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Integrating Hierarchical Statistical Models and machine-learning algorithms for Ground-Truthing Drone Images of the Vegetation: Taxonomy, Abundance and Population Ecological Models

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REMOTE SENSING 2021年第6期13卷 1161-1161页

作者： Damgaard, Christian Aarhus Univ Dept Biosci Vejlsovej 25 DK-8600 Silkeborg Denmark

In order to fit population ecological models, e.g., plant competition models, to new drone-aided image data, we need to develop statistical models that may take the new type of measurement uncertainty when applying machine-learning algorithms into account and quantify its importance for statistical inferences and ecological predictions. Here, it is proposed to quantify the uncertainty and bias of image predicted plant taxonomy and abundance in a hierarchical statistical model that is linked to ground-truth data obtained by the pin-point method. It is critical that the error rate in the species identification process is minimized when the image data are fitted to the population ecological models, and several avenues for reaching this objective are discussed. The outlined method to statistically model known sources of uncertainty when applying machine-learning algorithms may be relevant for other applied scientific disciplines.

关键词： measurement uncertainty machine-learning algorithms plant competition models hierarchical statistical model

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Lag-l forecasting and machine-learning algorithms

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EXPERT SYSTEMS 2011年第3期28卷 269-282页

作者： Ahn, Jae Joon Son, Il Suh Oh, Kyong Joo Kim, Tae Yoon Song, Gyu Moon Yonsei Univ Dept Informat & Ind Engn Seoul 120749 South Korea Keimyung Univ Dept Stat Taegu South Korea

In this study, we discuss the problem of lag-l forecasting, which has been solved using a machine-learning algorithm. The main aim of this study is to define the lag-l forecaster based on a precise classification approach and to discuss the technical issues involved in the lag-l forecasting problem, including a comparison of various machine-learning algorithms for proper implementation of the technique. This study focuses on an application that uses the lag-l forecaster in an early-warning system.

关键词： lag-l forecasting classifier machine-learning algorithms early warning

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Identification of geographical origin of winter jujube based on GC-MS coupled with machine-learning algorithms

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JOURNAL OF FOOD COMPOSITION AND ANALYSIS 2023年 124卷

作者： Wen, Jiangwei Li, Jiayu Wang, Dong Li, Chao Robbat, Albert Xia, Liya Hebei Univ Coll Qual & Tech Supervis Baoding 071002 Peoples R China Hebei Univ Ctr Metrol Instrument & Syst Natl & Local Joint Engn Res Baoding 071002 Peoples R China Hebei Univ Hebei Key Lab Energy Metering & Safety Testing Baoding 071002 Peoples R China Tufts Univ Dept Chem 62 Talbot Ave Medford MA 02155 USA

Headspace solid-phase microextraction (HS-SPME) combined with gas chromatography-mass (GC-MS) is the most often used technique for the analysis of volatile organic compounds (VOCs). When coupled with machine-learning algorithms, the two provide the means to differentiate the volatile metabolome of foods from different origins. In this study, the volatile metabolites of winter jujube, a fruit protected by China and the European Union (EU) based on geographical indications (GI), was analyzed by HS-SPME-GC-MS. Extreme gradient boosting (XGBoost) was applied, for the first time, to evaluate differences in winter jujube grown in four distinct regions. Locational differences yielded 33 VOCs that differed by functionality and concentration with a recurrence probability of >= 60%. Zhanhua winter jujube contained the most abundant VOCs, with Yuncheng winter jujube the least. The 33 winter jujube VOCs were analyzed by random forest (RF), support vector machine (SVM), and XGBoost to identify the place of origin. The discrimination rates were about 85% using RF, about 93% using SVM, and about 98% with XGBoost. Compared to RF and SVM algorithms, XGBoost was more efficient, pro-ducing higher accuracy rates, and providing new opportunities for origin traceability.

关键词： Winter jujube Volatile organic compounds machine-learning algorithms HS-SPME GC-MS XGBoost

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Automatic Detection of Faults in Race Walking: A Comparative Analysis of machine-learning algorithms Fed with Inertial Sensor Data

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SENSORS 2019年第6期19卷 1461.-1461.页

作者： Taborri, Juri Palermo, Eduardo Rossi, Stefano Univ Tuscia Dept Econ Engn Soc & Business Org DEIM I-01100 Viterbo Italy Sapienza Univ Rome Dept Mech & Aerosp Engn I-00184 Rome Italy

The validity of results in race walking is often questioned due to subjective decisions in the detection of faults. This study aims to compare machine-learning algorithms fed with data gathered from inertial sensors placed on lower-limb segments to define the best-performing classifiers for the automatic detection of illegal steps. Eight race walkers were enrolled and linear accelerations and angular velocities related to pelvis, thighs, shanks, and feet were acquired by seven inertial sensors. The experimental protocol consisted of two repetitions of three laps of 250 m, one performed with regular race walking, one with loss-of-contact faults, and one with knee-bent faults. The performance of 108 classifiers was evaluated in terms of accuracy, recall, precision, F1-score, and goodness index. Generally, linear accelerations revealed themselves as more characteristic with respect to the angular velocities. Among classifiers, those based on the support vector machine (SVM) were the most accurate. In particular, the quadratic SVM fed with shank linear accelerations was the best-performing classifier, with an F1-score and a goodness index equal to 0.89 and 0.11, respectively. The results open the possibility of using a wearable device for automatic detection of faults in race walking competition.

