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Preparation and Optimization of Mesoporous SnO2 Quantum Dot Thin Film Gas Sensors for H2S Detection Using xgboost Parameter Importance Analysis

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CHEMOSENSORS 2023年第10期11卷

作者： Liu, Jianqiao Wang, Yujia Sun, Yue Zhang, Kuanguang Ding, Yang Fu, Ce Wang, Junsheng Dalian Maritime Univ Coll Informat Sci & Technol Dalian 116026 Peoples R China

Tin oxide (SnO2) is a traditional gas-sensitive semiconductor with excellent response to various gases. However, its sensor performances are attenuated by the utility factor during gas diffusion in the sensing body. Therefore, the rational design of microstructure of devices is attractive and necessary because it may provide a sensible and controllable microstructure, which facilitates gas diffusion and inhibits the utility factor. Herein, the mesoporous tin oxide (MPTD) quantum dot thin film for H2S gas sensors is prepared by a facile route, which creates a mesoporous microstructure for thin films by the thermal decomposition of NH4Cl. The pore size of the thin films is controlled to be 19.36-40.13 nm. The mesoporous microstructure exhibits enhanced gas-sensing properties amounting to a 30-fold increase in response and 1/3 reduction in recovery time in H2S detection at room temperature (25 degrees C), with a limit of detection of 0.4 ppm. To determine the importance of sensor parameters such as pore size, film thickness, and grain size, an eXtreme Gradient Boosting (xgboost) algorithm model was developed to examine the feature importance of each parameter on the gas-sensing performance of the MPTD sensors. The visual illustration of parameter importance is revealed to facilitate the optimization of technical preparation parameters as well as the rational design of semiconductor gas sensors.

关键词： mesoporous microstructure SnO2 quantum dot thin film NH4Cl xgboost algorithm gas sensor

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Ensemble learning-based hierarchical retrieval of similar cases for site planning

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JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING 2021年第6期8卷 1548-1561页

作者： Wang, Pengyue Guo, Maozu Han, Yunsong Zhao, Lingling Zhou, Xiaoping Zhang, Dayu Beijing Univ Civil Engn & Architecture Sch Architecture & Urban Planning Beijing 100044 Peoples R China Beijing Key Lab Intelligent Proc Bldg Big Data Beijing 100044 Peoples R China Beijing Univ Civil Engn & Architecture Sch Elect & Informat Engn Beijing 100044 Peoples R China Harbin Inst Technol Sch Architecture Harbin 150001 Peoples R China Harbin Inst Technol Sch Comp Sci & Technol Harbin 150001 Peoples R China

Site planning not only involves the arrangement of a large number of elements but also considers the complexity of urban systems;hence, the design process involves large workloads and is time consuming. Retrieving similar existing cases helps architects optimize or accelerate the design process. This paper proposes a computational approach that provides valuable references by retrieving similar cases. Three types of attributes are extracted to represent a given parcel: land-use attributes, geometric attributes (area, orientation, etc.), and environmental attributes (greening rate, surrounding facilities, etc.). The complete hierarchical retrieval process is divided into three phases. The first phase selects cases whose land-use attributes are consistent with the target parcel. Then, the similarity distances between the given target parcel and the selected cases are calculated using geometric attributes. The eXtreme Gradient Boosting (xgboost) classifier is adopted to determine which case is similar to the target parcel. Finally, similarity scores of the retrieved cases are calculated based on the environmental attributes to provide more options during the actual design. In total, 1189 cases with different land-use types in Beijing were collected for the case base. The comparative experimental results confirmed that the proposed ensemble learning-based hierarchical retrieval of similar cases approach improves the accuracy of retrieval results. Furthermore, we use a real-world target parcel to demonstrate the superiority and flexibility of the retrieval process.

关键词： site planning similarity retrieval parcel representation xgboost algorithm

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Short-term urban rail transit passenger flow forecasting based on fusion model methods using univariate time series

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APPLIED SOFT COMPUTING 2023年 147卷

作者： Chuwang, Dung David Chen, Weiya Zhong, Ming Cent South Univ Sch Traff & Transportat Engn Changsha 410075 Peoples R China Rail Data Res & Applicat Key Lab Hunan Prov Changsha 410075 Peoples R China Wuhan Univ Technol Intelligent Transport Syst Res Ctr Wuhan 430063 Peoples R China

