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Monitoring yellow rust progression during spring critical wheat growth periods using multi-temporal Sentinel-2 imagery

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PEST MANAGEMENT SCIENCE 2024年第12期80卷 6082-6095页

作者： Ma, Huiqin Zhang, Jingcheng Huang, Wenjiang Ruan, Chao Chen, Dongmei Zhang, Hansu Zhou, Xianfeng Gui, Zhiqin Hangzhou Dianzi Univ Sch Automat Hangzhou Peoples R China Chinese Acad Sci Aerosp Informat Res Inst Key Lab Digital Earth Sci Beijing Peoples R China Chinese Acad Sci Hainan Res Inst Aerosp Informat Res Inst Key Lab Earth Observat Hainan Prov Sanya Peoples R China Anhui Univ Natl Engn Res Ctr Agroecol Big Data Anal & Applic Hefei Peoples R China

BACKGROUNDYellow rust (Puccinia striiformis f. sp. tritici) is a devastating hazard to wheat production, which poses a serious threat to yield and food security in the main wheat-producing areas in eastern China. It is necessary to monitor yellow rust progression during spring critical wheat growth periods to support its prediction by providing timely calibrations for disease prediction models and timely green prevention and *** Sentinel-2 images for the disease during the three wheat growth periods (jointing, heading, and filling) were acquired. Spectral, texture, and color features were all extracted for each growth period disease. Then three period-specific feature sets were obtained. Given the differences in field disease epidemic status in the three periods, three period-targeted monitoring models were established to map yellow rust damage progression in spring and track its spatiotemporal change. The models' performance was then validated based on the disease field truth data during the three periods (87 for the jointing period, 183 for the heading period, and 155 for the filling period). The validation results revealed that the representation of the wheat yellow rust damage progression based on our monitoring model group was realistic and credible. The overall accuracy of the healthy and diseased pixel classification monitoring model at the jointing period reached 87.4%, and the coefficient of determination (R2) of the disease index regression monitoring models at the heading and filling periods was 0.77 (heading period) and 0.76 (filling period). The model-group-result-based spatiotemporal change detection of the yellow rust progression across the entire study area revealed that the area proportions conforming to the expected disease spatiotemporal development pattern during the jointing-to-heading period and the heading-to-filling period reached 98.2% and 84.4% *** jointing, heading, and filling period-targeted monit

关键词： monitoring wheat yellow rust progression multi-temporal classification and regression satellite remote sensing

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Development of surrogate models for evaluating energy transfer quality of high-speed railway pantograph-catenary system using physics-based model and machine learning

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APPLIED ENERGY 2023年第1期333卷

作者： Huang, Guizao Wu, Guangning Yang, Zefeng Chen, Xing Wei, Wenfu Southwest Jiaotong Univ Sch Elect Engn Chengdu 611756 Peoples R China

High-speed railway pantograph-catenary system is the only energy transfer pathway to drive a train operation. Energy transfer quality deteriorates with the increasing train speed and harsh service environment, thereby quickly and accurately evaluating the energy transfer quality is very important to guarantee the normal oper-ation of a train. In this study, firstly, the physics-based model to simulate the dynamic interaction of pantograph -catenary system is established and validated. Eleven input parameters involve the essential line design and train operation parameters, and the output parameters that are crucially responsible for energy transfer quality are obtained by feature extraction. Secondly, a sampling strategy is employed to construct the input sampling points, based on which the outputs are computed via physics-based model, then combining them the dataset is obtained. Thirdly, five tree-based classification surrogate models are developed and compared to assess the level of energy transfer quality. Finally, eight regression surrogate models are developed in replacing physics-based model to evaluate the essential values of energy transfer quality. It is found that the gradient boosting decision tree (GBDT)-based surrogate model is the optimal classification model and the multi-layer feed-forward deep neural network (MLF-DNN)-based surrogate model for the optimal regression model. The two surrogate models are expected to quickly find the optimal design parameters and improve the operation control of trains of high-speed railway for the purpose of enhancing the energy transfer quality if coupled with optimization procedure.

