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Prediction of slope stability using Tree Augmented Naive-Bayes classifier: modeling and performance evaluation

MATHEMATICAL BIOSCIENCES AND ENGINEERING 2022年第5期19卷 4526-4546页

作者： Ahmad, Feezan Tang, Xiao-Wei Qiu, Jiang-Nan Wroblewski, Piotr Ahmad, Mahmood Jamil, Irfan Dalian Univ Technol State Key Lab Coastal & Offshore Engn Dalian 116024 Peoples R China Dalian Univ Technol Fac Econ & Management Dalian 116024 Peoples R China Univ Technol & Econ H Chodkowska Warsaw Fac Engn Jutrzenki 135 PL-02231 Warsaw Poland Mil Univ Technol Fac Mechatron Armament & Aerosp Sylwestra Kaliskiego 2 PL-00908 Warsaw Poland Int Islamic Univ Malaysia Dept Civil Engn Fac Engn Jalan Gombak Selangor 50728 Malaysia Univ Engn & Technol Peshawar Dept Civil Engn Bannu Campus Bannu 28100 Pakistan Univ Engn & Technol Peshawar Dept Civil Engn Peshawar 25120 Pakistan

Predicting slope stability is critical for identifying terrain that is prone to landslides and mitigating the damage caused by landslides. The relationships between factors that determine slope instability are complicated and multi-factorial, so it is sometimes difficult to mathematically characterize slope stability. In this paper, new Tree Augmented Naive-Bayes (TAN) model was developed to predict slope stability subjected to circular failures based on six input factors: cohesion, internal friction angle, pore pressure ratio, slope angle, unit weight, and slope angle. A total 87 slope stability case records obtained from published literature was used to train and test the proposed TAN model. According to the results of the performance indices-accuracy, precision, recall, F-score and Matthews correlation coefficient, the established TAN model was proven to be better at predicting slope stability with acceptable accuracy than other formerly developed empirical models in the literature. Furthermore, the slope height was revealed as the most sensitive factor in a sensitivity analysis.

关键词： machine learning algorithm Tree Augmented Naive-Bayes sensitivity analysis slope stability prediction

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Insights into forest vegetation changes and landscape fragmentation in Southeastern China: From a perspective of spatial coupling and machine learning

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ECOLOGICAL INDICATORS 2024年 166卷

作者： Lin, Yuying Jin, Yidong Ge, Yang Hu, Xisheng Weng, Aifang Wen, Linsheng Zhou, Yunrui Li, Baoyin Fujian Normal Univ Sch Culture Tourism & Publ Adm Fuzhou 350117 Peoples R China Fujian Normal Univ Postdoctoral Res Stn Ecol Fuzhou 350117 Peoples R China Fujian Normal Univ Sch Geog Sci Sch Carbon Neutral Future Technol Fuzhou 350117 Peoples R China Fujian Normal Univ Minist Sci & Technol & Fujian Prov State Key Lab Subtrop Mt Ecol Fuzhou 350117 Peoples R China Fujian Agr & Forestry Univ Coll Transportat & Civil Engn Fuzhou 350002 Peoples R China

A comprehensive quantification of the relationships between forest vegetation changes and forest landscape fragmentation is urgently required to provide valuable insights for informed forest management decisions. Previous efforts often focused on these two aspects separately, overlooking their intricate relationships, restricts the formulation of precise forest protection and recovery measures. To address this gap, taking Southeast China as a case, this study employed the Google Earth Engine (GEE) platform to calculate the annual Normalized Difference Vegetation Index (NDVI) from 2000 to 2020, a forest fragmentation comprehensive index (FFCI) was constructed to evaluate the static and dynamic forest landscape fragmentation over the study period, then the Theil-Sen and Mann-Kendall, the two-dimensional framework and the bivariate spatial autocorrelation, and a machine learning algorithm (random forest, RF) were employed to explore the forest vegetation change and forest landscape fragmentation, and their spatial coupling relationships and driving patterns, respectively. The results showed that the temporal trend of the forest vegetation can be categorized into three distinct phases, with the pattern of "decreasing-rising-decreasing", 78.2% of the area improved in forest vegetation, while 11.0% degraded. Approximately 6.0% of forest landscape showed a decline in fragmentation, while 9.3% experienced increased fragmentation trends, including 3.8% of forest landscape with moderate to high static fragmentation and 5.5% of forest landscape with low to very low static fragmentation. Throughout the study period, approximately 66.8% of forest landscape remained stability with the improved forest vegetation;however, there were cases (more than 7% of forests) with improved forest vegetation, but experienced severe landscape fragmentation. The RF outcome revealed that the forest vegetation change dynamics were influenced by the multiple factors, including distance to forest edg

