Data Prediction of Mobile Network Traffic in Public Scenes by SOS-vSVR Method.

Accurate base station traffic data in a public place with large changes in the amount of people could help predict the occurrence of network congestion, which would allow us to effectively allocate network resources. This is of great significance for festival network support, routine maintenance, and resource scheduling. However, there are a few related reports on base station traffic prediction, especially base station traffic prediction in public scenes with fluctuations in people flow. This study proposes a public scene traffic data prediction method, which is based on a v Support Vector Regression (vSVR) algorithm. To achieve optimal prediction of traffic, a symbiotic organisms search (SOS) was adopted to optimize the vSVR parameters. Meanwhile, the optimal input time step was determined through a large number of experiments. Experimental data was obtained at the base station of Huainan Wanda Plaza, in the Anhui province of China, for three months, with the granularity being one hour. To verify the predictive performance of vSVR, the classic regression algorithm extreme learning machine (ELM) and variational Bayesian Linear Regression (vBLR) were used. Their optimal prediction results were compared with vSVR predictions. Experimental results show that the prediction results from SOS-vSVR were the best. Outcomes of this study could provide guidance for preventing network congestion and improving the user experience.

The Future-oriented Repetitive Thought (FoRT) scale: Validation in Chinese samples and application in the schizophrenia spectrum.

BACKGROUND: Repetitive thoughts are usually associated with psychopathology. The Future-oriented Repetitive Thought (FoRT) Scale is a measure designed to capture frequency of repetitive thought about positive and negative future events. However, the validity of the scale in Chinese population and its application in the schizophrenia spectrum have not been examined. METHODS: The current study aimed to examine the psychometric properties of the Chinese version of the FoRT scale and to apply it to the schizophrenia spectrum. In Study 1, three samples (total N = 1875) of university students were recruited for exploratory factor analysis, confirmatory factor analysis, and validity test, respectively. In Study 2, we identified subsamples with high schizotypal traits (N = 89) and low schizotypal traits (N = 89), and recruited 36 inpatients with schizophrenia and 41 matched healthy controls. RESULTS: The three-factor (pessimistic repetitive future thinking, repetitive thinking about future goals, and positive indulging about the future) structure of the FoRT scale with one item deleted, fitted the Chinese samples. And the scale could distinguish patients with schizophrenia and individuals with high schizotypal traits from controls. CONCLUSION: These findings support that the Chinese version of the FoRT scale is a valid tool and provide evidence for the potential applications in the schizophrenia spectrum.

Relationship between theta/beta ratio and mind wandering in schizotypy.

Negative association was found between the frontal theta/beta ratio and mind wandering in participants with high schizotypal traits, while no such association was found in participants with low schizotypal traits. These findings provide insights into the neural mechanism of mind wandering in individuals with high schizotypal traits.

Coal gangue recognition based on spectral imaging combined with XGBoost.

The identification of coal gangue is of great significance for its intelligent separation. To overcome the interference of visible light, we propose coal gangue recognition based on multispectral imaging and Extreme Gradient Boosting (XGBoost). The data acquisition system is built in the laboratory, and 280 groups of spectral data of coal and coal gangue are collected respectively through the imager. The spectral intensities of all channels of each group of spectral data are averaged, and then the dimensionality is reduced by principal component analysis. XGBoost is used to identify coal and coal gangue based on the reduced dimension spectral data. The results show that PCA combined with XGBoost has the relatively best classification performance, and its recognition accuracy of coal and coal gangue is 98.33%. In this paper, the ensemble-learning algorithm XGBoost is combined with spectral imaging technology to realize the rapid and accurate identification of coal and coal gangue, which is of great significance to the intelligent separation of coal gangue and the intelligent construction of coal mines.

Research on SPDTRS-PNN based intelligent assistant diagnosis for breast cancer.

