La scoliose idiopathique de l’adolescent (SIA) est une déformation tri-dimensionelle du rachis. Son traitement comprend l’observation, l’utilisation de corsets pour limiter sa progression ou la chirurgie pour corriger la déformation squelettique et cesser sa progression. Le traitement chirurgical reste controversé au niveau des indications, mais aussi de la chirurgie à entreprendre. Malgré la présence de classifications pour guider le traitement de la SIA, une variabilité dans la stratégie opératoire intra et inter-observateur a été décrite dans la littérature. Cette variabilité s’accentue d’autant plus avec l’évolution des techniques chirurgicales et de l’instrumentation disponible. L’avancement de la technologie et son intégration dans le milieu médical a mené à l’utilisation d’algorithmes d’intelligence artificielle informatiques pour aider la classification et l’évaluation tridimensionnelle de la scoliose. Certains algorithmes ont démontré être efficace pour diminuer la variabilité dans la classification de la scoliose et pour guider le traitement. L’objectif général de cette thèse est de développer une application utilisant des outils d’intelligence artificielle pour intégrer les données d’un nouveau patient et les évidences disponibles dans la littérature pour guider le traitement chirurgical de la SIA. Pour cela une revue de la littérature sur les applications existantes dans l’évaluation de la SIA fut entreprise pour rassembler les éléments qui permettraient la mise en place d’une application efficace et acceptée dans le milieu clinique. Cette revue de la littérature nous a permis de réaliser que l’existence de “black box” dans les applications développées est une limitation pour l’intégration clinique ou la justification basée sur les évidence est essentielle. Dans une première étude nous avons développé un arbre décisionnel de classification de la scoliose idiopathique basé sur la classification de Lenke qui est la plus communément utilisée de nos jours mais a été critiquée pour sa complexité et la variabilité inter et intra-observateur. Cet arbre décisionnel a démontré qu’il permet d’augmenter la précision de classification proportionnellement au temps passé à classifier et ce indépendamment du niveau de connaissance sur la SIA. Dans une deuxième étude, un algorithme de stratégies chirurgicales basé sur des règles extraites de la littérature a été développé pour guider les chirurgiens dans la sélection de l’approche et les niveaux de fusion pour la SIA. Lorsque cet algorithme est appliqué à une large base de donnée de 1556 cas de SIA, il est capable de proposer une stratégie opératoire similaire à celle d’un chirurgien expert dans prêt de 70% des cas. Cette étude a confirmé la possibilité d’extraire des stratégies opératoires valides à l’aide d’un arbre décisionnel utilisant des règles extraites de la littérature. Dans une troisième étude, la classification de 1776 patients avec la SIA à l’aide d’une carte de Kohonen, un type de réseaux de neurone a permis de démontrer qu’il existe des scoliose typiques (scoliose à courbes uniques ou double thoracique) pour lesquelles la variabilité dans le traitement chirurgical varie peu des recommandations par la classification de Lenke tandis que les scolioses a courbes multiples ou tangentielles à deux groupes de courbes typiques étaient celles avec le plus de variation dans la stratégie opératoire. Finalement, une plateforme logicielle a été développée intégrant chacune des études ci-dessus. Cette interface logicielle permet l’entrée de données radiologiques pour un patient scoliotique, classifie la SIA à l’aide de l’arbre décisionnel de classification et suggère une approche chirurgicale basée sur l’arbre décisionnel de stratégies opératoires. Une analyse de la correction post-opératoire obtenue démontre une tendance, bien que non-statistiquement significative, à une meilleure balance chez les patients opérés suivant la stratégie recommandée par la plateforme logicielle que ceux aillant un traitement différent. Les études exposées dans cette thèse soulignent que l’utilisation d’algorithmes d’intelligence artificielle dans la classification et l’élaboration de stratégies opératoires de la SIA peuvent être intégrées dans une plateforme logicielle et pourraient assister les chirurgiens dans leur planification préopératoire. / Adolescent idiopathic scoliosis (AIS) is a three-dimensional deformity of the spine. Management of AIS includes conservative treatment with observation, the use of braces to limit its progression or surgery to correct the deformity and cease its progression. Surgical treatment of AIS remains controversial with respect to not only indications but also surgical strategy. Despite the existence of classifications to guide AIS treatment, intra- and inter-observer variability in surgical strategy has been described in the literature. Technological advances and their integration into the medical field have led to the use of artificial intelligence (AI) algorithms to assist with AIS classification and three-dimensional evaluation. With the evolution of surgical techniques and instrumentation, it is probable that the intra- and inter-observer variability could increase. However, some AI algorithms have shown the potential to lower variability in classification and guide treatment. The overall objective of this thesis was to develop software using AI tools that has the capacity to integrate AIS patient data