Automatic machine-learning based identification of jogging periods from accelerometer measurements of adolescents under field conditions.

Background: Assessment of health benefits associated with physical activity depend on the activity duration, intensity and frequency, therefore their correct identification is very valuable and important in epidemiological and clinical studies. The aims of this study are: to develop an algorithm for automatic identification of intended jogging periods; and to assess whether the identification performance is improved when using two accelerometers at the hip and ankle, compared to when using only one at either position.

Methods: The study used diarized jogging periods and the corresponding accelerometer data from thirty-nine, 15-year-old adolescents, collected under field conditions, as part of the GINIplus study.

SHOPIN: Semantic Homogeneity Optimization in Protein Interaction Networks.

Protein interaction networks (PINs) are argued to be the richest source of hidden knowledge of the intrinsic physical and/or functional meanings of the involved proteins. We propose a novel method for computational protein function prediction based on semantic homogeneity optimization in PIN (SHOPIN). The SHOPIN method creates graph representations of the PIN augmented by inclusion of the semantics of the proteins and their interacting contexts.

Nodes Localization in 3D Wireless Sensor Networks Based on Multidimensional Scaling Algorithm.

In the recent years, there has been a huge advancement in wireless sensor computing technology. Today, wireless sensor network (WSN) has become a key technology for different types of smart environment. Nodes localization in WSN has arisen as a very challenging problem in the research community.