The progress prediction of Parkinson's disease (PD) is one of the most important issues in early diagnosis of PD. Many researches have been conducted in this field, however, most existing methods focus on the selection of baseline features and regressors to reduce prediction errors. Different from the previous studies, the main goal of this paper is to obtain more effective features by feature transformation of baseline features to improve the prediction performance. Therefore, this paper proposes a prediction model based on graph wavelet transform (GWT) and attention weighted random forest (RF). Firstly, a clustering algorithm is adopted to reduce the prediction error of the model. Next, a multi-scale analysis of the feature vectors by GWT is conducted to yield a frequency feature representation that is more relevant to the target value. Finally, the frequency features are input into the attention weighted RF to predict the severity of PD, allowing the results of decision trees with better predictive performance in the RF to be highlighted while reducing the risk of overfitting. The effectiveness of the method is evaluated on the Parkinson's telemonitoring dataset collected by the University of Oxford. The experimental results show that the mean absolute error and root mean squared error of the proposed method for predicting PD severity (motor- and total-UPDRS) are 1.53, 2.13 and 1.91, 2.70, respectively. Compared with the quoted optimal method, the errors are reduced by 7.27%, 4.05% and 5.45%, 1.10%, respectively. This indicates that the proposed method has better prediction performance. 

Progress prediction of Parkinson's disease based on graph wavelet transform and attention weighted random forest

The rapid development of remote sensing and space technology provides multisource remote sensing image data for earth observation in the same area. Information provided by these images, however, is often complementary and cooperative, and multisource image fusion is still challenging. This paper proposes a novel multisource remote sensing image fusion algorithm. It integrates the contrast saliency map (CSM) and the sum-modified-Laplacian (SML) in the nonsubsampled shearlet transform (NSST) domain. The NSST is utilized to decompose the source images into low-frequency sub-bands and high-frequency sub-bands. Low-frequency sub-bands reflect the contrast and brightness of the source images, while high-frequency sub-bands reflect the texture and details of the source images. Using this information, the contrast saliency map and SML fusion rules are introduced into the corresponding sub-bands. Finally, the inverse NSST reconstructs the fusion image. Experimental results demonstrate that the proposed multisource remote image fusion technique performs well in terms of contrast enhancement and detail preservation.

https://www.mdpi.com/1424-8220/21/5/1756/pdf

Saliency-Guided Nonsubsampled Shearlet Transform for Multisource Remote Sensing Image Fusion.

The early fault diagnosis of rolling bearings has always been a difficult problem due to the interference of strong noise. This paper proposes a new method of early fault diagnosis for rolling bearings with entropy participation. First, a new signal decomposition method is proposed in this paper: intrinsic time-scale decomposition based on time-varying filtering. It is introduced into the framework of complete ensemble intrinsic time-scale decomposition with adaptive noise (CEITDAN). Compared with traditional intrinsic time-scale decomposition, intrinsic time-scale decomposition based on time-varying filtering can improve frequency-separation performance. It has strong robustness in the presence of noise interference. However, decomposition parameters (the bandwidth threshold and B-spline order) have significant impacts on the decomposition results of this method, and they need to be artificially set. Aiming to address this problem, this paper proposes rolling-bearing fault diagnosis optimization based on an improved coyote optimization algorithm (COA). First, the minimal generalized refined composite multiscale sample entropy parameter was used as the objective function. Through the improved COA algorithm, optimal intrinsic time-scale decomposition parameters based on time-varying filtering that match the input signal are obtained. By analyzing generalized refined composite multiscale sample entropy (GRCMSE), whether the mode component is dominated by the fault signal is determined. The signal is reconstructed and decomposed again. Finally, the mode component with the highest energy in the central frequency band is selected for envelope spectrum variation for fault diagnosis. Lastly, simulated and experimental signals were used to verify the effectiveness of the proposed method.

https://www.mdpi.com/1099-4300/23/4/451/pdf

A New Method Based on Time-Varying Filtering Intrinsic Time-Scale Decomposition and General Refined Composite Multiscale Sample Entropy for Rolling-Bearing Feature Extraction.

Deep Learning based Vertebral Body Segmentation with Extraction of Spinal Measurements and Disorder Disease Classification

Semantic segmentation is pixel-wise labeling of the image. Recently deep convolutional neural network (DCNN) providing progressive results in semantic segmentation. However, in remote sensing multispectral imagery very limited work has been done due to lack of training dataset. In this paper, a Res-Seg-net model is proposed for the semantic segmentation which is motivated by the existing Resnet and Segnet models. This model consists of encoder-decoder parts in which residual mapping is followed. For validation and testing of the proposed model, the RIT-18 dataset of multispectral imagery is used. The comparison results of the experiment on a multispectral imagery dataset have demonstrated the effectiveness of the proposed model.

Semantic Segmentation of Multispectral Images using Res-Seg-net Model

The objective of the multispectral pansharpening scheme is to obtain high spatial-spectral resolution multispectral (MS) images using high spectral resolution MS and high spatial resolution panchro...

Nidhi Saxena

Papers

Semantic Segmentation of Multispectral Images using Res-Seg-net Model

A pansharpening scheme using spectral graph wavelet transforms and convolutional neural networks