关键词： race walking illegal steps machine-learning algorithms inertial sensors activity recognition

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Evaluation of direct tensile strength for ultra-high-performance concrete using machine learning algorithms

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COMPUTERS AND CONCRETE 2024年第3期34卷 367-378页

作者： Kim, Sanghee Lim, Woo-Young Kyonggi Univ Dept Architectural Engn Suwon 16227 Kyonggi Do South Korea Wonkwang Univ Dept Architectural Engn Iksan 54538 Jeollabuk Do South Korea

This study evaluates the direct tensile strength of ultra-high-performance concrete (UHPC) using tests. A total of 45 dogbone-shaped specimens are tested, with the test variables being the fiber volume fraction and notch length. The test results showed that the material properties of UHPC were largely dependent on the fiber volume fraction and compressive strength. When steel fibers with more than 1% fiber volume fraction are mixed in the manufacturing of UHPC, the tensile strength can be more than twice that of plain UHPC. In addition, the incorporation of steel fibers enabled the significant improvement of the initial cracking strength. However, the effect of the notch length on the tensile behavior was insignificant. An assessment of the direct tensile strength is conducted using machine-learning algorithms (ML). For evaluation of the direct tensile strength of UHPC using ML, a total of 98 test data, including 53 data from other research works and 45 data from this experimental program, were collected. In total, 67 data with a 70% confidence interval on a normal distribution curve were selected, with 47 data among 67 used for ML training and 20 data used for ML testing. As a result, the machine-learning algorithm with a steel fiber volume fraction predicted that the tensile strength has an average of 0.98 and the lowest values of regression evaluation metrics among analytical and ML-based models. It is considered that an ML-based model can help to predict a more accurate tensile strength of UHPC.

关键词： direct tensile strength direct tension test fiber volume fraction machine-learning algorithms ultra-high- performance concrete (UHPC)

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Identification of novel characteristic biomarkers and immune infiltration profile for the anaplastic thyroid cancer via machine learning algorithms

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JOURNAL OF ENDOCRINOLOGICAL INVESTIGATION 2023年第8期46卷 1633-1650页

作者： Li, C. Dong, X. Yuan, Q. Xu, G. Di, Z. Yang, Y. Hou, J. Zheng, L. Chen, W. Wu, G. Wuhan Univ Dept Thyroid & Breast Surg Zhongnan Hosp Wuhan Peoples R China Wuhan Univ Dept Gastrointestinal Surg Zhongnan Hosp Wuhan Peoples R China Wuhan Univ Dept Gastr & Colorectal Surg Oncol Zhongnan Hosp Wuhan Peoples R China

PurposeAnaplastic thyroid cancer (ATC) is a rare and lethal malignant cancer. In recent years, the application of molecular-driven targeted therapy and immunotherapy has markedly improved the prognosis of ATC. This study aimed to identify characteristic genes for ATC diagnosis and revealed the role of ATC characteristic genes in drug sensitivity and immune cell *** downloaded ATC RNA-sequencing data from the GEO database. Following the combination and normalization of the dataset, we first divided the combined datasets into the training cohort and the validation cohort. We identified differentially expressed genes (DEGs) in ATC by differential expression analysis in the training cohort. We used two machine learning algorithms, least absolute shrinkage and selection operator (LASSO) and support vector machine-recursive feature elimination (SVM-RFE) to identify ATC characteristic genes. The CIBERSORT algorithm was performed to calculate the abundance of various immune cells in ATC. Finally, we validated the expression of ATC characteristic genes by quantitative RT-PCR (RT-qPCR) in ATC cell lines and immunohistochemistry (IHC).ResultsA total of 425 DEGs were identified in the training cohort, including 240 upregulated genes and 185 downregulated genes. Four ATC characteristic genes (ADM, PXDN, MMP1, and TFF3) were identified, and their diagnostic value was validated in the validation cohort (AUC in ROC analysis > 0.75). We established a practical gene expression-based nomogram to accurately predict the probability of ATC. We also found that ATC characteristic biomarkers are associated with the tumor immune microenvironment and drug ***, PXDN, MMP1, and TFF3 might serve as potential ATC diagnostic biomarkers and may be helpful for ATC molecular targeted therapy and immunotherapy.

