Parkinson's disease (PD) is a degenerative illness whose cardinal symptoms include rigidity, tremor, and slowness of movement. In addition to its widely recognized effects PD can have a profound effect on speech and voice.The speech symptoms most commonly demonstrated by patients with PD are reduced vocal loudness, monopitch, disruptions of voice quality, and abnormally fast rate of speech. This cluster of speech symptoms is often termed Hypokinetic Dysarthria.The disease can be difficult to diagnose accurately, especially in its early stages, due to this reason, automatic techniques based on Artificial Intelligence should increase the diagnosing accuracy and to help the doctors make better decisions. The aim of the thesis work is to predict the PD based on the audio files collected from various patients.Audio files are preprocessed in order to attain the features.The preprocessed data contains 23 attributes and 195 instances. On an average there are six voice recordings per person, By using data compression technique such as Discrete Cosine Transform (DCT) number of instances can be minimized, after data compression, attribute selection is done using several WEKA build in methods such as ChiSquared, GainRatio, Infogain after identifying the important attributes, we evaluate attributes one by one by using stepwise regression.Based on the selected attributes we process in WEKA by using cost sensitive classifier with various algorithms like MultiPass LVQ, Logistic Model Tree(LMT), K-Star.The classified results shows on an average 80%.By using this features 95% approximate classification of PD is acheived.This shows that using the audio dataset, PD could be predicted with a higher level of accuracy.

Submitted by MARAÍSA DA SILVA GUERRA null (maraisa.guerra@ifms.edu.br) on 2017-07-29T00:12:01Z No. of bitstreams: 1 relatorio.pdf: 8678616 bytes, checksum: 003c94cceff80c4879b62a63399f0ff9 (MD5) / Approved for entry into archive by Luiz Galeffi (luizgaleffi@gmail.com) on 2017-08-03T14:47:26Z (GMT) No. of bitstreams: 1 asseiss_msg_me_ilha.pdf: 8678616 bytes, checksum: 003c94cceff80c4879b62a63399f0ff9 (MD5) / Made available in DSpace on 2017-08-03T14:47:26Z (GMT). No. of bitstreams: 1 asseiss_msg_me_ilha.pdf: 8678616 bytes, checksum: 003c94cceff80c4879b62a63399f0ff9 (MD5) Previous issue date: 2017-06-30 / Nos últimos anos a quantidade de dados armazenados diarimente em empresas e instituições aumentou consideravelmente e um dos motivos que contribuiu para isso é a crescente importân- cia dada à informação. De forma geral, esses dados são meramente armazenados e, portanto, subutilizados pelos seus detentores, enquanto poderiam ser estudados a fim de obter novos co- nhecimentos, informações e relacionamentos. Neste contexto, surge o processo de descoberta de conhecimento em banco de dados. Este trabalho apresenta uma introdução a banco de dados, uma revisão bibliográfica sobre o processo de descoberta de conhecimento em banco de dados, a descrição de cada etapa deste processo, uma explanação sobre as tarefas de agrupamento e classificação, além de resumir brevemente as técnicas de particionamento e árvore de decisão. É exposto um estudo sobre o sistema Weka, em que apresenta-se conceitos, funcionalidades e exemplifica-se diversas formas de utilização do sistema. O objetivo principal deste trabalho é propor uma metodologia para descoberta de novos conhecimentos em bancos de dados acadê- micos baseada no processo de descoberta de conhecimento em banco de dados, sendo esta uma metodologia mais simplificada e de execução mais direcionada. Como parte da metodologia este trabalho contribui ainda com uma aplicação desenvolvida em Python como forma de apoio a etapas da metodologia. A metodologia proposta conta com a ferramenta Weka para execução dos algoritmos de data mining e prevê a execução das tarefas de agrupamento e classifica- ção. Por fim o trabalho retrata dois estudos de caso envolvendo bancos de dados acadêmicos reais e a execução de todas as etapas da metodologia proposta, com a utilização do sistema Weka. Os estudos de caso abordam as tarefas de agrupamento e classificação e as técnicas de particionamento e árvores de decisão, com a utilização dos algoritmos SimpleKMeans e J4.8, respectivamente. Os resultados obtidos através dos estudos mostram que a metodologia pro- posta é capaz de gerar conhecimentos novos e úteis, tanto na análise de dados de desempenho acadêmico quanto na análise de dados socioeconômicos dos alunos. / In the past years the amount of data stored daily in companies increased considerably and one of the reasons that contributed to this fact is the increasing importance given to information. In general these data are merely stored and therefore underused by its owners, while they could be studied in order to find out new knowledge, information and relationship. In this context, the knowledge discovery in database process arises. This work presents an introduction to databa- ses, a bibliographic review about the knowledge discovery in databases process, a description of each step of this process, an explanation about the clustering and classification tasks and the summarization os the partition and decision tree techniques. A study of the Weka system is shown, in wich are presented concepts, functionalities and examples of use forms for the sys- tem. The main objective of this work is the proposal of a methodology for knowledge discovery in academic databases based on the KDD process. The presented methodology is a more sim- plified and directed version of the KDD. As part of the methodology this work also presents an application developed in Python programming language as a support tool for the methodology steps. The presented methodology uses the Weka tool for running the data mining algorithms and considers the clustering and classification tasks. Lastly this work describes two case stu- dies involving real academic databases and the execution of all the steps from the proposted methodology using the Weka system. The case studies addresses the clustering and classifica- tion tasks, as well as the partitioning and decision trees techniques, using the SimpleKMeans and J4.8 algorithms respectively. The obtained results show that the methodology is capable of generating new and useful knowledge, both by analyzing academic performance data and by analyzing students’ socioeconomic data.

