Session Program


  • 10 July 2017
  • 04:30PM - 06:30PM
  • Room: Sveva
  • Chairs: Ahmad Lotfi and Faiyaz Doctor

Ambient Computational Intelligence

Abstract - In this paper, we address the problem of predicting the time of occurrence of next activity, given the current activity and the context. The models that predict activity and time of occurrence rely on the basic idea that human beings perform sequence of activities at specific times regularly. In other words, the models are dependent on human behavior. However, human behavior changes over time. Also, due to demands and goals to be attained, there may be change in human behavior. Therefore, one of the essential requirements of the predictive models for the given task is autonomous adaptation with time and without undergoing any retraining. Considering the requirement of an adaptive model, we propose an evolving fuzzy rule-based predictive model that can autonomously adapt with changes in the human behavior. The model performance is evaluated using real-life data and satisfactory results are obtained.
Abstract - In this paper we apply the Fuzzy Entropy and Approximate Entropy measures to the Activities of Daily Living (ADL) for a set of elderly subjects in their own homes, and compare the entropy measures against a simpler count of activity transitions. The aim is to assess whether a single relatively simple measure can give an overview of the ADL in order to provide summaries of the well-being of an elderly person for their carers or relatives. We find that both the entropy measures and activity count vary considerably between different elderly people, and from day to day. The absolute level of these measures seems to be indicative of different ADL levels. However, it appears that further analysis over a longer period and with more annotation by the volunteers is necessary before the measures can be appropriately interpreted.
Abstract - In the era of the Internet of Things (IoT), numerous application domains are emerging for a new generation of networked smart devices capable to process and communicate data over the Internet, for building a new smart world. While some large scale domains are certainly of a special interest, e.g., smart grid, some others small scale applications, e.g., smart home, give any user the chance to build his own IoT system. Thanks to the technological development, it is now possible to use and integrate cheap technologies to monitor the state of our homes. The paper is devoted to the implementation of an inexpensive system to measure the energy consumption of a home electrical appliances. An Arduino board equipped with a proper sensor is connected to the specific appliance one wants to monitor, and a web application running on a web server accessible through any device, i.e., pc, tablet or smartphone, makes it possible the real-time monitoring of the energy consumptions and to query for the historical energy rates. Moreover, a forecasting module based on a Radial Basis Function Neural Network trained, in the first layer, by a rough-fuzzy supervised clustering, provides future energy trends.
Abstract - Smart home systems have become increasingly widespread in the last few years. State-of-the-art smart home architectures concentrate on modeling the user physical behavior and on discovering possible behavioral pattern, while they provide very little personalization of the services based on the individual cognitive characteristics. In this direction, the analysis of the user activity on social networks offers a reliable and efficient way to obtain psychological traits of a human being in a manner that can be easily integrated with smart systems. In this paper, we outline a robot based architecture that blends ubiquitous computing and personality analysis to provide a custom-tailored system. An entertainment recommender scenario with a social robot is then analyzed as a case study.
Abstract - Road network quality condition should be monitored continuously. Several efforts for developing new technologies that automatically detect and recognize road events have been made, contributing improvement, traveling efficiency and good quality road state by implementing immediate corrective actions. In this work, a new model for identifying road events has been developed, classifying them in different road anomalies (potholes, cracks and planned events in bad condition) and events that are considered as part of the road (speed bumps, patches). This work presents a fuzzy classifier for recognizing this type of events using a set of fuzzy rules designed to identify each event through a statistical analysis and navigational data extracted from real environments. So that, the fuzzy model presents a good performance based on a parallel data processing with lower execution time than sequential algorithms present in the literature.