Musa Mammadov
Deakin University
87 Papers
337 Citations
Musa Mammadov is an academic researcher from Deakin University. The author has contributed to research in topics: Optimization problem & Naive Bayes classifier. The author has an hindex of 18, co-authored 83 publications. Previous affiliations of Musa Mammadov include Federation University Australia & University of Melbourne.
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Papers
Learning the naive Bayes classifier with optimization models
Sona Taheri,Musa Mammadov +1 more
TL;DR: Three novel optimization models for the naive Bayes classifier are introduced where both class probabilities and conditional probabilities are considered as variables, and it is demonstrated that the proposed models can significantly improve the performance of this classifier, yet at the same time maintain its simple structure.
Optimization of improved suspension system with inerter device of the quarter-car model in vibration analysis
TL;DR: In this paper, an improved suspension system with the incorporated inerter device of the quarter-car model was analyzed to obtain optimal design parameters for maximum comfort level for a driver and passengers.
70
Attribute weighted Naive Bayes classifier using a local optimization
TL;DR: This paper proposes a novel attribute weighted Naive Bayes classifier by considering weights to the conditional probabilities and reports the results of numerical experiments on several real-world data sets in binary classification, which show the efficiency of the proposed method.
A new reliability analysis method based on the conjugate gradient direction
TL;DR: A new method, called “Conjugate Gradient Analysis (CGA) Method”, is proposed to apply in the reliability analysis problems, based on the conjugate gradient method.
51
Coverage in WLAN with Minimum Number of Access Points
S. Kouhbor,Julien Ugon,Alex Rubinov,Alexander Y. Kruger,Musa Mammadov +4 more
- 07 May 2006
TL;DR: A novel mathematical model developed to find the optimal number of APs and their locations in an environment that includes obstacles is described and the results obtained indicate that the model and software is able to solve optimal coverage problems for a design area with different number of users.