关键词： Forest vegetation changes Forest landscape fragmentation Random forest Landscape pattern machine learning algorithm

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Combining Different Transformations of Ground Hyperspectral Data with Unmanned Aerial Vehicle (UAV) Images for Anthocyanin Estimation in Tree Peony Leaves

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REMOTE SENSING 2022年第9期14卷 2271页

作者： Luo, Lili Chang, Qinrui Gao, Yifan Jiang, Danyao Li, Fenling Northwest A&F Univ Coll Nat Resources & Environm Yangling 712100 Shaanxi Peoples R China

To explore rapid anthocyanin (Anth) detection technology based on remote sensing (RS) in tree peony leaves, we considered 30 species of tree peonies located in Shaanxi Province, China. We used an SVC HR similar to 1024i portable ground object spectrometer and mini-unmanned aerial vehicle (UAV)-borne RS systems to obtain hyperspectral (HS) reflectance and images of canopy leaves. First, we performed principal component analysis (PCA), first-order differential (FD), and continuum removal (CR) transformations on the original ground-based spectra;commonly used spectral parameters were implemented to estimate Anth content using multiple stepwise regression (MSR), partial least squares (PLS), back-propagation neural network (BPNN), and random forest (RF) models. The spectral transformation highlighted the characteristics of spectral curves and improved the relationship between spectral reflectance and Anth, and the RF model based on the FD spectrum portrayed the best estimation accuracy (R-c(2) = 0.91;R-v(2) = 0.51). Then, the RGB (red-green-blue) gray vegetation index (VI) and the texture parameters were constructed using UAV images, and an Anth estimation model was constructed using UAV parameters. Finally, the UAV image was fused with the ground spectral data, and a multisource RS model of Anth estimation was constructed, based on PCA + UAV, FD + UAV, and CR + UAV, using MSR, PLS, BPNN, and RF methods. The RF model based on FD+UAV portrayed the best modeling and verification effect (R-c(2) = 0.93;R-v(2) = 0.76);compared with the FD-RF model, R-c(2) increased only slightly, but R-v(2) increased greatly from 0.51 to 0.76, indicating improved modeling and testing accuracy. The optimal spectral transformation for the Anth estimation of tree peony leaves was obtained, and a high-precision Anth multisource RS model was constructed. Our results can be used for the selection of ground-based HS transformation in future plant Anth estimation, and as a theoretical basis for plant

关键词： hyperspectral tree peony anthocyanin (Anth) unmanned aerial vehicle (UAV) spectrum transform machine learning algorithm remote sensing (RS) Shaanxi Province China ground-based spectra

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SIGNAL IMAGE AND VIDEO PROCESSING 2022年第7期16卷 1831-1839页

作者： Roy, Ganesh Bhoi, Akash Kumar Das, Subir Bhaumik, Subhasis Indian Inst Engn Sci & Technol Dept Aerosp Engn & Appl Mech Howrah W Bengal India Sikkim Manipal Univ SMU Sikkim Manipal Inst Technol SMIT Dept Comp Sci & Engn Gangtok Sikkim India Indian Inst Engn Sci & Technol Sch Mechatron & Robot Sibpur Howrah India Indian Inst Engn Sci & Technol Dept Aerosp Engn & Appl Mech Sibpur Howrah India Cent Inst Technol Dept Instrumentat Engn Kokrajhar Assam India

