The main focus of situation assessment is to decide on the adequacy of process behaviour with respect to specifications. When is not possible to have a mathematical model to represent the system operation, other non-model-based techniques must be considered. Classification methods are typically proposed as strategies for diagnosis. Here, identification of the functional states is reduced to recognising the current shapes of variables as well-known states, commonly taking advantage of a process expert or past experiences. However, human knowledge is related to concepts and symbols whereas process acquisition systems provide monitoring systems with numerical data. Consequently, these type of knowledge-based decision systems are usually forced to work in a higher level of abstraction using symbolic representations. This thesis deals with the study of classification methods when performing qualitative trends analysis. The aim is to obtain qualitative trends and their classification by means of the extracted knowledge from past experiences. This doctoral dissertation deals with the study of classification methods when performing qualitative trends analysis. The aim is to obtain qualitative trends and their classification by means of the extracted knowledge from past experiences. / El objetivo principal de la evaluación de situaciones es decidir sobre la adecuación del comportamiento del proceso con respecto a las especificaciones. Cuando no es posible tener un modelo matemático para representar el funcionamiento del sistema, otras técnicas deben considerarse. Los métodos de clasificación suelen ser propuestos como estrategias para el diagnóstico. La identificación de los estados funcionales se reduce a reconocer las formas de las variables como estados conocidos, comúnmente adquiriendo conocimiento de un experto o experiencias anteriores. Sin embargo, el conocimiento humano se relaciona con conceptos y símbolos, mientras que los sistemas de adquisición proporcionan datos numéricos. En consecuencia, este tipo de sistemas basados en el conocimiento de decisiones trabajan en un nivel superior de abstracción a través de representaciones simbólicas. Esta tesis aborda el estudio de métodos de clasificación de las tendencias cualitativas. El objetivo es clasificarlas por medio del conocimiento extraído de las experiencias pasadas.

Fundação de Amparo à Pesquisa do Estado do Rio de Janeiro / Este trabalho de pesquisa descreve dois estudos de caso de métodos quimiométricos empregados para a quantificação de hidrocarbonetos policíclicos aromáticos HPAs (naftaleno, fluoreno, fenantreno e fluoranteno) em água potável usando espectroscopia de fluorescência molecular e a classificação e caracterização de sucos de uva e seus parâmetros de qualidade através de espectroscopia de infravermelho próximo. O objetivo do primeiro estudo é a aplicação combinada de métodos quimiométricos de segunda ordem (N-PLS, U-PLS, U-PLS/RBL e PARAFAC) e espectrofluorimetria para determinação direta de HPAs em água potável, visando contribuir para o conhecimento do potencial destas metodologias como alternativa viável para a determinação tradicional por cromatografia univariada. O segundo estudo de caso destinado à classificação e determinação de parâmetros de qualidade de sucos de uva, densidade relativa e teor de sólidos solúveis totais, foi medida por espectroscopia de infravermelho próximo e métodos quimiométricos. Diversos métodos quimiométricos, tais como HCA, PLS-DA, SVM-DA e SIMCA foram investigados para a classificação amostras de sucos de uva ao mesmo tempo que métodos de calibração multivariada de primeira ordem, tais como PLS, iPLS e SVM-LS foram usadas para a predição dos parâmetros de qualidade. O princípio orientador para o desenvolvimento dos estudos aqui descritos foi a necessidade de metodologias analíticas com custo, tempo de execução e facilidade de operação melhores e menor produção de resíduos do que os métodos atualmente utilizados para a quantificação de HPAs, em água de torneira, e classificação e caracterização das amostras de suco de uva e seus parâmetros de qualidade / This research work describes two studies of chemometric methods employed for the quantification of polycyclic aromatic hydrocarbons PAHs (naphthalene, fluorene, phenanthrene and fluoranthene) in tap water using molecular fluorescence technique, and the classification and characterization of grape juice and its quality parameters by near infrared spectroscopy. The goal of the first study is the combined application of the second-order chemometric methods (N-PLS, U-PLS, U-PLS/RBL, PARAFAC) and spectrofluorimetry technique for direct determination of HPAs in tap water, aiming to contribute for the growth of knowledge about the potential of these methodologies as viable alternatives to the traditional univariate chromatographic determination. The second study aimed at the classification and determination of grape juice quality parameters, as relative density and total soluble solids, were measured with the aid of near infrared spectroscopy and chemometric methods. Several chemometric methods, such as HCA, PLS-DA, SVM-DA, SIMCA, were investigated for the classification of grape juice samples as the same time first-order multivariate calibration methods, such as PLS, iPLS, SVM-LS, were used for prediction of quality parameters. The guiding principle for the development of the studies herein described was the need for analytical methodologies with cost, execution time, ease of operation, and residue output better or lower than present day methods employed for the quantification of PAHs in tap water and the classification and characterization of grape juice sample and its quality parameters