Breast cancer is the second dangerous cancer in the world. Breast cancer data often contains more redundant information. Redundant information makes the breast cancer auxiliary diagnosis less accurate and time consuming. Dimension reduction algorithm combined with machine learning can solve these problems well. This paper proposes the single parameter decision theoretic rough set (SPDTRS) combined with the probability neural network (PNN) model for breast cancer diagnosis. We find that when the parameter value of SPDTRS is 2.5 and the SPREAD value is 0.75, the number of 30 attributes of the original breast cancer data dropped to 12, the accuracy of the SPDTRS-PNN model training set is 99.25%, the accuracy of the test set is 97.04%, and the test time is 0.093 s. The experimental results show that the SPDTRS-PNN model can improve the ac-curacy of breast cancer recognition, reduce the time required for diagnosis.

A New Strategy for Identification of Coal Miners With Abnormal Physical Signs Based on EN-mRMR.

Coal miners' occupational health is a key part of production safety in the coal mine. Accurate identification of abnormal physical signs is the key to preventing occupational diseases and improving miners' working environment. There are many problems when evaluating the physical health status of miners manually, such as too many sign parameters, low diagnostic efficiency, missed diagnosis, and misdiagnosis. To solve these problems, the machine learning algorithm is used to identify miners with abnormal signs. We proposed a feature screening strategy of integrating elastic net (EN) and Max-Relevance and Min-Redundancy (mRMR) to establish the model to identify abnormal signs and obtain the key physical signs. First, the raw 21 physical signs were expanded to 25 by feature construction technology. Then, the EN was used to delete redundant physical signs. Finally, the mRMR combined with the support vector classification of intelligent optimization algorithm by Gravitational Search Algorithm (GSA-SVC) is applied to further simplify the rest of 12 relatively important physical signs and obtain the optimal model with data of six physical signs. At this time, the accuracy, precision, recall, specificity, G-mean, and MCC of the test set were 97.50%, 97.78%, 97.78%, 97.14%, 0.98, and 0.95. The experimental results show that the proposed strategy improves the model performance with the smallest features and realizes the accurate identification of abnormal coal miners. The conclusion could provide reference evidence for intelligent classification and assessment of occupational health in the early stage.

Multi-membrane search algorithm.

This research proposes a new multi-membrane search algorithm (MSA) based on cell biological behavior. Cell secretion protein behavior and cell division and fusion strategy are the main inspirations for the algorithm. In order to verify the performance of the algorithm, we used 19 benchmark functions to compare the MSA test results with MVO, GWO, MFO and ALO. The number of iterations of each algorithm on each benchmark function is 100, the population number is 10, and the running is repeated 50 times, and the average and standard deviation of the results are recorded. Tests show that the MSA is competitive in unimodal benchmark functions and multi-modal benchmark functions, and the results in composite benchmark functions are all superior to MVO, MFO, ALO, and GWO algorithms. This paper also uses MSA to solve two classic engineering problems: welded beam design and pressure vessel design. The result of welded beam design is 1.7252, and the result of pressure vessel design is 5887.7052, which is better than other comparison algorithms. Statistical experiments show that MSA is a high-performance algorithm that is competitive in unimodal and multimodal functions, and its performance in compound functions is significantly better than MVO, MFO, ALO, and GWO algorithms.

RF-PCA: A New Solution for Rapid Identification of Breast Cancer Categorical Data Based on Attribute Selection and Feature Extraction.

Breast cancer is one of the most common cancer diseases in women. The rapid and accurate diagnosis of breast cancer is of great significance for the treatment of cancer. Artificial intelligence and machine learning algorithms are used to identify breast malignant tumors, which can effectively solve the problems of insufficient recognition accuracy and long time-consuming in traditional breast cancer diagnosis methods. To solve these problems, we proposed a method of attribute selection and feature extraction based on random forest (RF) combined with principal component analysis (PCA) for rapid and accurate diagnosis of breast cancer. Firstly, RF was used to reduce 30 attributes of breast cancer categorical data. According to the average importance of attributes and out of bag error, 21 relatively important attribute data were selected for feature extraction based on PCA. The seven features extracted from PCA were used to establish an extreme learning machine (ELM) classification model with different activation functions. By comparing the classification accuracy and training time of these different models, the activation function of the hidden layer was determined as the sigmoid function. When the number of neurons in the hidden layer was 27, the accuracy of the test set was 98.75%, the accuracy of the training set was 99.06%, and the training time was only 0.0022 s. Finally, in order to verify the superiority of this method in breast cancer diagnosis, we compared with the ELM model based on the original breast cancer data and other intelligent classification algorithm models. The algorithm used in this article has a faster recognition time and a higher recognition accuracy than other algorithms. We also used the breast cancer data of breast tissue reactance features to verify the reliability of this method, and ideal results were obtained. The experimental results show that RF-PCA combined with ELM can significantly reduce the time required for the diagnosis of breast cancer, which has the ability of rapid and accurate identification of breast cancer and provides a theoretical basis for the intelligent diagnosis of breast cancer.

