Healthcare Critical Knowledge Monitor System Model : healthcare critical knowledge ontology component

“Proceedings of Safety, Health and Environment World Congress, ISSN 2317-3173. Vol. 13, nr. 1 (2013)”Healthcare organizations manage with personal information concerning to patients from many sources that, typically, are supported by computer-based systems therefore, demands cautious when there are ethical and legal aspects involved. Since not all clinical knowledge managed by healthcare organizations could be considered critical (or much critical) we need to define the value of clinical knowledge for further handle in risk management. With the key aspects of InfoSec: Confidentiality, Integrity, Availability and Privacy we intent to achieve the core critical knowledge that will be the source of the healthcare critical knowledge ontology. Critical knowledge ontology should be tailored to the healthcare organization in focus to comply with multiple factors, such as: organizational culture, terminology used, health department specifications, among others. With topic model approach we intent to automatically driven document topics and match with critical healthcare knowledge from ontology, thus, give value to the documents concerning its critical knowledge.This work is financed by FEDER funds through the Competitive Factors Operational Program – COMPETE and Portuguese national funds through FCT – Fundação para a ciência e tecnologia in project FCOMP-01-0124-FEDER- 022674

Critical Knowledge Monitor System Model: healthcare context

In order to provide a better service, sharing knowledge with partners and communities is becoming part of the healthcare organizations culture. Data, information and clinic knowledge require specific cautious, because it involves ethical and legal issues. The constant evolution of Information and Communication Technologies brings new opportunities with multiple forms of communication (web 2.0), therefore, new ways of sharing knowledge. Further, there is a wide knowledge sources: patient’s feedback; knowledge from Internet sources; knowledge from decision support systems; and inference knowledge (e.g. Knowledge from Data Mining techniques) justifying the use of knowledge management systems to get its benefits. The Critical Knowledge Monitor System Model, proposed here, allows knowledge sharing in a controlled ambient and could be a part of the answer to this paradigm that healthcare organizations face. To implement the Critical Knowledge Monitor System model we’ll need to apply knowledge engineering techniques such as ontology construction, text mining, techniques, Information retrieval, among others. Since not all knowledge manage by healthcare organizations could be considered critical (or much critical), it’s necessary to define constructs to classify clinic knowledge. To achieve this, we’ll implement a focus group approach with the use of risk management techniques to classify knowledge as critical and its critical level to driven ontology with the class and terms used by the healthcare organization under study. Essentially, these are the motives of this research.This work is financed by FEDER funds through the Competitive Factors Operational Program – COMPETE and Portuguese national funds through FCT – Fundação para a ciência e tecnologia in project FCOMP-01-0124-FEDER-022674

An ontology based approach to data surveillance

Nowadays the terrorist threat took proportions that concern governments and the national security organizations, all over the world. A successful terrorist incident usually brings catastrophic results. However if a terrorist attack can be predicted and characterized, it may be possible to organize a proper intervention in order to avoid it or to reduce its impact. The management of information is becoming an important issue in the domain of security information systems. The information access and association, analysis and assessment, and finally exploitation have become the focus for all security information services and governments. Current surveillance approaches are not very efficient leading innocent citizen to the confrontation of law enforcement services. One reason for this, result from the difficulties of the current system to extract knowledge or concepts abstracted from massive databases of information. Knowledge based methods, such as ontologies can integrate data surveillance, and enable a proper data analyse improving the performance of the security information services. This paper intends to present a perspective about the use of ontologies in the context of data surveillance, and present its importance in the current security services domain.(undefined

Grid data mining by means of learning classifier systems and distributed model induction

This paper introduces a distributed data mining approach suited to grid computing environments based on a supervised learning classifier system. Different methods of merging data mining models generated at different distributed sites are explored. Centralized Data Mining (CDM) is a conventional method of data mining in distributed data. In CDM, data that is stored in distributed locations have to be collected and stored in a central repository before executing the data mining algorithm. CDM method is reliable; however it is expensive (computational, communicational and implementation costs are high). Alternatively, Distributed Data Mining (DDM) approach is economical but it has limitations in combining local models. In DDM, the data mining algorithm has to be executed at each one of the sites to induce a local model. Those induced local models are collected and combined to form a global data mining model. In this work six different tactics are used for constructing the global model in DDM: Generalized Classifier Method (GCM); Specific Classifier Method (SCM); Weighed Classifier Method (WCM); Majority Voting Method (MVM); Model Sampling Method (MSM); and Centralized Training Method (CTM). Preliminary experimental tests were conducted with two synthetic data sets (eleven multiplexer and monks3) and a real world data set (intensive care medicine). The initial results demonstrate that the performance of DDM methods is competitive when compared with the CDM methods.Fundação para a Ciência e a Tecnologia (FCT

