Search results for "Machine"
showing 10 items of 2592 documents
Item Response Trees: a recommended method for analyzing categorical data in behavioral studies
2015
Behavioral data are notable for presenting challenges to their statistical analysis, often due to the difficulties in measuring behavior on a quantitative scale. Instead, a range of qualitative alternative responses is recorded. These can often be understood as the outcome of a sequence of binary decisions. For example, faced by a predator, an individual may decide to flee or stay. If it stays, it may decide to freeze or display a threat and if it displays a threat, it may choose from several alternative forms of display. Here we argue that instead of being analyzed using traditional nonparametric statistics or a series of separate analyses split by response categories, this kind of data ca…
Accessibility of Cultural Heritage in the Virtual Environment of Latvia Memory Institutions
2014
The aim of research is to evaluate the level of e-service provision in digital collections created by memory institutions (libraries and archives) of Latvia. The problem under study is as follows: digital collections have insufficient usability due to lack of appropriate e-services. The empirical basis for research is e-services of digital collections created by national and regional level libraries, as well as the National Archives of Latvia. The evaluation of e-services is based on 14 indicators within 7 categories: visibility, search, reference, personalization, user participation, instructions, and document delivery. The analysis reveals that the level of eservice development is quite l…
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 …
Machine learning for mortality analysis in patients with COVID-19
2020
This paper analyzes a sample of patients hospitalized with COVID-19 in the region of Madrid (Spain). Survival analysis, logistic regression, and machine learning techniques (both supervised and unsupervised) are applied to carry out the analysis where the endpoint variable is the reason for hospital discharge (home or deceased). The different methods applied show the importance of variables such as age, O2 saturation at Emergency Rooms (ER), and whether the patient comes from a nursing home. In addition, biclustering is used to globally analyze the patient-drug dataset, extracting segments of patients. We highlight the validity of the classifiers developed to predict the mortality, reaching…
A Curvature Based Method for Blind Mesh Visual Quality Assessment Using a General Regression Neural Network
2016
International audience; No-reference quality assessment is a challenging issue due to the non-existence of any information related to the reference and the unknown distortion type. The main goal is to design a computational method to objectively predict the human perceived quality of a distorted mesh and deal with the practical situation when the reference is not available. In this work, we design a no reference method that relies on the general regression neural network (GRNN). Our network is trained using the mean curvature which is an important perceptual feature representing the visual aspect of a 3D mesh. Relatively to the human subjective scores, the trained network successfully asses…
Varieties Generated by Certain Models of Reversible Finite Automata
2006
Reversible finite automata with halting states (RFA) were first considered by Ambainis and Freivalds to facilitate the research of Kondacs-Watrous quantum finite automata. In this paper we consider some of the algebraic properties of RFA, namely the varieties these automata generate. Consequently, we obtain a characterization of the boolean closure of the classes of languages recognized by these models.
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…
Comparison between Focused Electron/Ion Beam-Induced Deposition at Room Temperature and under Cryogenic Conditions
2019
This article belongs to the Special Issue Multi-Dimensional Direct-Write Nanofabrication.
Persistence in complex systems
2022
Persistence is an important characteristic of many complex systems in nature, related to how long the system remains at a certain state before changing to a different one. The study of complex systems' persistence involves different definitions and uses different techniques, depending on whether short-term or long-term persistence is considered. In this paper we discuss the most important definitions, concepts, methods, literature and latest results on persistence in complex systems. Firstly, the most used definitions of persistence in short-term and long-term cases are presented. The most relevant methods to characterize persistence are then discussed in both cases. A complete literature r…
BVLOS UAS Operations in Highly-Turbulent Volcanic Plumes.
2020
Long-range, high-altitude Unoccupied Aerial System (UAS) operations now enable in-situ measurements of volcanic gas chemistry at globally-significant active volcanoes. However, the extreme environments encountered within volcanic plumes present significant challenges for both air frame development and in-flight control. As part of a multi-disciplinary field deployment in May 2019, we flew fixed wing UAS Beyond Visual Line of Sight (BVLOS) over Manam volcano, Papua New Guinea, to measure real-time gas concentrations within the volcanic plume. By integrating aerial gas measurements with ground- and satellite-based sensors, our aim was to collect data that would constrain the emission rate of …