L'accident vasculaire cérébral et la sclérose en plaques figurent parmi les maladies neurologiques les plus destructrices du système nerveux central. L'accident vasculaire cérébral est la deuxième cause de décès et la principale cause de handicap chez l'adulte dans le monde alors que la sclérose en plaques est la maladie neurologique non traumatique la plus fréquente chez l'adulte jeune. L'imagerie par résonance magnétique est un outil important pour distinguer le tissu cérébral sain du tissu pathologique à des fins de diagnostic, de suivi de la maladie, et de prise de décision pour un traitement personnalisé des patients atteints d'accident vasculaire cérébral ou de sclérose en plaques. La prédiction de l'évolution individuelle de la maladie chez les patients atteints d'accident vasculaire cérébral ou de sclérose en plaques constitue un défi pour les cliniciens avant de donner un traitement individuel approprié. Cette prédiction est possible avec des approches statistiques appropriées basées sur des informations cliniques et d'imagerie. Toutefois, l'étiologie, la physiopathologie, les symptômes et l'évolution dans l'accident vasculaire cérébral et la sclérose en plaques sont très différents. Par conséquent, dans cette thèse, les méthodes statistiques utilisées pour ces deux maladies neurologiques sont différentes. Le premier objectif était l'identification du tissu à risque d'infarctus chez les patients atteints d'accident vasculaire cérébral. Pour cet objectif, les méthodes de classification (dont les méthodes de machine learning) ont été utilisées sur des données d'imagerie mesurées à l'admission pour prédire le risque d'infarctus à un mois. Les performances des méthodes de classification ont été ensuite comparées dans un contexte d'identification de tissu à haut risque d'infarctus à partir de données humaines codées voxel par voxel. Le deuxième objectif était de regrouper les patients atteints de sclérose en plaques avec une méthode non supervisée basée sur des trajectoires individuelles cliniques et d'imagerie tracées sur cinq ans. Les groupes de trajectoires aideraient à identifier les patients menacés d'importantes progressions et donc à leur donner des médicaments plus efficaces. Le troisième et dernier objectif de la thèse était de développer un modèle prédictif pour l'évolution du handicap individuel des patients atteints de sclérose en plaques sur la base de données démographiques, cliniques et d'imagerie obtenues a l'inclusion. L'hétérogénéité des évolutions du handicap chez les patients atteints de sclérose en plaques est un important défi pour les cliniciens qui cherchent à prévoir l'évolution individuelle du handicap. Le modèle mixte linéaire à classes latentes a été utilisé donc pour prendre en compte la variabilité individuelle et la variabilité inobservée entre sous-groupes de sclérose en plaques / Stroke and multiple sclerosis are two of the most destructive neurological diseases of the central nervous system. Stroke is the second most common cause of death and the major cause of disability worldwide whereas multiple sclerosis is the most common non-traumatic disabling neurological disease of adulthood. Magnetic resonance imaging is an important tool to distinguish healthy from pathological brain tissue in diagnosis, monitoring disease evolution, and decision-making in personalized treatment of patients with stroke or multiple sclerosis.Predicting disease evolution in patients with stroke or multiple sclerosis is a challenge for clinicians that are about to decide on an appropriate individual treatment. The etiology, pathophysiology, symptoms, and evolution of stroke and multiple sclerosis are highly different. Therefore, in this thesis, the statistical methods used for the study of the two neurological diseases are different.The first aim was the identification of the tissue at risk of infarction in patients with stroke. For this purpose, the classification methods (including machine learning methods) have been used on voxel-based imaging data. The data measured at hospital admission is performed to predict the infarction risk at one month. Next, the performances of the classification methods in identifying the tissue at a high risk of infarction were compared. The second aim was to cluster patients with multiple sclerosis using an unsupervised method based on individual clinical and imaging trajectories