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Peer Reviewed
See detailClassification of medicines according to their influence on driving ability
Maes, V.; Grenez, O.; Charlier, Corinne ULg et al

in Acta Clinica Belgica (1999), S1

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See detailClassification of periodic orbits of two-dimensional homogeneous granular crystals with no pre-compression
Detroux, Thibaut ULg; Starosvetsky, Yuli; Kerschen, Gaëtan ULg et al

in Nonlinear Dynamics (2014), 76(April 2014), 673-696

In the present study we classify the periodic orbits of a squarely packed, uncompressed and undamped, homogeneous granular crystal, assuming that all elastic granules oscillate with the same frequency (i ... [more ▼]

In the present study we classify the periodic orbits of a squarely packed, uncompressed and undamped, homogeneous granular crystal, assuming that all elastic granules oscillate with the same frequency (i.e., under condition of 1:1 resonance); this type of Hamiltonian periodic orbits have been labeled as nonlinear normal modes. To this end we formulate an auxiliary system which consists of a two-dimensional, vibro-impact lattice composed of non-uniform “effective particles” oscillating in an anti-phase fashion. The analysis is based on the idea of balancing linear momentum in both horizontal and vertical directions for separate, groups of particles, whereby each such a group is represented by the single effective particle of the auxiliary system. It is important to emphasize that the auxiliary model can be defined for general finite, squarely packed granular crystals composed of n rows and m columns. The auxiliary model is successful in predicting the total number of such periodic orbits, as well as the amplitude ratios for different periodic regimes including strongly localized ones. In fact this methodology enables one to systematically study the generation of mode localization in these strongly nonlinear, highly degenerate dynamical systems. Good correspondence between the results of the theoretical model and direct numerical simulations is observed. The results presented herein can be further extended to study the intrinsic dynamics of the more complex granular materials, such as heterogeneous two-dimensional and three-dimensional granular crystals and multi-layered structures. [less ▲]

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See detailClassification of positron emission tomography images using multiple kernel learning
Segovia-Román, Fermín ULg; Bastin, Christine ULg; Salmon, Eric ULg et al

in Proceeding of 3rd NIPS 2013 Workshop on Machine Learning and Interpretation in NeuroImaging (2013)

Over the last years, several approaches to analyze nuclear medicine imaging using computer systems have been proposed with the aim of assisting the diagnosis of neurodegenerative disorders. Probably one ... [more ▼]

Over the last years, several approaches to analyze nuclear medicine imaging using computer systems have been proposed with the aim of assisting the diagnosis of neurodegenerative disorders. Probably one of the most complex challenges facing these approaches is to deal with the huge amount of data provided by brain images. In this work, we propose an original approach based on multiple kernel learning. First the images were parcellated (according to the structure of the brain) by means of the automatic anatomical labeling atlas. Then, the importance of each region for the assisted diagnosis was estimated using a classifi- cation procedure. Finally, all the regions were combined in a multiple kernel method in which one kernel per region was computed and all the kernels were weighted according to the importance of the region they represented. For testing purposes, a database with 46 PET images from stable mild cognitive impairment subjects and early Alzheimer’s disease converter patients was used. An accuracy rate of 73.91% was achieved when differentiating between both groups. [less ▲]

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See detailClassification of riparian forest species (individual tree level) using UAV-based Canopy Height Model and multi-temporal orthophotos (Vielsalm, Eastern Belgium)
Michez, Adrien ULg; Lisein, Jonathan ULg; Toromanoff, François ULg et al

Poster (2013, September 09)

Introduction : Despite their relatively low area coverage, riparian forests are central landscape features providing several ecosystem services. Nevertheless, they are critically endangered in European ... [more ▼]

Introduction : Despite their relatively low area coverage, riparian forests are central landscape features providing several ecosystem services. Nevertheless, they are critically endangered in European countries by human pressures (livestock grazing, land use conflicts, canalizations, waste water, ...) andalso by natural hazards such as the recent black alder (Alnus glutinosa) extensive decline caused by Phytophthora alni. In this study UAV is used to improve the characterization of riparian areas. Riparian forest species are identified at the individual tree level. The health condition of black alder is assessed. For this purpose a computer based approach has been developped, with low needs of specific operator ability or training. Methods : We used the Gatewing X100 to acquire 16 aerial photographs datasets (7 in classic RGB and 9 in RG NIR) during 5 days (form Augustus to October 2012). We processed a CHM in ArcGIS by combining a national Digital Terrain Model with a photogrammetric DSM generated from a single flight photographs dataset with the "MicMac" opensource platform. The 16 orthophotos were computed with Agisoft Photoscan. Based on the CHM and some basic vegetation index (mean NDVI), a classification/segmentation process was developped in eCognition allowing tree crown extraction. An amount of 113 metrics were computed in eCognition for every tree crown object. The metrics were both derived from the CHM raster and spectral information. Metrics were computed by band (object spectral mean and CHM mean, Harralick entropy, Skewness) but also with band combination (Green NDVI and NDVI). A reference dataset was also acquired through a field survey of 624 individual tree positions accurately localized. The health condition of the black alder was recorded during the field survey. A supervised classification algorithm was developed in R (Random Forest package). Results : Several classification trees were assessed trough global accuracy using the Out Of Bag (OOB) error. The best global accuracy (82%) was obtained when distinguishing the black alder (with no regards for health condition during field survey) from the rest of riparian forest objects. The global accuracy tended to decline when other species were added. When separating healthy black alders from those with symptoms, the global accuracy is 77%. Conclusions : Our study highlights the potential of UAV-based multitemporal orthophotos to identify riparian forest species and health conditions at the tree level. Future studies will focus on quick radiometrics corrections. This could improve global accuracy by reducing the variability caused by illumination conditions [less ▲]

