1,934 research outputs found
Hybrid approaches based on computational intelligence and semantic web for distributed situation and context awareness
2011 - 2012The research work focuses on Situation Awareness and Context Awareness topics.
Specifically, Situation Awareness involves being aware of what is happening in the vicinity
to understand how information, events, and one’s own actions will impact goals and objectives,
both immediately and in the near future. Thus, Situation Awareness is especially
important in application domains where the information flow can be quite high and poor
decisions making may lead to serious consequences.
On the other hand Context Awareness is considered a process to support user applications
to adapt interfaces, tailor the set of application-relevant data, increase the precision of
information retrieval, discover services, make the user interaction implicit, or build smart
environments.
Despite being slightly different, Situation and Context Awareness involve common
problems such as: the lack of a support for the acquisition and aggregation of dynamic environmental
information from the field (i.e. sensors, cameras, etc.); the lack of formal approaches
to knowledge representation (i.e. contexts, concepts, relations, situations, etc.)
and processing (reasoning, classification, retrieval, discovery, etc.); the lack of automated
and distributed systems, with considerable computing power, to support the reasoning on a
huge quantity of knowledge, extracted by sensor data.
So, the thesis researches new approaches for distributed Context and Situation Awareness
and proposes to apply them in order to achieve some related research objectives such
as knowledge representation, semantic reasoning, pattern recognition and information retrieval.
The research work starts from the study and analysis of state of art in terms of
techniques, technologies, tools and systems to support Context/Situation Awareness. The
main aim is to develop a new contribution in this field by integrating techniques deriving
from the fields of Semantic Web, Soft Computing and Computational Intelligence. From
an architectural point of view, several frameworks are going to be defined according to the
multi-agent paradigm.
Furthermore, some preliminary experimental results have been obtained in some application
domains such as Airport Security, Traffic Management, Smart Grids and
Healthcare.
Finally, future challenges is going to the following directions: Semantic Modeling of
Fuzzy Control, Temporal Issues, Automatically Ontology Elicitation, Extension to other
Application Domains and More Experiments. [edited by author]XI n.s
Dealing with uncertain entities in ontology alignment using rough sets
This is the author's accepted manuscript. The final published article is available from the link below. Copyright @ 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.Ontology alignment facilitates exchange of knowledge among heterogeneous data sources. Many approaches to ontology alignment use multiple similarity measures to map entities between ontologies. However, it remains a key challenge in dealing with uncertain entities for which the employed ontology alignment measures produce conflicting results on similarity of the mapped entities. This paper presents OARS, a rough-set based approach to ontology alignment which achieves a high degree of accuracy in situations where uncertainty arises because of the conflicting results generated by different similarity measures. OARS employs a combinational approach and considers both lexical and structural similarity measures. OARS is extensively evaluated with the benchmark ontologies of the ontology alignment evaluation initiative (OAEI) 2010, and performs best in the aspect of recall in comparison with a number of alignment systems while generating a comparable performance in precision
Computational intelligence approaches to robotics, automation, and control [Volume guest editors]
No abstract available
Performance Evaluation of Smart Decision Support Systems on Healthcare
Medical activity requires responsibility not only from clinical knowledge and skill but
also on the management of an enormous amount of information related to patient care. It is
through proper treatment of information that experts can consistently build a healthy wellness
policy. The primary objective for the development of decision support systems (DSSs) is
to provide information to specialists when and where they are needed. These systems provide
information, models, and data manipulation tools to help experts make better decisions in a
variety of situations.
Most of the challenges that smart DSSs face come from the great difficulty of dealing
with large volumes of information, which is continuously generated by the most diverse types
of devices and equipment, requiring high computational resources. This situation makes this
type of system susceptible to not recovering information quickly for the decision making. As a
result of this adversity, the information quality and the provision of an infrastructure capable
of promoting the integration and articulation among different health information systems (HIS)
become promising research topics in the field of electronic health (e-health) and that, for this
same reason, are addressed in this research. The work described in this thesis is motivated
by the need to propose novel approaches to deal with problems inherent to the acquisition,
cleaning, integration, and aggregation of data obtained from different sources in e-health environments,
as well as their analysis.
