Graph learning is an effective dimensionality reduction (DR) manner to analyze the intrinsic properties of high dimensional data, it has been widely used in the fields of DR for hyperspectral image (HSI) data, but they ignore the collaborative relationship between sample pairs. In this paper, a novel supervised spectral DR method called local constrained manifold structure collaborative preserving embedding (LMSCPE) was proposed for HSI classification. At first, a novel local constrained collaborative representation (CR) model is designed based on the CR theory, which can obtain more effective collaborative coefficients to characterize the relationship between samples pairs. Then, an intraclass collaborative graph and an interclass collaborative graph are constructed to enhance the intraclass compactness and the interclass separability, and a local neighborhood graph is constructed to preserve the local neighborhood structure of HSI. Finally, an optimal objective function is designed to obtain a discriminant projection matrix, and the discriminative features of various land cover types can be obtained. LMSCPE can characterize the collaborative relationship between sample pairs and explore the intrinsic geometric structure in HSI. Experiments on three benchmark HSI data sets show that the proposed LMSCPE method is superior to the state-of-the-art DR methods for HSI classification.

Dimensionality Reduction of Hyperspectral Image Based on Local Constrained Manifold Structure Collaborative Preserving Embedding

Dynamic interactive multiview memory network for emotion recognition in conversation

Remotely sensed data from both in situ and satellite platforms in visible, near-infrared, and shortwave infrared (VNIR–SWIR, 400–2500 nm) regions have been widely used to characterize and model soil properties in a direct, cost-effective, and rapid manner at different scales. In this study, we assess the performance of machine-learning algorithms including random forest (RF), extreme gradient boosting machines (XGBoost), and support vector machines (SVM) to model salt marsh soil bulk density using multispectral remote-sensing data from the Landsat-7 Enhanced Thematic Mapper Plus (ETM+) platform. To our knowledge, use of remote-sensing data for estimating salt marsh soil bulk density at the vegetation rooting zone has not been investigated before. Our study reveals that blue (band 1; 450–520 nm) and NIR (band 4; 770–900 nm) bands of Landsat-7 ETM+ ranked as the most important spectral features for bulk density prediction by XGBoost and RF, respectively. According to XGBoost, band 1 and band 4 had relative importance of around 41% and 39%, respectively. We tested two soil bulk density classes in order to differentiate salt marshes in terms of their capability to support vegetation that grows in either low (0.032 to 0.752 g/cm3) or high (0.752 g/cm3 to 1.893 g/cm3) bulk density areas. XGBoost produced a higher classification accuracy (88%) compared to RF (87%) and SVM (86%), although discrepancies in accuracy between these models were small (<2%). XGBoost correctly classified 178 out of 186 soil samples labeled as low bulk density and 37 out of 62 soil samples labeled as high bulk density. We conclude that remote-sensing-based machine-learning models can be a valuable tool for ecologists and engineers to map the soil bulk density in wetlands to select suitable sites for effective restoration and successful re-establishment practices.

Machine-Learning Classification of Soil Bulk Density in Salt Marsh Environments.

Classifying Crop Types Using Two Generations of Hyperspectral Sensors (Hyperion and DESIS) with Machine Learning on the Cloud

Recently, long short-term memory (LSTM) networks are extensively utilized for text classification. Compared to feed-forward neural networks, it has feedback connections, and thus, it has the ability to learn long-term dependencies. However, the LSTM networks suffer from the parameter tuning problem. Generally, initial and control parameters of LSTM are selected on a trial and error basis. Therefore, in this paper, an evolving LSTM (ELSTM) network is proposed. A multiobjective genetic algorithm (MOGA) is used to optimize the architecture and weights of LSTM. The proposed model is tested on a well-known factory reports dataset. Extensive analyses are performed to evaluate the performance of the proposed ELSTM network. From the comparative analysis, it is found that the LSTM network outperforms the competitive models.

/pdf/evolving-long-short-term-memory-network-based-text-djuq967w.pdf

Evolving Long Short-Term Memory Network-Based Text Classification

The classification of hyperspectral remote sensing images is difficult due to the curse of dimensionality. Therefore, it is necessary to find an effective way to reduce the dimensions of such images. The Relief-F method has been introduced for supervising dimensionality reduction, but the band subset obtained by this method has a large number of continuous bands, resulting in a reduction in the classification accuracy. In this paper, an improved method—called Partitioned Relief-F—is presented to mitigate the influence of continuous bands on classification accuracy while retaining important information. Firstly, the importance scores of each band are obtained using the original Relief-F method. Secondly, the whole band interval is divided in an orderly manner, using a partitioning strategy according to the correlation between the bands. Finally, the band with the highest importance score is selected in each sub-interval. To verify the effectiveness of the proposed Partitioned Relief-F method, a classification experiment is performed on three publicly available data sets. The dimensionality reduction methods Principal Component Analysis (PCA) and original Relief-F are selected for comparison. Furthermore, K-Means and Balanced Iterative Reducing and Clustering Using Hierarchies (BIRCH) are selected for comparison in terms of partitioning strategy. This paper mainly measures the effectiveness of each method indirectly, using the overall accuracy of the final classification. The experimental results indicate that the addition of the proposed partitioning strategy increases the overall accuracy of the three data sets by 1.55%, 3.14%, and 0.83%, respectively. In general, the proposed Partitioned Relief-F method can achieve significantly superior dimensionality reduction effects.