plotted over five 5 years. Clusters of trajectories would help identifying patients who may have an important progression; thus, to treat them with more effective drugs irrespective of the clinical subtypes. The third and final aim of this thesis was to develop a predictive model for individual evolution of patients with multiple sclerosis based on demographic, clinical, and imaging data taken at study onset. The heterogeneity of disease evolution in patients with multiple sclerosis is an important challenge for the clinicians who seek to predict the disease evolution and decide on an appropriate individual treatment. For this purpose, the latent class linear mixed model was used to predict disease evolution considering individual and unobserved subgroup' variability in multiple sclerosis

Fundação de Amparo à Pesquisa do Estado do Rio de Janeiro / Este trabalho de pesquisa descreve dois estudos de caso de métodos quimiométricos empregados para a quantificação de hidrocarbonetos policíclicos aromáticos HPAs (naftaleno, fluoreno, fenantreno e fluoranteno) em água potável usando espectroscopia de fluorescência molecular e a classificação e caracterização de sucos de uva e seus parâmetros de qualidade através de espectroscopia de infravermelho próximo. O objetivo do primeiro estudo é a aplicação combinada de métodos quimiométricos de segunda ordem (N-PLS, U-PLS, U-PLS/RBL e PARAFAC) e espectrofluorimetria para determinação direta de HPAs em água potável, visando contribuir para o conhecimento do potencial destas metodologias como alternativa viável para a determinação tradicional por cromatografia univariada. O segundo estudo de caso destinado à classificação e determinação de parâmetros de qualidade de sucos de uva, densidade relativa e teor de sólidos solúveis totais, foi medida por espectroscopia de infravermelho próximo e métodos quimiométricos. Diversos métodos quimiométricos, tais como HCA, PLS-DA, SVM-DA e SIMCA foram investigados para a classificação amostras de sucos de uva ao mesmo tempo que métodos de calibração multivariada de primeira ordem, tais como PLS, iPLS e SVM-LS foram usadas para a predição dos parâmetros de qualidade. O princípio orientador para o desenvolvimento dos estudos aqui descritos foi a necessidade de metodologias analíticas com custo, tempo de execução e facilidade de operação melhores e menor produção de resíduos do que os métodos atualmente utilizados para a quantificação de HPAs, em água de torneira, e classificação e caracterização das amostras de suco de uva e seus parâmetros de qualidade / This research work describes two studies of chemometric methods employed for the quantification of polycyclic aromatic hydrocarbons PAHs (naphthalene, fluorene, phenanthrene and fluoranthene) in tap water using molecular fluorescence technique, and the classification and characterization of grape juice and its quality parameters by near infrared spectroscopy. The goal of the first study is the combined application of the second-order chemometric methods (N-PLS, U-PLS, U-PLS/RBL, PARAFAC) and spectrofluorimetry technique for direct determination of HPAs in tap water, aiming to contribute for the growth of knowledge about the potential of these methodologies as viable alternatives to the traditional univariate chromatographic determination. The second study aimed at the classification and determination of grape juice quality parameters, as relative density and total soluble solids, were measured with the aid of near infrared spectroscopy and chemometric methods. Several chemometric methods, such as HCA, PLS-DA, SVM-DA, SIMCA, were investigated for the classification of grape juice samples as the same time first-order multivariate calibration methods, such as PLS, iPLS, SVM-LS, were used for prediction of quality parameters. The guiding principle for the development of the studies herein described was the need for analytical methodologies with cost, execution time, ease of operation, and residue output better or lower than present day methods employed for the quantification of PAHs in tap water and the classification and characterization of grape juice sample and its quality parameters

The aim of this thesis is comparison of selected classification methods which are logistic regression (binary and multinominal), multilayer perceptron and classification trees, CHAID and CRT. The first part is reminiscent of the theoretical basis of these methods and explains the nature of parameters of the models. The next section applies the above classification methods to the six data sets and then compares the outputs of these methods. Particular emphasis is placed on the discriminatory power rating models, which a separate chapter is devoted to. Rating discriminatory power of the model is based on the overall accuracy, F-measure and size of the area under the ROC curve. The benefit of this work is not only a comparison of selected classification methods based on statistical models evaluating discriminatory power, but also an overview of the strengths and weaknesses of each method.

