In order to evaluate economics journals objectively and avoid the problem arising from artificial subjective factors, this paper put forward an evaluation of economics journals model based on reduction algorithm of rough set and grey correlation. Firstly, it used reduction algorithm of rough set based on equivalence relation to determine the key indicators. Secondly, it determined the key indicators weights by using grey correlation degree method, then used dominance relation of rough set method to determine another group of weights of key indicators. Lastly, it combined TOPSIS with two groups of weights above to evaluate and rank economics journals and compared the results, proved the evaluation model of economics journals based on reduction algorithm of rough set and grey correlation could be applied in evaluation of economics journals with high practicality and reasonability.

In order to evaluate food journals efficiently and reasonably, this study puts forward a comprehensive evaluation model for academic quality of food journals based on rough set and neural network. Firstly, we reduce evaluation indicators of journals based on discernibility matrix in rough set theory, removing the miscellaneous indicators and form the core evaluation indicator system, so as to have a more effective training for BP neural network. Then, we use methods defined in our study to generate enough training samples for the neural network modeling based on the core evaluation system. Lastly, with the help of BP neural network algorithm to rank journals, thereby we establish a comprehensive evaluation model for academic quality of journal. Instance analysis of food journals shows that the principle of generating the sample is feasible and effective and the modeling process is reliable and reasonable. What' more, the model established can be used for comprehensive evaluation for academic quality of food journals.

The autocorrelation algorithm is the most commonly used method for extracting fetal heart rate from ultrasound Doppler fetal monitors. The traditional autocorrelation algorithm can not always extract the detection cycle accurately. During the calculation process, the heartbeat cycle may not be recognized, or the cycle may be doubled or halved recognized. Combining the characteristics of envelope curve with average magnitude difference function curve, this paper designs a set of extreme point search scheme and a fetal heart cycle recognition model based on ensemble learning to assist in screening the best fetal heart cycle. The aim of this study is to improve the precision of the fetal heart rate calculation. The experimental results show that the proposed method can effectively screen out the best fetal heart cycle with enhanced reliability and robustness.

Fetal heart rate monitoring is a necessary routine examination item in obstetric clinic, which has important significance in the health examination of the perinatal fetus. Accurate extraction of fetal heart rate is a key technology in electronic fetal monitoring technology. There are still some difficulties and challenges in extracting the fetal heart rate from the ultrasound Doppler signals. The ultrasound Doppler fetal monitoring probe is difficult to maintain in the correct position, therefore, the Doppler ultrasound signals obtained may be the abdominal aorta signals which will cause fetal heart rate extraction error. In this paper, a signal source recognition model based on fast Fourier transform(FFT) and ensemble learning for ultrasound Doppler signals source recognition is proposed. The spectral features of the signals are extracted by FFT, and the spectral features are used as the input of ensemble learning model to decide whether the mother’s abdominal aorta signals are detected. The experimental results show that the proposed model can achieve the best recognition effect with the rule that the signals are regarded as from abdominal aorta if more than 93% of the signals get the negative output by the model within the time window of more than 13 seconds.

Fetal electronic monitoring is extensive and important in obstetrics. Although fetal movement is ususally used as an important indicator for quantifying fetal wellbeing, non-invasive and long-term monitoring of fetal movement remains challenging. The object of this study is to develop an algorithm for automatic detection of the fetal movements based on the analysis of Doppler ultrasound signals. In order to detect fetal movements automatically, a two-step process was proposed to track fetal movement. In Step 1, to suppress the problem of error detection, we calculated the baseline of the fetal movement signals from actography to extract new signals. In step2, we recalculated the threshold value of fetal movement detection by utilizing the information of fetal heart rate (FHR) acceleration to produced adaptive threshold values. The results showed that the union of results detected by the proposed method from actography and tocography achieved an encouraging performance with highest sensitivity and acceptable positive predictive value (PPV).