关键词： Surrogate model Machine learning Physics-based model Pantograph-catenary system Energy transfer classification and regression

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Team up GBDTs and DNNs: Advancing Efficient and Effective Tabular Prediction with Tree-hybrid MLPs 24

Team up GBDTs and DNNs: Advancing Efficient and Effective Ta...

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30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

作者： Yan, Jiahuan Chen, Jintai Wang, Qianxing Chen, Danny Z. Wu, Jian Zhejiang Univ Hangzhou Peoples R China Univ Illinois Urbana IL USA Univ Notre Dame Notre Dame IN USA

ISBN: (纸本)9798400704901

Tabular datasets play a crucial role in various applications. Thus, developing efficient, effective, and widely compatible prediction algorithms for tabular data is important. Currently, two prominent model types, Gradient Boosted Decision Trees (GBDTs) and Deep Neural Networks (DNNs), have demonstrated performance advantages on distinct tabular prediction tasks. However, selecting an effective model for a specific tabular dataset is challenging, often demanding time-consuming hyperparameter tuning. To address this model selection dilemma, this paper proposes a newframework that amalgamates the advantages of both GBDTs and DNNs, resulting in a DNN algorithm that is as efficient as GBDTs and is competitively effective regardless of dataset preferences for GBDTs or DNNs. Our idea is rooted in an observation that deep learning (DL) offers a larger parameter space that can represent a well-performing GBDT model, yet the current back-propagation optimizer struggles to efficiently discover such optimal functionality. On the other hand, during GBDT development, hard tree pruning, entropy-driven feature gate, and model ensemble have proved to be more adaptable to tabular data. By combining these key components, we present a Tree-hybrid simple MLP (T-MLP). In our framework, a tensorized, rapidly trained GBDT feature gate, a DNN architecture pruning approach, as well as a vanilla back-propagation optimizer collaboratively train a randomly initialized MLP model. Comprehensive experiments show that T-MLP is competitive with extensively tuned DNNs and GBDTs in their dominating tabular benchmarks (88 datasets) respectively, all achieved with compact model storage and significantly reduced training duration. The codes and full experiment results are available at https://***/jyansir/tmlp.

关键词： classification and regression tabular data green AI AutoML

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ES-HMS: Efficient Spatiotemporal Few-Shot Learning in Hyperbolic Space 24

ES-HMS: Efficient Spatiotemporal Few-Shot Learning in Hyperb...

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International Conference on Algorithms, Software Engineering, and Network Security (ASENS)

作者： Wei, Yifan Nie, Xueyun Chongqing Univ Posts & Telecommun Chongqing 400065 Peoples R China

ISBN: (纸本)9798400709784

Few-shot learning has the advantages of adapting to a small number of supervised samples, strong generalization performance, and a wide range of applications. Assuming a uniform geometric structure across all few-shot learning tasks, it can potentially introduce data bias, so it is a challenge to choose the appropriate embedding space. To address the problem, this paper designs a model ES-HMS for spatio-temporally efficient processing of Few-shot learning in hyperbolic manifold space, which focuses on the curvature generator LightCur and the class prototype generator SpaceTimeProto. In curvature generator LightCur, a new lightweight attentional feature fusion method LAFF is embedded to achieve independent characterization of each class, and the design is better adapted to complex data structures. In class prototype generator SpaceTimeProto, a multi-similarity metric learning algorithm is used to generate class prototypes, and SpaceTimeProto significantly reduces the spatio-temporal complexity of the model. The model is trained within a general adaptive FSL framework by learning some known classes information and applying them to a new few-shot learning task. Extensive experiments demonstrate that our model outperforms the state-of-the-art methods by clear margins. Code is available at: https://***/ivanthora/ES-HMS.

