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To detect false data injection attacks (FDIAs) in power grid reconstruction and solve the problem of high data dimension and bad abnormal data processing in the power system, thereby achieving safe and stable operation of the power grid system, this study introduces machine learning methods to explore the detection of FDIAs. First, through the utilization of the standard IEEE node system and the simulation of FDIAs under the condition of non-complete topology information, the construction of the attack data set is completed, and the MatPower tool is applied to simulate and analyze the data set. Second, based on the isolated Forest (iForest) abnormal score data processing algorithm combined with the Local Linear Embedding (LLE) data dimensionality reduction method, an algorithm for data feature extraction is constructed. Finally, based on the combination of the Convolutional Neural Network (CNN) and the Gated Recurrent Unit (GRU) network, an algorithm model for FDIAs detection is constructed. The results show that in the IEEE14-bus node and IEEE118-bus node systems, the overall distribution of the state estimated before and after the attack vector injection is consistent with the initial value. In the iFores algorithm, the number of iTree and the number of samples affect the extraction of abnormal score data. When the number of iTree n is determined to be 100, and the corresponding number of samples w is determined to be 10, the algorithm has the best detection effect. The FDIAs detection algorithm model based on CNN-GRU shows good detection effects under high attack intensity, with an accuracy rate of more than 95%, and its performance is better than other traditional detection algorithms. In this study, the bad data detection model based on deep learning has an active role in the realization of the safe and stable operation of the smart grid., Competing Interests: The authors have declared that no competing interests exist.

This study aimed to develop and evaluate a population pharmacokinetic (PPK) combined machine learning approach to predict tacrolimus trough concentrations for Chinese adult liver transplant recipients in the early posttransplant period. Tacrolimus trough concentrations were retrospectively collected from routine monitoring records of liver transplant recipients and divided into the training data set (1287 concentrations in 145 recipients) and the test data set (296 concentrations in 36 recipients). A PPK model was first established using NONMEM. Then a machine learning model of Xgboost was adapted to fit the estimated individual pharmacokinetic parameters obtained from the PPK model with Bayesian forecasting. The performance of the final PPK model and Xgboost model was compared in the test data set. In the final PPK model, tacrolimus daily dose, postoperative days, hematocrit, aspartate aminotransferase, and concomitant voriconazole, were identified to significantly influence the clearance. The postoperative days along with hematocrit significantly influence the volume of distribution. In the Xgboost model, the first 5 predictors for predicting the clearance were concomitant with voriconazole, sex, single nucleotide polymorphisms of CYP3A4*1G and CYP3A5*3 in recipients, and tacrolimus daily dose, for the volume of distribution were postoperative days, age, weight, total bilirubin and graft : recipient weight ratio. In the test data set, the Xgboost model showed the minimum median prediction error of tacrolimus concentrations, less than the PPK model with or without Bayesian forecasting. In conclusion, a PPK combined machine learning approach could improve the prediction of tacrolimus concentrations for Chinese adult liver transplant recipients in the early posttransplant period. [ABSTRACT FROM AUTHOR]

• N-MBL was compared with Machine Learning and local models. • N-MBL explained the non-linear relationship well between the property and spectra. • The performance of N-MBL was better than Machine Learning models. • The number of selected nearest neighbors was related to the accuracy of local model. Visible near-infrared (vis-NIR) spectroscopy has gained widespread recognition as an efficient and reliable approach for the rapid monitoring of soil properties. This technique relies on robust machine learning models that convert soil spectra information to soil properties. In particular, memory-based learning (MBL) has emerged as a powerful local modeling technique for soil spectral analysis. However, conventional MBL algorithms use linear models, disregarding the non-linear relationship between soil properties and vis-NIR spectra. Therefore, we hypothesize that non-linear memory-based learning (N-MBL) models can enhance prediction. This study develops and evaluates the N-MBL algorithm using the Lateritic Red soil spectral library (LRSSL) from Guangdong province in China. This library consists of 742 samples of vis-NIR spectra and corresponding soil properties, including pH, soil organic matter (SOM), total nitrogen (TN), total phosphorus (TP), and total potassium (TK). As a comparison, several commonly used supervised learning methods, such as Partial least squares regression (PLSR), Cubist, Random Forest (RF), Super vector machine (SVM), Convolution neural network (CNN), and local models (MBL), were compared to the proposed N-MBL. The results showed that local models generally outperformed supervised learning methods, particularly when applied to a large soil spectral library with a substantial number of samples (over 500). When comparing the two local models, MBL had more fluctuation of model performance compared to N-MBL as the number of selected nearest neighbors (k) varied between 30 and 250. As k increased, N-MBL showed higher R2 values for SOM and TN prediction than MBL but lower performance for pH and TK prediction. In addition, N-MBL outperformed MBL in predicting TP. In conclusion, N-MBL is a new local algorithm for predicting soil properties from vis-NIR spectra. It has a high potential to improve the accuracy of the prediction of multiple soil properties. [ABSTRACT FROM AUTHOR]