and available evidence from the literature to guide AIS surgical treatment. To do so, a literature review on existing computer applications developed with regards to AIS evaluation and management was undertaken to gather all the elements that would lead to usable software in the clinical setting. This review highlighted the fact that many applications use a non-descript “black box” between input and output, which limits clinical integration where management based on evidence is essential. In the first study, we developed a decision tree to classify AIS based on the Lenke scheme. The Lenke scheme was popular in the past, but has recently been criticized for its complexity leading to intra and inter-observer variability. The resultant decision tree demonstrated an ability to increase classification accuracy in proportion to the time spent classifying. Importantly, this increase in accuracy was independently of previous knowledge about AIS. In the second study, a surgical strategy rule-based algorithm was developed using rules extracted from the literature to guide surgeons in the selection of the approach and levels of fusion for AIS. When this rule-based algorithm was tested against a database of 1,556 AIS cases, it was able to output a surgical strategy similar to the one undertaken by an expert surgeon in 70% of cases. This study confirmed the ability of a rule-based algorithm based on the literature to output valid surgical strategies. In the third study, classification of 1,776 AIS patients was undertaken using Kohonen Self-Organizing-Maps (SOM), which is a kind of neural network that demonstrates there are typical AIS curve types (i.e: single curves and double thoracic curves) for which there is little variability in surgical treatment when compared to the recommendations from the Lenke scheme. Other curve types (i.e: multiple curves or in transition zones between typical curves) have much greater variability in surgical strategy. Finally, a software platform integrating all the above studies was developed. The interface of this software platform allows for: 1) the input of AIS patient radiographic measurements; 2) classification of the curve type using the decision tree; 3) output of surgical strategy options based on rules extracted from the literature. A comparison of surgical correction obtained by patients receiving surgical treatment suggested by the software showed a tendency to obtain better balance -though non-statistically significant - than those who were treated differently from the surgical strategies outputted by the software. Overall, studies from this thesis suggest that the use of AI algorithms in the classification and selection of surgical strategies for AIS can be integrated in a software platform that could assist the surgeon in the planning of appropriate surgical treatment.

Os Sistemas de Distribuição (SDs), devido as suas topologias e configurações, dentre outros fatores, apresentam um desafio para a localização física das situações de faltas passíveis de ocorrência. Como fato, tem-se que uma localização de faltas, rápida e precisa, possibilita atenuar os transtornos que os usuários finais dos SDs viriam a ter em relação à qualidade do serviço prestado pelas distribuidoras. No contexto das redes elétricas inteligentes, e considerando medidores de qualidade da energia elétrica previamente alocados de forma otimizada, esta pesquisa propõe uma metodologia baseada em árvores de decisão e redes neurais artificiais para a localização de faltas em SDs radiais e aéreos. Foram realizados testes da metodologia proposta considerando variações no tipo, na impedância e no ângulo de incidência da falta aplicadas sobre o SD de 34 barras do IEEE (Institute of Electrical and Electronics Engineers). Para os testes de sensibilidade da metodologia desenvolvida, foram consideradas variações no carregamento do sistema, os erros inerentes ao sistema de medição, a variação no número de medidores disponível, o impacto de uma alocação não otimizada dos medidores e uma redução na taxa amostral. Os resultados encontrados foram promissores e indicam que a metodologia como desenvolvida poderá ser aplicada para SDs diferentes do caso teste utilizado. / Due to the distribution systems (DS) topologies, configurations and among other factors, it is a challenge to physically locate situations of faults. As a matter of fact, a fast and accurate fault location will make it possible to mitigate the inconvenience that the end users of DS would have due to the quality of the service provided by the distributors. In the context of intelligent electric grids, and considering the electric power quality meters optimally alocated, this research proposes a methodology based in decision trees and artificial neural networks for a fault location in radial and aerial DS. The proposed methodology was tested considering variations on the type, impedance and angle of incidence of the fault applied on the DS of 34 bars of the IEEE (Institute of Electrical and Electronic Engineers). For a sensitivity test of the developed methodology, it were considered the variations in system loading, the errors inherent to the measurement system, a variation in number of meters available, the impact of the non-optimized allocation of the meters and a redution on the sampling rate. The results were promising and indicated that the methodology developed can be applied to different DS from the test case used.

