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Major depressive disorder (MDD) is a serious and heterogeneous psychiatric disorder that needs accurate diagnosis. Resting-state functional MRI (rsfMRI), which captures multiple perspectives on brain structure, function, and connectivity, is increasingly applied in the diagnosis and pathological research of MDD. Different machine learning algorithms are then developed to exploit the rich information in rsfMRI and discriminate MDD patients from normal controls. Despite recent advances reported, the MDD discrimination accuracy has room for further improvement. The generalizability and interpretability of the discrimination method are not sufficiently addressed either. Here, we propose a machine learning method (MFMC) for MDD discrimination by concatenating multiple features and stacking multiple classifiers. MFMC is tested on the REST-meta-MDD data set that contains 2428 subjects collected from 25 different sites. MFMC yields 96.9% MDD discrimination accuracy, demonstrating a significant improvement over existing methods. In addition, the generalizability of MFMC is validated by the good performance when the training and testing subjects are from independent sites. The use of XGBoost as the meta classifier allows us to probe the decision process of MFMC. We identify 13 feature values related to 9 brain regions including the posterior cingulate gyrus, superior frontal gyrus orbital part, and angular gyrus, which contribute most to the classification and also demonstrate significant differences at the group level. The use of these 13 feature values alone can reach 87% of MFMC's full performance when taking all feature values. These features may serve as clinically useful diagnostic and prognostic biomarkers for MDD in the future. • A new MDD discrimination method MFMC is proposed. • MFMC is tested on REST-meta-MDD including over 2400 subjects. • MFMC outperforms existing methods with significant improvements. • The good generalizability of MFMC is verified. • MFMC identifies 13 features related with 9 brain regions important for MDD. [ABSTRACT FROM AUTHOR]

Accuracy of flow rate determination is very critical to an ultrasonic gas flowmeter, which is sensitive to the flow profile under measurement and cannot be obviously improved with traditional weighted integration methods for multipath transducers. Therefore, on one hand, more attentions are paid to intelligent learning algorithms (e.g. artificial neural networks) in recent studies to accurately construct the mapping relation between multipath velocities and the flow rate. However, the bottleneck that a trained network is only customized for a certain flow profile greatly restricts its application. On the other hand, many researchers turn to reconstruct the flow field but so far all flow visualization methods cost too much and are difficult to realize online. In this paper, an intelligent method of flow profile identification for multipath ultrasonic flowmeters is developed to solve the above predicament. Based on support vector machine, a multiclass identifier is constructed to automatically identify the flow profile to be measured from 65 typical flow patterns. Extremely high identification accuracy of 99.49% for test and 100% for training is achieved while the test result still reaches 88.46% even when a measurement uncertainty of ±2% is considered. A comparison with extreme leaning machine further reveals the identification performance and robustness of the proposed flow profile identifier. Different piping configurations, installation positions and angles can be therefore accurately identified, which indirectly realizes the flow visualization and can comprise an intelligent system of ultrasonic flow measurement when combined with the intelligent flow rate determination algorithms. [ABSTRACT FROM AUTHOR]

To reduce the large integration errors brought by traditional methods in gas flow measurement under complex flow field, artificial neural network, support vector machine and other intelligent algorithms have been put into use. But these intelligent methods consume much time for training and need intensive user intervention for network design. This paper proposes to apply extreme learning machine to multipath ultrasonic flowmeters, which can analytically determine the output weights of networks instead of error backpropagation algorithm and iterative tuning of the parameters, and therefore provide high metering accuracy at extremely fast learning speed as well as require least human intervention. To test its effectiveness under different flow field and sensitivity to complex flow profiles, extreme learning machine is applied to determine the flow rate under two piping configurations, which can produce mild and severe flow disturbances. The determination errors are compared with a traditional integration method on the position of 5D and 10D as well as with the path orientation of 0° and 90°. Then 7 installation angles and 9 installation positions are respectively configured to study the performance and sensitivity of UFMs to installation effects. Finally, a comparison between extreme learning machine and other two intelligent algorithms is made in training and test time, the mean squared error and the maximal metering error under severe flow disturbance. It is found that extreme learning machine has rather high determination accuracy for flow rate at extremely fast learning speed and it is insensitive to the installation effects of ultrasonic gas flowmeter. [ABSTRACT FROM AUTHOR]