Search results for "Extraction"

Generic attribute deviation metric for assessing mesh simplification algorithm quality

2002

International audience; This paper describes an efficient method to compare two triangular meshes. Meshes considered here contain geometric features as well as other surface attributes such as material colors, texture, temperature, radiation, etc. Two deviation measurements are presented to assess the differences between two meshes. The first measurement, called geometric deviation, returns geometric differences. The second measurement , called attribute deviation, returns attribute differences regardless of the attribute type. In this paper we present an application of this method to the Mesh Simplification Algorithm (MSA) quality assessment according to the appearance attributes. This ass…

Cover Picture: Complexation and Extraction of PAHs to the Aqueous Phase with a Dinuclear Pt II Diazapyrenium‐Based Metallacycle (Chem. Eur. J. 41/201…

2010

Irrelevant Features, Class Separability, and Complexity of Classification Problems

2011

In this paper, analysis of class separability measures is performed in attempt to relate their descriptive abilities to geometrical properties of classification problems in presence of irrelevant features. The study is performed on synthetic and benchmark data with known irrelevant features and other characteristics of interest, such as class boundaries, shapes, margins between classes, and density. The results have shown that some measures are individually informative, while others are less reliable and only can provide complimentary information. Classification problem complexity measurements on selected data sets are made to gain additional insights on the obtained results.

Creating a semantically-enhanced cloud services environment through ontology evolution

2014

Currently, the availability of Web resources has grown enormously to the point that whatever a user needs at a given moment can potentially be found on the Internet. These resources are not limited to data items anymore, functionality delivered through some sort of service architectural model is also offered on the Internet. In the last few years, cloud computing has emerged as one of the most popular computing models to provide services over the Internet. However, as the number of available cloud services increases, the problem of service discovery and selection arises. Experience indicates that semantic technologies can provide the basis for enhanced and more precise search processes. In …

Challenges in the determination of engineered nanomaterials in foods

2016

Detection, characterization, and quantification of engineering nanomaterials (ENMs) in foods is still a pending issue that needs to be tackle to protect consumers and to fix some related aspects (e.g. labelling or control). The global challenge for the analytical sciences is that ENMs are a new sort of analytes, involving both chemical (composition, mass and number concentration) and physical information (e.g. size, shape, aggregation). In this critical review, we evaluate and compare the procedures involved in the analytical methods and studies developed thus far for the identification and quantification of ENMs in food. We discuss advantages and limitation as well as prospects. We pointed…

Information Abstraction from Crises Related Tweets Using Recurrent Neural Network

2016

Social media has become an important open communication medium during crises. The information shared about a crisis in social media is massive, complex, informal and heterogeneous, which makes extracting useful information a difficult task. This paper presents a first step towards an approach for information extraction from large Twitter data. In brief, we propose a Recurrent Neural Network based model for text generation able to produce a unique text capturing the general consensus of a large collection of twitter messages. The generated text is able to capture information about different crises from tens of thousand of tweets summarized only in a 2000 characters text.

Combining conjunctive rule extraction with diffusion maps for network intrusion detection

2013

Network security and intrusion detection are important in the modern world where communication happens via information networks. Traditional signature-based intrusion detection methods cannot find previously unknown attacks. On the other hand, algorithms used for anomaly detection often have black box qualities that are difficult to understand for people who are not algorithm experts. Rule extraction methods create interpretable rule sets that act as classifiers. They have mostly been combined with already labeled data sets. This paper aims to combine unsupervised anomaly detection with rule extraction techniques to create an online anomaly detection framework. Unsupervised anomaly detectio…

The impact of sample reduction on PCA-based feature extraction for supervised learning

2006

"The curse of dimensionality" is pertinent to many learning algorithms, and it denotes the drastic raise of computational complexity and classification error in high dimensions. In this paper, different feature extraction (FE) techniques are analyzed as means of dimensionality reduction, and constructive induction with respect to the performance of Naive Bayes classifier. When a data set contains a large number of instances, some sampling approach is applied to address the computational complexity of FE and classification processes. The main goal of this paper is to show the impact of sample reduction on the process of FE for supervised learning. In our study we analyzed the conventional PC…

The role of green extraction techniques in Green Analytical Chemistry

2015

Abstract Greening extraction techniques to improve the sensitivity and the selectivity of analytical methods is the sustainable alternative to classical sample-preparation procedures used in the past. In this update, we review the main strategies employed in the scientific literature to reduce deleterious side-effects of extraction techniques. We demonstrate that the evolution of sample-treatment procedures is focused on the simultaneous improvement of the main analytical features of the method and its practical aspects, including the economic case.

Area-Based Depth Estimation for Monochromatic Feature-Sparse Orthographic Capture

2018

With the rapid development of light field technology, depth estimation has been highlighted as one of the critical problems in the field, and a number of approaches have been proposed to extract the depth of the scene. However, depth estimation by stereo matching becomes difficult and unreliable when the captured images lack both color and feature information. In this paper, we propose a scheme that extracts robust depth from monochromatic, feature-sparse scenes recorded in orthographic sub-aperture images. Unlike approaches which rely on the rich color and texture information across the sub-aperture views, our approach is based on depth from focus techniques. First, we superimpose shifted …