Global urbanization has made the urban rail transit system an essential service for a growing population. To help urban rail transit stations design optimal operational plans, previous studies have devoted extensive efforts to passenger flow forecasting, especially short-term predictions. By considering the complex pattern of passenger flow, previous research investigated the feasibility of machine learning (ML) methods on different data features and found the limited application of a single ML method. Based on the dynamic historical passenger flow data at an urban rail station, this study proposes an ML-fusion strategy to enhance prediction accuracy, including data aggregation, time series forecasting model selection, and fusion model strategy. First, this study aggregates the data into working days, weekends, and hourly time series for single model development. Based on the predictive performance of single model development, this study selects xgboost, AdaBoost, and LightGBM from the widely used ML-method pool. To overcome prediction errors caused by the discrepancy between characteristics of passenger flow and single prediction models, the proposed ML-fusion model combines single forecasting models with dynamically predicted passenger flow to enhance the accuracy and efficiency of the prediction. Based on the experimental results, the mean absolute error is 1.54, and the regression coefficient is 0.99, which is in close agreement with unity, which validates that the proposed ML-fusion method has displayed superiority over all other single models tested both in accuracy and stability.(c) 2023 Elsevier B.V. All rights reserved.

关键词： Short-term passenger flow forecast xgboost algorithm LightGBM algorithm AdaBoost algorithm Fusion model strategy

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Predicting Green Supply Chain Impact With SNN-Stacking Model in Digital Transformation Context

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JOURNAL OF ORGANIZATIONAL AND END USER COMPUTING 2023年第1期35卷

作者： Li, Te Donta, Praveen Kumar Dongbei Univ Finance & Econ Sch Business Adm Dalian Peoples R China TU Wien Distributed Syst Grp Vienna Austria

Green supply chain management is crucial for sustainable enterprises. Achieving it hinges on creating a greener supply chain through AI-driven data analysis. This enables precise market alignment, optimized management, and sustainable development. This study explores the link between digital transformation and green supply chain management. It leverages AI, specifically the xgboost algorithm, to gauge sample contributions to market demand. It extracts multi-dimensional features in green supply chain management using NSCNN and CSCNN, combining them with the Stacking ensemble learning algorithm to form a new predictive model. This model, SNN-Stacking ensemble learning, outperforms traditional models, aiding resource planning, enhancing supply chain transparency, and promoting sustainable development by reducing environmental risks and resource waste. This research underscores the potential of digital technology in green supply chain management.

关键词： Demand forecast Digital transformation Green supply chain management SNN-Stacking ensemble learning xgboost algorithm

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Soft computing for determining base resistance of super-long piles in soft soil: A coupled SPBO-xgboost approach

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COMPUTERS AND GEOTECHNICS 2023年 162卷

作者： Nguyen, Tan Ly, Duy-Khuong Huynh, Thien Q. Nguyen, Thanh T. Ton Duc Thang Univ Fac Civil Engn Ho Chi Minh City Vietnam Ton Duc Thang Univ Smart Comp Civil Engn Res Grp Ho Chi Minh City Vietnam Van Lang Univ Inst Computat Sci & Artificial Intelligence Lab Computat Mech Ho Chi Minh City Vietnam Van Lang Univ Fac Mech Elect & Comp Engn Sch Technol Ho Chi Minh City Vietnam Duy Tan Univ Inst Res & Dev Danang 550000 Vietnam Univ Technol Sydney Sch Civil & Environm Engn Ultimo NSW 2007 Australia

Prediction of base resistance for long piles is usually challenging because of the complex mobilization of load over the depth. This study hence proposes a novel machine learning (ML) method where the eXtreme Gradient Boosting (xgboost) algorithm is coupled with the state-of-the-art Student Psychology-Based Optimization method (SPBO) to predict base resistance of piles. A dataset (796 datapoints) from well-instrumented static load tests among various projects in the soft soil region of Ho Chi Minh City is used to develop the model. Five input parameters including the applied load, loading point displacement, axial stiffness, SPT value of the soil beneath the pile toe, and the distance from the loading point to the pile toe are considered. Performance of the coupled SPBO-xgboost model is comprehensively assessed by comparing it with other optimized xgboost models including the Teaching-Learning Based Optimization, Bayesian Optimization, and other well-known ML techniques. The established model is used to predict data obtained from independent studies and compared with practice design codes of pile foundation. The results show that the proposed SPBO-xgboost model outperforms all other models considering the prediction accuracy and reliability. Sensitivity analysis and Partial Dependence Plots are conducted to gain insights into the base resistance mechanism, giving considerable implications to design practice of pile foundation.

