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- Context-based multi-agent recommender system, supported on IoT, for guiding the occupants of a building in case of a firePublication . Neto, Joaquim; Morais, A. Jorge; Gonçalves, Ramiro Manuel Ramos Moreira; Coelho, António LeçaThe evacuation of buildings in case of fire is a sensitive issue for civil society that also motivates the academic community to develop and study solutions to improve the efficiency of evacuating these spaces. The study of human behavior in fire emergencies has been one of the areas that have deserved the attention of researchers. However, this modeling of human behavior is difficult and complex because it depends on factors that are difficult to know and that vary from country to country. In this paper, a paradigm shift is proposed which, instead of focusing on modeling the behavior of occupants, focuses on conditioning this behavior by providing real-time information on the most efficient evacuation routes. Making this information available to occupants is possible with a solution that takes advantage of the growing use of the IoT (Internet of Things) in buildings to help occupants adapt to the environment. Supported by the IoT, multi-agent recommender systems can help users to adapt to the environment and provide the occupants with the most efficient evacuation routes. This paradigm shift is achieved through a context-based multi-agent recommender system based on contextual data obtained from IoT devices, which recommends the most efficient evacuation routes at any given time. The obtained results suggest that the proposed solution can improve the efficiency of evacuating buildings in the event of a fire; for a scenario with two hundred people following the system recommendations, the time they take to reach a safe place decreases by 17.7%.
- Sistema de recomendação Web usando agentesPublication . Morais, A. Jorge; Neto, Joaquim; Oliveira, Eugénio; Jorge, Alípio MárioO crescimento da Web trouxe vários problemas aos utilizadores. A grande quantidade de informação existente hoje em dia em alguns sítios Web torna a procura de informação útil muito difícil. Os objetivos dos proprietários dos sítios Web e dos utilizadores nem sempre coincidem. O conhecimento dos padrões de visitas dos utilizadores é crucial para que os proprietários possam transformar e adaptar o sítio Web. Este é o princípio do sítio Web adaptativo: o sítio Web adapta-se de forma a melhorar a experiência do utilizador. Alguns algoritmos foram propostos para adaptar um sítio da Web. Neste artigo, descrevemos uma proposta de um sistema de recomendação Web baseado em agentes que combina dois algoritmos: regras de associação e filtragem colaborativa. Ambos os algoritmos são incrementais e funcionam com dados binários. Os resultados mostram que, em algumas situações, a abordagem multiagente melhora a capacidade preditiva quando comparada com os agentes individuais.
- Multi-agent web recommendationsPublication . Neto, Joaquim; Morais, A. JorgeDue to the large amount of pages in Websites it is important to collect knowledge about users’ previous visits in order to provide patterns that allow the customization of the Website. In previous work we proposed a multi-agent approach using agents with two different algorithms (associative rules and collaborative filtering) and showed the results of the offline tests. Both algorithms are incremental and work with binary data. In this paper we present the results of experiments held online. Results show that this multi-agent approach combining different algorithms is capable of improving user’s satisfaction.
- Abordagem multiagente em sistemas de recomendação WebPublication . Neto, Joaquim; Morais, A. JorgeO crescimento exponencial da informação disponível na Web torna difícil para os utilizadores a tarefa de obter a informação que pretendem e quando dela necessitam. Para ultrapassar o problema, os sítios Web têm vindo a incorporar sistemas de recomendação que, baseados no histórico de acessos, têm como objetivo maximizar a satisfação dos utilizadores, disponibilizando-lhes recomendações de alta qualidade. A complexidade do problema e a natureza distribuída da Web justificam abordagens baseadas na tecnologia dos agentes inteligentes autónomos e sistemas multiagente, permitindo combinar múltiplos algoritmos de recomendação, aumentando assim as hipóteses das recomendações sugeridas serem efetivamente do interesse do utilizador. É este o tipo de abordagem explorada pelo sistema de recomendação multiagente AMAAFWA (A Multi-Agent Approach for Web Adaptation) (Morais, 2013). Os testes realizados em modo offline mostraram que essa abordagem multiagente, baseada em agentes implementando diferentes algoritmos, apresenta um desempenho superior ao dos algoritmos considerados individualmente. O objetivo desta dissertação é adaptar e testar o sistema AMAAFWA em tempo real, com o objetivo de validar os resultados obtidos em modo offline, pelo que se procedeu à sua adaptação para funcionamento online, integrando-o num sítio Web. O sistema AMAAFWA baseia-se numa classificação implícita dos itens e os algoritmos de recomendação são baseados em memória e incrementais. Foi também criada e testada uma versão do sistema que considera uma classificação explícita dos itens por parte dos utilizadores, com o propósito de comparar o desempenho de ambos os tipos de classificação. Demonstra-se na presente dissertação que o sistema de recomendação multiagente AMAAFWA, em funcionamento online, apresenta um desempenho superior ao dos algoritmos considerados individualmente, sendo ainda capaz de melhorar a satisfação do utilizador e contribuir para o aumento do sucesso do sítio Web em que se insere. Relativamente à comparação dos tipos de classificação implícita e explícita dos itens, os resultados mostram desempenhos similares.
- An ontology for fire building evacuationPublication . Neto, Joaquim; Morais, A. Jorge; Gonçalves, Ramiro Manuel Ramos Moreira; Coelho, António LeçaGuiding the building occupants under fire emergency to a safe place is an open research problem, and finding solutions to address the problem requires a perfect knowledge of the fire building evacuation domain. The use of ontologies to model knowledge of a domain allows a common and shared understanding of that domain, between people and heterogeneous systems. This paper presents an ontology that aims to build a knowledge model to understand the referred domain better and help develop more capable building evacuation solutions and systems. The herein proposed ontology considers the different variables and actors involved in the fire building evacuation process. We followed the Methontology methodology for its developing, and we present all the development steps, from the specification to its implementation with the Protégé tool.
