The purpose of this project is to develop a novel proof-of-concept system in attempt to measure affective states during learning-tasks and investigate whether machine learning models trained with this data has the potential to enhance the learning experience for an individual. By considering biometric signals from a user during a learning session, the affective states anxiety, engagement and boredom will be classified using different signal transformation methods and finally using machine-learning models from the Weka Java API. Data is collected using an Empatica E4 Wristband which gathers skin- and heart related biometric data which is streamed to an Android application via Bluetooth for processing. Several machine-learning algorithms and features were evaluated for best performance.

Para explorar as relações existentes entre os dados abriu-se espaço para a procura de conhecimento e informações úteis não conhecidas, a partir de grandes conjuntos de dados armazenados. A este campo deu-se o nome de Descoberta de Conhecimento em Base de Dados (DCBD), o qual foi formalizado em 1989. O DCBD é composto por um processo de etapas ou fases, de natureza iterativa e interativa. Este trabalho baseou-se na metodologia CRISP-DM . Independente da metodologia empregada, este processo tem uma fase que pode ser considerada o núcleo da DCBD, a “mineração de dados” (ou modelagem conforme CRISP-DM), a qual está associado o conceito “classe de tipo de problema”, bem como as técnicas e algoritmos que podem ser empregados em uma aplicação de DCBD. Destacaremos as classes associação e agrupamento, as técnicas associadas a estas classes, e os algoritmos Apriori e K-médias. Toda esta contextualização estará compreendida na ferramenta de mineração de dados escolhida, Weka (Waikato Environment for Knowledge Analysis). O plano de pesquisa está centrado em aplicar o processo de DCBD no Poder Judiciário no que se refere a sua atividade fim, julgamentos de processos, procurando por descobertas a partir da influência da classificação processual em relação à incidência de processos, ao tempo de tramitação, aos tipos de sentenças proferidas e a presença da audiência. Também, será explorada a procura por perfis de réus, nos processos criminais, segundo características como sexo, estado civil, grau de instrução, profissão e raça. O trabalho apresenta nos capítulos 2 e 3 o embasamento teórico de DCBC, detalhando a metodologia CRISP-DM. No capítulo 4 explora-se toda a aplicação realizada nos dados do Poder Judiciário e por fim, no capítulo 5, são apresentadas as conclusões. / With the purpose of exploring existing connections among data, a space has been created for the search of Knowledge an useful unknown information based on large sets of stored data. This field was dubbed Knowledge Discovery in Databases (KDD) and it was formalized in 1989. The KDD consists of a process made up of iterative and interactive stages or phases. This work was based on the CRISP-DM methodology. Regardless of the methodology used, this process features a phase that may be considered as the nucleus of KDD, the “data mining” (or modeling according to CRISP-DM) which is associated with the task, as well as the techniques and algorithms that may be employed in an application of KDD. What will be highlighted in this study is affinity grouping and clustering, techniques associated with these tasks and Apriori and K-means algorithms. All this contextualization will be embodied in the selected data mining tool, Weka (Waikato Environment for Knowledge Analysis). The research plan focuses on the application of the KDD process in the Judiciary Power regarding its related activity, court proceedings, seeking findings based on the influence of the procedural classification concerning the incidence of proceedings, the proceduring time, the kind of sentences pronounced and hearing attendance. Also, the search for defendants’ profiles in criminal proceedings such as sex, marital status, education background, professional and race. In chapters 2 and 3, the study presents the theoretical grounds of KDD, explaining the CRISP-DM methodology. Chapter 4 explores all the application preformed in the data of the Judiciary Power, and lastly, in Chapter conclusions are drawn

This thesis explores possible approaches to detecting robotic activity of botnets on network. Initially, the detection based on full packet analysis in consideration of DNS, HTTP and IRC communication, is described. However, this detection is found inapplicable for technical and ethical reasons. Then it focuses on the analysis based on network flow metadata, compiling them to be processable in machine learning. It creates detection models using different machine learning methods, to compare them with each other. Bayes net method is found to be acceptable for detecting robotic activity of botnets. The Bayesian model is only able to identify the botnet that already executes the commands sent by its C&C server. "Sleeping" botnets are not reliably detectable by this model.

