IEEE Sensors Journal Top 25

Today we received the following email:


Dear Mr. Yang, Mr. Zhang, Mr. Dong, Dr. Alelaiwi, and Prof. El Saddik,


On behalf of the IEEE Sensors Council, I am pleased to congratulate you as a co-author of the paper Evaluating and Improving the Depth Accuracy of Kinect for Windows v2IEEE Sensors Journal, Vol. 15, No. 8, August 2015, for your paper being one of the 25 most downloaded Sensors Journal papers in the months of May and June 2017. It is exciting to note that included in this count are all Sensors Journal papers published since the Journal’s foundation, about 4500 papers in total, and that last year, 439,609 Sensors Journal papers were downloaded from IEEE Xplore. You can view the latest Top 50 papers at:

Thank you for your contribution to the IEEE Sensors Journal!


Best regards,

Mike McShane

President, IEEE Sensors Council



Days Spent Together Using Soft Sensory Information on OSNs

Days Spent Together Using Soft Sensory Information on OSNs—A Case Study on Facebook by Alzamzami et al. (2016) considered an interesting problem: How to measure the number of days spent together from face-to-face interactions on online social networks? By introducing the concept of soft sensory information on online social networks

Link to the paper:

, Volume 21, Issue 15, pp 4227–4238


El Saddik: Entrevistas a participantes en las II Jornadas eMadrid sobre e-Learning

Prof. El Saddik in an Entrevistas a participantes en las II Jornadas eMadrid sobre e-Learning: 



PhD candidate Faisal Arafsha wins 3rd Place competition

Congratulations for Faisal Arafsha to win the 3rd place for his work titles: Cloud-based Tactile Health system in Foot


Fake News: Determining trust in media-rich websites using semantic similarity

Our paper “Determining trust in media-rich websites using semantic similarity” published in Multimedia Tools and Applications (2012) 60:69–96 DOI 10.1007/s11042-011-0798-x,  presented a method to dynamically compute and evolve the trust level of a “not-so-trusted” website of a particular domain (e.g. politics, health, sports, economy, information technology, etc.) based on how similar its content is with a trusted website of the same domain. This ‘trust level’ in informative websites allows us to determine the trustworthiness and credibility of the factual information presented in a not-so-trusted website.