Submitted by SBI Biblioteca Digital (sbi.bibliotecadigital@puc-campinas.edu.br) on 2018-02-21T17:47:30Z No. of bitstreams: 1 JOSE DIVINO DE LIMA.pdf: 3046765 bytes, checksum: a793279094d547961482cafe99be62cb (MD5) / Made available in DSpace on 2018-02-21T17:47:30Z (GMT). No. of bitstreams: 1 JOSE DIVINO DE LIMA.pdf: 3046765 bytes, checksum: a793279094d547961482cafe99be62cb (MD5) Previous issue date: 2017-08-31 / The present dissertation work analyses telecommunication systems failures caused by internal and external agents. This analysis can be very challenging since such systems are complex and heterogeneous. Within this context, this work proposed a model that can be used to predict consequent failures from data samples. To do so, we have used a data mining tool and prediction algorithms that create decision trees. Applying the proposed model to a set of faults, generated by the system of a major telecommunications operator, it was demonstrated that it is possible to group faults with an accuracy of 85.96%. In this way, a process can be established that assists in the definition of grouping and correlation of failures, which allows that high level management systems can be configured more efficiently by their administrators. / O presente trabalho de disserta??o tem como principal objetivo a an?lise dos sistemas de telecomunica??o, os quais est?o cada vez mais complexos e heterog?neos e, em fun??o disso, suscet?veis a diversos tipos de falhas causadas tanto por fatores internos como externos, sendo estes ?ltimos devido ? integra??o com sistemas de terceiros. Dentro desse contexto, este trabalho apresenta, ent?o, um modelo que pode ser utilizado para prever falhas consequentes a partir de uma amostra de dados. Para tanto, utilizou-se uma ferramenta de minera??o de dados e algoritmos de predi??o, que criam ?rvores de decis?o. Aplicado o modelo proposto a um conjunto de falhas, gerado pelo sistema de uma grande operadora de telecomunica??es, demonstrou-se que ? poss?vel agrupar falhas com precis?o de 85,96%. Logo, pode-se estabelecer um processo que auxilia na defini??o do agrupamento e correla??o de falhas, permitindo que os sistemas de gest?o de alto n?vel possam ser configurados de maneira mais eficiente pelos administradores.

Esta pesquisa objetivou uma melhor compreensão do processo de escolha do modo de viagem. Empregou-se a abordagem indutiva dirigida a dados livre de suposições a priori da mineração em banco de dados (Data Mining), utilizando redes neurais artificiais (RNA) como ferramenta mineradora, à procura de conhecimento, ou informação útil, a respeito de escolha e capaz de indicar qual das estruturas de decisão subjacentes aos modelos de escolha modal considerados mais se aproximaria ao do observado. Partindo-se da ideia de que nesse processo exista um padrão o qual pode ser captado por uma RNA, ajustou-se um modelo de RNA aos dados e extraiu-se então o conhecimento contido no modelo de RNA ajustado através de um algoritmo de extração de árvore de decisão de RNA chamado Trepan (Trees parroting network), que foi analisado e interpretado à luz dos objetivos desta pesquisa. Os dados que foram utilizados nesse processo de descoberta de conhecimento são provenientes de uma pesquisa de entrevista domiciliar realizada na cidade de Bauru - SP, para fins de estimativa da matriz de deslocamentos origem-destino dessa cidade. Obteve-se quatro árvores de decisão com estruturas simples e com a araucária preditiva de 75% aproximadamente para os três modos de viagem estudados. Embora o conhecimento extraído dos modelos neurais ajustados não tenham proporcionado a indicação de qual das estruturas de decisão subjacentes aos modelos de escolha modal mais se aproxima da obtida com o modelo neural, foi constatada nas árvores resultantes do processo de descoberta do conhecimento uma relação de compensação entre o atributo sexo e os atributos relacionados à capacidade econômica do domicílio na decisão de escolha do modo carro para a realização de uma viagem. Os resultados também sugerem a não necessidade de mais um atributo de entrada referente ao deslocamento realizado em uma viagem para modelagem por RNA do processo de escolha do modo de viagem no contexto estudado. / This research aimed at a better understanding of the mode travel choice process. The