关键词： Large and long piles Base resistance xgboost algorithm Machine learning Partial dependence plots

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Application of unsupervised learning and deep learning for rock type prediction and petrophysical characterization using multi-scale data

GEOENERGY SCIENCE AND ENGINEERING

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GEOENERGY SCIENCE AND ENGINEERING 2023年 230卷

作者： Iraji, Shohreh Soltanmohammadi, Ramin Matheus, Gabriela Fernandes Basso, Mateus Vidal, Alexandre Campane State Univ Campinas UNICAMP Dept Mech Engn FEM BR-13083860 Campinas SP Brazil State Univ Campinas UNICAMP Ctr Energy & Petr Studies CEPETRO BR-13083896 Campinas SP Brazil State Univ Campinas UNICAMP Geosci Inst IG Dept Geol & Nat Resources BR-13083855 Campinas SP Brazil

This study integrates well log data, routine core analyses, microcomputed X-ray tomography (mu CT) images, and sedimentary petrography to accurately characterize and evaluate the carbonate reservoirs of the Barra Velha formation (Aptian) of the Santos Basin within Brazilian pre-salt region. In these carbonate reservoirs, the porous system is extremely diverse and variable, making it challenging to establish rock typing with comparable petrophysical properties. Based on this integrated study, the reservoir sequences were characterized and a precise definition of four reservoir rock types (RRTs) was performed by integrating the petrophysical values from the plugs and their corresponding well log data of two cored wells using K-means unsupervised classification algorithm. The classification results were combined with various conventional techniques to evaluate the quality and geological characteristics of the studied sequence. This evaluation encompassed different parameters such as flow and storage capacity, reservoir quality index, flow zone indicator, pore spaces interpretation, and average pore and throat radius. The study involved a detailed analysis of thin sections to identify various facies, including shrubstones, reworked, and spherulitestone, and to classify various forms of porosity such as interparticle, intraparticle, intercrystalline, vug, moldic, fracture, and growth framework porosity. Pore Network Modeling from mu CT analysis of plugs was used specifically for the characterization of pores and throats of plug samples from each RRT. These datasets were utilized as supporting evidence to offer a more accurate and inclusive knowledge of reservoir quality. The study aimed to develop predictive models by implementing deep learning and machine learning algorithms trained on well log data to estimate plug porosity and rock type. Two deep learning models, ResNet and 1D CNN, were trained and evaluated for plug porosity prediction, with the 1D CNN model showing

关键词： Micro -computed tomography ( mu CT) images K -means unsupervised classification algorithm Reservoir rock types (RRTs) Pore network modeling Deep learning algorithms ResNet 1D CNN xgboost algorithm

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An Interpretable Prediction Model for Stroke Based on xgboost and SHAP

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Journal of Clinical and Nursing Research 2023年第3期7卷 96-106页

作者： Tianshu Fang Jiacheng Deng Nanchang University Queen Mary School Nanchang UniversityNanchang 330031Jiangxi ProvinceChina Nanchang Key Laboratory of Medical and Technology Research Nanchang UniversityNanchang 330031Jiangxi ProvinceChina

Objective:To establish a stroke prediction and feature analysis model integrating xgboost and SHAP to aid the clinical diagnosis and prevention of ***:Based on the open data set on Kaggle,with the help of data preprocessing and grid parameter optimization,an interpretable stroke risk prediction model was established by integrating xgboost and SHAP and an explanatory analysis of risk factors was ***:The xgboost model’s accuracy,sensitivity,specificity,and area under the receiver operating characteristic(ROC)curve(AUC)were 96.71%,93.83%,99.59%,and 99.19%,*** explanatory analysis showed that age,type of residence,and history of hypertension were key factors affecting the incidence of ***:Based on the data set,our analysis showed that the established model can be used to identify stroke,and our explanatory analysis based on SHAP increases the transparency of the model and facilitates medical practitioners to analyze the reliability of the model.

关键词： Stroke risk prediction xgboost algorithm SHAP model Risk factor analysis

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Photovoltaic Power Prediction Model Using Pre-train and Fine-tune Paradigm Based on LightGBM and xgboost

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Procedia Computer Science 2023年 224卷 407-412页

作者： Jiandong Ye Binqi Zhao Hua Deng Yangjiang Power Supply Bureau Guangdong Power Grid Co. Ltd Yangjiang China Institute of Tropical and Marine Meteorology Guangzhou CMA

