Machine Learning Algorithms at Myocardial Perfusion Imaging - a Preliminary Study

Introduction: A major focus of data mining process - especially machine learning researches - is to automatically learn to recognize complex patterns and help to take the adequate decisions strictly based on the acquired data. Since imaging techniques like MPI – Myocardial Perfusion Imaging on Nuclear Cardiology, can implicate a huge part of the daily workflow and generate gigabytes of data, there could be advantages on Computerized Analysis of data over Human Analysis: shorter time, homogeneity and consistency, automatic recording of analysis results, relatively inexpensive, etc.Objectives: The aim of this study relates with the evaluation of the efficacy of this methodology on the evaluation of MPI Stress studies and the process of decision taking concerning the continuation – or not – of the evaluation of each patient. It has been pursued has an objective to automatically classify a patient test in one of three groups: “Positive”, “Negative” and “Indeterminate”. “Positive” would directly follow to the Rest test part of the exam, the “Negative” would be directly exempted from continuation and only the “Indeterminate” group would deserve the clinician analysis, so allowing economy of clinician’s effort, increasing workflow fluidity at the technologist’s level and probably sparing time to patients. Methods: WEKA v3.6.2 open source software was used to make a comparative analysis of three WEKA algorithms (“OneR”, “J48” and “Naïve Bayes”) - on a retrospective study using the comparison with correspondent clinical results as reference, signed by nuclear cardiologist experts - on “SPECT Heart Dataset”, available on University of California – Irvine, at the Machine Learning Repository. For evaluation purposes, criteria as “Precision”, “Incorrectly Classified Instances” and “Receiver Operating Characteristics (ROC) Areas” were considered. Results: The interpretation of the data suggests that the Naïve Bayes algorithm has the best performance among the three previously selected algorithms. Conclusions: It is believed - and apparently supported by the findings - that machine learning algorithms could significantly assist, at an intermediary level, on the analysis of scintigraphic data obtained on MPI, namely after Stress acquisition, so eventually increasing efficiency of the entire system and potentially easing both roles of Technologists and Nuclear Cardiologists. In the actual continuation of this study, it is planned to use more patient information and significantly increase the population under study, in order to allow improving system accuracy.

Intelligent recommendation system for e-learning platforms

As more and more digital resources are available, finding the appropriate document becomes harder. Thus, a new kind of tools, able to recommend the more appropriated resources according the user needs, becomes even more necessary. The current project implements an intelligent recommendation system for elearning platforms. The recommendations are based on one hand, the performance of the user during the training process and on the other hand, the requests made by the user in the form of search queries. All information necessary for decision-making process of recommendation will be represented in the user model. This model will be updated throughout the target user interaction with the platform.

Sistema de recomendação para plataformas de e-learning

O aumento do número de recursos digitais disponíveis dificulta a tarefa de pesquisa dos recursos mais relevantes, no sentido de se obter o que é mais relevante. Assim sendo, um novo tipo de ferramentas, capaz de recomendar os recursos mais apropriados às necessidades do utilizador, torna-se cada vez mais necessário. O objetivo deste trabalho de I&D é o de implementar um módulo de recomendação inteligente para plataformas de e-learning. As recomendações baseiam-se, por um lado, no perfil do utilizador durante o processo de formação e, por outro lado, nos pedidos efetuados pelo utilizador, através de pesquisas [Tavares, Faria e Martins, 2012]. O e-learning 3.0 é um projeto QREN desenvolvido por um conjunto de organizações e tem com objetivo principal implementar uma plataforma de e-learning. Este trabalho encontra-se inserido no projeto e-learning 3.0 e consiste no desenvolvimento de um módulo de recomendação inteligente (MRI). O MRI utiliza diferentes técnicas de recomendação já aplicadas noutros sistemas de recomendação. Estas técnicas são utilizadas para criar um sistema de recomendação híbrido direcionado para a plataforma de e-learning. Para representar a informação relevante, sobre cada utilizador, foi construído um modelo de utilizador. Toda a informação necessária para efetuar a recomendação será representada no modelo do utilizador, sendo este modelo atualizado sempre que necessário. Os dados existentes no modelo de utilizador serão utilizados para personalizar as recomendações produzidas. As recomendações estão divididas em dois tipos, a formal e a não formal. Na recomendação formal o objetivo é fazer sugestões relacionadas a um curso específico. Na recomendação não-formal, o objetivo é fazer sugestões mais abrangentes onde as recomendações não estão associadas a nenhum curso. O sistema proposto é capaz de sugerir recursos de aprendizagem, com base no perfil do utilizador, através da combinação de técnicas de similaridade de palavras, um algoritmo de clustering e técnicas de filtragem [Tavares, Faria e Martins, 2012].