Brain-computer interface (BCI) and assistive robotics are the emerging topics for research in the Biomedical Engineering field. Several remarkable findings have already been observed in both the areas. But still, there are some research efforts required for improving the previous results. A robust real-time guideline for controlling an assistive robotics device through motor imagery electroencephalography (EEG) signal is still under investigation. The most challenging issue in BCI is to develop the appropriate feature extraction technique for controlling the assistive device with the EEG signals. Although several feature extraction algorithms are available, cross-correlation is famous for its noise removal capability. On the other hand, entropy-based methods are most suitable for non-stationary signal characterization. A new feature extraction method is investigated using the power of cross-correlation and spectral entropy in the present work. For classification of this feature set, machine learning algorithms viz. Linear discriminant analysis, quadratic discriminant analysis, and logistic regression are used and obtained average accuracy results of 98.67%, 98.41%, and 98.48%, respectively. The other objective of the present work is to control a lower limb exoskeleton (LLE) device with this classified signal. The LLE device has been designed using a computer-aided design model and assembled in a MATLAB Simscape simulation environment. The satisfactory motion of the LLE device has been observed after triggered through the classified signal.

关键词： EEG signal Brain-computer interface (BCI) Feature extraction machine learning algorithm Lower limb exoskeleton (LLE)

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Analysis of the Improvement of Engineering Mechanics Experimental Methods Based on IoT and machine learning

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SENSORS 2023年第7期23卷 3416页

作者： Sun, Yi Sheng, Dongfa Liu, Dewen Southwest Forestry Univ Inst Civil Engn Kunming 650224 Peoples R China

With the rapid development of sensor technology, machine learning, and the Internet of Things, wireless sensor networks have gradually become a research hotspot. In order to improve the data fusion performance of wireless sensor networks and ensure network security in the event of external attacks, this paper proposes a wireless sensor optimization algorithm model, involving wireless sensor networks, the Internet of Things, and other related fields. This paper first analyzes the role of the Internet of Things in wireless sensor networks, studies the localization mechanism and hierarchy of the Internet of Things based on wireless sensor networks, and improves the LE-RLPCCA (Position Estimation Robust Local Retention Criteria Correlation Analysis) localization algorithm model based on sensor grids. This paper discusses the problems of machine learning in wireless sensor networks, constructs a sensor-based machine learning model, and designs a data fusion algorithm for a wireless sensor networks' machine learning model. The application of wireless sensors in engineering mechanics experiments is summarized, and the optimization algorithm model of the wireless sensor in engineering mechanics experiments is proposed. The analysis results show that the average accuracy of the DKFCM-FSVM (Density aware Kernel-based Fuzzy C-means Clustering algorithm Fuzzy Support Vector machine) algorithm in detecting five behaviors is 0.997, 0.992, 0.904, 0.996, and 0.946, respectively, and the accuracy in detecting different behaviors is the best, 0.005, 0.01, 0.003, and 0.006 respectively. It achieves the lowest false positive rate in the detection of different behaviors, and the average false positive rate is 0.004, 0.003, 0.003, 0.008, and 0.005, respectively, which shows that the DKFCM-FSVM algorithm model of wireless sensor networks in engineering mechanics experiments is the optimal solution. The work of this paper has good reference value for the application of wireless sensor netw

关键词： intelligent sensor Internet of Things application machine learning algorithm LE-LPCCA model engineering test optimization

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Prediction models for macro shrinkage of aluminum alloys based on machine learning algorithms

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MATERIALS TODAY COMMUNICATIONS 2019年 21卷

作者： Liao, Hengcheng Zhao, Baojun Suo, Xiaojin Wang, Qigui Southeast Univ Sch Mat Sci & Engn Jiangsu Key Lab Adv Metall Mat Nanjing 211189 Jiangsu Peoples R China GM Global Prod Grp Mat Technol 800 North Glenwood Ave Pontiac MI 48340 USA

