Unsupervised learning to cluster the disease stages in parkinson's disease

Parkinson's disease (PD) is the second most common neurodegenerative disorder (after Alzheimer's disease) and directly affects upto 5 million people worldwide. The stages (Hoehn and Yaar) of disease has been predicted by many methods which will be helpful for the doctors to give the dosage according to it. So these methods were brought up based on the data set which includes about seventy patients at nine clinics in Sweden. The purpose of the work is to analyze unsupervised technique with supervised neural network techniques in order to make sure the collected data sets are reliable to make decisions. The data which is available was preprocessed before calculating the features of it. One of the complex and efficient feature called wavelets has been calculated to present the data set to the network. The dimension of the final feature set has been reduced using principle component analysis. For unsupervised learning k-means gives the closer result around 76% while comparing with supervised techniques. Back propagation and J4 has been used as supervised model to classify the stages of Parkinson's disease where back propagation gives the variance percentage of 76-82%. The results of both these models have been analyzed. This proves that the data which are collected are reliable to predict the disease stages in Parkinson's disease.

Machine learning and small robot navigation

Developing successful navigation and mapping strategies is an essential part of autonomous robot research. However, hardware limitations often make for inaccurate systems. This project serves to investigate efficient alternatives to mapping an environment, by first creating a mobile robot, and then applying machine learning to the robot and controlling systems to increase the robustness of the robot system. My mapping system consists of a semi-autonomous robot drone in communication with a stationary Linux computer system. There are learning systems running on both the robot and the more powerful Linux system. The first stage of this project was devoted to designing and building an inexpensive robot. Utilizing my prior experience from independent studies in robotics, I designed a small mobile robot that was well suited for simple navigation and mapping research. When the major components of the robot base were designed, I began to implement my design. This involved physically constructing the base of the robot, as well as researching and acquiring components such as sensors. Implementing the more complex sensors became a time-consuming task, involving much research and assistance from a variety of sources. A concurrent stage of the project involved researching and experimenting with different types of machine learning systems. I finally settled on using neural networks as the machine learning system to incorporate into my project. Neural nets can be thought of as a structure of interconnected nodes, through which information filters. The type of neural net that I chose to use is a type that requires a known set of data that serves to train the net to produce the desired output. Neural nets are particularly well suited for use with robotic systems as they can handle cases that lie at the extreme edges of the training set, such as may be produced by "noisy" sensor data. Through experimenting with available neural net code, I became familiar with the code and its function, and modified it to be more generic and reusable for multiple applications of neural nets.

Estágio Supervisionado na formação de professores em serviço dos anos iniciais do ensino fundamental

This report aims to present the results of research on the possibilities and limits of Supervised Traineeship in a the course of Training of Teachers in Service of the early years of primary education conducted through an agreement between the Federal University of Piauí and the Teresina city council (PI) UFPI/PMT, offers subsidies to teaching practices that address the reality of students who live, work and/or studying in school located in rural areas. The research is based on a theoretical and methodological approach that places the object of study in the critical perspective and using procedures such as: literature (bibliographical research), documentary analysis, semi-structured interview and photographic record. We researched 10 (ten) teachers of the course promoted by UFPI graduating in 2006 and who develop teaching activities in schools located in rural areas in the city of Teresina-PI, 02 (two) teachers who are trainee supervisor of UFPI and the coordinator of the course. The analysis evidenced that the object, historically, educational policies have changed, but did not break the traditional patterns of teacher education, enough, especially in the curricular proposals in light of the changes required by the information and global society. We also note that in the supervised training, the process of critical reflection on teaching practice was made possible, in part, and raised the reconstruction of specific knowledge to make teaching in order to conduct the teaching-learning process articulating different realities of primary teaching and facing situations due to conservative practices in relation to education in rural areas. It contributed, therefore, this curricular component to resize, in part, the pedagogical action of the teachers graduated. The study also drives the research toward to similar studies in the perspective of overcoming the current model of teacher training that does not correspond to the demands of society in change and to the crisis of capital, with immeasurable consequences in the workplace. The contemporary claims for a University to form professionals capable of directing the destiny of society, where teacher training is a social and political demands on which is co-responsible

O estágio supervisionado como contexto de formação docente específica para educação infantil: o que dizem os formandos sobre suas aprendizagens?