- Multi-agent-based recommender systems: a literature reviewPublication . Neto, Joaquim; Morais, A. Jorge; Gonçalves, Ramiro Manuel Ramos Moreira; Coelho, António LeçaConsidering the growing volume of information and services available on the Web, it has become essential to provide Web sites and applications with tools, such as recommender systems, capable of helping their users to obtain the information and services appropriate to their interests. Due to the complexity of Web adaptation and the ability of multi-agent systems to deal with complex problems, the multi-agent systems technology have been increasing. In this paper, we present a thorough survey of the use of multi-agent-based recommender systems. The results show that the use of multi-agent systems in recommender systems is increasing. The review shows the diversity of applications of multi-agent systems in recommender systems, namely on what concerns to the diversity of domains, different types of approaches, contributing to improving the performance of the recommendation systems.
- Implementation of neural networks to frontal electroencephalography for the identification of the transition responsiveness/unresponsiveness during induction of general anesthesiaPublication . Ferreira, Ana Isabel Leitão; Vide, Sérgio; Nunes, Catarina S.; Neto, Joaquim; Amorim, Pedro; Mendes, JoaquimObjective: General anesthesia is a reversible drug-induced state of altered arousal characterized by loss of responsiveness (LOR) due to brainstem inactivation. Precise identification of the LOR during the induction of general anesthesia is extremely important to provide personalized information on anesthetic requirements and could help maintain an adequate level of anesthesia throughout surgery, ensuring safe and effective care and balancing the avoidance of intraoperative awareness and overdose. So, main objective of this paper was to investigate whether a Convolutional Neural Network (CNN) applied to bilateral frontal electroencephalography (EEG) dataset recorded from patients during opioid-propofol anesthetic procedures identified the exact moment of LOR. Material and methods: A clinical protocol was designed to allow for the characterization of different clinical endpoints throughout the transition to unresponsiveness. Fifty (50) patients were enrolled in the study and data from all was included in the final dataset analysis. While under a constant estimated effect-site concentration of 2.5 ng/mL of remifentanil, an 1% propofol infusion was started at 3.3 mL//h until LOR. The level of responsiveness was assessed by an anesthesiologist every six seconds using a modified version of the Richmond Agitation-Sedation Scale (aRASS). The frontal EEG was acquired using a bilateral bispectral (BIS VISTA (TM) v2.0, Medtronic, Ireland) sensor. EEG data was then split into 5-second epochs, and for each epoch, the anesthesiologist's classification was used to label it as responsiveness (no-LOR) or unresponsiveness (LOR). All 5-second epochs were then used as inputs for the CNN model to classify the untrained segment as no-LOR or LOR. Results: The CNN model was able to identify the transition from no-LOR to LOR successfully, achieving 97.90 +/- 0.07% accuracy on the cross-validation set. Conclusion: The obtained results showed that the proposed CNN model was quite efficient in the responsiveness/unresponsiveness classification. We consider our approach constitutes an additional technique to the current methods used in the daily clinical setting where LOR is identified by the loss of response to verbal commands or mechanical stimulus. We therefore hypothesized that automated EEG analysis could be a useful tool to detect the moment of LOR, especially using machine learning approaches.
- An ontological model for fire evacuation route recommendation in buildingsPublication . Neto, Joaquim; Morais, A. Jorge; Gonçalves, Ramiro Manuel Ramos Moreira; Coelho, António LeçaGuiding the occupants of a building to a safe place is an area of research that deserves the attention of researchers. Finding solutions for the problem of guiding the building occupants requires a perfect knowledge of the fire building evacuation domain. The use of ontologies to model the knowledge of a domain allows a common and shared understanding of that domain. This paper presents an ontology that has the purpose to deepen the understanding of that domain and help develop building evacuation solutions and systems capable of guiding the occupants during a building evacuation process. The proposed ontology considers the different variables and actors involved in the fire building evacuation process. The ontology development followed the Methontology methodology, and for implementation, the Protégé tool was used. The ontological model was successfully submitted to a thorough evaluation process and is publicly available on the Web.
- Geometric and physical building representation and occupant’s movement models for fire building evacuation simulationPublication . Neto, Joaquim; Morais, A. Jorge; Gonçalves, Ramiro Manuel Ramos Moreira; Coelho, António LeçaBuilding evacuation simulation allows for a better assessment of fire safety conditions in existing buildings, which is why it is of interest to develop an easy-to-use web platform that helps fire safety technicians in this assessment. To achieve this goal, the geometric and physical representation of the building and installed fire safety devices are necessary, as well as the modelling of occupant movement. Although these are widely studied areas, in this paper, we present two new model approaches, either for the physical and geometric representation of a building or for the occupant's movement simulation, during a building evacuation process. To test both models, we develop a Multi-Agent Web Simulator Platform. The tests carried out show the suitability of the model approaches herein presented.
- A multi-agent system for recommending fire evacuation routes in buildings, based on context and IoTPublication . Neto, Joaquim; Morais, A. Jorge; Gonçalves, Ramiro Manuel Ramos Moreira; Coelho, A. LeçaThe herein proposed research project brings together the area of the multi-agent recommender systems and the IoT and aims to study the extent to which a context-based multi-agent recommender system can contribute to improving efficiency in the evacuation of buildings under a fire emergency, recommending the most adequate and efficient evacuation routes in real time.