need for sleep research. Therefore, this paper aims to develop an end-to-end deep learning architecture using raw polysomnographic recordings to automate sleep scoring. The proposed model adopts two-dimensional convolutional neural networks (2D-CNN) to automatically learn features from multi-modality signals, together with a "squeeze and excitation" block for recalibrating channel-wise feature responses. The learnt representations are finally fed to a softmax classifier to generate predictions for each sleep stage. The model pe…
Orientation Adaptive Minimal Learning Machine for Directions of Atomic Forces
2021
Machine learning (ML) force fields are one of the most common applications of ML in nanoscience. However, commonly these methods are trained on potential energies of atomic systems and force vectors are omitted. Here we present a ML framework, which tackles the greatest difficulty on using forces in ML: accurate prediction of force direction. We use the idea of Minimal Learning Machine to device a method which can adapt to the orientation of an atomic environment to estimate the directions of force vectors. The method was tested with linear alkane molecules. peerReviewed