The diploma thesis deals with machine learning, expert systems, fuzzy logic, genetic algorithms, neural networks and chaos theory, which fall into the category of artificial intelligence. The aim of this work is to describe and implement three different classification methods, according to which the data set will be processed. The GNU Octave software environment was chosen for the data application for licensing reasons. Further evaluate the success of data classification, including visualization. Three different classification methods are used for comparison, so that we can compare the processed data with each other.

El disseny molecular de sistemes d’interès per a la química mèdica i per al disseny de fàrmacs sempre s’ha trobat molt lligat a la disponibilitat sintètica dels resultats. Des del moment que la química combinatòria s’incorpora dins de l’esquema sintètic, canvia el paper que ha de jugar la química computacional: la diversitat d’estructures possibles a sintetitzar fa necessària la introducció de mètodes, com el cribratge virtual, que permetin avaluar la viabilitat de grans quimioteques virtuals amb un temps raonable. Els mètodes quimioinformàtics responen a la necessitat anterior, posant a l’abast de l’usuari mètodes eficaços per a la predicció teòrica d’activitats biològiques o propietats d’interès. Dins d’aquests destaquen els mètodes basats en la relació quantitativa d’estructura-activitat (QSAR). Aquests han demostrat ser eficaços per l’establiment de models de predicció en l’àmbit farmacològic i biomèdic. S’ha avaluat la utilització de mètodes QSAR no lineals en la teràpia fotodinàmica del càncer, donat que és una de les línies de recerca d’interès del Grup d’Enginyeria Molecular (GEM) de l’IQS. El disseny de fotosensibilitzadors es pot realitzar a partir de la predicció de propietats fisicoquímiques (com l’espectre d’absorció i la hidrofobicitat del sistema molecular), i de l’estudi de la seva localització subcel•lular preferent, la qual ha demostrat recentment jugar un paper molt important en l’eficàcia del procés global. Per altra banda, les xarxes neuronals artificials són actualment un dels mètodes més ben valorats per a l’establiment de models QSAR no lineals. Donat l’interès de disposar d’un programari capaç d’aplicar aquests mètodes i que, a més, sigui prou versàtil i adaptable com per poder-se aplicar a diferents problemes, s’ha desenvolupat el programari ArIS. Aquest inclou els principals mètodes de xarxes neuronals artificials, per realitzar tasques de classificació i predicció quantitativa, necessaris per a l’estudi de problemes d’interès, com és la predicció de l’activitat anti-VIH d’anàlegs de l’AZT, l’optimització de formulacions químiques o el reconeixement estructural de grans sistemes moleculars / El diseño molecular de sistemas de interés para la química médica y para el diseño de fármacos siempre ha estado condicionado por la disponibilidad sintética de los resultados. Desde el momento en que la química combinatoria se incorpora en el esquema sintético, cambia el papel de la química computacional: la diversidad de estructuras que pueden sintetizarse hace necesaria la introducción de métodos, como el cribado virtual, que permitan evaluar la viabilidad de grandes quimiotecas virtuales en un tiempo razonable. Los métodos quimioinformáticos responden a la necesidad anterior, ofreciendo al usuario métodos eficaces para la predicción teórica de actividades biológicas o propiedades de interés. Entre ellos destacan los métodos basados en la relación cuantitativa de estructura-actividad (QSAR), que han demostrado ser eficaces para establecer modelos de predicción en el ámbito farmacológico y biomédico. Se ha evaluado la utilización de métodos QSAR no lineales en terapia fotodinámica del cáncer, dado que es una de las líneas de investigación de interés del Grup d’Enginyeria Molecular (GEM) del IQS. El diseño de fotosensibilizadores se puede realizar a partir de la predicción de propiedades fisicoquímicas (como su espectro de absorción o su hidrofobicidad) y del estudio de su localización subcelular preferente, la cual ha demostrado recientemente jugar un papel muy importante en la