inductive data driven free from a priori assumptions of the data mining approach was employed, using artificial neural networks (ANN) as a mining tool, looking for knowledge or useful information, concerning the choice process and capable of indicating which of the underlying decision structures to the considered modal choice models would come closer to the observed one. Taking into consideration that there is a pattern in this process that can be captured by ANN, an ANN model was fitted (trained) to the data, and the knowledge contained in the trained ANN model was extracted by employing an ANN decision tree extraction algorithm called Trepan (Trees parroting network), which was analysed and interpreted in the light of the object of this research. The data which was employed in this knowledge discovery process come from a household survey carried out in Bauru - SP in order to estimate the O-D matrix in this city. Four decision trees with simple structures and predicting accuracy of approximately 75% for the three travel modes studied were obtained. Even though the knowledge extracted from the trained ANN model has not yielded the indication of which of the underlying decision structures to the modal choice models was closer to the neural model, a compensating relation between the sex attribute and the household economic-related attribute in the decision of choosing the car mode in order to travel was evidenced in the trees resulting from the process of knowledge discovery. The results also suggest the lack of necessity of more than one input travel attribute concerning the displacement performed in a trip for the ANN modelling of the mode travel choice process in the studied context.

Os Sistemas de Distribuição (SDs), devido as suas topologias e configurações, dentre outros fatores, apresentam um desafio para a localização física das situações de faltas passíveis de ocorrência. Como fato, tem-se que uma localização de faltas, rápida e precisa, possibilita atenuar os transtornos que os usuários finais dos SDs viriam a ter em relação à qualidade do serviço prestado pelas distribuidoras. No contexto das redes elétricas inteligentes, e considerando medidores de qualidade da energia elétrica previamente alocados de forma otimizada, esta pesquisa propõe uma metodologia baseada em árvores de decisão e redes neurais artificiais para a localização de faltas em SDs radiais e aéreos. Foram realizados testes da metodologia proposta considerando variações no tipo, na impedância e no ângulo de incidência da falta aplicadas sobre o SD de 34 barras do IEEE (Institute of Electrical and Electronics Engineers). Para os testes de sensibilidade da metodologia desenvolvida, foram consideradas variações no carregamento do sistema, os erros inerentes ao sistema de medição, a variação no número de medidores disponível, o impacto de uma alocação não otimizada dos medidores e uma redução na taxa amostral. Os resultados encontrados foram promissores e indicam que a metodologia como desenvolvida poderá ser aplicada para SDs diferentes do caso teste utilizado. / Due to the distribution systems (DS) topologies, configurations and among other factors, it is a challenge to physically locate situations of faults. As a matter of fact, a fast and accurate fault location will make it possible to mitigate the inconvenience that the end users of DS would have due to the quality of the service provided by the distributors. In the context of intelligent electric grids, and considering the electric power quality meters optimally alocated, this research proposes a methodology based in decision trees and artificial neural networks for a fault location in radial and aerial DS. The proposed methodology was tested considering variations on the type, impedance and angle of incidence of the fault applied on the DS of 34 bars of the IEEE (Institute of Electrical and Electronic Engineers). For a sensitivity test of the developed methodology, it were considered the variations in system loading, the errors inherent to the measurement system, a variation in number of meters available, the impact of the non-optimized allocation of the meters and a redution on the sampling rate. The results were promising and indicated that the methodology developed can be applied to different DS from the test case used.

