Approximation of Ensemble Boundary Using Spectral Coefficients.

A spectral analysis of a Boolean function is proposed for approximating the decision boundary of an ensemble of classifiers, and an intuitive explanation of computing Walsh coefficients for the functional approximation is provided. It is shown that the difference between the first- and third-order coefficient approximations is a good indicator of optimal base classifier complexity. When combining neural networks, the experimental results on a variety of artificial and real two-class problems demonstrate under what circumstances ensemble performance can be improved. For tuned base classifiers, the first-order coefficients provide performance similar to the majority vote. However, for weak/fast base classifiers, higher order coefficient approximation may give better performance. It is also shown that higher order coefficient approximation is superior to the Adaboost logarithmic weighting rule when boosting weak decision tree base classifiers.

An Energy-Efficient Multi-Tier Architecture for Fall Detection Using Smartphones.

Automatic detection of fall events is vital to providing fast medical assistance to the causality, particularly when the injury causes loss of consciousness. Optimization of the energy consumption of mobile applications, especially those which run 24/7 in the background, is essential for longer use of smartphones. In order to improve energy-efficiency without compromising on the fall detection performance, we propose a novel 3-tier architecture that combines simple thresholding methods with machine learning algorithms. The proposed method is implemented on a mobile application, called uSurvive, for Android smartphones. It runs as a background service and monitors the activities of a person in daily life and automatically sends a notification to the appropriate authorities and/or user defined contacts when it detects a fall. The performance of the proposed method was evaluated in terms of fall detection performance and energy consumption. Real life performance tests conducted on two different models of smartphone demonstrate that our 3-tier architecture with feature reduction could save up to 62% of energy compared to machine learning only solutions. In addition to this energy saving, the hybrid method has a 93% of accuracy, which is superior to thresholding methods and better than machine learning only solutions.

Survey of Turkish practice evaluating the management of postdural puncture headache in the obstetric population (1).

Many surveys and meta-analysis concerning the management of postdural puncture headache (PDPH) in the obstetric population were published in the literature. Therefore, we aimed to determine the current practice and ideas in the management of PDPH in the Turkish obstetric population and to provide awareness of the responders about new solutions with a survey. The response rate was 70%. The management strategies against accidental dural puncture during epidural insertion were to leave the catheter in situ as a spinal catheter (36%, n = 28) or to re-site it at a different level (64%, n = 50). Although these results might reflect the current practice of this small sample, in order to follow the changes in these strategies and to catch almost a standard approach for the prevention and management of PDPH which is a serious complication affecting morbidity in this particular population, further surveys including most of the centers are required.