Malicious programs have been a serious threat for the confidentiality, integrity and availability of a system. Different researches have been done to detect them. Two approaches have been derived for it i.e. Signature Based Detection and Heuristic Based Detection. These approaches performed well against known malicious programs but cannot catch the new malicious programs. Different researchers tried to find new ways of detecting malicious programs. The application of data mining and machine learning is one of them and has shown good results compared to other approaches. A new category of malicious programs has gained momentum and it is called Spyware. Spyware are more dangerous for confidentiality of private data of the user of system. They may collect the data and send it to third party. Traditional techniques have not performed well in detecting Spyware. So there is a need to find new ways for the detection of Spyware. Data mining and machine learning have shown promising results in the detection of other malicious programs but it has not been used for detection of Spyware yet. We decided to employ data mining for the detection of spyware. We used a data set of 137 files which contains 119 benign files and 18 Spyware files. A theoretical taxonomy of Spyware is created but for the experiment only two classes, Benign and Spyware, are used. An application Binary Feature Extractor have been developed which extract features, called n-grams, of different sizes on the basis of common feature-based and frequency-based approaches. The number of features were reduced and used to create an ARFF file. The ARFF file is used as input to WEKA for applying machine learning algorithms. The algorithms used in the experiment are: J48, Random Forest, JRip, SMO, and Naive Bayes. 10-fold cross-validation and the area under ROC curve is used for the evaluation of classifier performance. We performed experiments on three different n-gram sizes, i.e.: 4, 5, 6. Results have shown that extraction of common feature approach has produced better results than others. We achieved an overall accuracy of 90.5 % with an n-gram size of 6 from the J48 classifier. The maximum area under ROC achieved was 83.3 % with Random Forest. / +46709325761, +46762782550

[pt] Este trabalho apresenta o EasyLearn, um framework para apoiar o desenvolvimento de aplicações voltadas ao aprendizado supervisionado. O EasyLearn define uma camada intermediaria, de simples configuração e entendimento, entre a aplicação e o WEKA, um framework de aprendizado de máquina criado pela Universidade de Waikato. Todos os classificadores e filtros implementados pelo WEKA podem ser facilmente encapsulados para serem utilizados pelo EasyLearn. O EasyLearn recebe como entrada um conjunto de arquivos de configuração no formato XML contendo a definição do fluxo de processamento a ser executado, além da fonte de dados a ser processada, independente do formato. Sua saída é adaptável e pode ser configurada para produzir, por exemplo, relatórios de acurácia da classificação, a própria da fonte de dados classificada, ou o modelo de classificação já treinado. A arquitetura do EasyLearn foi definida após a análise detalhada dos processos de classificação, permitindo identificar inúmeras atividades em comum entre os três processos estudados aprendizado, avaliação e classificação). Através desta percepção e tomando as linguagens orientadas a objetos como inspiração, foi criado um framework capaz de comportar os processos de classificação e suas possíveis variações, além de permitir o reaproveitamento das configurações, através da implementação de herança e polimorfismo para os seus arquivos de configuração. A dissertação ilustra o uso do framework criado através de um estudo de caso completo sobre classificação de produtos do comércio eletrônico, incluindo a criação do corpus, engenharia de atributos e análise dos resultados obtidos. / [en] This dissertation presents EasyLearn, a framework to support the development of supervised learning applications. EasyLearn dfines an intermediate layer, which is easy to configure and understand, between the application and WEKA, a machine learning framework created by the University of Waikato. All classifiers and filters implemented by WEKA can be easily encapsulated to be used by EasyLearn. EasyLearn receives as input a set of configuration files in XML format containing the definition of the processing flow to be executed, in addition to the data source to be classified, regardless of format. Its output is customizable and can be configured to produce classification accuracy reports, the classified data source, or the trained classification model. The architecture of EasyLearn was defined after a detailed analysis of the classification process, which identified a set of common activities among the three analyzed processes (learning, evaluation and classification). Through this insight and taking the object-oriented languages as inspiration, a framework was created which is able to support the classification processes and its variations, and which also allows reusing settings by implementing inheritance and polymorphism in their configuration files. This dissertation also illustrates the use of the created framework presenting a full case study about e-commerce product classification, including corpus creation, attribute engineering and result analysis.

