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Displaying 1-5 out of 5 total
Business Model Specific Charging Mechanism in Cognitive Radio
Software Engineering and Advanced Applications, Euromicro Conference
By Gülfem Isiklar Alptekin, Ayse Basar Bener
Issue Date:September 2008
Dynamic Spectrum Access allows the cognitive radio touse or share the spectrum in an opportunistic manner, whichimproves the efficiency of spectrum usage. In this research, we first present a theoretic framework and its process flow as a possible future sc...
Bayesian Networks For Evidence-Based Decision-Making in Software Engineering
IEEE Transactions on Software Engineering
By Ayse Tosun Misirli,Ayse Basar Bener
Issue Date:June 2014
Recommendation systems in software engineering (SE) should be designed to integrate evidence into practitioners experience. Bayesian networks (BNs) provide a natural statistical framework for evidence-based decision-making by incorporating an integrated su...
Super Peer Web Service Discovery Architecture
Data Engineering, International Conference on
By Evren Ayorak, Ayse Basar Bener
Issue Date:April 2007
Web Service discovery is currently performed with centralized registries such as UDDI. In this paper, we propose a super-peer network protocol to combine the efficiency of a centralized protocols and P2P networks. For avoidinga flooding the network with se...
Web Service Standards and Real Business Scenario Challenges
By Çigdem Patlak, Ayse Basar Bener, Haluk Bingöl
Issue Date:September 2003
The Web services paradigm is expected to transform the Web into a distributed application-to-application network. The Web services landscape is in an evolving state with core specifications almost mature and gaining widespread acceptance. Yet for some spec...
ENNA: software effort estimation using ensemble of neural networks with associative memory
Found in: Proceedings of the 16th ACM SIGSOFT International Symposium on Foundations of software engineering (SIGSOFT '08/FSE-16)
By Ayse Basar Bener, Burak Turhan, Yigit Kultur
Issue Date:November 2008
Companies usually have limited amount of data for effort estimation. Machine learning methods have been preferred over parametric models due to their flexibility to calibrate the model for the available data. On the other hand, as machine learning methods ...
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