A New Method Based on CEEMD Combined With Iterative Feature Reduction for Aided Diagnosis of Epileptic EEG.

In the clinical diagnosis of epileptic diseases, the intelligent diagnosis of epileptic electroencephalogram (EEG) signals has become a research focus in the field of brain diseases. In order to solve the problem of time-consuming and easily influenced by human subjective factors, artificial intelligence pattern recognition algorithm has been applied to EEG signals recognition. However, at present, the common empirical mode decomposition (EMD) signal decomposition algorithm does not consider the problem of mode aliasing. The EEG features obtained by feature extraction may be mixed with some unimportant features that affect the classification accuracy. In this paper, we proposed a new method based on complementary ensemble empirical mode decomposition (CEEMD) combined with iterative feature reduction for aided diagnosis of epileptic EEG. First of all, the evaluation indexes of decomposing and reconstructing signals by several methods were compared. The CEEMD was selected as the decomposition method of the signals. Then, the support vector machine recursive elimination (SVM-RFE) was used to reduce 9 features extracted from EEG data. The support vector classification of the gray wolf optimizer (GWO-SVC) recognition model was established for different feature subsets. By comparing the classification accuracy of training set and test set of different feature subsets, and considering the complexity of the model reflected by the number of features selected by SVM-RFE, the analysis showed that the 6 feature subsets with fewer features and higher classification accuracy could reflect the key information of epileptic EEG. The accuracy of the training set classification was 99.38% and the test set was as high as 100%. The recognition time was only 1.6551 s. Finally, in order to verify the reliability of the algorithm proposed in this paper, the proposed algorithm compared with the classification model established by the raw EEG signals and the optimization model established by other intelligent optimization algorithms. It is found that the algorithm used in this paper has higher classification accuracy and faster recognition time than other processing methods. The experimental results show that CEEMD combined with SVM-RFE is feasible for rapid and accurate recognition of EEG signals, which provides a theoretical basis for the aided diagnosis of epilepsy.

Selection of characteristic wavelengths using SPA for laser induced fluorescence spectroscopy of mine water inrush.

In the process of prevention and control of water inrush disaster, it is of great significance to identify the type of water inrush source for coal mine safety production accurately and quickly. The application of laser induced fluorescence (LIF) technology to identify the water inrush in coal mine broke the shortage of the traditional hydrochemical method, which could realize the accurate and rapid identification of water inrush types. Firstly, in order to avoid the influence of random variations of spectral data, four kinds of common pretreatment methods were analyzed and compared, and the moving average smoothing method was chosen to preprocess the original fluorescence spectral data. Then, for the purpose of selecting the appropriate sample division method to improve the predictive performance of the model, four common sample division methods were compared, and the sample set partitioning based on joint x-y distance (SPXY) method was used to divide the samples into training set and test set. Further, the 10 characteristic wavelengths were selected by successive projections algorithm (SPA) to reduce the amount of data. Finally, the selected data was taken as input, the sigmoid function was selected as the activation function of extreme learning machine (ELM), and the number of hidden layer neurons was set to 34, which realized the construction of water source identification model. The prediction accuracy of ELM model for the training set and test set were 99.0% and 94.0%, respectively. In addition, the water samples collected at different time were mixed in the same way to form the independent verification set, and the prediction accuracy of the ELM water source identification model for independent verification set was 91.5%. The results shown that it was feasible to select the characteristic wavelengths of fluorescence spectra by using the SPA. The data of 10 characteristic wavelengths could fully represent the effective information of whole band spectrum. And it also provided a theoretical basis for the development of a special online identification instrument for mine water inrush.

