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machine learning-based mapping of indoor radon potential using geogenic factors

Environmental and Ecological Statistics 2025年 1-36页

作者： Daviran, Mehrdad Masoumi, Iman Ghezelbash, Reza Maggio, Sabrina De Iaco, Sandra Department of Mining Engineering Amirkabir University of Technology Tehran Iran Department of Economic Sciences University of Salento Lecce Italy National Biodiversity Future Center Palermo Italy School of Mining Engineering College of Engineering University of Tehran Tehran Iran National Centre for HPC Big Data and Quantum Computing Bologna Italy

This study presents a comprehensive, data-driven analysis of mapping indoor radon potential areas in the Apulia region of southeastern Italy, relying exclusively on geogenic factors. Key geogenic radon factors intrinsic to the Earth’s geological makeup are examined, including the presence of uranium in bedrocks and soil, geological inhomogeneities, such as faults and karstic formations, and soil properties affecting gas permeability. The methodology utilizes Frequency Ratio (FR) analysis to empirically derive the relevance of 14 geogenic subfactors which are then used as standardized inputs for machine learning algorithms including Support Vector machine (SVM), Multilayer Perceptron Artificial Neural Network (MLP-ANN), and Random Forest. Each model is trained and validated using a balanced dataset derived from indoor radon measurements and geospatial geogenic predictors. SHAP (SHapley Additive exPlanations) analysis is used to enhance interpretability by showing how geogenic factors influenced model predictions. Model performance is evaluated using confusion matrices, ROC (Receiver Operator Characteristic) curves, and AUC (Area Under the Curve) metrics. Notably, SVM emerges as the top-performing model among them. Key findings highlight the significant influence of geological inhomogeneities, uranium content in soil and bedrock, as well as soil permeability on indoor radon concentration. The proposed workflow combining FR and SHAP for interpretability demonstrates an effective and reproducible strategy for regional indoor radon potential mapping. The research contributes to advancing the understanding of indoor radon exposure dynamics, providing valuable insights for policymakers, urban planners, and public health authorities to mitigate radon-related health risks effectively by identifying high indoor radon potential areas based on geogenic factors and elucidating underlying mechanisms; this study prepares the groundwork for targeted intervention strategies and info

关键词： Geogenic factors Indoor radon potential machine learning algorithm

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Impact of Enterprises Relationship on Ml's Ability to Forecast SME's Credit Risk in SCF: A Research Based on Complex Network

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EMERGING MARKETS FINANCE AND TRADE 2024年第11期60卷 2594-2612页

作者： Peng, Qiaosheng Xie, Chi Li, Jing Zhu, You Wang, Gang-Jin Hunan Univ Business Sch Changsha Hunan Peoples R China Hunan Univ Ctr Finance & Investment Management Changsha Hunan Peoples R China Hunan Univ Business Sch Changsha 410082 Hunan Peoples R China

In this study, we investigate the impact of introducing enterprise relationship to train machine learning (ML) algorithms on improving its ability of forecasting small and medium-sized enterprises' (SMEs) credit risk in supply chain finance (SCF). First, we incorporate attributes of SME node in the CEs-SMEs network, a bipartite network constructed with transaction data in the Chinese automobile industry, into financial indicators to train ML algorithms for improving forecasting performance. Second, we employ the weighted one-mode projection approach to extract a projected-SMEs network that reveals the relationship among SMEs from the CEs-SMEs network and examine whether the ML algorithms' forecasting performances is improved when considering attributes in the projected-SMEs network. Third, we analyze the correlation between SME's credit risk and individual network attribute to specifically understand the impact of enterprise relationship on credit risk. Overall, the empirical results indicate that the forecasting performance is obviously better when enterprise transaction relationship is considered, and it is further enhanced after we apply weighted one-mode projection approach. Meanwhile, the variable importance analysis and the binomial logistic regression demonstrate how significantly each network attribute is correlated with credit risk, and the discussion on partial dependence plot shows that SMEs with large degree are non-risky generally.