Objective: Compared logistic regression (LR) with machine learning (ML) models, to predict the risk of ischemic stroke in an elderly population in China. Methods: We applied 2208 records from the Rugao Longitudinal Ageing Study (RLAS) for ischemic stroke risk prediction assessment. Input variables included 103 phenotypes. For 3‐year ischemic stroke risk prediction, we compared the discrimination and calibration of LR model and ML methods, where ML methods include Random Forest (RF), Gaussian kernel Support Vector Machines (SVM), Multilayer perceptron (MLP), K‐Nearest Neighbors Algorithm (KNN), and Gradient Boosting Decision Tree (GBDT) to develop an ischemic stroke risk prediction model. Results: Age, pulse, waist circumference, education level, β2‐microglobulin, homocysteine, cystatin C, folate, free triiodothyronine, platelet distribution width, QT interval, and QTc interval were significant induced predictors of ischemic stroke. For ischemic stroke prediction, the ML approach was able to tap more biochemical and ECG‐related multidimensional phenotypic indicators compared to the LR model, which placed more importance on general demographic indicators. Compared to the LR model, SVM provided the best discrimination and calibration (C‐index: 0.79 vs. 0.71, 11.27% improvement in model utility), with the best performance in both validation and test data. Conclusion: In a comparison of LR with five ML models, the accuracy of ischemic stroke prediction was higher by combining ML with multiple phenotypes. Combined with other studies based on elderly populations in China, ML techniques, especially SVM, have shown good long‐term predictive performance, inspiring the potential value of ML use in clinical practice. [ABSTRACT FROM AUTHOR]

• Reasonable feature dimension is estimated while protect local features with high computational efficiency. • The extracted feature parameters describe well the atrophy of featured ROIs of the brain as clinical parameters but show better performance in AD identification than clinical parameters. • Feature extraction and classification are performed compartmentally and automatically instead of extracting clinical features preliminarily. In recent years, some clinical parameters, such as the volume of gray matter (GM) and cortical thickness, have been used as anatomical features to identify Alzheimer's disease (AD) from Healthy Controls (HC) in some feature-based machine learning methods. However, fewer image-based feature parameters have been proposed, which are equivalent to these clinical parameters, to describe the atrophy of regions-of-interest (ROIs) of the brain. In this study, we aim to extract effective image-based feature parameters to improve the diagnostic performance of AD with magnetic resonance imaging (MRI) data. A new subspace-based sparse feature learning method is proposed, which builds a union-of-subspace representation model to realize feature extraction and disease identification. Specifically, the proposed method estimates feature dimensions reasonably, at the same time, it protects local features for the specified ROIs of the brain, and realizes image-based feature extraction and classification automatically instead of computing the volume of GM or cortical thickness preliminarily. Experimental results illustrate the effectiveness and robustness of the proposed method on feature extraction and classification, which are based on the sampled clinical dataset from Peking University Third Hospital of China and the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. The extracted image-based feature parameters describe the atrophy of ROIs of the brain well as clinical parameters but show better performance in AD identification than clinical parameters. Based on them, the important ROIs for AD identification can be identified even for correlated variables. The extracted features and the proposed identification parameters show high correlation with the volume of GM and the clinical mini-mental state examination (MMSE) score respectively. The proposed method will be useful in denoting the changes of cerebral pathology and cognitive function in AD patients. [ABSTRACT FROM AUTHOR]