Esta pesquisa objetivou uma melhor compreensão do processo de escolha do modo de viagem. Empregou-se a abordagem indutiva dirigida a dados livre de suposições a priori da mineração em banco de dados (Data Mining), utilizando redes neurais artificiais (RNA) como ferramenta mineradora, à procura de conhecimento, ou informação útil, a respeito de escolha e capaz de indicar qual das estruturas de decisão subjacentes aos modelos de escolha modal considerados mais se aproximaria ao do observado. Partindo-se da ideia de que nesse processo exista um padrão o qual pode ser captado por uma RNA, ajustou-se um modelo de RNA aos dados e extraiu-se então o conhecimento contido no modelo de RNA ajustado através de um algoritmo de extração de árvore de decisão de RNA chamado Trepan (Trees parroting network), que foi analisado e interpretado à luz dos objetivos desta pesquisa. Os dados que foram utilizados nesse processo de descoberta de conhecimento são provenientes de uma pesquisa de entrevista domiciliar realizada na cidade de Bauru - SP, para fins de estimativa da matriz de deslocamentos origem-destino dessa cidade. Obteve-se quatro árvores de decisão com estruturas simples e com a araucária preditiva de 75% aproximadamente para os três modos de viagem estudados. Embora o conhecimento extraído dos modelos neurais ajustados não tenham proporcionado a indicação de qual das estruturas de decisão subjacentes aos modelos de escolha modal mais se aproxima da obtida com o modelo neural, foi constatada nas árvores resultantes do processo de descoberta do conhecimento uma relação de compensação entre o atributo sexo e os atributos relacionados à capacidade econômica do domicílio na decisão de escolha do modo carro para a realização de uma viagem. Os resultados também sugerem a não necessidade de mais um atributo de entrada referente ao deslocamento realizado em uma viagem para modelagem por RNA do processo de escolha do modo de viagem no contexto estudado. / This research aimed at a better understanding of the mode travel choice process. The inductive data driven free from a priori assumptions of the data mining approach was employed, using artificial neural networks (ANN) as a mining tool, looking for knowledge or useful information, concerning the choice process and capable of indicating which of the underlying decision structures to the considered modal choice models would come closer to the observed one. Taking into consideration that there is a pattern in this process that can be captured by ANN, an ANN model was fitted (trained) to the data, and the knowledge contained in the trained ANN model was extracted by employing an ANN decision tree extraction algorithm called Trepan (Trees parroting network), which was analysed and interpreted in the light of the object of this research. The data which was employed in this knowledge discovery process come from a household survey carried out in Bauru - SP in order to estimate the O-D matrix in this city. Four decision trees with simple structures and predicting accuracy of approximately 75% for the three travel modes studied were obtained. Even though the knowledge extracted from the trained ANN model has not yielded the indication of which of the underlying decision structures to the modal choice models was closer to the neural model, a compensating relation between the sex attribute and the household economic-related attribute in the decision of choosing the car mode in order to travel was evidenced in the trees resulting from the process of knowledge discovery. The results also suggest the lack of necessity of more than one input travel attribute concerning the displacement performed in a trip for the ANN modelling of the mode travel choice process in the studied context.

<p>The thesis proposes novel full decision tree and decision tree ensemble<br />induction algorithms EFTI and EEFTI, and various possibilities for their<br />implementations are explored. The experiments show that the proposed EFTI<br />algorithm is able to infer much smaller DTs on average, without the<br />significant loss in accuracy, when compared to the top-down incremental DT<br />inducers. On the other hand, when compared to other full tree induction<br />algorithms, it was able to produce more accurate DTs, with similar sizes, in<br />shorter times. Also, the hardware architectures for acceleration of these<br />algorithms (EFTIP and EEFTIP) are proposed and it is shown in experiments<br />that they can offer substantial speedups.</p> / <p>У овоj дисертациjи, представљени су нови алгоритми EFTI и EEFTI за<br />формирање стабала одлуке и њихових ансамбала неинкременталном<br />методом, као и разне могућности за њихову имплементациjу.<br />Експерименти показуjу да jе предложени EFTI алгоритам у могућности<br />да произведе драстично мања стабла без губитка тачности у односу на<br />постојеће top-down инкременталне алгоритме, а стабла знатно веће<br />тачности у односу на постојеће неинкременталне алгоритме. Такође су<br />предложене хардверске архитектуре за акцелерацију ових алгоритама<br />(EFTIP и EEFTIP) и показано је да је уз помоћ ових архитектура могуће<br />остварити знатна убрзања.</p> / <p>U ovoj disertaciji, predstavljeni su novi algoritmi EFTI i EEFTI za<br />formiranje stabala odluke i njihovih ansambala neinkrementalnom<br />metodom, kao i razne mogućnosti za njihovu implementaciju.<br />Eksperimenti pokazuju da je predloženi EFTI algoritam u mogućnosti<br />da proizvede drastično manja stabla bez gubitka tačnosti u odnosu na<br />postojeće top-down inkrementalne algoritme, a stabla znatno veće<br />tačnosti u odnosu na postojeće neinkrementalne algoritme. Takođe su<br />predložene hardverske arhitekture za akceleraciju ovih algoritama<br />(EFTIP i EEFTIP) i pokazano je da je uz pomoć ovih arhitektura moguće<br />ostvariti znatna ubrzanja.</p>

An important challenge in natural language surface realization is the generation of grammatical sentences from incomplete sentence plans. Realization can be broken into a two-stage process consisting of an over-generating rule-based module followed by a ranker that outputs the most probable candidate sentence based on a statistical language model. Thus far, an n-gram language model has been evaluated in this context. More sophisticated syntactic knowledge is expected to improve such a ranker. In this thesis, a new language model based on featurized functional dependency syntax was developed and evaluated. Generation accuracies and cross-entropy for the new language model did not beat the comparison bigram language model.