关键词： Adaptive algorithms Artificial neural networks classification and regression Graphical models Meta-learning

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Data-driven decision support for rail traffic control: A predictive approach

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EXPERT SYSTEMS WITH APPLICATIONS 2022年 207卷

作者： Luo, Jie Peng, Qiyuan Wen, Chao Wen, Wen Huang, Ping Southwest Jiaotong Univ Sch Transportat & Logist Chengdu 610031 Peoples R China Southwest Jiaotong Univ Natl United Engn Lab Integrated & Intelligent Tra Chengdu 610031 Peoples R China Swiss Fed Inst Technol Inst Transport Planning & Syst CH-8093 Zurich Switzerland

Advanced decision support for rail traffic control is significant for enhancing the safety and quality of railway transport service. Data-driven methods have shown powerful learning ability and wide extensibility for prediction, classification, and decision-making problems. In this paper, we propose a hybrid prediction model based on deep forest (DF) ensemble learning to analyze the two common rail traffic control actions, i.e., changing the dwelling times and running times. This basic concept is to mimic the decision-making of rail traffic controllers, providing them with advanced decisions/control actions using data-driven learning algorithms. According to the decision-making approach of rail traffic controllers, the learning process of the model is split into two stages, i.e., learning the type of action (ToA) and the number of changes (NoC) in the dwelling times and running times (i.e., how many dwelling times or running times have been changed compared with the planned ones). The first stage is a classification problem;thus, DF classifier with the synthetic minority oversampling technique (SMOTE) is employed to deal with imbalanced data. In the second stage, the DF regressor treats the NoC in the dwelling and running times as numerical variables and utilizes the information from stage one, i.e., the prediction results of the classification model, to make predictions. The proposed model is calibrated on train operation data from two high-speed railway lines in China. The experimental results and comparative analyses show that the proposed method provides advanced and timely decision support for controllers. These characteristics of the proposed model are imperative for supporting the dynamic decision-making of controllers to manage railway traffic.

关键词： Train dispatching Control actions Dwelling times and running times classification and regression Deep forest

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Receiver operating characteristic (ROC) movies, universal ROC (UROC) curves, and coefficient of predictive ability (CPA)

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MACHINE LEARNING 2022年第8期111卷 2769-2797页

作者： Gneiting, Tilmann Walz, Eva-Maria Heidelberg Inst Theoret Studies Heidelberg Germany Karlsruhe Inst Technol Inst Stochast Karlsruhe Germany

Throughout science and technology, receiver operating characteristic (ROC) curves and associated area under the curve (AUC) measures constitute powerful tools for assessing the predictive abilities of features, markers and tests in binary classification problems. Despite its immense popularity, ROC analysis has been subject to a fundamental restriction, in that it applies to dichotomous (yes or no) outcomes only. Here we introduce ROC movies and universal ROC (UROC) curves that apply to just any linearly ordered outcome, along with an associated coefficient of predictive ability (CPA) measure. CPA equals the area under the UROC curve, and admits appealing interpretations in terms of probabilities and rank based covariances. For binary outcomes CPA equals AUC, and for pairwise distinct outcomes CPA relates linearly to Spearman's coefficient, in the same way that the C index relates linearly to Kendall's coefficient. ROC movies, UROC curves, and CPA nest and generalize the tools of classical ROC analysis, and are bound to supersede them in a wealth of applications. Their usage is illustrated in data examples from biomedicine and meteorology, where rank based measures yield new insights in the WeatherBench comparison of the predictive performance of convolutional neural networks and physical-numerical models for weather prediction.