Kindergarten teachers training gains the spotlight with the passing of Law number 9.394/96 (Guidelines and Basis Law) that defines this segment as the initial step of basic education, with pedagogical function. In this spectrum, the discussion about teacher training unravels to ensure social quality to education as well as the teacher s specificities towards child singularities. Adding to that, the growing propagation of Pedagogy in an undergraduate level, given that such course has been continually transformed by the National Curriculum Guidelines for Pedagogy (2006), highlighting the addition of curriculum components that are specific to upbringing. The complex debate circa kindergarten teachers training has advancements and hardships that need to be unveiled in order to improve both formation and social quality of education in the 0- 5 years old range. This investigation inserts itself in said context and aims to analyze which knowledge, specific to kindergarten teaching are constructed, according to undergraduate trainees, in Pedagogy s supervised internship. The study was conducted alongside the discipline: Supervised Internship in Child Education ministered by the Advanced Campus of Rio Grande do Norte s State University s in the municipality of Patu-RN (CAP-UERN-Patu) and was conducted through 2012 by accompanying four undergraduate interns. We first assumed that the development of teaching knowledge is a complex process of appropriation of cultural-social practices and is symbolically mediated by interactions that occur in the formation context, and the supervised internship can be understood as a space for the articulation and enlargement of theoretical and practical knowledge, directly related to the specificities of child education. The theoretical-methodological foundation was based upon the historiccultural approach of L. S. Vygotsky and M. Bakhtin s dialogism on human sciences research, as well as his postulates on learning and developmental processes, conceived as both essentially social and discursive. The investigation approached the principles of the qualitative perspective and to the construction and analysis of data, involved documental analysis and, specially, semi-structured interviews, both individual and collective, whose fundamental premise was the production-comprehension of meanings in a dialogical perspective. The participants texts/speeches produced a synthesis that points to the occurrence, within the supervised internship at CAP/UERN, of internalization/appropriation processes and, as such, of formulation of meanings that are pertinent to child education: child, childhood, kindergarten and teacher signification and this stage s specific teaching knowledge. It stood out that the internship, alongside other curriculum components, is, in fact, one of the primeval formation environment for the teachers, in which the interns interact with their colleagues, supervisor professor, collaborator professor, and of course, the children to construct their erudition. Such interactions allow the undergraduate interns to develop attitudes and procedures to reflect on what they know, what they ve done and what they can achieve. We have concluded that the undergraduate internship can constitute itself as an articulatorconsolidator environment in the future teacher s formation process and, since well oriented, can provide the effective initiation, not only to the practice, but to the praxis as a movement of non dissociability between theory and practice

Aquatic weed automatic classification using machine learning techniques

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Efficient supervised optimum-path forest classification for large datasets

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Improving the accuracy of the optimum-path forest supervised classifier for large datasets

In this work, a new approach for supervised pattern recognition is presented which improves the learning algorithm of the Optimum-Path Forest classifier (OPF), centered on detection and elimination of outliers in the training set. Identification of outliers is based on a penalty computed for each sample in the training set from the corresponding number of imputable false positive and false negative classification of samples. This approach enhances the accuracy of OPF while still gaining in classification time, at the expense of a slight increase in training time. © 2010 Springer-Verlag.

Precipitates segmentation from scanning electron microscope images through machine learning techniques

The presence of precipitates in metallic materials affects its durability, resistance and mechanical properties. Hence, its automatic identification by image processing and machine learning techniques may lead to reliable and efficient assessments on the materials. In this paper, we introduce four widely used supervised pattern recognition techniques to accomplish metallic precipitates segmentation in scanning electron microscope images from dissimilar welding on a Hastelloy C-276 alloy: Support Vector Machines, Optimum-Path Forest, Self Organizing Maps and a Bayesian classifier. Experimental results demonstrated that all classifiers achieved similar recognition rates with good results validated by an expert in metallographic image analysis. © 2011 Springer-Verlag Berlin Heidelberg.

Estratégias pedagógicas no ensino da clínica ampliada na atenção primária à saude: percepções e vivências dos alunos de graduação médica

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Preliminary diagnosis of ophtalmological diseases through machine learning techniques

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Word sense disambiguation via high order of learning in complex networks

Complex networks have been employed to model many real systems and as a modeling tool in a myriad of applications. In this paper, we use the framework of complex networks to the problem of supervised classification in the word disambiguation task, which consists in deriving a function from the supervised (or labeled) training data of ambiguous words. Traditional supervised data classification takes into account only topological or physical features of the input data. On the other hand, the human (animal) brain performs both low- and high-level orders of learning and it has facility to identify patterns according to the semantic meaning of the input data. In this paper, we apply a hybrid technique which encompasses both types of learning in the field of word sense disambiguation and show that the high-level order of learning can really improve the accuracy rate of the model. This evidence serves to demonstrate that the internal structures formed by the words do present patterns that, generally, cannot be correctly unveiled by only traditional techniques. Finally, we exhibit the behavior of the model for different weights of the low- and high-level classifiers by plotting decision boundaries. This study helps one to better understand the effectiveness of the model. Copyright (C) EPLA, 2012

Central catheter dressing in a simulator: the effects of tutor's assistance or self-learning tutorial

Aims: to compare the performance of undergraduate students concerning semi-implanted central venous catheter dressing in a simulator, with the assistance of a tutor or of a self-learning tutorial. Method: Randomized controlled trial. The sample consisted of 35 undergraduate nursing students, who were divided into two groups after attending an open dialogue presentation class and watching a video. One group undertook the procedure practice with a tutor and the other with the assistance of a self-learning tutorial. Results: in relation to cognitive knowledge, the two groups had lower performance in the pre-test than in the post-test. The group that received assistance from a tutor performed better in the practical assessment. Conclusion: the simulation undertaken with the assistance of a tutor showed to be the most effective learning strategy when compared to the simulation using a self-learning tutorial. Advances in nursing simulation technology are of upmost importance and the role of the tutor in the learning process should be highlighted, taking into consideration the role this professional plays in knowledge acquisition and in the development of critical-reflexive thoughts and attitudes. (ClinicalTrials.gov Identifier: NCT 01614314).