To ensure the success of data integration and analysis in e-health environments, it
is essential that machine-learning (ML) algorithms ensure system reliability. However, in this
type of environment, it is not possible to guarantee a reliable scenario. This scenario makes
intelligent SAD susceptible to predictive failures, which severely compromise overall system
performance. On the other hand, systems can have their performance compromised due to the
overload of information they can support.
To solve some of these problems, this thesis presents several proposals and studies
on the impact of ML algorithms in the monitoring and management of hypertensive disorders
related to pregnancy of risk. The primary goals of the proposals presented in this thesis are
to improve the overall performance of health information systems. In particular, ML-based
methods are exploited to improve the prediction accuracy and optimize the use of monitoring
device resources. It was demonstrated that the use of this type of strategy and methodology
contributes to a significant increase in the performance of smart DSSs, not only concerning precision
but also in the computational cost reduction used in the classification process.
The observed results seek to contribute to the advance of state of the art in methods
and strategies based on AI that aim to surpass some challenges that emerge from the integration
and performance of the smart DSSs. With the use of algorithms based on AI, it is possible to
quickly and automatically analyze a larger volume of complex data and focus on more accurate
results, providing high-value predictions for a better decision making in real time and without
human intervention.A atividade médica requer responsabilidade não apenas com base no conhecimento
e na habilidade clĂnica, mas tambĂ©m na gestĂŁo de uma enorme quantidade de informações
relacionadas ao atendimento ao paciente. É através do tratamento adequado das informações
que os especialistas podem consistentemente construir uma polĂtica saudável de bem-estar. O
principal objetivo para o desenvolvimento de sistemas de apoio à decisão (SAD) é fornecer informações
aos especialistas onde e quando são necessárias. Esses sistemas fornecem informações,
modelos e ferramentas de manipulação de dados para ajudar os especialistas a tomar melhores
decisões em diversas situações.
A maioria dos desafios que os SAD inteligentes enfrentam advĂŞm da grande dificuldade
de lidar com grandes volumes de dados, que Ă© gerada constantemente pelos mais diversos
tipos de dispositivos e equipamentos, exigindo elevados recursos computacionais. Essa situação
torna este tipo de sistemas suscetĂvel a nĂŁo recuperar a informação rapidamente para a
tomada de decisão. Como resultado dessa adversidade, a qualidade da informação e a provisão
de uma infraestrutura capaz de promover a integração e a articulação entre diferentes sistemas
de informação em saúde (SIS) tornam-se promissores tópicos de pesquisa no campo da saúde
eletrônica (e-saúde) e que, por essa mesma razão, são abordadas nesta investigação. O trabalho
descrito nesta tese Ă© motivado pela necessidade de propor novas abordagens para lidar
com os problemas inerentes à aquisição, limpeza, integração e agregação de dados obtidos de
diferentes fontes em ambientes de e-saúde, bem como sua análise.
Para garantir o sucesso da integração e análise de dados em ambientes e-saúde é
importante que os algoritmos baseados em aprendizagem de máquina (AM) garantam a confiabilidade
do sistema. No entanto, neste tipo de ambiente, nĂŁo Ă© possĂvel garantir um cenário
totalmente confiável. Esse cenário torna os SAD inteligentes suscetĂveis Ă presença de falhas
de predição que comprometem seriamente o desempenho geral do sistema. Por outro lado, os
sistemas podem ter seu desempenho comprometido devido à sobrecarga de informações que
podem suportar.
Para tentar resolver alguns destes problemas, esta tese apresenta várias propostas e
estudos sobre o impacto de algoritmos de AM na monitoria e gestĂŁo de transtornos hipertensivos
relacionados com a gravidez (gestação) de risco. O objetivo das propostas apresentadas nesta
tese é melhorar o desempenho global de sistemas de informação em saúde. Em particular, os
métodos baseados em AM são explorados para melhorar a precisão da predição e otimizar o
uso dos recursos dos dispositivos de monitorização. Ficou demonstrado que o uso deste tipo
de estratégia e metodologia contribui para um aumento significativo do desempenho dos SAD
inteligentes, não só em termos de precisão, mas também na diminuição do custo computacional
utilizado no processo de classificação.