The main theme of this master's thesis is a description of text mining. This document is specialized to English texts and their automatic data preprocessing. The main part of this thesis analyses various stemming algorithms (Lovins, Porter and Paice/Husk). Stemming is a procedure for automatic conflating semantically related terms together via the use of rule sets. Next part of this thesis describes design of an application for various types of stemming algorithms. Application is based on the Java platform with using of graphic library Swing and MVC architecture. Next chapter contains description of implementation of the application and stemming algorithms. In the last part of this master's thesis experiments with stemming algorithms and comparing the algorithm from viewpoint to the results of classification the text are described.

The thesis generally describes matters of data warehouses and knowledge discovery in databases. Then it focuses on the meteorological databases and their problems. The practical part of thesis describes design methods for data mining project, NOAA Global Surface Summary of the Day (GSOD), which is then implemented in two different ways using the Pentaho tools. Finally, an evaluation and comparison of these two approaches.

Nowadays email is commonly used by citizens to establish communication with their government. On the received emails, governments deal with some common queries and subjects which some handling officers have to manually answer. Automatic email classification of the incoming emails allows to increase the communication efficiency by decreasing the delay between the query and its response. This thesis takes part within the IMAIL project, which aims to provide an automatic answering solution to the Swedish Social Insurance Agency (SSIA) (“Försäkringskassan” in Swedish). The goal of this thesis is to analyze and compare the classification performance of different sets of features extracted from SSIA emails on different automatic classifiers. The features extracted from the emails will depend on the previous preprocessing that is carried out as well. Compound splitting, lemmatization, stop words removal, Part-of-Speech tagging and Ngrams are the processes used in the data set. Moreover, classifications will be performed using Support Vector Machines, k- Nearest Neighbors and Naive Bayes. For the analysis and comparison of different results, precision, recall and F-measure are used. From the results obtained in this thesis, SVM provides the best classification with a F-measure value of 0.787. However, Naive Bayes provides a better classification for most of the email categories than SVM. Thus, it can not be concluded whether SVM classify better than Naive Bayes or not. Furthermore, a comparison to Dalianis et al. (2011) is made. The results obtained in this approach outperformed the results obtained before. SVM provided a F-measure value of 0.858 when using PoS-tagging on original emails. This result improves by almost 3% the 0.83 obtained in Dalianis et al. (2011). In this case, SVM was clearly better than Naive Bayes.

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.

