Search results for "Random"

Randomized Hough Transform for Ellipse Detection with Result Clustering

2005

Our research is focused on the development of robust machine vision algorithms for pattern recognition. We want to provide robotic systems the ability to understand more on the external real world. In this paper, we describe a method for detecting ellipses in real world images using the randomized Hough transform with result clustering. A preprocessing phase is used in which real world images are transformed - noise reduction, greyscale transform, edge detection and final binarization - in order to be processed by the actual ellipse detector. The ellipse detector filters out false ellipses that may interfere with the final results. Due to the fact that usually more "virtual" ellipses are de…

Emulating Human Supervision in an Intelligent Tutoring System for Arithmetical Problem Solving

2014

This paper presents an intelligent tutoring system (ITS) for the learning of arithmetical problem solving. This is based on an analysis of a) the cognitive processes that take place during problem solving; and b) the usual tasks performed by a human when supervising a student in a one-to-one tutoring situation. The ITS is able to identify the solving strategy that the student is following and offer adaptive feedback that takes into account both the problem's constraints and the decisions previously made by the user. An observational study shows the ITS's accuracy at emulating expert human supervision, and a randomized experiment reveals that the ITS significantly improves students' learning…

Evaluating Classifiers for Mobile-Masquerader Detection

2006

As a result of the impersonation of a user of a mobile terminal, sensitive information kept locally or accessible over the network can be abused. The means of masquerader detection are therefore needed to detect the cases of impersonation. In this paper, the problem of mobile-masquerader detection is considered as a problem of classifying the user behaviour as originating from the legitimate user or someone else. Different behavioural characteristics are analysed by designated one-class classifiers whose classifications are combined. The paper focuses on selecting the classifiers for mobile-masquerader detection. The selection process is conducted in two phases. First, the classification ac…

Diversity in search strategies for ensemble feature selection

2005

Ensembles of learnt models constitute one of the main current directions in machine learning and data mining. Ensembles allow us to achieve higher accuracy, which is often not achievable with single models. It was shown theoretically and experimentally that in order for an ensemble to be effective, it should consist of base classifiers that have diversity in their predictions. One technique, which proved to be effective for constructing an ensemble of diverse base classifiers, is the use of different feature subsets, or so-called ensemble feature selection. Many ensemble feature selection strategies incorporate diversity as an objective in the search for the best collection of feature subse…

Oral Dabigatran Etexilate Versus Enoxaparin for Prevention of Venous Thromboembolism After Total Hip or Knee Arthroplasty: A Pooled Analysis of Four …

2011

Abstract Abstract 2312 Introduction: Thromboprophylaxis after major orthopaedic surgery reduces the risk of venous thromboembolism (VTE). Four randomized, double-blind, non-inferiority trials compared oral dabigatran etexilate doses of 220 mg or 150 mg once daily (qd) with subcutaneous enoxaparin for the primary prevention of VTE in patients undergoing elective total hip or knee arthroplasty. In the hip arthroplasty trials (RE-NOVATE® and RE-NOVATE® II) the treatment duration was 28–35 days; in the knee arthroplasty trials it was 6–10 days (RE-MODEL™) and 12–15 days (RE-MOBILIZE®). Three of the trials used a comparator enoxaparin regimen of 40 mg qd started the evening before surgery, while…

Machine Learning Methods for Spatial and Temporal Parameter Estimation

2020

Monitoring vegetation with satellite remote sensing is of paramount relevance to understand the status and health of our planet. Accurate and constant monitoring of the biosphere has large societal, economical, and environmental implications, given the increasing demand of biofuels and food by the world population. The current democratization of machine learning, big data, and high processing capabilities allow us to take such endeavor in a decisive manner. This chapter proposes three novel machine learning approaches to exploit spatial, temporal, multi-sensor, and large-scale data characteristics. We show (1) the application of multi-output Gaussian processes for gap-filling time series of…

Intravitreal dexamethasone as an enhancer for the anti-VEGF treatment in neovascular ARMD: recovering an old ally

2010

Maximum Displacement Variability of Stochastic Structures Subject to Deterministic Earthquake Loading

1998

The variability of the maximum response displacement of random frame structures under deterministic earthquake loading are examined in this paper using stochastic finite element techniques. The elastic modulus and the mass density are assumed to be described by cross-correlated stochastic fields. Specifically, a variability response function formulation is used for this problem, which allows for calculation of spectral-distribution-free upper bounds of the maximum displacement variance. Further, under the assumption of prespecified correlation functions describing the spatial variation of the material properties, variability response functions are used to calculate the corresponding maximum…

Probabilistic models for the fatigue resistance of welded steel joints subjected to constant amplitude loading

2022

Abstract S-N curves found in various rules and regulations are the basic tool for the practicing engineer when carrying out life predictions for welded details in dynamically loaded structures. The present work is investigating the expected fatigue life and associated scatter for welded steel joints subjected to Constant Amplitude (CA) loading. The objective is to obtain more reliable life predictions based on advancements in the probabilistic model fitted to collected life data. A Random Fatigue Limit Model (RFLM) is proposed to obtain fatigue resistance curves at given probability levels of survival. As a distinction to more conventional statistical methods, the model is treating both the…

Electrocatheter-mediated High-voltage Pulsed Radiofrequency of the Dorsal Root Ganglion in the Treatment of Chronic Lumbosacral Neuropathic Pain

2019

Objectives:Despite the interest in scientific community, there is still poor evidence about pulsed radiofrequency (PRF) efficacy in the treatment of neuropathic pain. In order to determine whether high-voltage PRF and epidural adhesiolysis (PRF-EA) showed better results than epidural adhesiolysis al