Os resultados observados buscam contribuir para o avanço do estado da arte em métodos
e estratégias baseadas em inteligência artificial que visam ultrapassar alguns desafios que
advêm da integração e desempenho dos SAD inteligentes. Como o uso de algoritmos baseados
em inteligĂŞncia artificial Ă© possĂvel analisar de forma rápida e automática um volume maior de
dados complexos e focar em resultados mais precisos, fornecendo previsões de alto valor para uma melhor tomada de decisão em tempo real e sem intervenção humana
The Semantic Shadow : Combining User Interaction with Context Information for Semantic Web-Site Annotation
This thesis develops the concept of the Semantic Shadow (SemS), a model for managing contentual and structural annotations on web page elements and their values. The model supports a contextual weighting of the annotated information, allowing to specify the annotation values in relation to the evaluation context. A procedure is presented, which allows to manage and process this context-dependent meta information on web page elements using a dedicated programming interface. Two distinct implementations for the model have been developed: One based on Java objects, the other using the Resource Description Framework (RDF) as modeling backend. This RDF-based storage allows to integrate the annotations of the Semantic Shadow with other information of the Semantic Web. To demonstrate the application of the Semantic Shadow concept, a procedure to optimize web based user interfaces based on the structural semantics has been developed: Assuming a mobile client, a requested web page is dynamically adapted by a proxy prototype, where the context-awareness of the adaptation can be directly modeled alongside with the structural annotations. To overcome the drawback of missing annotations for existing web pages, this thesis introduces a concept to derive context-dependent meta-information on the web pages from their usage: From the observation of the users' interaction with a web page, certain context-dependent structural information about the concerned web page elements can be derived and stored in the annotation model of the Semantic Shadow concept.In dieser Arbeit wird das Konzept des Semantic Shadow (dt. Semantischer Schatten) entwickelt, ein Programmier-Modell um Webseiten-Elemente mit inhaltsbezogenen und strukturellen Anmerkungen zu versehen. Das Modell unterstützt dabei eine kontextabhängige Gewichtung der Anmerkungen, so dass eine Anmerkung in Bezug zum Auswertungs-Kontext gesetzt werden kann. Zur Verwaltung und Verarbeitung dieser kontextbezogenen Meta-Informationen für Webseiten-Elemente wurde im Rahmen der Arbeit eine Programmierschnittstelle definiert. Dazu wurden zwei Implementierungen der Schnittstelle entwickelt: Eine basiert ausschließlich auf Java-Objekten, die andere baut auf einem RDF-Modell auf. Die RDF-basierte Persistierung erlaubt eine Integration der Semantic-Shadow-Anmerkungen mit anderen Anwendungen des Semantic Webs. Um die Anwendungsmöglichkeiten des Semantic-Shadow-Konzepts darzustellen, wurde eine Vorgehensweise zur Optimierung von webbasierten Benutzerschnittstellen auf Grundlage von semantischen Strukturinformationen entwickelt: Wenn ein mobiler Benutzer eine Webseite anfordert, wird diese dynamisch durch einen Proxy angepasst. Die Kontextabhängigkeit dieser Anpassung wird dabei bereits direkt mit den Struktur-Anmerkungen modelliert. Für bestehende Webseiten liegen zumeist keine Annotationen vor. Daher wird in dieser Arbeit ein Konzept vorgestellt, kontextabhängige Meta-Informationen aus der Benutzung der Webseiten zu bestimmen: Durch Beobachtung der Benutzer-Interaktionen mit den Webseiten-Elementen ist es möglich bestimmte kontextabhängige Strukturinformationen abzuleiten und als Anmerkungen im Modell des Semantic-Shadow-Konzepts zu persistieren
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