Search results for "Data mining"
showing 10 items of 907 documents
A novel dynamic multi-model relevance feedback procedure for content-based image retrieval
2016
This paper deals with the problem of image retrieval in large databases with a big semantic gap by a relevance feedback procedure. We present a novel algorithm for modelling the users's preferences in the content-based image retrieval system.The proposed algorithm considers the probability of an image belonging to the set of those sought by the user, and estimates the parameters of several local logistic regression models whose inputs are the low-level image features. A Principal Component Analysis method is applied to the original vector to reduce its high dimensionality. The relevance probabilities predicted by these local models are combined by means of a weighted average. These weights …
Inter-Model Consistency and Complementarity: Learning from ex-vivo Imaging and Electrophysiological Data towards an Integrated Understanding of Cardi…
2011
International audience; Computational models of the heart at various scales and levels of complexity have been independently developed, parameterised and validated using a wide range of experimental data for over four decades. However, despite remarkable progress, the lack of coordinated efforts to compare and combine these computational models has limited their impact on the numerous open questions in cardiac physiology. To address this issue, a comprehensive dataset has previously been made available to the community that contains the cardiac anatomy and fibre orientations from magnetic resonance imaging as well as epicardial transmembrane potentials from optical mapping measured on a per…
Visual data mining with self-organising maps for ventricular fibrillation analysis
2012
Detection of ventricular fibrillation (VF) at an early stage is being deeply studied in order to lower the risk of sudden death and allows the specialist to have greater reaction time to give the patient a good recovering therapy. Some works are focusing on detecting VF based on numerical analysis of time-frequency distributions, but in general the methods used do not provide insight into the problem. However, this study proposes a new methodology in order to obtain information about this problem. This work uses a supervised self-organising map (SOM) to obtain visually information among four important groups of patients: VF (ventricular fibrillation), VT (ventricular tachycardia), HP (healt…
Application of non-invasive technologies in dry-cured ham: An overview
2019
Background: Dry-cured ham is one of the most valued food products by Mediterranean consumers. In this sense, the appropriate development of its different production stages is essential to ensure the quality requirements. For this reason, non-invasive technologies have gained popularity and have been reported as useful not only to ensure the food safety of different products, but also to monitor fundamental stages in the production process, such as the salting stage, to analyze the content of different compounds without sample losses, and to correct possible defects in the final product. Scope and approach: This work has been focused on summarizing the studies that describe and have successf…
Contribution of virtual reality to functional rehabilitation
2010
Virtual reality has grown immensely. Practical applications for the use of this technology encompass many fields in both engineering science and human science. In the field of medicine, one of the newest fields to benefit from the advances in VR technology, virtual reality has become a major new therapeutic tool not only in medicine and surgery but also for the treatment of psychological disorders and rehabilitation for impaired person. Our research presented in this thesis aims at developing utilities to aid in functional rehabilitation using virtual reality technology. The main research question of our work concerns the effect of virtual metaphors in learning and training human gestures f…
Application of the group method of data handling (GMDH) approach for landslide susceptibility zonation using readily available spatial covariates
2022
Abstract Landslide susceptibility (LS) mapping is an essential tool for landslide risk assessment. This study aimed to provide a new approach with better performance for landslide mapping and adopting readily available variables. In addition, it investigates the capability of a state-of-the-art model developed using the group method of data handling (GMDH) to spatially model LS. Furthermore, hybridized models of GMDH were developed using different metaheuristic algorithms. The study area was the Bonghwa region of South Korea, for which an accurate landslide inventory dataset is available. We considered a total of 13 spatial covariates (altitude, slope, aspect, topographic wetness index, val…
Ultimate Order Statistics-Based Prototype Reduction Schemes
2013
Published version of a chapter in the book: AI 2013: Advances in Artificial Intelligence. Also available from the publisher at: http://dx.doi.org/10.1007/978-3-319-03680-9_42 The objective of Prototype Reduction Schemes (PRSs) and Border Identification (BI) algorithms is to reduce the number of training vectors, while simultaneously attempting to guarantee that the classifier built on the reduced design set performs as well, or nearly as well, as the classifier built on the original design set. In this paper, we shall push the limit on the field of PRSs to see if we can obtain a classification accuracy comparable to the optimal, by condensing the information in the data set into a single tr…
Putting the user into the active learning loop : Towards realistic but efficient photointerpretation
2012
In recent years, several studies have been published about the smart definition of training set using active learning algorithms. However, none of these works consider the contradiction between the active learning methods, which rank the pixels according to their uncertainty, and the confidence of the user in labeling, which is related both to the homogeneity of the pixel context and to the knowledge of the user of the scene. In this paper, we propose a two-steps procedure based on a filtering scheme to learn the confidence of the user in labeling. This way, candidate training pixels are ranked according both to their uncertainty and to the chances of being labeled correctly by the user. In…
Intelligent Sampling for Vegetation Nitrogen Mapping Based on Hybrid Machine Learning Algorithms
2021
Upcoming satellite imaging spectroscopy missions will deliver spatiotemporal explicit data streams to be exploited for mapping vegetation properties, such as nitrogen (N) content. Within retrieval workflows for real-time mapping over agricultural regions, such crop-specific information products need to be derived precisely and rapidly. To allow fast processing, intelligent sampling schemes for training databases should be incorporated to establish efficient machine learning (ML) models. In this study, we implemented active learning (AL) heuristics using kernel ridge regression (KRR) to minimize and optimize a training database for variational heteroscedastic Gaussian processes regression (V…
Data Mining Approach for Detection of DDoS Attacks Utilizing SSL/TLS Protocol
2015
Denial of Service attacks remain one of the most serious threats to the Internet nowadays. In this study, we propose an algorithm for detection of Denial of Service attacks that utilize SSL/TLS protocol. These protocols encrypt the data of network connections on the application layer which makes it impossible to detect attackers activity based on the analysis of packet payload. For this reason, we concentrate on statistics that can be extracted from packet headers. Based on these statistics, we build a model of normal user behavior by using several data mining algorithms. Once the model has been built, it is used to detect DoS attacks. The proposed framework is tested on the data obtained w…