Partitioned Relief-F Method for Dimensionality Reduction of Hyperspectral Images

Bidirectional Gated Temporal Convolution with Attention for text classification

This paper proposes a Nested Sliding Window (NSW) method based on the correlation between pixel vectors, which can extract spatial information from the hyperspectral image (HSI) and reconstruct the original data. In the NSW method, the neighbourhood window constructed with the target pixel as the centre contains relevant pixels that are spatially adjacent to the target pixel. In the neighbourhood window, a nested sliding sub-window contains the target pixel and a part of the relevant pixels. The optimal sub-window position is determined according to the average value of the Pearson correlation coefficients of the target pixel and the relevant pixels, and the target pixel can be reconstructed by using the pixels and the corresponding correlation coefficients in the optimal sub-window. By combining NSW with Principal Component Analysis (PCA) and Support Vector Machine (SVM), a classification model, namely NSW-PCA-SVM, is obtained. This paper conducts experiments on three public datasets, and verifies the effectiveness of the proposed model by comparing with two basic models, i.e., SVM and PCA-SVM, and six state-of-the-art models, i.e., CDCT-WF-SVM, CDCT-2DCT-SVM, SDWT-2DWT-SVM, SDWT-WF-SVM, SDWT-2DCT-SVM and Two-Stage. The proposed approach has the following advantages in overall accuracy (OA)—take the experimental results on the Indian Pines dataset as an example: (1) Compared with SVM (OA = 53.29%) and PCA-SVM (OA = 58.44%), NSW-PCA-SVM (OA = 91.40%) effectively utilizes the spatial information of HSI and improves the classification accuracy. (2) The performance of the proposed model is mainly determined by two parameters, i.e., the window size in NSW and the number of principal components in PCA. The two parameters can be adjusted independently, making parameter adjustment more convenient. (3) When the sample size of the training set is small (20 samples per class), the proposed NSW-PCA-SVM approach achieves 2.38–18.40% advantages in OA over the six state-of-the-art models.

https://www.mdpi.com/2072-4292/13/1/114/pdf

An SVM-Based Nested Sliding Window Approach for Spectral─Spatial Classification of Hyperspectral Images

Computer-aided color parameter imaging (CPI) is a novel technique for contrast-enhanced ultrasound (CEUS) that can highlight hemodynamic features of focal lesions. The purpose of the study was to investigate the role of CPI in evaluation of hepatocellular carcinoma (HCC) hemodynamic features and prognosis after radiofrequency ablation (RFA). One hundred twenty-one patients with HCC underwent CEUS with CPI analysis before RFA. Eighty-nine patients had pathologically proven well- to moderately differentiated HCC (WM-HCC), and 32 patients had poorly differentiated or undifferentiated HCC (PU-HCC). Perfusion features of CEUS and contrast-enhanced computed tomography/magnetic resonance imaging were compared with CPI parameters for WM-HCC and PU-HCC. The results indicated that 67.4% of WM-HCC had a centrifugal perfusion CPI pattern, whereas 84.4% of PU-HCC tumors had a centripetal pattern (p < 0.001, odds ratio = 11.2). The specificity, sensitivity and accuracy of the CPI perfusion pattern regarding HCC pathological grade were higher than those with routine CEUS (84.4% vs. 9.4%, p < 0.001; 67.4% vs. 3.4%, p < 0.001; 71.9% vs. 5.0%, p < 0.001). Moreover, multivariable analysis revealed that the CPI perfusion pattern was an independent risk factor for progression-free survival post-RFA (centripetal group: 28.3 ± 4.1 mo vs. centrifugal group: 45.8 ± 4.4 mo, p = 0.002). A novel CPI technique for CEUS could non-invasively provide valuable hemodynamic information and predict prognosis for HCC patients treated by RFA. 

Computer-Aided Color Parameter Imaging of Contrast-Enhanced Ultrasound Evaluates Hepatocellular Carcinoma Hemodynamic Features and Predicts Radiofrequency Ablation Outcome.

Abstract Background High-throughput sequencing of genes indicating susceptibility to noise-induced hearing loss has not previously been reported. Aims/Objectives To identify and analyze genes associated with susceptibility to noise-induced hearing loss (NIHL) and characterize differences in susceptibility to hearing loss by genotype. Material and methods Pure tone audiometry tests were performed on 113 workers exposed to high-intensity noise. Whole-exome sequencing (WES) was conducted and NIHL susceptibility genes screened for training unsupervised and supervised machine learning models. Immunofluorescence staining of mouse cochlea was used to observe patterns of NIHL susceptibility gene expression. Results Participants were divided into a NIHL and a control group, according to the results of audiometry tests. Seventy-three possible NIHL susceptibility genes were input into the machine learning model. Two subgroups of NIHL could be distinguished by unsupervised machine learning and the classification was evaluated by the supervised machine learning algorithm. The VWF gene had the highest mutation frequency in the NIHL group and was expressed mainly in the spiral ligament. Conclusions and significance NIHL susceptibility genes were screened and NIHL subgroups could be distinguished. VWF may be a novel NIHL susceptibility gene.

https://www.tandfonline.com/doi/pdf/10.1080/00016489.2023.2201287?needAccess=true&role=button

Gang Liu

Papers

Partitioned Relief-F Method for Dimensionality Reduction of Hyperspectral Images

Bidirectional Gated Temporal Convolution with Attention for text classification

An SVM-Based Nested Sliding Window Approach for Spectral─Spatial Classification of Hyperspectral Images

Computer-Aided Color Parameter Imaging of Contrast-Enhanced Ultrasound Evaluates Hepatocellular Carcinoma Hemodynamic Features and Predicts Radiofrequency Ablation Outcome.

Whole-exome sequencing for screening noise-induced hearing loss susceptibility genes