Accurate photovoltaic power prediction is crucial for optimizing the performance of photovoltaic power plants and ensuring the stability of the power grid. However, existing forecasting methods are challenged when predicting the power output of multiple plants with different geographical locations and environmental conditions simultaneously, due to the complexity of the underlying factors that affect power generation. To address this issue, this paper proposes a novel LightGBM-xgboost photovoltaic power prediction model based on a pre-training and fine-tuning paradigm. The model leverages a large dataset to pre-train and learn rich feature representations within meteorological elements, which are then fine-tuned on specific downstream tasks to improve prediction accuracy and generalization ability. Experimental verification is conducted using data from three photovoltaic generators operating in Guangdong, China. Results demonstrate that the proposed model outperforms other methods when predicting the power output of multiple plants with different conditions simultaneously, highlighting the effectiveness of the proposed approach. Overall, this work contributes to advancing the state-of-the-art in photovoltaic power prediction and has practical implications for improving the control and scheduling of photovoltaic power plants, as well as ensuring the stability and security of the power grid.

关键词： Photovoltaic power prediction Pre-train and Fine-tune model LightGBM algorithm xgboost algorithm

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Deep learning based intelligent identification system for ripening stages of banana

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POSTHARVEST BIOLOGY AND TECHNOLOGY 2023年 203卷

作者： Shuprajhaa, Thayumanavan Raj, J. Mathav Paramasivam, Suresh Kumar Sheeba, K. N. Uma, Subbaraya ICAR Natl Res Ctr Banana Tiruchirappalli 620102 Tamil Nadu India Indian Inst Technol Gauhati 781039 Assam India Natl Inst Technol Tiruchirappalli 620015 Tamil Nadu India

Ripening of banana hands during handling, on board transit, shipping and storage leads to higher post-harvest loss and impede the trade. Identification of ripening is paramount importance to reduce loss. Bulk handlers and food processing industries requires automated non-destructive methods of ripening stage identification meth-odologies. This paper proposes a deep learning based non-destructive method of classification of banana fruit under four categories - unripe, under ripe, ripe and over ripe. A customized dataset was prepared with sufficient images in each class. A convolution neural network (CNN) combined with an eXtreme Gradient Boosting (xgboost) algorithm (CNN-xgboost) is introduced for the effective determination of the ripening stage of banana. CNN acts as the trainable feature extractor of the images and xgboost acts as the identifier of ripening stage. Linear Discriminant Analysis (LDA) is incorporated in order to eliminate the need to have data augmentation or a huge data set. Thus, the proposed deep learning approach possesses capability to perform classification even with a smaller data set compared to conventional deep and machine learning techniques. The performance accuracy of the proposed duo is found to be 91.25 % and it is higher than that obtained with a Support Vector Classifier (SVC), Gaussian Naive Bayesian Classifier (GNB) or k-Nearest Neighbours (KNN) algorithms.

关键词： Convolution Neural Network xgboost algorithm Ripeness Supply chain Post-harvest loss Machine learning

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The modernization strategy of constructing teacher-student relationship in the new era of universities

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APPLIED MATHEMATICS AND NONLINEAR SCIENCES 2023年

作者： Ruan, Hongni Jiangsu Coll Engn & Technol Sch Art Design Nantong 226007 Peoples R China

Teacher-student relationship is the most central relationship in educational activities, and without high-quality teacher-student relationship, there is no high level of educational activities. In the new period of social development, the outstanding problems in teacher-student relationship have attracted wide attention of public opinion. This paper takes a full-time comprehensive higher vocational college formed by the merger of several specialized colleges as the target of the study, from which a valid target sample is selected. Based on the idea of SHIFU algorithm and greedy algorithm to construct the teacher-student relationship recognition model, we use xgboost (eXtreme Gradient Boosting) for optimization. Based on the four types of teacher-student relationships identified, a systematic strategy for building modern teacher-student relationships is structured from three aspects: society, school, and teachers and students. The following data were obtained by applying the proposed strategy to the target institutions: the values of the number of students answering questions and solving problems in the experimental class were 46.88% and 40.99% of the total number of students, respectively, which were 4.87% and 5.56% higher than those in the control class. The value of the number and frequency of cooperative exchanges between teachers and students in the experimental class is 52.76% of the total number and frequency, and the satisfaction rate between teachers and students is 49.57%. And the control class is only 44.16% vs. 39.77%. The number of students completing their homework in the experimental class was 95.82%, and the average assessment score was 64.16, which was higher than that of the control class. The strategy has a modernist philosophical orientation and a practical quality, which can make the teacher-student relationship in the new era present a state of moral clarity, intimacy and harmony.

关键词： Stochastic distributed greedy algorithm Teacher-student relationship recognition model Modernized teacher-student relationship strategy SHIFU algorithm xgboost algorithm

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