Macro shrinkage percentage is one of the most important measures of the castability of alloys. To quickly and accurately predict the macro shrinkage of aluminum alloys is greatly meaningful before developing new components or new alloys. In this study, based on the dataset of macro shrinkage percentage of aluminum alloys obtained by our experiments, three algorithms, Support Vector Regression (SVR), General Regression Neural Network (GRNN) and Random Forest (RF), were adopted to construct the prediction models for macro shrinkage, respectively. K-fold cross-validation and analysis on stability and reliability of models indicate the RF model is the best one. Compared the prediction values of macro shrinkage percentage in binary, ternary and multicomponent aluminum alloys by RF model with the experiment data of this study and the data from literatures, the constructed model has been verified to have high prediction accuracy and reliability.

关键词： Aluminum alloys Macro shrinkage machine learning algorithm Prediction model

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Prognosis of automated sleep staging based on two-layer ensemble learning stacking model using single-channel EEG signal

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SOFT COMPUTING 2021年第24期25卷 15445-15462页

作者： Satapathy, Santosh Kumar Loganathan, D. Pondicherry Engn Coll Puducherry Pondicherry India

Sleep is important part for human health and quality of life in the daily routine basis. However, numerous individuals face sleep problems due to rapid changes occurred in both social and professional lifestyles. These problems can lead to several neurological and physical disorder diseases, and therefore, decrease their overall life quality. machine learning methods for automated sleep stage classification (ASSC) are a fundamental approach to evaluate and treat this public health challenge. The main objective of this study is to propose a high-effective and high-accuracy based multiple sleep staging classification model based on single-channel electroencephalogram (EEG) signals using machine learning (ML) model. The proposed automated sleep staging system followed four basic stages: signal preprocessing, feature extraction and screening, classification algorithms, and performance evaluation. In this research work, a novel method is applied for signal preprocessing, feature screening and classification models. In signal preprocessing we obtain the wiener filter techniques for removing the different types of artifacts from input sleep recordings. In feature extraction, we obtain a total of 28 features based on both time and frequency domain features and non-linear features. The relevant features are screened through ReliefF weight feature selection algorithm, and eliminating the redundant features using Pearson correlation coefficients. The important contribution of this research work is establishes two layers an ensembling learning stacking model for classifying the multiple sleep stages. Three different subgroups of ISRUC-Sleep (SG-I/SG-II/SG-III) subjects sleep recordings having different health condition obtained for our proposed experimental work. Comparing with the recent contributions on sleep staging performances using single-channel EEG signals, it has found that our proposed ensemble learning stacking model was reported excellent in sleep staging classifica

关键词： Sleep stage EEG signal Feature screening machine learning algorithm Stacking model

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Automatic Detection of Human Maxillofacial Tumors by Using Thermal Imaging: A Preliminary Study

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SENSORS 2022年第5期22卷 1985页

作者： Macianskyte, Diana Adaskevicius, Rimas Lithuanian Univ Hlth Sci Clin Radiol LT-50009 Kaunas Lithuania Kaunas Univ Technol Dept Elect Power Syst LT-51368 Kaunas Lithuania

Traditional computed tomography (CT) delivers a relatively high dose of radiation to the patient and cannot be used as a method for screening of pathologies. Instead, infrared thermography (IRT) might help in the detection of pathologies, but interpreting thermal imaging (TI) is difficult even for the expert. The main objective of this work is to present a new, automated IRT method capable to discern the absence or presence of tumor in the orofacial/maxillofacial region of patients. We evaluated the use of a special feature vector extracted from face and mouth cavity thermograms in classifying TIs against the absence/presence of tumor (n = 23 patients per group). Eight statistical features extracted from TI were used in a k-nearest neighbor (kNN) classifier. Classification accuracy of kNN was evaluated by CT, and by creating a vector with the true class labels for TIs. The presented algorithm, constructed from a training data set, gives good results of classification accuracy of kNN: sensitivity of 77.9%, specificity of 94.9%, and accuracy of 94.1%. The new algorithm exhibited almost the same accuracy in detecting the absence/presence of tumor as CT, and is a proof-of-principle that IRT could be useful as an additional reliable screening tool for detecting orofacial/maxillofacial tumors.