关键词： Supply chain finance small and-medium-sized enterprise's credit risk machine learning algorithm complex network weighted one-mode projection D85 G10 G21 G33 L14 M10

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Balancing Plug-In for Stream-Based Classification 1

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11st World Conference on Information Systems and Technologies (WorldCIST)

作者： de Arriba-Perez, Francisco Garcia-Mendez, Silvia Leal, Fatima Malheiro, Benedita Burguillo-Rial, Juan Carlos Univ Vigo atlanTTic Informat Technol Grp Vigo Spain Univ Portucalense REMIT Porto Portugal INESC TEC Porto Portugal Polytech Inst Porto ISEP Porto Portugal

ISBN: (数字)9783031456428

ISBN: (纸本)9783031456411;9783031456428

The latest technological advances drive the emergence of countless real-time data streams fed by users, sensors, and devices. These data sources can be mined with the help of predictive and classification techniques to support decision-making in fields like e-commerce, industry or health. In particular, stream-based classification is widely used to categorise incoming samples on the fly. However, the distribution of samples per class is often imbalanced, affecting the performance and fairness of machine learning models. To overcome this drawback, this paper proposes Bplug, a balancing plug-in for stream-based classification, to minimise the bias introduced by data imbalance. First, the plugin determines the class imbalance degree and then synthesises data statistically through non-parametric kernel density estimation. The experiments, performed with real data from Wikivoyage and Metro of Porto, show that Bplug maintains inter-feature correlation and improves classification accuracy. Moreover, it works both online and offline.

关键词： Data bias fairness imbalanced data sets machine learning algorithm stream classification

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Research on prediction and analysis of supercritical water heat transfer coefficient based on support vector machine

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NUCLEAR ENGINEERING AND TECHNOLOGY 2023年第11期55卷 4102-4111页

作者： Ma, Dongliang Li, Yi Zhou, Tao Huang, Yanping Hebei Finance Univ Sch Informat Engn & Comp Sci Baoding 071051 Peoples R China Hebei Finance Univ Sch Financial Technol Baoding 071051 Hebei Peoples R China Southeast Univ Sch Energy & Environm Nanjing 211189 Peoples R China Nucl Power Inst China Sci & Technol Reactor Syst Design Technol Lab Chengdu 610041 Peoples R China

In order to better perform thermal hydraulic calculation and analysis of supercritical water reactor, based on the experimental data of supercritical water, the model training and predictive analysis of the heat transfer coefficient of supercritical water were carried out by using the support vector machine (SVM) algorithm. The changes in the prediction accuracy of the supercritical water heat transfer coefficient are analyzed by the changes of the regularization penalty parameter C, the slack variable epsilon and the Gaussian kernel function parameter gamma. The predicted value of the SVM model obtained after parameter optimization and the actual experi-mental test data are analyzed for data verification. The research results show that: the normalization of the data has a great influence on the prediction results. The slack variable has a relatively small influence on the accuracy change range of the predicted heat transfer coefficient. The change of gamma has the greatest impact on the accuracy of the heat transfer coefficient. Compared with the calculation results of traditional empirical formula methods, the trained algorithm model using SVM has smaller average error and standard deviations. Using the SVM trained algorithm model, the heat transfer coefficient of supercritical water can be effectively predicted and analyzed.

关键词： Supercritical water Support vector machine Radial basis kernel function Data normalization Heat transfer coefficient machine learning algorithm

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LiDAR Image-Based Climate Change Detection with Their Economic Ecosystem Modelling: Artificial Intelligence Application

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Remote Sensing in Earth Systems Sciences 2025年第1期8卷 173-181页

作者： Sravani, Chekka Ananth, Prithiviraj Sasikala, C. Kumar, N. Sathish Kumari, Ch D. V. P. Thrimurthulu, V. Computer Science and Engineering Marri Laxman Reddy Institute of Technology and Management Telangana Hyderabad 500043 India Department of CSE Sona College of Technology Salem-5 Salem India Department of CSE Srinivasa Ramanujan Institute of Technology (Autonomous) Andhra Pradesh Ananthapuramu India Department of ECE Sri Ramakrishna Engineering College Coimbatore 22 India Department of Computer Science and Engineering Aditya University Andhra Pradesh Surampalem India Department of Computer Science and Engineering MLR Institute of Technology Telangana Dundigal Hyderabad India

Over the past few decades, change detection has been the subject of more research in both close-quarters and distant sensing due to its significance in environment monitoring and database updating. The advancement of sensing technology has made data collecting more accessible and economical, which has increased the amount of data available from different sensing platforms. Studying these atmospheric components has become much easier because of LiDAR technology, which can measure backscattered signals and explore the atmosphere at very high spatial and temporal resolution. This research proposes a novel technique in satellite data security analysis with climate change detection utilizing ML algorithm. Input has been collected as input as satellite data based on security and processed for noise removal as well as normalisation. Processed data has been segmented and classified using SegNet histogram graph cut contour model and transfer AlexNet adversarial Markov model. The experimental analysis has been carried out in terms of data integrity, precision, normalised coefficient, recall and F-1 score. The proposed technique attained data integrity of 98%, F-1 score 95%, PRECISION of 97%, normalised coefficient of 93% and RECALL of 92%. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Adversarial Markov Climate change detection Graph cut contour model LiDAR technology machine learning algorithm

