Keynote Speakers
Discovering patterns from big data attracts a lot of attention due to its importance in discovering accurate patterns and features that are used in predictions of decision making.
The challenges in big data analytics are the high dimensionality and complexity in data representation. Granular computing and feature selection are among the challenge to deal with big data analytics that is used for Decision making. We will discuss these challenges in this talk and provide new projection on ensemble learning for health care risk prediction. In decision making most approaches are taking into account objective criteria, however the subjective correlation among different ensembles provided as preference utility is necessary to be presented to provide confidence preference additive among it reducing ambiguity and produce better utility preferences measurement for good quality predictions. Most models in Decision support systems are assuming criteria as independent. Different type of data (time series, linguistic values, interval data, etc.) imposes some difficulties to data analytics due to preprocessing and normalization processes which are expensive and difficult when data sets are raw and imbalanced. We will highlight these issues though project applied to health-care for elderly, by merging heterogeneous metrics for providing health care predictions for elderly at home. We have utilized ensemble learning as multi-classification techniques on multi-data streams that collected from multi-sensing devices.
Subjectivity (i.e., service personalization) would be examined based on correlations between different contextual structures that are reflecting the framework of personal context, for example in nearest neighbor based correlation analysis fashion. Some of the attributes incompleteness also may lead to affect the approximation accuracy. Attributes with preference-ordered domain relations properties become one aspect in ordering properties in rough approximations. We outline issues on Virtual Doctor Systems, and highlights its innovation in interactions with elderly patients, also discuss these challenges in granular computing and decision support systems research domains. In this talk I will present the current state of art and focus it on health care risk analysis with examples from our experiments.
Situation Awareness is usually defined in terms of what information is important for a particular job or goal.
Most of the problems with Situation Awareness occur at the level “Perception” and “Comprehension” because of missing information, information overload, information perceived in a wrong way (e.g., noise) or also information not pertinent with respect to the specific goal. Thus, the current situation must be identified, in general, in uncertainty conditions and within complex and critical environments. In this case, it is needed an effective hybridization of the human component with the technological (automatic) component to succeed in tasks related to Situation Awareness.
Situation Awareness oriented systems have to organize information around goals and provide a proper level of abstraction of meaningful information. To answer these issues, we propose a Cognitive Architecture, for defining Situation Awareness oriented systems, that is defined by starting from the well known Endsley’s Model and integrating a set of Computational Intelligence techniques (e.g., Fuzzy Cognitive Maps and Formal Concept Analysis) to support the three main processes of the model (perception, comprehension and projection). One of these techniques is Granular Computing that makes information observable at different levels of granularity and approximation to allow humans to focus on specific details, overall picture or on any other level with respect to their specific goals, constraints, roles, characteristics and so on.
Furthermore, the proposed Cognitive Architecture considers some enabling technologies like multi-agents systems and semantic modelling to provide a solution to face the complexity and heterogeneity of the monitored environment and the capability to represent, in a machine-understandable way, procedural, factual and other kind of knowledge and all the memory facilities that could be required.
Practical experiences deriving from the realization of complex systems in the domain of Smart Cities will be presented during the talk.
The trend of mHealth, or mobile health, emerge recently as a result of technological evolution and miniaturization and the users’ interest in monitoring and improving health and well being. Other that improving of informing about personal health, it also allows for the development of better assistive environments. Nonetheless, existing approaches are generally based on physiological sensors, which are intrusive and cannot be realistically used. This is especially true in environments in which the transparency, pervasiveness and sensitivity are of importance, such as in Ambient Intelligence and Electronic Commerce.
We put forward a new approach to the problem in which user behavioral cues are used as an input to assess the individual’s inner state. This innovative approach has been validated by research in the last years and has characteristics that may enable the development of true unobtrusive, pervasive and sensitive ambient intelligent systems. Specifically, this approach has been proven to be useful in quantifying aspects such as stress, fatigue or attention.
