Search results for "support"
showing 10 items of 2310 documents
V I G — A Visual and Dynamic Decision Support System for Multiple Objective Linear Programming
1989
In this paper we describe the principles of VIG (Visual Interactive Goal Programming), a Multiple Criteria Decision Support System, recently developed by Korhonen. PARETO RACE is a corner-stone of this system, which is designed to support both the modelling and solving of a multiple objective linear programming problem. The interface is based on one main menu, spreadsheets, and interactive use of computer graphics. VIG provides the decision-maker with the possibility to approach his/her decision problem by using an “evolutionary approach”. This means that the decision-maker does not have to specify the model precisely prior to solving the problem. In fact, the model evolves progressively. W…
Deep CNN for IIF Images Classification in Autoimmune Diagnostics
2019
The diagnosis and monitoring of autoimmune diseases are very important problem in medicine. The most used test for this purpose is the antinuclear antibody (ANA) test. An indirect immunofluorescence (IIF) test performed by Human Epithelial type 2 (HEp-2) cells as substrate antigen is the most common methods to determine ANA. In this paper we present an automatic HEp-2 specimen system based on a convolutional neural network method able to classify IIF images. The system consists of a module for features extraction based on a pre-trained AlexNet network and a classification phase for the cell-pattern association using six support vector machines and a k-nearest neighbors classifier. The class…
A review of thermochemical energy storage systems for power grid support
2020
Power systems in the future are expected to be characterized by an increasing penetration of renewable energy sources systems. To achieve the ambitious goals of the “clean energy transition”, energy storage is a key factor, needed in power system design and operation as well as power-to-heat, allowing more flexibility linking the power networks and the heating/cooling demands. Thermochemical systems coupled to power-to-heat are receiving an increasing attention due to their better performance in comparison with sensible and latent heat storage technologies, in particular, in terms of storage time dynamics and energy density. In this work, a comprehensive review of the state of art of theore…
CSCL for NGO's Cross cultural Virtual Teams in Africa: An Ethiopian Children Advocacy Case Study against Exclusion and toward Facilitation of Express…
2005
This exploratory pilot study shows that NGO's involved in Children Advocacy through Arts in Africa are willing to use a groupware, meaning a computer supported collaborative learning (CSCL) environment. Innovative ideas and best practices among NGOs would be shared easily worldwide. Little scientific information is available to help them make a sound choice. This study suggests that some NGOs based in Ethiopia/Africa have specific needs which should translate in specific context analysis and interface development: 1) an intercultural approach to creativity, arts and innovation, and 2) emphasis should be placed on tools to facilitate asynchronous systematic conception and sharing of intra an…
Multimodal Images Classification using Dense SURF, Spectral Information and Support Vector Machine
2019
International audience; The multimodal image classification is a challenging area of image processing which can be used to examine the wall painting in the cultural heritage domain. In such classification, a common space of representation is important. In this paper, we present a new method for multimodal representation learning, by using a pixel-wise feature descriptor named dense Speed Up Robust Features (SURF) combined with the spectral information carried by the pixel. For classification of extracted features we have used support vector machine (SVM). Our database was extracted from acquisition on cultural heritage wall paintings that contain four modalities UV, Visible, IRR and fluores…
A Wavelet approach to extract main features from indirect immunofluorescence images
2019
A number of previous studies have shown that IIF image analysis requires complex and sometimes heterogeneous and diversified methods. Robust solutions can be proposed but they need to orchestrate several methods from low-level analysis up to more complex neural networks or SVM for data classification. The contribution intends to highlight the versatility of Wavelet Transform (WT) and their use in various levels of analysis for the classification of IIF images in order to develop a system capable of performing: image enhancement, ROI segmentation and object classification. Therefore, WT was adopted in the de-noise section, segmentation and classification. This analysis allows frequencies cha…
Recent advances of HCI in decision-making tasks for optimized clinical workflows and precision medicine.
2020
The ever-increasing amount of biomedical data is enabling new large-scale studies, even though ad hoc computational solutions are required. The most recent Machine Learning (ML) and Artificial Intelligence (AI) techniques have been achieving outstanding performance and an important impact in clinical research, aiming at precision medicine, as well as improving healthcare workflows. However, the inherent heterogeneity and uncertainty in the healthcare information sources pose new compelling challenges for clinicians in their decision-making tasks. Only the proper combination of AI and human intelligence capabilities, by explicitly taking into account effective and safe interaction paradigms,…
Kernel-Based Framework for Multitemporal and Multisource Remote Sensing Data Classification and Change Detection
2008
The multitemporal classification of remote sensing images is a challenging problem, in which the efficient combination of different sources of information (e.g., temporal, contextual, or multisensor) can improve the results. In this paper, we present a general framework based on kernel methods for the integration of heterogeneous sources of information. Using the theoretical principles in this framework, three main contributions are presented. First, a novel family of kernel-based methods for multitemporal classification of remote sensing images is presented. The second contribution is the development of nonlinear kernel classifiers for the well-known difference and ratioing change detectio…
A Comparison of Advanced Regression Algorithms for Quantifying Urban Land Cover
2014
Quantitative methods for mapping sub-pixel land cover fractions are gaining increasing attention, particularly with regard to upcoming hyperspectral satellite missions. We evaluated five advanced regression algorithms combined with synthetically mixed training data for quantifying urban land cover from HyMap data at 3.6 and 9 m spatial resolution. Methods included support vector regression (SVR), kernel ridge regression (KRR), artificial neural networks (NN), random forest regression (RFR) and partial least squares regression (PLSR). Our experiments demonstrate that both kernel methods SVR and KRR yield high accuracies for mapping complex urban surface types, i.e., rooftops, pavements, gras…
Structured Output SVM for Remote Sensing Image Classification
2011
Traditional kernel classifiers assume independence among the classification outputs. As a consequence, each misclassification receives the same weight in the loss function. Moreover, the kernel function only takes into account the similarity between input values and ignores possible relationships between the classes to be predicted. These assumptions are not consistent for most of real-life problems. In the particular case of remote sensing data, this is not a good assumption either. Segmentation of images acquired by airborne or satellite sensors is a very active field of research in which one tries to classify a pixel into a predefined set of classes of interest (e.g. water, grass, trees,…