关键词： Anaplastic thyroid cancer machine-learning algorithms Diagnostic markers Immune cell infiltration Drug sensitivity

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Assessment of landslide susceptibility along mountain highways based on different machine learning algorithms and mapping units by hybrid factors screening and sample optimization

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GONDWANA RESEARCH 2023年 123卷 89-106页

作者： Sun, Deliang Gu, Qingyu Wen, Haijia Xu, Jiahui Zhang, Yalan Shi, Shuxian Xue, Mengmeng Zhou, Xinzhi Chongqing Normal Univ Key Lab GIS Applicat Res Chongqing 401331 Peoples R China Chongqing Univ Natl Joint Engn Res Ctr Geohazards Prevent Reservo Sch Civil Engn Key Lab New Technol Construction Cities Mt Area Chongqing 400045 Peoples R China East China Normal Univ Minist Educ Key Lab Geog Informat Sci Shanghai 200241 Peoples R China

To develop a better spatial prediction model of landslide susceptibility along mountain highways, this study compared assessment models of landslide susceptibility along mountain highways based on different machine-learning-algorithms (random forest (RF), support vector machine (SVM)), and mapping units (grid and slope units) using factor screening and sample optimization. Using a typical mountainous county, Chengkou, as a case study, a landslide inventory was prepared based on field investigations, satellite images, and historical records. In total, 334 landslides were identified. Twenty landslide conditioning factors influenced by topography, geology, environmental conditions, and human activities were initially identified. Less important conditioning factors were identified and eliminated using a geographical detector. Two common non-linear machine-learning models (RF and SVM) were used under the grid and slope units, and on all cells (including landslides and non-landslides);10-fold cross-validation was utilized to select the training and validation datasets for these models. Subsequently, the trained models were utilized for landslide susceptibility mapping in the entire study area, divided into five classes. Subsequently, the performance of the models under different units was evaluated using a confusion matrix and receiver operating characteristic curve. Finally, partial dependency plots (PDP) and local interpretable modelagnostic explanations (LIME) were used to study the interpretability of landslide susceptibility model. The research results show that after removing the noise-generating factors, the model trained by the remaining 11 landslide-conditioning factors can deliver ideal forecast accuracy. In this study, the evaluation results of the slope unit for the RF or SVM model were more accurate and reasonable than those of the grid unit. In this case, the slope unit was found to be ideal. Under both the slope and grid units, the performance of the RF mod

关键词： Landslide susceptibility machine-learning algorithms Mapping units Factors screening Sample optimization

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Creation of a Rat Takotsubo Syndrome Model and Utilization of machine learning algorithms for Screening Diagnostic Biomarkers

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JOURNAL OF INFLAMMATION RESEARCH 2023年 16卷 4833-4843页

作者： Huai, Hongyu Li, Junliang Zhang, Xiangjie Xu, Qiang Lan, Huan Southwest Med Univ Inst Cardiovasc Res Key Lab Sichuan Prov Key Lab Med ElectrophysiolMinist Educ & Med Elect Luzhou Peoples R China Southwest Med Univ Affiliated Hosp Dept Otolaryngol Head & Neck Surg Luzhou Sichuan Peoples R China Southwest Med Univ Sch Basic Med Sci Luzhou Peoples R China

Introduction: Ferroptosis, a crucial type of programmed cell death, is directly linked to various cardiac disorders. However, the contribution of ferroptosis-related genes (FRGs) to Takotsubo syndrome (TTS) has not been completely understood. Purpose: The objective of this study was to investigate the relationship between the FRGs and TTS. Methods: TTS rat models were established by isoprenaline injection. Heart tissues were subsequently harvested for total RNA extraction and library construction. Transcriptome data wereobtained transcriptome data for TTS and FRGs from our laboratory, and sources such as the Ferroptosis Database (FerrDb) and the Gene Expression Omnibus Database (GEO). 57 differentially expressed FRGs (DE-FRGs) were discovered. The LASSO and SVM-RFE algorithms were employed to identify Enpp2, Pla2g6, Etv4, and Il1b as marker genes, and logistic regression was applied to construct a diagnostic model. The important genes were validated by real time PCR and the external dataset. Finally, the extent of immune infiltration was explored. Results: Among the 57 genes, there were 36 up-regulated and 21 down-regulated genes that exhibited distinct expression patterns in the TTS and healthy control samples. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis indicated that the enriched pathways were primarily associated with pathways of neurodegeneration-multiple disease, while Gene Ontology (GO) analysis revealed that these genes were primarily linked to cellular response to external stimuli, outer membrane functions, and ubiquitin protein ligase binding. After the identification of four marker genes as potentially effective biomarkers for TTS diagnosis, subsequent logistic regression modeling revealed a receiver operating characteristic curve (ROC) with an AUC of 1.0. The examination of immune cell infiltration showed significantly higher prevalence of activated CD4+ T cells, mast cells, etc., in TTS. Conclusion: Our findings support the theoretical impor

关键词： ferroptosis diagnosis takotsubo syndrome machine-learning algorithms

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The Kullback-Leibler Divergence Used in machine learning algorithms for Health Care Applications and Hypertension Prediction: A Literature Review 9

The Kullback-Leibler Divergence Used in Machine Learning Alg...

