By Helena Lindgren, Juan F. De Paz, Paulo Novais, Antonio Fernández-Caballero, Hyun Yoe, Andres Jiménez Ramírez, Gabriel Villarrubia

Ambient Intelligence (AmI) is a up to date paradigm rising from man made Intelligence, the place desktops are used as proactive instruments helping individuals with their daily actions, making everyone’s lifestyles extra comfortable.

Another major predicament of AmI originates from the human desktop interplay area and makes a speciality of supplying how one can engage with structures in a extra normal manner through skill person pleasant interfaces. This box is evolving quick as should be witnessed via the rising usual language and gesture established sorts of interaction.

The inclusion of computational energy and conversation applied sciences in daily items is transforming into and their embedding into our environments will be as invisible as attainable. to ensure that AmI to achieve success, human interplay with computing strength and embedded structures within the atmosphere might be gentle and occur with out humans really noticing it. the one understanding humans must have arises from AmI: extra protection, convenience and health, rising in a average and inherent way.

ISAmI is the overseas Symposium on Ambient Intelligence and aiming to collect researchers from a number of disciplines that represent the medical box of Ambient Intelligence to offer and speak about the newest effects, new rules, tasks and classes realized, specifically when it comes to software program and purposes, and goals to compile researchers from a variety of disciplines which are drawn to all points of this zone.


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This work focuses on detection of ties in MSN and it fits this last trend of research. Specifically, we aim at finding ties based on a short-term analysis of information about the user’s actions and interactions detected by the sensors embedded in conventional smartphones. Short-term analysis means that ties are detected on the base of short temporal sequences (a few minutes at most), in order to provide timely information to the community detection algorithms used to support the information diffusion in MSN.

The idea is that the driving assistant proposes an average speed for the road section, which minimizes the driver stress and does not increase the trip time significantly. This speed does not require a sudden change in driving habits because the proposed average speed is based on the driver. The objective is to avoid one of the biggest problems of the driving assistant: frustration and impatience. 1 33 3 Estimation of th the Optimum Speed to Avoid Stress Particle Swarm Optimizaation (PSO) [7] and Deep Learning [8] are used to esttimate the average speed foor each section of the road.

Fig. 1 Flowchart to estimate the optimum speed for reducing stress and fuel consumption Each solution is repressented by a particle in the search space. It has the follow wing elements: ● ● Position: In our case is the recommended speed for reducing the streess level. Pbest: This is thhe best position on the current particle (speed that min nimizes the heart rrate and improves the driving style). C. Magaña et al. ● ● Gbest: It is the best position among all particles. Speed: Is calculated using equation 1.

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