Due to the proliferation of low cost wireless sensors, there is growing research interest in their applications, for example, in home healthcare and location tracking. However, due to sensors’ energy resource constraint, some possible applications of sensors have been restricted. In particular, in applications concerning deployment of mobile sensors in dynamic environments, high amounts of energy are consumed by sensors to maintain routing tables. Although existing methods have been proposed to query data from sensors without the use of any routing tables, these methods typically require redundant data to be sent back to the sink and not all of the aggregation functions could be executed precisely. In this paper, we modify an existing method to provide more accurate query answers and extend the lifetime of a wireless sensor network (WSN). According to our simulation, this method outperforms the existing method our approach modifies.
Recommendation systems have a prevalent cold-start problem. The problem is occurred might due to new users or new items (music) are added into the system. In this paper, the meaning of the cold-start is narrowed to that the systems do not understand the new user’s preferences. Therefore, systems can not recommend the music to users. Although many recommendation systems have a solution to reduce the cold-start, e.g., general systems utilize random to select songs. The systems random select some music works to user so that systems will know the user’s preferences after they rated the music works. However, systems may cost much time to collect the necessary information when the new user is interesting in some special types of music. Therefore, if systems select various type of music initially, the user’s preferences will be extracted more quickly. That is the cold-start problem can be reduced when the types of initial recommended music are various. In our approach, we utilize SOM to select some music from clusters. According to experiment, SOM selects type of music more average than k-means and random selection. Therefore, SOM can improve the cold-start problem and increase the precision of recommendation results.
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