An ensemble deep learning method as data fusion system for remote sensing multisensor classification

Credit and Loan Approval Classification Using a Bio-Inspired Neural Network

Numerous people are applying for bank loans as a result of the banking industry's expansion, but because banks only have a certain amount of assets to lend to, they can only do so to a certain number of applicants. Therefore, the banking industry is very interested in finding ways to reduce the risk factor involved in choosing the safe applicant in order to save lots of bank resources. These days, machine learning greatly reduces the amount of work needed to choose the safe applicant. Taking this into account, a novel weights and structure determination (WASD) neural network has been built to meet the aforementioned two challenges of credit approval and loan approval, as well as to handle the unique characteristics of each. Motivated by the observation that WASD neural networks outperform conventional back-propagation neural networks in terms of sluggish training speed and being stuck in local minima, we created a bio-inspired WASD algorithm for binary classification problems (BWASD) for best adapting to the credit or loan approval model by utilizing the metaheuristic beetle antennae search (BAS) algorithm to improve the learning procedure of the WASD algorithm. Theoretical and experimental study demonstrate superior performance and problem adaptability. Furthermore, we provide a complete MATLAB package to support our experiments together with full implementation and extensive installation instructions.

A novel fault classification feature extraction method for rolling bearing based on multi-sensor fusion technology and EB-1D-TP encoding algorithm

To improve the accuracy of bearing fault diagnosis in a multisensor monitoring environment, it is necessary to obtain more accurate and effective fault classification features for bearings. Accordingly, a bearing fault classification feature extraction method based on multisensor fusion technology and an enhanced binary one-dimensional ternary pattern (EB-1D-TP) algorithm were proposed in this study. First, an optimal equalization weighting algorithm was established to realize high-precision fusion of bearing signals by introducing an optimal equalization factor and determining the theoretical optimal equalization factor value. Second, an enhanced binary encoding method similar to balanced ternary encoding was developed, which increases the difference between the different fault features of the bearing. Finally, the new sequence obtained after encoding was used as the input to a support vector machine to classify and diagnose the faults of the rolling bearing. The experimental results show that the algorithm can significantly improve the accuracy and speed of rolling-bearing fault classification. Combining fusion-encoding features with other intelligent classification methods, the classification results were improved.

Synergetic application of thermal imaging and CCD imaging techniques to detect mutton adulteration based on data-level fusion and deep residual network

To improve the performance of single thermal imaging and single CCD imaging in detecting unknown adulterated meat samples, these two imaging techniques combined with a deep residual network were synergistically applied to detect mutton adulteration. Considering the importance of spatial and detailed information in improving stability and accuracy, three data-level fusion methods, namely, colour image stitching, grey image stitching and grey channel stacking, were proposed for the fusion of thermal images and CCD images. Classification and prediction models were further developed based on fusion images. The results showed that the models with colour image stitching achieved the best performance. For the external validation set, the accuracy of the best classification model in discriminating five categories was 99.30%. In predicting pork proportions, the R2, RMSE, RPD and RER of the best prediction model were 0.9717, 0.0238, 7.8696 and 21.28, respectively. The best prediction model for duck proportions had a R2 of 0.9616, RMSE of 0.0277, RPD of 5.1015, and RER of 14.44. Therefore, the synergetic application of thermal imaging and CCD imaging can provide a novel and promising tool to detect mutton adulteration and the quality of other food items.