Η ραγδαία εξάπλωση του Internet έχει προκαλέσει σημαντικές αλλαγές σε πολλούς κλάδους της οικονομίας και της κοινωνίας παγκόσμια. Με τη ραγδαία ανάπτυξη των Τεχνολογιών της Πληροφορικής και της Τεχνολογίας, μια νέα μορφή εκπαίδευσης εμφανίστηκε, που δεν είναι άλλη από το e-learning (εκπαίδευση από απόσταση), που έφερε την επανάσταση στο εκπαιδευτικό γίγνεσθαι. Επιπρόσθετα ο Παγκόσμιος Ιστός σταδιακά μετεξελίσσεται στο Σημασιολογικό Παγκόσμιο Ιστό (Semantic Web) νέα μοντέλα και πρότυπα (XML, RDF, OWL) αναπτύσσονται για την προώθηση αυτής της διαδικασίας. Η έκφραση, μετάδοση και αναζήτηση πληροφοριών με χρήση αυτών των προτύπων ανοίγει νέους ορίζοντες στη χρήση του Διαδικτύου. Οι οντολογίες κερδίζουν ολοένα έδαφος για την αναπαράσταση γνώσης. Σε μια μεγάλη οντολογία που περιέχει χρήσιμα δεδομένα για ένα σύστημα εξ’ αποστάσεως εκπαίδευσης, αξίζει κάποιος να ερευνήσει την «κρυμμένη γνώση», δηλαδή να ανακαλύψει πιθανές συσχετίσεις ή συνειρμούς, να βρει πρότυπα ή μορφές που επαναλαμβάνονται ή ακραία φαινόμενα. Η παρούσα διπλωματική εργασία αποτελεί μια επίδειξη τεχνολογίας για την έγκυρη και έγκαιρη πρόβλεψη της απόδοσης των φοιτητών σε ένα σύστημα εξ’ αποστάσεως εκπαίδευσης. Η βασική ιδέα προκύπτει από την ανάγκη να σχεδιαστεί μία οντολογία η οποία θα μπορεί να αποθηκεύσει τη γνώση σχετικά με τις ικανότητες φοιτητών (user profile) σε σχέση με ένα συγκεκριμένο εκπαιδευτικό αντικείμενο (ΠΛΗ23 – Τηλεματική, Διαδίκτυο του Ελληνικού Ανοικτού Πανεπιστημίου (ΕΑΠ) )η οποία έχει πολύ συγκεκριμένη ύλη και 4 υποχρεωτικές γραπτές εργασίες ανά έτος). Στη συνέχεια παρουσιάζονται τα αποτελέσματα μελέτης της ανάλυσης των δεδομένων των φοιτητών με τεχνικές εξόρυξης γνώσης. Η εύρεση των κανόνων πραγματοποιήθηκε μέσω του εργαλείου Weka. Το αποτέλεσμα που προέκυψε είναι μία βάση γνώσης βάσει της οποίας γίνεται έγκαιρα και έγκυρα η πρόβλεψη της συμπεριφοράς του φοιτητή, δηλαδή αν θα καταφέρει να ολοκληρώσει επιτυχώς ή μη τη Θεματική Ενότητα που έχει αναλάβει στο ΕΑΠ, ώστε ο διδάσκων να μπορεί από πολύ νωρίς να υποστηρίξει το φοιτητή με επιπλέον υλικό αν απαιτείται. / The rapid spread of Internet has caused significant changes in many sectors of the economy and society worldwide. From those changes could not be left out of education. With the rapid development of information technologies and technology, a new form of education appears, e-learning (distance education), which revolutionized the educational process. Furthermore, while the World Wide Web gradually transforms into Semantic Web, new standards and models (XML, RDF, OWL) are evolving in order to launch this inquiry. The storage, presentation, transmission and search of information according to those standards open up new horizons in the utilization of the Web. Ontologies are increasingly get used for knowledge representation. A large ontology contains useful data for a system of distance education, deserves someone to investigate the "hidden knowledge", i.e. to discover possible associations or to find patterns or forms that are repeated or extreme events. This thesis is a demonstration of technology for accurate and timely prediction of the performance of students in a system of distance education. The basic idea was to design an ontology that can store knowledge about the students’ skills (user profile) in relation to a specific educational purpose (PLI23 - Telematics, Internet of the Hellenic Open University, which has a very specific matter and 4 mandatory projects per year). Then we present the results of a study analyzing student data mining techniques (data mining-classification). The discovery rules took place via the tool Weka. The result is a knowledge base which is the appropriate tool (Interface teacher) may provide that a student needs on a particular topic (in addition to material help from the teacher), etc.

Tato práce pojednává o vytvoření mobilní aplikace zvažující kontext zařízení, která vybírá a doporučuje hudební skladby dle aktuálního stavu kontextu zařízení. Kontext je získáván na základě naměřených hodnot, které jsou získány z vestavěných senzorů mobilního zařízení a z ostatních systémových hodnot zařízení. Výběr konkrétní skladby je poté založen na výstupu modelu strojového učení, který klasifikuje kontext na základě aktuálních získaných dat a následně zvolí skladbu připadající k danému kontextu.

The thesis is focused on an introduction of data mining. Data mining is focused on finding of a hidden data correlation. Interest in this area is dated back to the 60th the 20th century. Data analysis was first used in marketing. However, later it expanded to more areas, and some of its options are still unused. One of methodologies is useful used for creating of this process. Methodology offers a concise guide on how you can create a data mining procedure. The data mining analysis contains a wide range of algorithms for data modification. The interest in data mining causes that number of data mining software is increasing. This thesis contains overviews some of this programs, some examples and assessment.