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See detailClassification of riparian forest species and health condition using multi-temporal and hyperspatial imagery from unmanned aerial system
Michez, Adrien ULg; Piégay, Hervé; Lisein, Jonathan ULg et al

in Environmental Monitoring and Assessment (2016), 188(3),

Riparian forests are critically endangered many anthropogenic pressures and natural hazards. The importance of riparian zones has been acknowledged by European Directives, involving multi-scale monitoring ... [more ▼]

Riparian forests are critically endangered many anthropogenic pressures and natural hazards. The importance of riparian zones has been acknowledged by European Directives, involving multi-scale monitoring. The use of this very high resolution and hyperspatial imagery in a multi-temporal approach is an emerging topic. The trend is reinforced by the recent and rapid growth of the use of the unmanned aerial system (UAS), which has prompted the development of innovative methodology. Our study proposes a methodological framework to explore how a set of multi-temporal images acquired during a vegetative period can differentiate some of the deciduous riparian forest species and their health conditions. More specifically, the developed approach intends to identify, through a process of variable selection, which variables derived from UAS imagery and which scale of image analysis are the most relevant to our objectives. The methodological framework is applied to two study sites to describe the riparian forest through two fundamental characteristics: the species composition and the health condition. These characteristics were selected not only because of their use as proxies for the riparian zone ecological integrity but also because of their use for river management. The comparison of various scales of image analysis identified the smallest OBIA objects (ca. 1 m²) as the most relevant scale. Variables derived from spectral information (bands ratio's) were identified as the most appropriate, followed by variables related to the vertical structure of the forest. Classification results show good overall accuracies for the species composition of the riparian forest (five classes, 79.5 and 84.1 % for Site 1 and Site 2). The classification scenario regarding the health condition of the black alders of the Site 1 performed the best (90.6 %). The quality of the classification models developed with a UAS-based, cost-effective, and semi-automatic approach competes successfully with those developed using more expensive imagery, such as multispectral and hyperspectral airborne imagery. The high overall accuracy results obtained by the classification of the diseased alders open the door to applications dedicated to monitoring of the health conditions of riparian forest. Our methodological framework will allow UAS users to manage large imagery metrics datasets derived from those dense time series. [less ▲]

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See detailClassification of scenarios for persons crossing a door frame based on the joint measurements of two radial distance sensors
Barnich, Olivier; Van Droogenbroeck, Marc ULg

Report (2009)

This report is the final report concerning the research project that involved the TELIM group from the University of Liège and BEA, a Belgian company active, among others, in the design and manufacturing ... [more ▼]

This report is the final report concerning the research project that involved the TELIM group from the University of Liège and BEA, a Belgian company active, among others, in the design and manufacturing of devices for people and vehicle detection. [less ▲]

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See detailClassification of sporadic Creutzfeldt-Jakob disease based on clinical and neuropathological characteristics
Abrahantes, J. C.; Aerts, M.; van Everbroeck, B. et al

in European Journal of Epidemiology (2007), 22(7), 457-465

Creutzfeldt-Jakob disease (CJD) is a rare and fatal neurodegenerative disease of unknown cause. Patients are usually aged between 50 and 75 and typical clinical features include rapidly progressive ... [more ▼]

Creutzfeldt-Jakob disease (CJD) is a rare and fatal neurodegenerative disease of unknown cause. Patients are usually aged between 50 and 75 and typical clinical features include rapidly progressive dementia associated with myoclonus and a characteristic electroencephalographic pattern. Neuropathological examination reveals cortical spongiform change, hence the term 'spongiform encephalopathy'. Several statistical techniques were applied to classify patients with sporadic CJD (sCJD), based on clinical and neuropathological investigation. We focus on the classification of neuropathologically confirmed sCJD patients. In order to obtain a classification rule that correctly classifies this type of patients and at the same time controls the overall error rate, we apply several classification techniques, which in general, produce comparable results. The boosting method produces the best results and the variable 14-3-3 protein in cerebrospinal fluid plays the most important role in the prediction of neuropathologically confirmed sCJD. [less ▲]