eficacia del proceso global. Por otro lado, las redes neuronales artificiales son actualmente uno de los métodos mejor valorados para establecer modelos QSAR no lineales. Es por ello que resulta muy interesante disponer de un programa capaz de aplicar estos métodos y que, además, sea lo suficientemente versátil y adaptable como para poder aplicarse a distintos problemas, según las necesidades del usuario. Por este motivo se ha desarrollado el programa ArIS, el cual incluye los principales métodos de redes neuronales artificiales para realizar tareas de clasificación y predicción cuantitativa, necesarios para el estudio de problemas de interés como la predicción de la actividad anti-VIH de análogos del AZT, la optimización de formulaciones químicas o el reconocimiento estructural de grandes sistemas moleculares. / Molecular modelling of interesting systems for medicinal chemistry and drug design highly depends on availability of synthetic results. Since combinatorial chemistry was incorporated into the synthetic scheme, the role of computational chemistry has changed: the structural diversity of candidates to be synthesized requires the introduction of computational methods which are able to screen large virtual libraries. Answering to this requirement, chemoinformatics offers many kinds of different methods for predicting biological activities and molecular properties. One of the most relevant techniques among them is Quantitative Structure-Activity Relationships (QSAR), which can be used to establish prediction models for both, pharmacological and biomedical sectors. The use of non- linear QSAR methods has been evaluated in photodynamic therapy of cancer, one of the research areas of the Grup d’Enginyeria Molecular (GEM) at IQS. Molecular design of photosensitizers can be performed by computational studies of their physicochemical properties (absorption spectra or hydrophobicity, for example) and subcellular localization, which becomes a key factor in the efficacy of the overall process. Furthermore, artificial neural networks are nowadays rated as one of the very best methods for establishing non-linear QSAR models. Developing software that includes all these methods would be certainly interesting. Implemented algorithms should be versatile and easily adaptable for their use in any problems. We have developed ArIS software, which includes the most important methods of artificial neural networks for classification and quantitative prediction. ArIS has been used to predict anti-HIV activity of AZT-analogues, for optimization of chemical formulations and for structural recognition in large molecular systems, among others.

Indiana University-Purdue University Indianapolis (IUPUI) / The positive identification of human remains that are decomposed, burnt, or otherwise disfigured can prove especially challenging in forensic anthropology, resulting in the need for specialized methods of analysis. Due to the unique morphological characteristics of the frontal sinus, a positive identification can be made in cases of unknown human remains, even when remains are highly cremated or decomposed. This study retrospectively reviews 3D CBCT images of a total of 43 Caucasian patients between the ages of 20-38 from the Indiana University School of Dentistry to quantify frontal sinus differences between adult males and females and investigate the usefulness of frontal sinus morphology for forensic identification. Digit codes with six sections and eleven-digit numbers were created to classify each individual sinus. It was shown that 3D CBCT images of the frontal sinus could be used to make a positive forensic identification. Metric measurements displayed a high degree of variability between sinuses and no two digit codes were identical. However, it was also shown that there were almost no quantifiable and significant sexually dimorphic differences between male and female frontal sinuses. This study confirms that sex determination should not be a primary goal of frontal sinus analysis and highlights the importance of creating a standard method of frontal sinus evaluation based on metric measurements.

No description available.