This work focuses on the regression analysis of morphological parameters calculated from the ECG for estimating the position of the heart to the measuring electrodes. It consists of a theoretical analysis of the problems of ECG recording and description of the data obtained from experiments on isolated animal hearts. On the theoretical part is followed by a description of the calculation parameters suitable for regression analysis and their application in the training and testing of the following regression models to estimate the position of the heart to the measuring electrode.

No description available.

Στην παρούσα διπλωματική εργασία γίνεται μια προσπάθεια αντικειμενοποίησης και μοντελοποίησης του μετεγχειρητικού πόνου συνεπεία προγραμματισμένων ορθοπαιδικών επεμβάσεων στην άρθρωση του γόνατος με την βοήθεια εργαλείων μηχανικής μάθησης. Σκοπός της εν λόγω μοντελοποίησης είναι η δημιουργία ενός ευφυούς συστήματος αξιολόγησης και εκτίμησης του μετεγχειρητικού πόνου και η εξέταση της υπόθεσης του κατά πόσο η χρήση ως παραμέτρου μιας αντικειμενικής τιμής όπως αυτή που προέρχεται από την καταγραφή των σωματοαισθητικών προκλητών δυναμικών μπορεί να επηρεάσει την ακρίβεια του συστήματος μας. Συγκεκριμένα χρησιμοποιήθηκαν παράμετροι από το ιστορικό του ασθενούς, τα σωματομετρικά του χαρακτηριστικά, τα δεδομένα του χειρουργείου και της αναλγησίας που δόθηκε σε αυτό, η αυτοαξιολόγηση του ίδιου του ασθενούς μέσω της κλίμακας αυτοαξιολόγησης του πόνου NRS και τέλος αυτό που διαφοροποιεί την παρούσα διπλωματική είναι η προσπάθεια συσχέτισης μιας αντικειμενικής παραμέτρου που τα τελευταία χρόνια έχει συσχετιστεί με τον πόνο, αυτή των σωματοαισθητικών προκλητών δυναμικών. / In this paper we made an attempt of objectification and modeling of postoperative pain as a result of planned orthopedic surgery in the knee joint with the help of machine learning tools. The purpose of this modeling is to create an intelligent system evaluation and assessment of postoperative pain and the case is whether the use as an objective parameter value as derived from the recording of somatosensory evoked potentials may affect the accuracy of the system our. Specific parameters used by the patient's history, the anthropometric characteristics, data of surgery and analgesia given to this, self-evaluation of the patient using the scale of self-assessment of pain NRS and finally what differentiates this thesis is the attempt correlation of an objective parameter in recent years has been associated with pain, that of somatosensory evoked potentials.

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

Businesses today are often operating in a highly competitive environment where information is a noticeably valuable asset. Businesses are therefore in need of powerful tools for extracting actionable business knowledge. Research show that SME companies are lagging behind large companies in the use of data analytics; even though they know the potential benefits. We want to study and compare different tools for data analytics and how they can be used by small companies. Our research questions are therefore: what analytical tools are today available on the market, and what are their possibilities and challenges for small companies? And: how can these analytical tools aid in the development of a business, product or service? We conclude in our research that there are several data analytics tools available for small businesses, that their different usages can be applied successfully and without big cost, and that their relevance, both in business development and innovation, depends on the business objectives and goals of their utilization.

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