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See detailA classification of spores by support vectors based on an analysis of their ornament spatial distribution - An application to Emsian miospores from Saudi Arabia
Breuer, P.; Dislaire, G.; Filatoff, J. et al

in Carnets de Géologie = Notebooks on Geology (2007), CG2007(M01/02), 9-15

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See detailClassification of the algebras $\mathbb{O}_{p,q}$
Kreusch, Marie ULg; Morier-Genoud, Sophie

in Communications in Algebra (2015), 43(9), 3799-3815

We study a series of real nonassociative algebras $O_{p,q}$ introduced in [5]. These algebras have a natural $Z^n_2$-grading, where $n = p + q$, and they are characterized by a cubic form over the field $Z ... [more ▼]

We study a series of real nonassociative algebras $O_{p,q}$ introduced in [5]. These algebras have a natural $Z^n_2$-grading, where $n = p + q$, and they are characterized by a cubic form over the field $Z_2$. We establish all the possible isomorphisms between the algebras $O_{p,q}$ preserving the structure of $Z^n_2$-graded algebra. The classification table of $O_{p,q}$ is quite similar to that of the real Clifford algebras $Cl_{p,q}$, the main difference is that the algebras $O_{n,0}$ and $O_{0,n}$ are exceptional. [less ▲]

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See detailClassification of worldwide bovine tuberculosis risk factors in cattle: a stratified approach.
Humblet, Marie-France ULg; Boschiroli, Maria Laura; Saegerman, Claude ULg

in Veterinary Research (2009), 40(5), 50

The worldwide status of bovine tuberculosis (bTB) as a zoonosis remains of great concern. This article reviews the main risk factors for bTB in cattle based on a three-level classification: animal, herd ... [more ▼]

The worldwide status of bovine tuberculosis (bTB) as a zoonosis remains of great concern. This article reviews the main risk factors for bTB in cattle based on a three-level classification: animal, herd and region/country level. A distinction is also made, whenever possible, between situations in developed and developing countries as the difference of context might have consequences in terms of risk of bTB. Recommendations are suggested to animal health professionals and scientists directly involved in the control and prevention of bTB in cattle. The determination of Millennium Development Goals for bTB is proposed to improve the control/eradication of the disease worldwide. [less ▲]

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See detailClassification performance resulting from of 2-means
Ruwet, Christel ULg; Haesbroeck, Gentiane ULg

in Journal of Statistical Planning & Inference (2013), 143(2), 408-418

The k-means procedure is probably one of the most common nonhierachical clustering techniques. From a theoretical point of view, it is related to the search for the k principal points of the underlying ... [more ▼]

The k-means procedure is probably one of the most common nonhierachical clustering techniques. From a theoretical point of view, it is related to the search for the k principal points of the underlying distribution. In this paper, the classification resulting from that procedure for k=2 is shown to be optimal under a balanced mixture of two spherically symmetric and homoscedastic distributions. Then, the classification efficiency of the 2-means rule is assessed using the second order influence function and compared to the classification efficiencies of the Fisher and logistic discriminations. Influence functions are also considered here to compare the robustness to infinitesimal contamination of the 2-means method w.r.t. the generalized 2-means technique. [less ▲]

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See detailClassification trees based on infrared spectroscopic data to discriminate between genuine and counterfeit medecines
Deconinck, Eric; Sacré, Pierre-Yves ULg; De Beer, Jacques

Conference (2011, September 23)

Classification trees built with the Classification And Regression Tree algorithm were evaluated for modelling infrared spectroscopic data in order to discriminate between genuine and counterfeit drug ... [more ▼]

Classification trees built with the Classification And Regression Tree algorithm were evaluated for modelling infrared spectroscopic data in order to discriminate between genuine and counterfeit drug samples and to classify counterfeit samples in different classes following the RIVM classification system. Models were built for two data sets consisting of the Fourier Transform Infrared spectra, the Near Infrared spectra and the Raman spectra for genuine and counterfeit samples of respectively Viagra® and Cialis®. Easy interpretable models were obtained for both models. The models were validated for their descriptive and predictive properties. The predictive properties were evaluated using both cross validation as an external validation set. The obtained models for both data sets showed a 100% correct classification for the discrimination between genuine and counterfeit samples and 83.3% and 100% correct classification for the counterfeit samples for the Viagra® and the Cialis® data set respectively. [less ▲]