Learning Democracy Together in School? : Student and Teacher Attitudes in Bosnia and Herzegovina

The principal aim of this study is to examine attitudes and values, through questionnaires, among students and teachers in the last grade of primary school (grade 8) regarding issues related to authoritarianism, democracy, human rights, children rights, conflict resolution and legislation in Bosnia and Herzegovina. A second aim is to explore and analyze the role of the international community in the democratization and education processes in the light of globalization in this country through secondary sources of data, site visits and observations. Analysis of the student sample reveals suspicion towards democracy, especially when democracy was associated with politics and politicians. When the issue of democracy was de-contextualized from Bosnia and Herzegovina realities in the questionnaire, students showed more positive attitudes towards it. Students generally agreed with very strong authoritarian statements. High achieving students were more democratic, more socially responsible, more tolerant regarding attitudes towards religion, race and disabilities, and less authoritarian compared to low achievers. High achievers felt that they had influence over daily events, and were positive towards social and civil engagement. High achievers viewed politics negatively, but had high scores on the democracy scale. High achievers also agreed to a larger extent that it is acceptable to break the law. The more authoritarian students were somewhat more prone to respond that it is not acceptable to break the law. The major findings from the teacher sample show that teachers who agreed with non-peaceful mediation, and had a non-forgiving and rigid approach to interpersonal conflicts, also agreed with strong authoritarian statements and were less democratic. In general, teachers valued students who behave respectfully, have a good upbringing and are obedient. They were very concerned about the general status of education in society, which they felt was becoming marginalized. Teachers were not happy with the overloaded curricula and they showed an interest in more knowledge and skills to help children with traumatic war experiences. When asked about positive reforms, teachers were highly critical of, and dissatisfied with, the educational situation. Bosnia and Herzegovina is undergoing a transition from a state-planned economy and one party system to a market economy and a multi party system. During this transition, the country has become more involved in the globalization process than ever. Today the country is a semi-protectorate where international authorities intervene when necessary. The International community is attempting to introduce western democracy and some of the many complexities in this process are discussed in this study. Globalization processes imply contradictory demands and pressures on the education system. On one hand, economic liberalization has affected education policies —a closer alignment between education and economic competitiveness. On the other hand, there is a political and ideological globalization process underlying the importance of human rights, and the inclusiveness of education for all children. Students and teachers are caught between two opposing ideals — competition and cooperation.

Lesion detection and classification in breast cancer: evaluation of approaches based on morphological features, tracer kinetic modelling and semi-quantitative parameters in MR functional imaging (DCE-MRI)

The diagnosis, grading and classification of tumours has benefited considerably from the development of DCE-MRI which is now essential to the adequate clinical management of many tumour types due to its capability in detecting active angiogenesis. Several strategies have been proposed for DCE-MRI evaluation. Visual inspection of contrast agent concentration curves vs time is a very simple yet operator dependent procedure, therefore more objective approaches have been developed in order to facilitate comparison between studies. In so called model free approaches, descriptive or heuristic information extracted from time series raw data have been used for tissue classification. The main issue concerning these schemes is that they have not a direct interpretation in terms of physiological properties of the tissues. On the other hand, model based investigations typically involve compartmental tracer kinetic modelling and pixel-by-pixel estimation of kinetic parameters via non-linear regression applied on region of interests opportunely selected by the physician. This approach has the advantage to provide parameters directly related to the pathophysiological properties of the tissue such as vessel permeability, local regional blood flow, extraction fraction, concentration gradient between plasma and extravascular-extracellular space. Anyway, nonlinear modelling is computational demanding and the accuracy of the estimates can be affected by the signal-to-noise ratio and by the initial solutions. The principal aim of this thesis is investigate the use of semi-quantitative and quantitative parameters for segmentation and classification of breast lesion. The objectives can be subdivided as follow: describe the principal techniques to evaluate time intensity curve in DCE-MRI with focus on kinetic model proposed in literature; to evaluate the influence in parametrization choice for a classic bi-compartmental kinetic models; to evaluate the performance of a method for simultaneous tracer kinetic modelling and pixel classification; to evaluate performance of machine learning techniques training for segmentation and classification of breast lesion.