Aim of this study is to investigate the effects of menstrual cycle on cardiac autonomic nerve function parameters in young healthy women by means of heart rate variability (HRV). Comparative analysis has been done using linear and non linear methods of HRV for analysis of real life stress during menstrual cycle. This study considers three phases of menstrual cycle i.e. menstrual phase, luteal phase, and follicular phase. Electrocardiograms (ECG) of twenty young healthy women were recorded in all the three phases of menstrual cycle and RR intervals were then obtained. Scaling exponent alpha (α) in detrended Fluctuation Analysis (DFA) successfully distinguish the stress in three phases of menstrual cycle based on their HRV's. In DFA mean values of scaling exponent alpha (α) are lowest in luteal phase (0.77) indicates maximum stress as compared to the menstrual phase (0.88) and follicular phase (0.86) of menstrual cycle. In contrast to DFA, linear methods of HRV i.e. standard deviation of all NN intervals (SDNN) and square root of the mean of the sum of the squares of differences between adjacent NN intervals (RMSSD) and low frequency (LF), high frequency (HF) and LF/HF ratio in frequency domain method of HRV failed to clearly distinguishes the HRV's in all phases of menstrual cycle. And also it has been concluded that the menstrual characteristics in young women vary with autonomic nerve function.

No abstract

No abstract

PurposeExtraction of leaf region from the plant leaf images is a prerequisite process for species recognition, disease detection and classification and so on, which are required for crop management. Several approaches were developed to implement the process of leaf region segmentation from the background. However, most of the methods were applied to the images taken under laboratory setups or plain background, but the application of leaf segmentation methods is vital to be used on real-time cultivation field images that contain complex backgrounds. So far, the efficient method that automatically segments leaf region from the complex background exclusively for black gram plant leaf images has not been developed.Design/methodology/approachExtracting leaf regions from the complex background is cumbersome, and the proposed PLRSNet (Plant Leaf Region Segmentation Net) is one of the solutions to this problem. In this paper, a customized deep network is designed and applied to extract leaf regions from the images taken from cultivation fields.FindingsThe proposed PLRSNet compared with the state-of-the-art methods and the experimental results evident that proposed PLRSNet yields 96.9% of Similarity Index/Dice, 94.2% of Jaccard/IoU, 98.55% of Correct Detection Ratio, Total Segmentation Error of 0.059 and Average Surface Distance of 3.037, representing a significant improvement over existing methods particularly taking into account of cultivation field images.Originality/valueIn this work, a customized deep learning network is designed for segmenting plant leaf region under complex background and named it as a PLRSNet.

No abstract