Adaptive learning in agents behaviour: a framework for electricity markets simulation

Electricity markets are complex environments with very particular characteristics. A critical issue regarding these specific characteristics concerns the constant changes they are subject to. This is a result of the electricity markets’ restructuring, which was performed so that the competitiveness could be increased, but it also had exponential implications in the increase of the complexity and unpredictability in those markets scope. The constant growth in markets unpredictability resulted in an amplified need for market intervenient entities in foreseeing market behaviour. The need for understanding the market mechanisms and how the involved players’ interaction affects the outcomes of the markets, contributed to the growth of usage of simulation tools. Multi-agent based software is particularly well fitted to analyze dynamic and adaptive systems with complex interactions among its constituents, such as electricity markets. This dissertation presents ALBidS – Adaptive Learning strategic Bidding System, a multiagent system created to provide decision support to market negotiating players. This system is integrated with the MASCEM electricity market simulator, so that its advantage in supporting a market player can be tested using cases based on real markets’ data. ALBidS considers several different methodologies based on very distinct approaches, to provide alternative suggestions of which are the best actions for the supported player to perform. The approach chosen as the players’ actual action is selected by the employment of reinforcement learning algorithms, which for each different situation, simulation circumstances and context, decides which proposed action is the one with higher possibility of achieving the most success. Some of the considered approaches are supported by a mechanism that creates profiles of competitor players. These profiles are built accordingly to their observed past actions and reactions when faced with specific situations, such as success and failure. The system’s context awareness and simulation circumstances analysis, both in terms of results performance and execution time adaptation, are complementary mechanisms, which endow ALBidS with further adaptation and learning capabilities.

Planning and implementing a new assessment strategy using an e-learning platform

One of the most difficult issues of e-Learning is the students’ assessment. Being this an outstanding task regarding theoretical topics, it becomes even more challenging when the topics under evaluation are practical. ISCAP’s Information Systems Department is composed of about twenty teachers who have been for several years using an e-learning environment (at the moment Moodle 2.3) combined with traditional assessment. They are now planning and implementing a new e-learning assessment strategy. This effort was undertaken in order to evaluate a practical topic (the use of spreadsheets to solve management problems) common to shared courses of several undergraduate degree programs. The same team group is already experienced in the assessment of theoretical information systems topics using the b-learning platform. Therefore, this project works as an extension to previous experiences being the team aware of the additional difficulties due to the practical nature of the topics. This paper describes this project and presents two cycles of the action research methodology, used to conduct the research. The first cycle goal was to produce a database of questions. When it was implemented in order to be used with a pilot group of students, several problems were identified. Subsequently, the second cycle consisted in solving the identified problems preparing the database and all the players to a broader scope implementation. For each cycle, all the phases, its drawbacks and achievements are described. This paper suits all those who are or are planning to be in the process of shifting their assessment strategy from a traditional to one supported by an e-learning platform.