Analytic knowledge about the systematic variation in a language has an important place in the description of the language. Such knowledge is interesting e.g. in the language teaching domain, as a background for various types of linguistic studies, and in the development of more dynamic speech technology applications. In previous studies, the effects of single variables or relatively small groups of related variables on the pronunciation of words have been studied separately. The work described in this thesis takes a holistic perspective on pronunciation variation and focuses on a method for creating general descriptions of phone-level pronunciation in discourse context. The discourse context is defined by a large set of linguistic attributes ranging from high-level variables such as speaking style, down to the articulatory feature level. Models of phone-level pronunciation in the context of a discourse have been created for the central standard Swedish language variety. The models are represented in the form of decision trees, which are readable for both machines and humans. A data-driven approach was taken for the pronunciation modelling task, and the work involved the annotation of recorded speech with linguistic and related information. The decision tree models were induced from the annotation. An important part of the work on pronunciation modelling was also the development of a pronunciation lexicon for Swedish. In a cross-validation experiment, several sets of pronunciation models were created with access to different parts of the attributes in the annotation. The prediction accuracy of pronunciation models could be improved by 42.2% by making information from layers above the phoneme level accessible during model training. Optimal models were obtained when attributes from all layers of annotation were used. The goal for the models was to produce pronunciation representations representative for the language variety and not necessarily for the individual speakers, on whose speech the models were trained. In the cross-validation experiment, model-produced phone strings were compared to key phonetic transcripts of actual speech, and the phone error rate was defined as the share of discrepancies between the respective phone strings. Thus, the phone error rate is the sum of actual errors and discrepancies resulting from desired adaptations from a speaker-specific pronunciation to a pronunciation reflecting general traits of the language variety. The optimal models gave an average phone error rate of 8.2%. / QC 20100901

Η ραγδαία ανάπτυξη και διείσδυση των νέων τεχνολογιών πληροφορίας και επικοινωνίας έχει επιφέρει ριζικές αλλαγές σε όλους τους τομείς της ανθρώπινης δράσης (Castells, 1998). Ιδιαίτερο ενδιαφέρον παρουσιάζει η επιρροή των τεχνολογιών αυτών στον τομέα της εκπαίδευσης. Οι εξελίξεις στον χώρο της τεχνολογίας και επικοινωνίας καθώς και η διάδοση του Internet μετεξέλιξαν αναπόφευκτα την εκπαιδευτική διαδικασία, από το κλασσικό συγκεντρωτικό μοντέλο σε ένα πιο άμεσο και ευέλικτο: η «εξ’ Αποστάσεως Εκπαίδευση» (e-learning) είναι μια εναλλακτική μορφή εκπαίδευσης, που επιδιώκει να καλύψει τους περιορισμούς της παραδοσιακής εκπαίδευσης. Στην παρούσα μεταπτυχιακή διπλωματική εργασία σχεδιάστηκε και υλοποιήθηκε ένα ολοκληρωμένο σύστημα Δυναμικής Ανάλυσης και Πρόβλεψης της επίδοσης των εκπαιδευομένων, για ένα σύστημα εξ΄ αποστάσεως εκπαίδευσης. Η βασική ιδέα εμφορείται από την ανάγκη των ιδρυμάτων εξ΄ αποστάσεως εκπαίδευσης, για την κάλυψη των εκπαιδευτικών αναγκών και την παροχή υψηλής ποιότητας σπουδών. Η εξόρυξη γνώσης