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See detailClassification trees based on infrared spectroscopic data to discriminate between genuine and counterfeit medicines
Deconinck, Eric; Sacré, Pierre-Yves ULg; Coomans, Danny et al

in Journal of Pharmaceutical & Biomedical Analysis (2012), 57(1), 68-75

Classification trees built with the Classification And Regression Tree algorithm were evaluated for modelling infrared spectroscopic data in order to discriminate between genuine and counterfeit drug ... [more ▼]

Classification trees built with the Classification And Regression Tree algorithm were evaluated for modelling infrared spectroscopic data in order to discriminate between genuine and counterfeit drug samples and to classify counterfeit samples in different classes following the RIVM classification system. Models were built for two data sets consisting of the Fourrier Transformed Infrared spectra, the Near Infrared spectra and the Raman spectra for genuine and counterfeit samples of respectively Viagra® and Cialis®. Easy interpretable models were obtained for both models. The models were validated for their descriptive and predictive properties. The predictive properties were evaluated using both cross validation as an external validation set. The obtained models for both data sets showed a 100% correct classification for the discrimination between genuine and counterfeit samples and 83.3% and 100% correct classification for the counterfeit samples for the Viagra® and the Cialis® data set respectively. [less ▲]

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See detailClassification Trees based on infrared spectroscopic data to discriminate between genuine and counterfeit medicines.
Deconinck, Eric; Sacre, Pierre-Yves ULg; Coomans, Danny et al

Poster (2012)

Due to the extension of the internet, counterfeit drugs represent a growing threat for public health in the developing countries but also more and more in the industrial world. In literature several ... [more ▼]

Due to the extension of the internet, counterfeit drugs represent a growing threat for public health in the developing countries but also more and more in the industrial world. In literature several analytical techniques were applied in order to discriminate between genuine and counterfeit medecines. One thing all these techniques have in common is that they generate a huge amount of data, which is often difficult to interpret in order to see differences between the different samples and to determine the cause of the differences. The majority of the authors make use of explorative chemometric tools to visualise the differences in the data obtained for the different samples. Even if some of the applied methods could be able to give a model with predictive ability, only a few authors created a model able to predict if a sample is counterfeit or not. Classification trees built with the Classification And Regression Tree algorithm were evaluated for modelling infrared spectroscopic data in order to discriminate between genuine and counterfeit drug samples and to classify counterfeit samples in different classes following the RIVM classification system. Models were built for two data sets consisting of the Fourrier Transformed Infrared spectra, the Near Infrared spectra and the Raman spectra for genuine and counterfeit samples of respectively Viagra® and Cialis®. Easy interpretable models were obtained for both models. The models were validated for their descriptive and predictive properties. The predictive properties were evaluated using both cross validation as an external validation set. The obtained models for both data sets showed a 100% correct classification for the discrimination between genuine and counterfeit samples and 83.3% and 100% correct classification for the counterfeit samples for the Viagra® and the Cialis® data set respectively. [less ▲]

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See detailClassification, photo-z and environment of X-ray selected sources in the XMM-LSS field
Melnyk, Olga ULg; Plionis, M; Elyiv, Andrii ULg et al

in Proceedings of the XXL Consortium meeting in the Castle of the Meudon observatory, from 9 to 13 July, 2012 (2012, July 09)

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See detailClassifying microarray data with association rules
Antonie, Luiza; Bessonov, Kyrylo ULg

in Proceedings of the 2011 ACM Symposium on Applied Computing (2011)

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See detailClassifying pairs with trees for biological network inference
Schrynemackers, Marie ULg

Conference (2012, November 28)

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See detailClassifying pairs with trees for supervised biological network inference
Schrynemackers, Marie ULg; Wehenkel, Louis ULg; Madan Babu, Mohan et al

in Molecular Biosystems (2015), 11(8), 2116-2125

Networks are ubiquitous in biology, and computational approaches have been largely investigated for their inference. In particular, supervised machine learning methods can be used to complete a partially ... [more ▼]

Networks are ubiquitous in biology, and computational approaches have been largely investigated for their inference. In particular, supervised machine learning methods can be used to complete a partially known network by integrating various measurements. Two main supervised frameworks have been proposed: the local approach, which trains a separate model for each network node, and the global approach, which trains a single model over pairs of nodes. Here, we systematically investigate, theoretically and empirically, the exploitation of tree-based ensemble methods in the context of these two approaches for biological network inference. We first formalize the problem of network inference as a classification of pairs, unifying in the process homogeneous and bipartite graphs and discussing two main sampling schemes. We then present the global and the local approaches, extending the latter for the prediction of interactions between two unseen network nodes, and discuss their specializations to tree-based ensemble methods, highlighting their interpretability and drawing links with clustering techniques. Extensive computational experiments are carried out with these methods on various biological networks that clearly highlight that these methods are competitive with existing methods. [less ▲]

Detailed reference viewed: 78 (26 ULg)