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A Fine-Grained Flash-Memory Fragment Recognition Approach for Low-Level Data Recovery

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Chinese Journal of Electronics 2022年第4期31卷 732-740页

作者： ZHANG Li HAO Shengang ZHANG Quanxin Department of Media Engineering Zhejiang University of Media and Communication School of Computer Science Beijing Institute of Technology

Data recovery from flash memory in the mobile device can effectively reduce the loss caused by data corruption. Type recognition of data fragment is an essential prerequisite to the low-level data recovery. Previous works in this field classify data fragment based on its file type. Still, the classification efficiency is low, especially when the data fragment is a part of a composite *** propose a fine-grained approach to classifying data fragment from the low-level flash memory to improve the classification accuracy and efficiency. The proposed method redefines flash-memory-page data recognition problem based on the encoding format of the data segment, and applies a hybrid machine learning algorithm to detect the data type of the flash page. The hybrid algorithm can significantly decompose the given data space and reduce the cost of training. The experimental results show that our method achieves better classification accuracy and higher time performance than the existing methods.

关键词： Flash fragmentation recognition Fine-grained machine learning algorithm Support vector machine Decision tree Digital forensics

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machine learning modelling of structural response for different seismic signal characteristics: A parametric analysis

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APPLIED SOFT COMPUTING 2024年 164卷

作者： De Iuliis, M. Miceli, E. Castaldo, P. Politecn Torino Dept Struct Geotech & Bldg Engn DISEG Turin Italy

The present study investigates the best seismic parameters for modeling the dynamic response of various nonlinear structural systems by comparing different machine learning (ML) algorithms. A total of 400 synthetic excitations were generated and analyzed against 23 seismic parameters. These signals were used in a step-by-step numerical analysis to calculate the dynamic responses of 1000 single-degree-of-freedom (SDOF) systems with varying mechanical properties. The data obtained from these responses were processed using 20 ML algorithms, including linear regression, tree, support vector machine (SVM), boosted and bagged trees, and artificial neural network (ANN). Each ML algorithm used a single seismic parameter as input to determine the most predictive parameters for modeling structural responses, defining the high predictive seismic parameters (HPSP) set. To validate the obtained results, the most effective model predictions have been compared with the results of the parametric step-by-step analyses performed for a new group of natural ground motions. The findings demonstrate that with a properly calibrated training phase, considering the specific site hazard and selecting seismic parameters from the HPSP set, the ML model can accurately estimate seismic responses whit a significantly reduced computational effort. This study underscores the potential of integrating ML techniques into the performance-based seismic design approach.

关键词： machine learning algorithm Artificial excitation Non-linear SDOF system Seismic parameter Peak relative displacement Predictive metrics

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Optimization-based stacked machine-learning method for seismic probability and risk assessment of reinforced concrete shear walls

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EXPERT SYSTEMS WITH APPLICATIONS 2024年第PartD期255卷

作者： Kazemi, Farzin Asgarkhani, Neda Jankowski, Robert Gdansk Univ Technol Fac Civil & Environm Engn ul Narutowicza 11-12 PL-80233 Gdansk Poland Univ Naples Federico II Sch Polytech & Basic Sci Dept Struct Engn & Architecture Naples Italy

Efficient seismic risk assessment aids decision-makers in formulating citywide risk mitigation plans, providing insights into building performance and retrofitting costs. The complexity of modeling, analysis, and postprocessing of the results makes it hard to fast-track the seismic probabilities, and there is a need to optimize the computational time. This research addresses seismic probability and risk assessment of reinforced concrete shear walls (RCSWs) by introducing stacked machine learning (Stacked ML) models based on Bayesian optimization (BO), genetic algorithm (GA), particle swarm optimization (PSO), and gradient-based optimization (GBO) algorithms. The study investigates 4-, to 15-Story RCSWs assuming different bay lengths and soil types to build a comprehensive database based on the incremental dynamic analysis (IDA) subjected to 56 near-field pulse-like and no-pulse records. Having 227,200 and 63,384 data points for a median of IDA curve (MIDA) and seismic probability curve, respectively, the proposed Stacked ML models have shown good performance on curve fitting ability by accuracy of 99.1% and 99.4% for MIDA and seismic fragility curves, respectively. In addition, the proposed models can estimate the mean annual frequency, lambda, which is a key parameter in seismic risk assessment of buildings. To provide the results of the study for general buildings, a user-friendly GUI is proposed that facilitates result utilization, offering insights into seismic performance levels, providing the estimated MIDA and seismic failure probability curves, and mean annual frequency calculations for specific performance levels and seismic hazard curves.