关键词： C index classification and regression Evaluation metric Rank correlation coefficient ROC analysis

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Machine learning models for prediction of co-occurrence of diabetes and cardiovascular diseases: a retrospective cohort study

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JOURNAL OF DIABETES AND METABOLIC DISORDERS 2022年第1期21卷 251-261页

作者： Abdalrada, Ahmad Shaker Abawajy, Jemal Al-Quraishi, Tahsien Islam, Sheikh Mohammed Shariful Wasit Univ Fac Comp Sci & Informat Technol Al Kut Iraq Deakin Univ Sch Informat Technol Melbourne Vic Australia Deakin Univ Inst Phys Act & Nutr 221 Burwood Highway Melbourne Vic 3125 Australia

Background Diabetic mellitus (DM) and cardiovascular diseases (CVD) cause significant healthcare burden globally and often co-exists. Current approaches often fail to identify many people with co-occurrence of DM and CVD, leading to delay in healthcare seeking, increased complications and morbidity. In this paper, we aimed to develop and evaluate a two-stage machine learning (ML) model to predict the co-occurrence of DM and CVD. Methods We used the diabetes complications screening research initiative (DiScRi) dataset containing >200 variables from >2000 participants. In the first stage, we used two ML models (logistic regression and Evimp functions) implemented in multivariate adaptive regression splines model to infer the significant common risk factors for DM and CVD and applied the correlation matrix to reduce redundancy. In the second stage, we used classification and regression algorithm to develop our model. We evaluated the prediction models using prediction accuracy, sensitivity and specificity as performance metrics. Results Common risk factors for DM and CVD co-occurrence was family history of the diseases, gender, deep breathing heart rate change, lying to standing blood pressure change, HbA1c, HDL and TC\HDL ratio. The predictive model showed that the participants with HbA1c >6.45 and TC\HDL ratio > 5.5 were at risk of developing both diseases (97.9% probability). In contrast, participants with HbA1c >6.45 and TC\HDL ratio <= 5.5 were more likely to have only DM (84.5% probability) and those with HbA1c <= 5.45 and HDL >1.45 were likely to be healthy (82.4%. probability). Further, participants with HbA1c <= 5.45 and HDL <1.45 were at risk of only CVD (100% probability). The predictive accuracy of the ML model to detect co-occurrence of DM and CVD is 94.09%, sensitivity 93.5%, and specificity 95.8%. Conclusions Our ML model can significantly predict with high accuracy the co-occurrence of DM and CVD in people attending a screening program. This might help

关键词： Diabetes mellitus Cardiovascular disease Multi-diseases prediction classification and regression Co-morbidity

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Enhancing construction safety: Machine learning-based classification of injury types

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SAFETY SCIENCE 2023年 162卷

作者： Alkaissy, Maryam Arashpour, Mehrdad Golafshani, Emadaldin Mohammadi Hosseini, M. Reza Khanmohammadi, Sadegh Bai, Yu Feng, Haibo Monash Univ Dept Civil Engn Melbourne Vic 3800 Australia Deakin Univ Sch Architecture & Built Environm Burwood Vic Australia Univ British Columbia Fac Forestry Vancouver BC Canada

The construction industry is a hazardous industry with significant injuries and fatalities. Few studies have used data-driven analysis to investigate injuries due to construction accidents. This study aims to deploy machine learning (ML) models to predict four injury types (ITs): Upper limbs, lower limbs, head/neck, and back/trunk. A total of 16,878 construction accident records in Australia were collected and fed into several ML algorithms, including fine trees, ensemble of boosted trees, xgboost, random forest, two types of support vector machines, and logistic regression. Six performance metrics of precision, recall, accuracy, F1 score, the area under the receiver operating curve (AUROC), and the area under precision recall curve (AUPRC) were used to evaluate modeling outputs. Random forest showed superior performance in predicting injury types (accuracy 79.3%;recall 78.0%;F1 score 78.5%;precision 77.1%;AUROC 0.98;and AUPRC 0.78). The critical features of injury types were analyzed using the feature importance method and accident nature and mechanism had significant impacts. The study's findings contribute to safety enhancement by providing quantitative prediction models of injury types and subsequent development of safety controls in construction.