Identification of mine water inrush using laser-induced fluorescence spectroscopy combined with one-dimensional convolutional neural network.

The application of laser-induced fluorescence (LIF) combined with machine learning methods can make up for the shortcomings of traditional hydrochemical methods in the accurate and rapid identification of mine water inrush in coal mines. However, almost all of these methods require preprocessing such as principal component analysis (PCA) or drawing the spectral map as an essential step. Here, we provide our solution for the classification of mine water inrush, in which a one-dimensional convolutional neural network (1D CNN) is trained to automatically identify mine water inrush according to the LIF spectroscopy without the need for preprocessing. First, the architecture and parameters of the model were optimized and the 1D CNN model containing two convolutional blocks was determined to be the best model for the identification of mine water inrush. Then, we evaluated the performance of the 1D CNN model using the LIF spectral dataset of mine water inrush containing 540 training samples and 135 test samples, and we found that all 675 samples could be accurately identified. Finally, superior classification performance was demonstrated by comparing with a traditional machine learning algorithm (genetic algorithm-support vector machine) and a deep learning algorithm (two-dimensional convolutional neural network). The results show that LIF spectroscopy combined with 1D CNN can be used for the fast and accurate identification of mine water inrush without the need for complex pretreatments.

Efficacy of transthoracic device closure versus traditional surgical repair on atrial septal defects: A systematic review and meta-analysis / 中国胸心血管外科临床杂志

@#Objective    To compare the effects of transthoracic device closure and traditional surgical repair on atrial septal defect systemically. Methods    A systematic literature search was conducted using the PubMed, EMbase, The Cochrane Library, VIP, CNKI, CBM, Wanfang Database up to July 31, 2018 to identify trials according to the inclusion and exclusion criteria. Quality was assessed and data of included articles were extracted. The meta-analysis was conducted by RevMan 5.3 and Stata 12.0 software. Results    Thirty studies were identified, including 3 randomized controlled trials (RCTs) and 27 cohort studies involving 3 321 patients. For success rate, the transthoracic closure group was lower than that in the surgical repair group (CCT, OR=0.34, 95%CI 0.16 to 0.69, P=0.003). There was no statistical difference in mortality between the two groups (CCT, OR=0.43, 95%CI 0.12 to 1.52, P=0.19). Postoperative complication occurred less frequently in the transthoracic closure group than that in the surgical repair group (RCT, OR=0.30, 95%CI 0.12 to 0.77,  P=0.01; CCT, OR=0.27, 95%CI 0.17 to 0.42, P<0.000 01). The risk of postoperative arrhythmia in the transthoracic closure group was lower than that in the surgical repair group (CCT, OR=0.56, 95%CI 0.34 to 0.90, P=0.02). There was no statistical difference in the incidence of postoperative residual shunt in postoperative one month (CCT, OR=4.52, 95%CI 0.45 to 45.82, P=0.20) and in postoperative one year (CCT, OR=1.03, 95%CI 0.29 to 3.68, P=0.97) between the two groups. Although the duration of operation (RCT MD=–55.90, 95%CI –58.69 to –53.11, P<0.000 01; CCT MD=–71.68, 95%CI -– 79.70 to –63.66, P<0.000 01), hospital stay (CCT, MD=–3.31, 95%CI –4.16, –2.46, P<0.000 01) and ICU stay(CCT, MD=–10.15, 95%CI –14.38 to –5.91, P<0.000 01), mechanical ventilation (CCT, MD=–228.68, 95%CI –247.60 to – 209.77, P<0.000 01) in the transthoracic closure group were lower than those in the traditional surgical repair group, the transthoracic closure costed more than traditional surgical repair during being in the hospital (CCT, MD=1 221.42, 95%CI 1 124.70 to 1 318.14, P<0.000 01). Conclusion    Compared with traditional surgical repair, the transthoracic closure reduces the hospital stay, shortens the length of ICU stay and the duration of ventilator assisted ventilation, while has less postoperative complications. It is safe and reliable for patients with ASD within the scope of indication.