N-ary trees classifier

This paper addresses the problem of automatic detection and prediction of abnormal human behaviours in public spaces. For this propose a novel classifier, called N-ary trees, is presented. The classifier processes time series of attributes like the object position, velocity, perimeter and area, to infer the type of action performed. This innovative classifier can detect three types of events: normal; unusual; or abnormal events. In order to evaluate the performance of the N-ary trees classifier, we carry out a preliminary study with 180 synthetic tracks and one restricted area. The results revealed a great level of accuracy and that the proposed method can be used in surveillance systems.Fundação para a Ciência e a Tecnologia (FCT)

Prediction of abnormal behaviors for intelligent video surveillance systems

IEEE Copyright Policies This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.The OBSERVER is a video surveillance system that detects and predicts abnormal behaviors aiming at the intelligent surveillance concept. The system acquires color images from a stationary video camera and applies state of the art algorithms to segment, track and classify moving objects. In this paper we present the behavior analysis module of the system. A novel method, called Dynamic Oriented Graph (DOG) is used to detect and predict abnormal behaviors, using real-time unsupervised learning. The DOG method characterizes observed actions by means of a structure of unidirectional connected nodes, each one defining a region in the hyperspace of attributes measured from the observed moving objects and having assigned a probability to generate an abnormal behavior. An experimental evaluation with synthetic data was held, where the DOG method outperforms the previously used N-ary Trees classifier.Fundação para a Ciência e a Tecnologia (FCT) - SFRH/BD/17259/2004

Moving object detection unaffected by cast shadows, highlights and ghosts

IEEE Copyright Policies: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.This paper describes a new approach to perform segmentation of moving objects in real-time from images acquired by a fixed color video camera and is the first tool of a major project that aspires to recognize abnormal human behavior in public areas. The moving objects detection is based on background subtraction and it is unaffected by changes in illumination, i.e., cast shadows and highlights. Furthermore it does not require a special attention during the initialization process, due to its ability to detect and rectify ghosts. The results show that with image resolutions of 380x280 at 24 bits per pixel, the time spent in the segmentation process is around 80ms, in a 32 bits 3GHz processor based computer.Fundação para a Ciência e a Tecnologia (FCT

Previsão de eventos anormais em sistemas de vídeo-vigilância

Este trabalho tem como propósito a detecção e previsão de comportamentos passíveis de originar uma quebra de segurança. Tais comportamentos são reconhecidos por meio da observação de padrões de actividade humana, extraídos de sequências de imagens digitalizadas adquiridas por intermédio de uma câmara de vídeo a cores, monocular e fixa. A aferição dos comportamentos é suportada pela informação resultante dos processos de detecção, classificação e seguimento de objectos em movimento, minimizando a utilização de informação de contexto na cena observada, e sem recurso a descrições de comportamentos previamente definidos. Para a detecção e previsão automática de comportamentos desenvolveu-se um novo classificador (Dynamic Oriented Graph) proposto no âmbito deste trabalho e que, utilizando os dados provenientes das funções de processamento e análise de imagem, permite modelar sequências temporais. O sistema, constituído pela junção das várias componentes desenvolvidas e implementado numa câmara de vídeo inteligente, foi testado com um conjunto de dados sintéticos

Biometria e autenticação

Com a utilização cada vez maior de Tecnologias da Informação e das Comunicações (TIC) nos Sistemas de Informação (SI) das organizações, surgem com crescente evidência os problemas de segurança e, em particular, a questão da autenticação do utilizador. Esta questão é hoje fundamental já que o acesso indevido a informação sensível pode provocar grandes prejuízos à organização. Neste trabalho descreve-se uma das técnicas utilizadas na autenticação, a biometria, como forma de aumentar a qualidade da autenticação. Nesse sentido, é analisado o estado da arte, são identificadas algumas vantagens, desvantagens e limitações das principais tecnologias desenvolvidas e procura-se perceber o impacto que a autenticação biométrica pode ter nas organizações, quando conjugada com a tecnologia proporcionada pelos cartões com capacidade de processamento e armazenamento seguro, conhecidos como Smart Cards. Finalmente, é brevemente introduzido o projecto de investigação em curso para o desenvolvimento de um sistema que explora estas tecnologias

The OBSERVER: an intelligent and automated video surveillance system

Comunicação apresentada na ICIAR, 3, Póvoa de Varzim, Portugal, 2006.In this work we present a new approach to learn, detect and predict unusual and abnormal behaviors of people, groups and vehicles in real-time. The proposed OBSERVER video surveillance system acquires images from a stationary color video camera and applies state-of-the-art algorithms to segment and track moving objects. The segmentation is based in a background subtraction algorithm with cast shadows, highlights and ghost’s detection and removal. To robustly track objects in the scene, a technique based on appearance models was used. The OBSERVER is capable of identifying three types of behaviors (normal, unusual and abnormal actions). This achievement was possible due to the novel N-ary tree classifier, which was successfully tested on synthetic data.Fundação para a Ciência e a Tecnologia (FCT)