关键词： Reinforced concrete shear wall machine learning algorithm Seismic failure probability Optimization algorithm Seismic performance curve Seismic retrofit

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Efficient optimization based thresholding technique for analysis of alzheimer MRIs

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INTERNATIONAL JOURNAL OF NEUROSCIENCE 2023年第2期133卷 201-214页

作者： Prabha, S. Sankaran, K. Sakthidasan Chitradevi, D. Hindustan Inst Technol & Sci Dept Elect & Commun Engn Chennai Tamil Nadu India Hindustan Inst Technol & Sci Dept Comp Sci & Engn Chennai Tamil Nadu India

Purpose study: Alzheimer is a type of dementia that usually affects older adults by creating memory loss due to damaged brain cells. The damaged brain cells lead to shrinkage in the size of the brain and it is very difficult to extract the grey matter (GM) and white matter (WM). The segmentation of GM and WM is a challenging task due to its homogeneous nature between the neighborhood tissues. In this proposed system, an attempt has been made to extract GM and WM tissues using optimization-based segmentation techniques. Materials and methods: The optimization method is considered for the classification of normal and alzheimer disease (ad) through magnetic resonance images (mri) using a modified cuckoo search algorithm. Gray Level Co-Occurrence Matrix (GLCM) features are calculated from the extracted GM and WM. Principal Component Analysis (PCA) is adopted for selecting the best features from the GLCM features. Support Vector machine (SVM) is a classifier which is used to classify the normal and abnormal images. Results: The proposed optimization algorithm provides most promising and efficient level of image segmentation compared to fuzzy c means (fcm), otsu, particle swarm optimization (pso) and cuckoo search (cs). The modified cuckoo yields high accuracy of 96%, sensitivity of 97% and specificity of 94% than other methods due to its powerful searching potential for the proper identification of gray and WM tissues. Conclusions: The results of the classification process proved the effectiveness of the proposed technique in identifying alzheimer affected patients due to its very strong optimization ability. The proposed pipeline helps to diagnose early detection of AD and better assessment of the neuroprotective effect of a therapy.

关键词： Alzheimer classification machine learning algorithm magnetic resonance imaging optimization segmentation

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A novel method for assessing cardiac function in patients with coronary heart disease based on wrist pulse analysis

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IRISH JOURNAL OF MEDICAL SCIENCE 2023年第6期192卷 2697-2706页

作者： Wu, Wen-jie Chen, Rui Guo, Rui Yan, Jian-jun Zhang, Chun-ke Wang, Yi-qin Yan, Hai-xia Zhang, Ye-qing Shanghai Univ Tradit Chinese Med Dept Basic Med Sci 1200 Cailun Rd Shanghai 201203 Peoples R China East China Univ Sci & Technol Inst Intelligent Percept & Diag Sch Mech & Power Engn 130 Meilong Rd Shanghai 200237 Peoples R China Shanghai Univ Tradit Chinese Med Shanghai Municipal Hosp Tradit Chinese Med Dept Chinese Internal Med Shanghai 200071 Peoples R China

BackgroundThe timely assessment of B-type natriuretic peptide (BNP) marking chronic heart failure risk in patients with coronary heart disease (CHD) helps to reduce patients' *** evaluate the potential of wrist pulse signals for use in the cardiac monitoring of patients with *** total of 419 patients with CHD were assigned to Group 1 (BNP < 95 pg/mL, n = 249), 2 (95 < BNP < 221 pg/mL, n = 85), and 3 (BNP > 221 pg/mL, n = 85) according to BNP levels. Wrist pulse signals were measured noninvasively. Both the time-domain method and multiscale entropy (MSE) method were used to extract pulse features. Decision tree (DT) and random forest (RF) algorithms were employed to construct models for classifying three groups, and the models' performance metrics were *** pulse features of the three groups differed significantly, suggesting different pathological states of the cardiovascular system in patients with CHD. Moreover, the RF models outperformed the DT models in performance metrics. Furthermore, the optimal RF model was that based on a dataset comprising both time-domain and MSE features, achieving accuracy, average precision, average recall, and average F1-score of 90.900%, 91.048%, 90.900%, and 90.897%, *** wrist pulse detection technology employed in this study is useful for assessing the cardiac function of patients with CHD.

关键词： B-type natriuretic peptide Chronic heart failure Coronary heart disease machine learning algorithm Multiscale entropy method Time-domain method Wrist pulse signal

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