Search results for "oftware"

Bagging and Boosting with Dynamic Integration of Classifiers

2000

One approach in classification tasks is to use machine learning techniques to derive classifiers using learning instances. The co-operation of several base classifiers as a decision committee has succeeded to reduce classification error. The main current decision committee learning approaches boosting and bagging use resampling with the training set and they can be used with different machine learning techniques which derive base classifiers. Boosting uses a kind of weighted voting and bagging uses equal weight voting as a combining method. Both do not take into account the local aspects that the base classifiers may have inside the problem space. We have proposed a dynamic integration tech…

Real-time flaw detection on a complex object: comparison of results using classification with a support vector machine, boosting, and hyperrectangle-…

2006

We present a classification work performed on industrial parts using artificial vision, a support vector machine (SVM), boost- ing, and a combination of classifiers. The object to be controlled is a coated heater used in television sets. Our project consists of detect- ing anomalies under manufacturer production, as well as in classi- fying the anomalies among 20 listed categories. Manufacturer speci- fications require a minimum of ten inspections per second without a decrease in the quality of the produced parts. This problem is ad- dressed by using a classification system relying on real-time ma- chine vision. To fulfill both real-time and quality constraints, three classification algorit…

Improving clustering of Web bot and human sessions by applying Principal Component Analysis

2019

View references (18) The paper addresses the problem of modeling Web sessions of bots and legitimate users (humans) as feature vectors for their use at the input of classification models. So far many different features to discriminate bots’ and humans’ navigational patterns have been considered in session models but very few studies were devoted to feature selection and dimensionality reduction in the context of bot detection. We propose applying Principal Component Analysis (PCA) to develop improved session models based on predictor variables being efficient discriminants of Web bots. The proposed models are used in session clustering, whose performance is evaluated in terms of the purity …

Preoperative preparation of «loss of domain» hernia. Progressive pneumoperitoneum and botulinum toxin type A.

2017

Preoperative progressive pneumoperitoneum and botulinum toxin type A are useful tools in the preparation of patients with loss of domain hernias. Both procedures are complementary in the surgical repair, especially with the use of prosthetic techniques without tension, that allow a integral management of these patients. The aim of this paper is to update concepts related to both procedures, emphasizing the advantages that take place in the preoperative management of loss of domain hernias.

A COLLABORATIVE VIRTUAL REALITY ENVIRONMENT FOR NEUROSURGICAL PLANNING AND TRAINING

2007

OBJECTIVE We have developed a highly interactive virtual environment that enables collaborative examination of stereoscopic three-dimensional (3-D) medical imaging data for planning, discussing, or teaching neurosurgical approaches and strategies. MATERIALS AND METHODS The system consists of an interactive console with which the user manipulates 3-D data using hand-held and tracked devices within a 3-D virtual workspace and a stereoscopic projection system. The projection system displays the 3-D data on a large screen while the user is working with it. This setup allows users to interact intuitively with complex 3-D data while sharing this information with a larger audience. RESULTS We have…

Functional connectivity inference from fMRI data using multivariate information measures

2022

Abstract Shannon’s entropy or an extension of Shannon’s entropy can be used to quantify information transmission between or among variables. Mutual information is the pair-wise information that captures nonlinear relationships between variables. It is more robust than linear correlation methods. Beyond mutual information, two generalizations are defined for multivariate distributions: interaction information or co-information and total correlation or multi-mutual information. In comparison to mutual information, interaction information and total correlation are underutilized and poorly studied in applied neuroscience research. Quantifying information flow between brain regions is not explic…

Class discovery from semi-structured EEG data for affective computing and personalisation

2017

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link. Many approaches to recognising emotions from metrical data such as EEG signals rely on identifying a very small number of classes and to train a classifier. The interpretation of these classes varies from a single emotion such as stress [24] to features of emotional model such as valence-arousal [4]. There are two major issues here. First classification approach limits the analysis of the data within the selected classes and is also highly dependent on training data/cycles, all of which limits generalisation. Second issue is that it does not exp…

A Biologically Inspired Representation of the Intelligence of a University Campus

2016

Abstract Intelligence or smartness in an urban environment implies several factors directed to improve quality of life and efficiency. It is important to note that in this context the inclusion of citizens and their devices is a key factor for reaching smartness. Data from mobile devices are increasingly used in everyday activities and have to be considered a useful means for handling and analyzing knowledge and communications. This paper shows how to represent important data when dealing with smartness by creating an analogy between the representation of human brain areas, activated when specific tasks are performed, and groups of students when behaviors or needs arise. The brain traffic c…

An ecological model of bridging the digital divide in education: A case study of OLPC deployment in Nepal

2018

Angiocardiographic digital still images compressed via irreversible methods: concepts and experiments.

1997

Abstract We defined, implemented and tested two new methods for irreversible compression of angiocardiographic still images: brightness error limitation (BEL) and pseudo-gradient adaptive brightness and contrast error limitation (PABCEL). The scan path used to compress the digital images is based on the Peano–Hilbert plane-filling curve. The compression methods limit, for each pixel, the brightness errors introduced when approximating the original image (i.e. the difference between the values of corresponding pixels as grey levels). Additional limitations are imposed to the contrast error observed when considering along the scan path consecutive pixels of both the original and the reconstru…