关键词： infrared thermal image CT machine learning algorithm kNN classifier orofacial maxillofacial tumor

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A Method for Fault Detection in Wireless Sensor Network Based on Pearson's Correlation Coefficient and Support Vector machine Classification

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WIRELESS PERSONAL COMMUNICATIONS 2022年第3期123卷 2649-2664页

作者： Biswas, Priyajit Samanta, Tuhina Indian Inst Engn Sci & Technol Dept Informat Technol Howrah 711103 W Bengal India

Sensor nodes are tiny low-cost devices prone to various faults. So, it is imperative to detect those faults. This paper presents a sensor measurement fault detection algorithm based on Pearson's correlation coefficient and the Support Vector machine(SVM) algorithm. As environmental phenomena are spatially and temporally correlated but faults are somewhat uncorrelated, Pearson's correlation coefficient is used to measure correlation. Then SVM was used to classify faulty readings from normal readings. After classification, faulty readings are discarded. Here each sensor nodes periodically collects environmental features and sends them to their associated cluster heads. Each cluster head analyze collected data using the classification algorithm to detect whether any fault is present or not. Network simulator NS-2.35 and Matlab are used for evaluation of our proposed method. The fault detection algorithm was evaluated using performance metrics, namely, Accuracy, Precision, Sensitivity, Specificity, Recall, F-1 Score, Geometric Mean(G_mean), Receiver Operating Characteristics (ROC), and Area Under Curve(AUC). Performance evaluation shows, the proposed method performs well for high fault percentages.

关键词： Wireless sensor network machine learning algorithm Correlation coefficient SVM Measurement fault

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Spectrum Reconstruction with Filter-Free Photodetectors Based on Graded-Band-Gap Perovskite Quantum Dot Heterojunctions

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ACS APPLIED MATERIALS & INTERFACES 2022年第12期14卷 14455-14465页

作者： Wang, Xiao-Lin Chen, Yantao Chu, Yingli Liu, Wen-Jun Zhang, David Wei Ding, Shi-Jin Wu, Xiaohan Fudan Univ Sch Microelectron State Key Lab ASIC & Syst Shanghai 200433 Peoples R China Zhangjiang Fudan Int Innovat Ctr Shanghai 201203 Peoples R China Natl Integrated Circuit Innovat Ctr Shanghai 201203 Peoples R China

Spectrum reconstruction with filter-free microspectrometers has attracted much attention owing to their promising potential in in situ analysis systems, on-chip spectroscopy characterizations, hyperspectral imaging, etc. Further efforts in this field can be devoted to improving the performance of microspectrometers by employing high-performance photosensitive materials and optimizing the reconstruction algorithms. In this work, we demonstrate spectrum reconstruction with a set of photodetectors based on graded-band-gap perovskite quantum dot (PQD) heterojunctions using both calculation and machine learning algorithms. The photodetectors exhibit good photosensitivities with nonlinear current-voltage curves, and the devices with different PQD band gaps show various spectral responsivities with different cutoff wavelength edges covering the entire visible range. Reconstruction performances of monochromatic spectra with the set of PQD photodetectors using two different algorithms are compared, and the machine learning method achieves relatively better accuracy. Moreover, the nonlinear current-voltage variation of the photodetectors can provide increased data diversity without redundancy, thus further improving the accuracy of the reconstructed spectra for the machine learning algorithm. A spectral resolution of 10 nm and reconstruction of multipeak spectra are also demonstrated with the filter-free photodetectors. Therefore, this study provides PQD photodetectors with the corresponding optimized algorithms for emerging flexible microspectrometer systems.

关键词： spectrum reconstruction machine learning algorithm photodetectors perovskite quantum dots graded band gap

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