για την πρόβλεψη της επίδοσης των εκπαιδευομένων συμβάλλει καθοριστικά στην επίτευξη υψηλής ποιότητας σπουδών. Η ικανότητα και η δυνατότητα πρόβλεψης της απόδοσης των εκπαιδευομένων μπορεί να φανεί χρήσιμη με αρκετούς τρόπους για την διαμόρφωση ενός συστήματος, που θα μπορεί να αποτρέψει την αποτυχία καθώς και την παραίτηση των εκπαιδευομένων. Αξίζει να σημειωθεί ότι στα συστήματα εξ’ αποστάσεως εκπαίδευσης η συχνότητα «εγκατάλειψης» είναι αρκετά υψηλότερη από αυτή στα συμβατικά πανεπιστήμια. Για την πρόβλεψη της επίδοσης των εκπαιδευομένων, η απαιτούμενη πληροφορία βρίσκεται «κρυμμένη» στο εκπαιδευτικό σύνολο δεδομένων (δλδ. βαθμοί γραπτών εργασιών, βαθμοί τελικής εξέτασης, παρουσίες φοιτητών) και είναι εξαγώγιμη με τεχνικές εξόρυξης. Η χρήση μεθόδων εξόρυξης δεδομένων (data mining) στον τομέα της εκπαίδευσης παρουσιάζει αυξανόμενο ερευνητικό ενδιαφέρον. Ο νέος αυτός «αναπτυσσόμενος» τομέας έρευνας, που ονομάζεται «Εκπαιδευτική Εξόρυξη Δεδομένων», ασχολείται με την ανάπτυξη μεθόδων εξόρυξης «γνώσης» από τα εκπαιδευτικά σύνολα δεδομένων. Πράγμα που επιτυγχάνεται με τη χρήση τεχνικών όπως τα δέντρα απόφασης, τα Νευρωνικά Δίκτυα, Naïve Bayes, k-means, κλπ. Η παρούσα εργασία έχει σχεδιαστεί να προσφέρει ένα μοντέλο εξόρυξης δεδομένων χρησιμοποιώντας τη μέθοδο των δέντρων απόφασης, για το σύστημα τριτοβάθμιας εκπαίδευσης στο ανοιχτό πανεπιστήμιο. Η «γνώση» που προκύπτει από τα δεδομένα εξόρυξης θα χρησιμοποιηθεί με στόχο την διευκόλυνση και την ενίσχυση της μάθησης, καθώς επίσης και στη λήψη αποφάσεων. Στην παρούσα εργασία, εξάγουμε «γνώση» που σχετίζεται με τις επιδόσεις των μαθητών στην τελική εξέταση. Επίσης, γίνεται εντοπισμός των ατόμων που εγκαταλείπουν το μάθημα και των μαθητών που χρειάζονται ιδιαίτερη προσοχή και εντέλει δίνει τη δυνατότητα στους καθηγητές να παράσχουν την κατάλληλη παροχή συμβουλών. / The rapid development and intrusion of information technology and communications have caused radical changes in all sectors of human’s activity. (Castells, 1998). Of particular interest is the great technology’s influence on education. Due to the adoption of the new technologies, e-learning has been emerged and developed. As a result, distance learning has transformed and new possibilities have appeared. It is remarkable that distance learning became and considered as a scout of the new era in education and contributed to the quality of education: e-learning is trying to cover the limitations of conventional teaching environment. In the present thesis, an integrated system of dynamic analysis and prediction of the performance of students in distance education has been designed and implemented. The initial idea for designing this system came from the higher distance education institutes’ need to provide quality education to its students and to improve the quality of managerial decisions. One way to achieve highest level of quality in higher distance education e-learning system is by discovering knowledge from educational data to study the main attributes that may affect the students’ performance. The discovered knowledge can be used to offer a helpful and constructive recommendations to the academic planners in higher distance education institutes to enhance their decision making process, to improve students’ academic performance, trim down failure rate and dropout rate, to assist instructors, to improve teaching and many other benefits. Dropout rates in university level distance learning are definitely higher than those inconventional universities, thus limiting dropout is essential in university-level distance learning.