E-Learning as a shared service in shared services centers

Book Subtitle International Conference, CENTERIS 2010, Viana do Castelo, Portugal, October 20-22, 2010, Proceedings, Part II

A B-learning strategy for Therapeutics at the Bachelor Level

Background Information:The incorporation of distance learning activities by institutions of higher education is considered an important contribution to create new opportunities for teaching at both, initial and continuing training. In Medicine and Nursing, several papers illustrate the adaptation of technological components and teaching methods are prolific, however, when we look at the Pharmaceutical Education area, the examples are scarce. In that sense this project demonstrates the implementation and assessment of a B-Learning Strategy for Therapeutics using a “case based learning” approach. Setting: Academic Pharmacy Methods:This is an exploratory study involving 2nd year students of the Pharmacy Degree at the School of Allied Health Sciences of Oporto. The study population consists of 61 students, divided in groups of 3-4 elements. The b-learning model was implemented during a time period of 8 weeks. Results:A B-learning environment and digital learning objects were successfully created and implemented. Collaboration and assessment techniques were carefully developed to ensure the active participation and fair assessment of all students. Moodle records show a consistent activity of students during the assignments. E-portfolios were also developed using Wikispaces, which promoted reflective writing and clinical reasoning. Conclusions:Our exploratory study suggests that the “case based learning” method can be successfully combined with the technological components to create and maintain a feasible online learning environment for the teaching of therapeutics.

Educational in a global environment: non-formal examples of learning practices at the international office

23rd SPACE AGM and Conference from 9 to 12 May 2012 Conference theme: The Role of Professional Higher Education: Responsibility and Reflection Venue: Mikkeli University of Applied Sciences, Mikkeli, Finland

Implementação de cursos em regime de e-learning em ambiente hospitalar. A MEDUCA como suporte

A sociedade moderna encontra-se numa evolução progressiva e constante no que respeita às novas tecnologias. Independentemente da área de conhecimento, é de senso comum, que cada vez mais é necessária formação sólida, sendo fundamental a preparação e a consolidação das futuras gerações na utilização das novas tecnologias. As plataformas de e-learning são hoje em dia uma realidade mais que afirmada, e com aplicação em todos os sectores de actividade. A área da saúde não foge à regra, verificando-se que os seus profissionais evidenciam falta de disponibilidade para participação nas formações presenciais, fundamentais para o seu processo de formação contínua (LLL – Long Liffe Learning). Estes profissionais necessitam de estar continuamente actualizados, de forma a melhor poderem contribuir para o desempenho das suas funções, como o aconselhamento dos utentes, acompanhamento de doentes crónicos, uso correcto dos medicamentos, entre outros. O presente trabalho pretende implementar em ambiente hospitalar o modelo de formação à distância em regime de e-learning ou b-learning, identificando potenciais vantagens e constrangimentos inerentes ao processo. Para o efeito, será utilizada a plataforma MEDUCA criada pelo GILT-ISEP e baseada em Moodle, como uma plataforma destinada á formação para profissionais da área da Saúde. Esta dissertação apresenta uma investigação sobre a implementação da dita plataforma em ambiente hospitalar. Esta dissertação apresenta todo o estudo/trabalho desenvolvido para a implementação da plataforma MEDUCA nalgumas entidades contactadas da área da saúde. Espera-se que esta ferramenta ofereça uma alternativa interactiva de educação e aprendizagem, visando melhorar constantemente o nível formativo de cada profissional de saúde.

Defining and Classifying Learning Outcomes: A case study

Bologne came to globalize education in higher education, creating a unified architecture that potentiates higher education and enhances the continued interconnection of the spaces of education policy in higher education in the world, in particular in Europe. The aim of this work consists in the presentation of an identification model and skills’ classification and learning outcomes, based on the official documents of the course units (syllabus and assessment components) of a course of Higher Education. We are aware that the adoption of this model by different institutions, will contribute to interoperability learning outcomes, thus enhancing the mobility of teachers and students in the EHEA (European Higher Education Area) and third countries.