关键词： Artificial intelligence classification and regression Construction management Leading and lagging indicators Machine learning algorithms Modeling and simulation Neural networks Quantitative analysis Risk analysis Safety engineering

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Joint Augmented and Compressed Dictionaries for Robust Image classification

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ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS 2023年第3期19卷 136-136页

作者： Zeng, Shaoning Rao, Yunbo Zhang, Bob Xu, Yong Univ Elect Sci & Technol China Yangtze Delta Reg Inst Huzhou B1 Floor 8South Taihu Technol Innovat Bldg819 R Huzhou 313000 Peoples R China Univ Elect Sci & Technol China Sch Informat & Software Engn Xiyuan AveWest Hitech Zone Chengdu 611731 Peoples R China Univ Macau Dept Comp & Informat Sci Avenida Univ Taipa 999078 Macau Peoples R China Harbin Inst Technol Shenzhen Dept Comp Sci Coll Town Shenzhen 518000 Peoples R China

Dictionary-based classification (DC) has been a promising learning theory in multimedia computing. Previous studies focused on learning a discriminative dictionary as well as the sparsest representation based on the dictionary, to cope with the complex conditions in real-world applications. However, robustness by learning only one single dictionary is far from the optimal level. What is worse, it cannot take advantage of the available techniques proven in modern machine learning, like data augmentation, to mitigate the same problem. In this work, we propose a novel method that utilizes joint Augmented and Compressed Dictionaries for Robust Dictionary-based classification (ACD-RDC). For optimization under the noise model introduced by real-world conditions, the objective function of ACD-RDC incorporates only two simple, but well-designed constraints, including one enhanced sparsity constraint by the general data augmentation, which requires less case-by-case and sophisticated tuning, and another discriminative constraint solved by a jointly learned dictionary. The optimization of the objective function is then deduced theoretically to an approximate linear problem. The sparsity and discrimination enhanced by data augmentation guarantees the robustness for image classification under various conditions, which constructs the first positive case using data augmentation to obtain robust dictionary-based classification. Numerous experiments have been conducted on popular facial and object image datasets. The results demonstrate that ACD-RDC obtains more promising classification on diversely collected images than the current dictionary-based classification methods. ACD-RDC is also confirmed to be a state-of-the-art classification method when using deep features as inputs.

关键词： classification and regression inference algorithms supervised learning machine learning

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Exploiting pivot words to classify and summarize discourse facets of scientific papers

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SCIENTOMETRICS 2020年第3期125卷 3139-3157页

作者： La Quatra, Moreno Cagliero, Luca Baralis, Elena Politecn Torino Corso Duca Abruzzi 24 I-10129 Turin Italy

The ever-increasing number of published scientific articles has prompted the need for automated, data-driven approaches to summarizing the content of scientific articles. The Computational Linguistics Scientific Document Summarization Shared Task (CL-SciSumm 2019) has recently fostered the study and development of new text mining and machine learning solutions to the summarization problem customized to the academic domain. In CL-SciSumm, a Reference Paper (RP) is associated with a set of Citing Papers (CPs), all containing citations to the RP. In each CP, the text spans (i.e., citances) have been identified that pertain to a particular citation to the RP. The task of identifying the spans of text in the RP that most accurately reflect the citance is addressed using supervised approaches. This paper proposes a new, more effective solution to the CL-SciSumm discourse facet classification task, which entails identifying for each cited text span what facet of the paper it belongs to from a predefined set of facets. It proposes also to extend the set of traditional CL-SciSumm tasks with a new one, namely thediscourse facet summarizationtask. The idea behind is to extract facet-specific descriptions of each RP consisting of a fixed-length collection of RP's text spans. To tackle both the standard and the new tasks, we propose machine learning supported solutions based on the extraction of a selection of discriminating words, calledpivot words. Predictive features based on pivot words are shown to be of great importance to rate the pertinence and relevance of a text span to a given facet. The newly proposed facet classification method performs significantly better than the best performing CL-SciSumm 2019 participant (i.e., the classification accuracy has increased by + 8%), whereas regression methods achieved promising results for the newly proposed summarization task.

关键词： Discourse facet classification Faceted summarization classification and regression Deep natural language processing

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