RT-Ensemble Pred: A tool for retention time prediction of metabolites on different LC-MS systems

Liquid chromatography-mass spectrometry (LC-MS) could provide a large amount of information to assist in metabolites identification. Different liquid chromatographic methods (CMs) could produce different retention times to the same metabolite. To predict the retention time of local dataset by online datasets has become a trend, but the datasets downloaded from different databases were differences in quantity levels. And the imbalanced data could produce bad influence in model prediction. Thus, based on quantitative structure-retention relationships (QSRRs), an ensemble model, named RT-Ensemble Pred, has been successfully built to predict retention time of different LC-MS systems in this study. A total of 76, 807 metabolites (76, 909 retention times) have been collected across 9 CMs, and 19 natural products and 1 antifungal drug (20 retention times) have been collected to test the model applicability. An ensemble sampling was applied for the preprocessing procedure to solve the problem of imbalanced data. Based on the ensemble sampling, RT-Ensemble Pred could better utilize online datasets for the prediction of retention time. RT-Ensemble Pred was built based on the online datasets and tested by local dataset. The predictive accuracy of RT-Ensemble Pred was higher than the models without any sampling methods. The results showed that RT-Ensemble Pred could predict the metabolites which was not included in the database and the metabolites which were from new CMs. It could also be used for the prediction of other compounds beside metabolites. Furthermore, a tool of RT-Ensemble Pred was packed and can be freely downloaded at https://gitlab.com/mikic93/rt-ensemble-pred. It provides convenience for the users who need to predict the retention time of metabolites.

Land Cover Classification of UAV Remote Sensing Based on Transformer-CNN Hybrid Architecture

High-precision land cover maps of remote sensing images based on an intelligent extraction method are an important research field for many scholars. In recent years, deep learning represented by convolutional neural networks has been introduced into the field of land cover remote sensing mapping. In view of the problem that a convolution operation is good at extracting local features but has limitations in modeling long-distance dependence relationships, a semantic segmentation network, DE-UNet, with a dual encoder is proposed in this paper. The Swin Transformer and convolutional neural network are used to design the hybrid architecture. The Swin Transformer pays attention to multi-scale global features and learns local features through the convolutional neural network. Integrated features take into account both global and local context information. In the experiment, remote sensing images from UAVs were used to test three deep learning models including DE-UNet. DE-UNet achieved the highest classification accuracy, and the average overall accuracy was 0.28% and 4.81% higher than UNet and UNet++, respectively. It shows that the introduction of a Transformer enhances the model fitting ability.

Multisensor Feature Fusion Based Rolling Bearing Fault Diagnosis Method

To fully utilize the fault information and improve the diagnosis accuracy of rolling bearings, a multisensor feature fusion method is proposed. The method contains two steps. First, the intrinsic mode function (IMF) of each sensor vibration signal is calculated by variational mode decomposition (VMD), and the redundant information such as noise is eliminated. Then, the time-domain, frequency-domain and multiscale entropy features are extracted based on the preferred IMF and fused into one multidomain feature dataset. In the second step, the deep autoencoder network (DAEN) is constructed and the multisensor fusion features of the first step are used as input of the DAEN, and the multisensor fusion features are further extracted and classified. The experimental results show that the proposed model has a higher classification accuracy compared with the existing methods.

Gully Erosion Susceptibility Mapping in Highly Complex Terrain Using Machine Learning Models

Gully erosion is the most severe type of water erosion and is a major land degradation process. Gully erosion susceptibility mapping (GESM)’s efficiency and interpretability remains a challenge, especially in complex terrain areas. In this study, a WoE-MLC model was used to solve the above problem, which combines machine learning classification algorithms and the statistical weight of evidence (WoE) model in the Loess Plateau. The three machine learning (ML) algorithms utilized in this research were random forest (RF), gradient boosted decision trees (GBDT), and extreme gradient boosting (XGBoost). The results showed that: (1) GESM were well predicted by combining both machine learning regression models and WoE-MLC models, with the area under the curve (AUC) values both greater than 0.92, and the latter was more computationally efficient and interpretable; (2) The XGBoost algorithm was more efficient in GESM than the other two algorithms, with the strongest generalization ability and best performance in avoiding overfitting (averaged AUC = 0.947), followed by the RF algorithm (averaged AUC = 0.944), and GBDT algorithm (averaged AUC = 0.938); and (3) slope gradient, land use, and altitude were the main factors for GESM. This study may provide a possible method for gully erosion susceptibility mapping at large scale.