SemiSupervised Ovulation Detection Based on Multiple Properties Despite being a wellresearched problem, ovulation detection in human female remains a difficult task. Most current methods for ovulation detection rely on measurements of a single property (e.g. morning body temperature) or at most on two properties (e.g. both salivary and vaginal electrical resistance). In this paper we present a machine learning based method for detecting the day in which ovulation occurs. Our method considered measurements of five different properties. We crawled a dataset from the web and showed that our method outperforms current stateoftheart methods for ovulation detection. Our method performs well also when considering measurements of fewer properties. We show that our methodu0027s performance can be further improved by using unlabeled data, that is, mensuration cycles without a know ovulation date. Our resulted machine learning model can be very useful for women trying to conceive that have trouble in recognizing their ovulation period, especially when some measurements are missing.

Gender Recognition Using Motion Data from Multiple Smart Devices Abstract   Using multiple smart devices, such as smartphone and smartwatch simultaneously, is becoming a popular life style with the popularity of wearables. This multiplesensor setting provides new opportunities for enhanced user trait analysis via multiple data fusion. In this study, we explore the task of gender recognition by using motion data collected from multiple smart devices. Specifically, motion data are collected from smartphone and smart band simultaneously. Motion features are extracted from the collected motion data according to three aspects: time, frequency, and wavelet domains. We present a feature selection method considering the redundancies between motion features. Gender recognition is performed using four supervised learning methods. Experimental results demonstrate that using motion data collected from multiple smart devices can significantly improve the accuracy of gender recognition. Evaluation of our method on a dataset of 56 subjects shows that it can reach an accuracy of 98.7% compared with the accuracies of 93.7% and 88.2% when using smartphone and smart band individually.