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9th International Conference on Emerging Ubiquitous Systems and Pervasive Networks (EUSPN) / 8th International Conference on Current and Future Trends of Information and Communication Technologies in Healthcare (ICTH)

作者： Clim, Antonio Zota, Razvan Daniel Tinica, Grigore Bucharest Acad Econ Studies 15-17 Dorobanti Bucharest 010552 Romania Univ Med & Pharm Grigore T Popa 16 Univ St Iasi 700115 Romania

Kullback-Leibler divergence class or relative entropy is an exceptional instance of a more extensive divergence. It is an estimation of how a particular dissemination wanders from another, normal likelihood appropriation. Kullback-Leibler divergence has a considerable measure of ongoing applications. Despite the fact that there is an advance in the drug field, it still requires a measurable examination for supporting developing prerequisites. This paper discusses the use of Kullback-Leibler divergence as a conceivable technique to foresee hypertension, utilizing chest sound accounts and machine-learning calculations. It would have a unique, elevated advantage in the crisis medical services framework. Interpreting the chest sound example gives a wide and varying degree of recognition concerning different abnormalities and prosperity conditions of the restorative field. The proposed technique to estimate circulatory strain is chest sound examination, using a strategy that makes a record of sounds conveyed by the contracting heart, coming to fruition in view of valves and related vessel vibration, finally investigating it with the assistance of Kullback-Leibler divergence and machine calculation. An investigation employing the Kullback-Leibler divergence strategy will permit finding the distinction in chest sound chronicles, which can be assessed by a machine-learning calculation. Likewise, the report proposes the strategy for examining the chest sound accounts in Kullback-Leibler divergence class. (C) 2018 The Authors. Published by Elsevier Ltd.

关键词： Kullback-Leibler Divergence Class chest sound analysis machine-learning algorithms continuous monitoring of hypertension probability distribution Clinical Decision Support

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A Hybrid Model with Applying machine learning algorithms and Optimization Model to Forecast Greenhouse Gas Emissions with Energy Market Data

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SUSTAINABLE CITIES AND SOCIETY 2022年 82卷

作者： Javanmard, Majid Emami Ghaderi, S. F. Univ Tehran Coll Engn Sch Ind Engn Tehran Iran Univ Tehran Res Inst Energy Management & Planning Tehran Iran

In recent decades, many countries have encountered air pollution and environmental problems caused by greenhouse gas (GHG) emissions. One of the essential approaches to managing and planning for GHG emissions reduction is an accurate prediction. This study has presented a hybrid machine learning and mathematical programming approach that enjoys a high prediction accuracy and is implemented with small data to predict the emission of greenhouse gases (CO2, N2O, CH4, and Fluorinated-Gas). Data in the energy sector affecting GHG emissions and also related data to the rates of GHG emissions from 1990 to 2018 in Iran have been collected. Then, the forecast of emission of each gas has been performed by nine algorithms of ANN, AR, ARIMA, SARIMA, SARIMAX, RF, SVR, KNN, and LSTM. The forecast accuracy of each algorithm has been evaluated with five indicators. The results of GHG emissions till 2028 have been forecasted and presented with algorithms with higher forecast accuracy. In the next step, the outcomes of the machine learning algorithms have been regarded as the input data to the mathematical model, which has been then implemented by the PSO and GWO meta heuristic algorithms, and the emission of greenhouse gases has been predicted until the year 2028. The prediction accuracy of the proposed approach has been evaluated by five indices and compared with the results of machine learning algorithms. Finally, the Stepwise Regression algorithm has been applied to evaluate the relationships between the data. In general, the results obtained from this study indicated an increase in forecast accuracy using an optimization model. This optimization model has increased the forecast accuracy compared to the machine learning algorithms applied in this study. The improvements were at least 31.7% with the PSO algorithm and 12.8% with the GWO algorithm. Based on the high-accurate prediction of the proposed approach, the emission of greenhouses gases in Iran will exceed 1096 Mt/year in 20

关键词： Greenhouse Gas Emissions Energy Market Data Air Pollution Forecasting machine-learning algorithms Optimization Model

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