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Toplam kayıt 220, listelenen: 31-40
A hybrid fingerprint based indoor positioning with extreme learning machine [Aşiri Ögrenme Makinesi ile Hibrid Parmakizi Tabanli iç Ortam Konumlandirma]
(Institute of Electrical and Electronics Engineers Inc., 2017)
Though global positioning system is a well-accepted technology for outdoor positioning, it is ineffective for indoor environment. Therefore, the search for effective and cheap solutions still continues. In this work, it ...
Artificial intelligence aided recommendation based mobile trip planner for Eskisehir city
(Institute of Electrical and Electronics Engineers Inc., 2015)
Recent years have seen a proliferation of applications aimed for the mobile users. Although there are some mobile trip planning applications available for big cities such Istanbul, they lack some important features that ...
Joint features regression for Cold-Start Recommendation on VideoLectures.Net
(CEUR-WS, 2011)
Recommender systems are popular information filtering systems used in various domains. Cold-start problem is a key challenge in a recommender system. In newitem/existing-user case of the cold-start problem, which is ...
Integration of classification algorithms for indoor positioning system
(Institute of Electrical and Electronics Engineers Inc., 2017)
Positioning applications become more popular with the advancement of location aware services. Global Positioning System is a successful solution for outdoors whereas it is not suitable for indoor environments due to the ...
Privacy-preserving item-based recommendations over partitioned data with overlaps
(Inderscience Enterprises Ltd., 2017)
User ratings are vital elements to drive recommender systems and, in the case of an insufficient amount of ratings, companies may prefer to operate recommender services over partitioned data. To make this feasible, there ...
Reconstructing rated items from perturbed data
(Elsevier Science BV, 2016)
The basic idea behind privacy-preserving collaborative filtering schemes is to prevent data collectors from deriving the actual rating values and the rated items. Different data perturbation methods have been proposed to ...
Privacy-preserving top-N recommendation on distributed data
(Wiley, 2008)
Traditional collaborative filtering (CF) systems perform filtering tasks on existing databases; however, data collected for recommendation purposes may split between different online vendors. To generate better predictions, ...
A survey: deriving private information from perturbed data
(Springer, 2015)
Privacy-preserving data mining has attracted the attention of a large number of researchers. Many data perturbation methods have been proposed to ensure individual privacy. Such methods seem to be successful in providing ...
Private predictions on hidden Markov models
(Springer, 2010)
Hidden Markov models (HMMs) are widely used in practice to make predictions. They are becoming increasingly popular models as part of prediction systems in finance, marketing, bio-informatics, speech recognition, signal ...
SOM-based recommendations with privacy on multi-party vertically distributed data
(Palgrave Macmillan LTD, 2012)
Data collected for providing recommendations can be partitioned among different parties. Offering distributed data-based predictions is popular due to mutual advantages. It is almost impossible to present trustworthy ...