Informal learning recognition through a cloud ecosystem

Learning and teaching processes, like all human activities, can be mediated through the use of tools. Information and communication technologies are now widespread within education. Their use in the daily life of teachers and learners affords engagement with educational activities at any place and time and not necessarily linked to an institution or a certificate. In the absence of formal certification, learning under these circumstances is known as informal learning. Despite the lack of certification, learning with technology in this way presents opportunities to gather information about and present new ways of exploiting an individual’s learning. Cloud technologies provide ways to achieve this through new architectures, methodologies, and workflows that facilitate semantic tagging, recognition, and acknowledgment of informal learning activities. The transparency and accessibility of cloud services mean that institutions and learners can exploit existing knowledge to their mutual benefit. The TRAILER project facilitates this aim by providing a technological framework using cloud services, a workflow, and a methodology. The services facilitate the exchange of information and knowledge associated with informal learning activities ranging from the use of social software through widgets, computer gaming, and remote laboratory experiments. Data from these activities are shared among institutions, learners, and workers. The project demonstrates the possibility of gathering information related to informal learning activities independently of the context or tools used to carry them out.

TRAILER Project (Tagging, Recognition, Acknowledgment of Informal Learning Experiences) a methodology to make learners' informal learning activities visible to the institutions

The application of information technologies (specially the Internet, Web 2.0 and social tools) make informal learning more visible. This kind of learning is not linked to an institution or a period of time, but it is important enough to be taken into account. On the one hand, learners should be able to communicate to the institutions they are related to, what skills they possess, whether they were achieved in a formal or informal way. On the other hand the companies and educational institutions need to have a deeper knowledge about the competencies of their staff. The TRAILER project provides a methodology supported by a technological framework to facilitate communication about informal learning between businesses, employees and learners. The paper presents the project and some of the work carried out, an exploratory analysis about how informal learning is considered and the technological framework proposed. Whilst challenges remain in terms of establishing the meaningfulness of technological engagement for employees and businesses, the continuing transformation of the social, technological and educational environment is likely to lead to greater emphasis for the effective exploitation of informal learning.

CrimsonHex: a learning objects repository for programming exercises

A repository of learning objects is a system that stores electronic resources in a technology-mediated learning process. The need for this kind of repository is growing as more educators become eager to use digital educa- tional contents and more of it becomes available. The sharing and use of these resources relies on the use of content and communication standards as a means to describe and exchange educational resources, commonly known as learning objects. This paper presents the design and implementation of a service-oriented reposi- tory of learning objects called crimsonHex. This repository supports new definitions of learning objects for specialized domains and we illustrate this feature with the definition of programming exercises as learning objects and its validation by the repository. The repository is also fully compliant with existing commu- nication standards and we propose extensions by adding new functions, formalizing message interchange and providing a REST interface. To validate the interoperability features of the repository, we developed a repository plug-in for Moodle that is expected to be included in the next release of this popular learning management system.

Supporting learning through tagging systems

With the advent of Web 2.0, new kinds of tools became available, which are not seen as novel anymore but are widely used. For instance, according to Eurostat data, in 2010 32% of individuals aged 16 to 74 used the Internet to post messages to social media sites or instant messaging tools, ranging from 17% in Romania to 46% in Sweden (Eurostat, 2012). Web 2.0 applications have been used in technology-enhanced learning environments. Learning 2.0 is a concept that has been used to describe the use of social media for learning. Many Learning 2.0 initiatives have been launched by educational and training institutions in Europe. Web 2.0 applications have also been used for informal learning. Web 2.0 tools can be used in classrooms, virtual or not, not only to engage students but also to support collaborative activities. Many of these tools allow users to use tags to organize resources and facilitate their retrieval at a later date or time. The aim of this chapter is to describe how tagging has been used in systems that support formal or informal learning and to summarize the functionalities that are common to these systems. In addition, common and unusual tagging applications that have been used in some Learning Objects Repositories are analysed.

Remote lab experiments: opening possibilities for distance learning in engineering fields

Remote experimentation laboratories are systems based on real equipment, allowing students to perform practical work through a computer connected to the internet. In engineering fields lab activities play a fundamental role. Distance learning has not demonstrated good results in engineering fields because traditional lab activities cannot be covered by this paradigm. These activities can be set for one or for a group of students who work from different locations. All these configurations lead to considering a flexible model that covers all possibilities (for an individual or a group). An inter-continental network of remote laboratories supported by both European and Latin American institutions of higher education has been formed. In this network context, a learning collaborative model for students working from different locations has been defined. The first considerations are presented.