Hüseyin Polat

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Recognition of lung sounds is an important goal in pulmonary medicine. In this work, we present a study for neural networks–genetic algorithm approach intended to aid in lung sound classification. Lung sound was captured from the chest wall of The subjects with different pulmonary diseases and also from the healthy subjects. Sound intervals with duration of(More)
In addition to memoryand model-based prediction methods, hybrid schemes are widely used due to their advantages like higher accuracy and improved online performance. Such methods should provide accurate predictions efficiently with privacy. Also, they need to be robust against profile injection or shilling attacks. These attacks inser t fake profiles into(More)
Listening to various lung sounds has proven to be an important diagnostic tool for detecting and monitoring certain types of lung diseases. In this study a computer-based system has been designed for easy measurement and analysis of lung sound using the software package DasyLAB. The designed system presents the following features: it is able to digitally(More)
The scope of this study is to diagnose vertebral arterial inefficiency by using Doppler measurements from both right and left vertebral arterials. Total of 96 patients’ Doppler measurements, consisting of 42 of healthy, 30 of spondylosis, and 24 of clinically proven vertebrobasillary insufficiency (VBI), were examined. Patients’ age and sex information as(More)
With the evolution of the Internet and e-commerce, collaborative filtering (CF) and privacy-preserving collaborative filtering (PPCF) have become popular. The goal in CF is to generate predictions with decent accuracy, efficiently. The main issue in PPCF, however, is achieving such a goal while preserving users’ privacy. Many implementations of CF and PPCF(More)
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