Chronic kidney disease (CKD) is a global health problem with high morbidity and mortality rate, and it induces other diseases. Since there are no obvious symptoms during the early stages of CKD, patients often fail to notice the disease. Early detection of CKD enables patients to receive timely treatment to ameliorate the progression of this disease. Machine learning models can effectively aid clinicians achieve this goal due to their fast and accurate recognition performance. In this study, we propose a machine learning methodology for diagnosing CKD. The CKD data set was obtained from the University of California Irvine (UCI) machine learning repository, which has a large number of missing values. KNN imputation was used to fill in the missing values, which selects several complete samples with the most similar measurements to process the missing data for each incomplete sample. Missing values are usually seen in real-life medical situations because patients may miss some measurements for various reasons. After effectively filling out the incomplete data set, six machine learning algorithms (logistic regression, random forest, support vector machine, k-nearest neighbor, naive Bayes classifier and feed forward neural network) were used to establish models. Among these machine learning models, random forest achieved the best performance with 99.75% diagnosis accuracy. By analyzing the misjudgments generated by the established models, we proposed an integrated model that combines logistic regression and random forest by using perceptron, which could achieve an average accuracy of 99.83% after ten times of simulation. Hence, we speculated that this methodology could be applicable to more complicated clinical data for disease diagnosis.

Emotion is a human attitude experience and corresponding behavioral response to objective things. Effective emotion recognition is important for the intelligence and humanization of brain-computer interface (BCI). Although deep learning has been widely used in emotion recognition in recent years, emotion recognition based on electroencephalography (EEG) is still a challenging task in practical applications. Herein, we proposed a novel hybrid model that employs generative adversarial networks to generate potential representations of EEG signals while combining graph convolutional neural networks and long short-term memory networks to recognize emotions from EEG signals. Experimental results on DEAP and SEED datasets show that the proposed model achieved the promising emotion classification performance compared with the state-of-the-art methods.

A localized surface plasmon resonance sensor based on molecularly imprinted polymer (MIP) coated Au nanoparticle (AuNP) was developed to selectively detect α-pinene vapor. AuNPs were deposited on glass slides via Au ion sputtering method. The MIP was synthesized using methacrylic acid as monomer, ethylene glycol dimethylacrylate as cross-linker, and α-pinene as template. Adsorption characteristics of MIP powder and coating films were evaluated using gas chromatography mass spectrometry. The influence of molar ratio of template (α-pinene) to monomer on the absorption characteristics of MIP film was investigated. The largest affinity absorption amount of polymer film was achieved when the molar ratio of template to monomer was 1:4. Prepolymerized MIP mixture was spin coated on the sputtered AuNPs. The results showed that plasmon resonance peak in spectrum and the refractive index sensitivity depended on the polymer layer thickness. Better sensitivity and selectivity of MIP coated AuNPs sensor were realized at the spin coating speed about 1400 r/min. The real-time responses of α-pinene imprinted polymer coated AuNP sensor to three terpene vapors: α-pinene, limonene, and y-terpinene were verified to be selective, rapid, and reversible.