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Assessment of peak particle velocity of blast vibration using hybrid soft computing approaches

JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING 2025年第2期12卷 154-176页

作者： Yuan, Haiping Zou, Yangyao Li, Hengzhe Ji, Shuaijie Gu, Ziang He, Liu Hu, Ruichao Hefei Univ Technol Coll Civil Engn Hefei 230009 Peoples R China State Key Lab Min Induced Response & Disaster Prev Huainan 232001 Peoples R China

Blasting vibration is a major adverse effect in rock blasting excavation, and accurately predicting its peak particle velocity (PPV) is vital for ensuring engineering safety and risk management. This study proposes an innovative IHO-VMD-catboost model that integrates variational mode decomposition (VMD) and the catboost algorithm, with hyperparameters globally optimized using the improved hippopotamus optimization (IHO) algorithm. Compared to existing models, the proposed method improves feature extraction from vibration signals and significantly enhances prediction accuracy, especially in complex geological conditions. Using measured data from open-pit mine blasting, the model extracts key features such as maximum section charge, total charge, and horizontal distance, achieving superior performance compared to 13 traditional models. It reports a root mean square error of 0.28 cm/s, a mean absolute error of 0.17 cm/s, an index of agreement of 0.993, and a variance accounted for value of 97.28%, demonstrating superior prediction accuracy, a high degree of fit with observed data, and overall robustness in PPV prediction. Additionally, analyses based on the SHapley Additive Explanations framework provide insights into the complex nonlinear relationships between factors like horizontal distance and maximum section charge, improving the model's interpretability. The model demonstrates robustness, stability, and applicability in various tests, confirming its reliability in complex engineering scenarios, and offering a valuable solution for safe mining and optimized blasting design.

关键词： blast vibration peak particle velocity catboost algorithm variational mode decomposition improved hippopotamus optimization SHapley Additive exPlanations

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Novel models based on machine learning to predict the prognosis of metaplastic breast cancer

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BREAST 2025年 79卷 103858-103858页

作者： Zhang, Yinghui An, Wenxin Wang, Cong Liu, Xiaolei Zhang, Qihong Zhang, Yue Cheng, Shaoqiang Harbin Med Univ Dept Breast Surg Canc Hosp Harbin 150081 Heilongjiang Peoples R China Harbin Med Univ Canc Hosp Dept Urol Surg Harbin Peoples R China Harbin Med Univ Canc Hosp Dept Gastrointestinal Surg Harbin Peoples R China Harbin Med Univ Canc Hosp Dept Breast Med Harbin 150081 Heilongjiang Peoples R China

Background: Metaplastic breast cancer (MBC) is a rare and highly aggressive histological subtype of breast cancer. There remains a significant lack of precise predictive models available for use in clinical practice. Methods: This study utilized patient data from the SEER database (2010-2018) for data analysis. We utilized prognostic factors to develop a novel machine learning model (catboost) for predicting patient survival rates. Simultaneously, our hospital's cohort of MBC patients was utilized to validate our model. We compared the benefits of radiotherapy among the three groups of patients. Results: The catboost model we developed exhibits high accuracy and correctness, making it the best-performing model for predicting survival outcomes in patients with MBC (1-year AUC = 0.833, 3-year AUC = 0.806;5-year AUC = 0.810). Furthermore, the catboost model maintains strong performance in an external independent dataset, with AUC values of 0.937 for 1-year survival, 0.907 for 3-year survival, and 0.890 for 5-year survival, respectively. Radiotherapy is more suitable for patients undergoing breast-conserving surgery with M0 stage [group1: (OS:HR = 0.499, 95%CI 0.320-0.777 p < 0.001;BCSS: HR = 0.519, 95%CI 0.290-0.929 p = 0.008)] and those with T3-4/N2-3M0 stage undergoing mastectomy [group2: (OS:HR = 0.595, 95%CI 0.437-0.810 p < 0.001;BCSS: HR = 0.607, 95%CI 0.427-0.862 p = 0.003)], compared to patients with stage T1-2/N0-1M0 undergoing mastectomy [group3: (OS:HR = 1.090, 95%CI 0.673-1.750 p = 0.730;BCSS: HR = 1.909, 95%CI 1.036-3.515 p = 0.038)]. Conclusion: We developed three machine learning prognostic models to predict survival rates in patients with MBC. Radiotherapy is considered more appropriate for patients who have undergone breast-conserving surgery with M0 stage as well as those in stage T3-4/N2-3M0 undergoing mastectomy.

关键词： Metaplastic breast cancer catboost algorithm SEER SHAP Radiotherapy

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Machine Learning for Predicting Required Cross-Sectional Dimensions of Circular Concrete-Filled Steel Tubular Columns

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BUILDINGS 2025年第9期15卷 1438-1438页

作者： Chepurnenko, Anton Al-Zgul, Samir Tyurina, Vasilina Don State Tech Univ Struct Mech & Theory Struct Dept Rostov Na Donu 344003 Russia

Machine learning methods are widely used to predict the bearing capacity of concrete-filled steel tubular (CFST) columns. However, in addition to this task, the engineer often faces the inverse problem: to determine what cross-section dimensions of the CFST column are required for given loads. This paper is devoted to the development of machine learning models for predicting the geometric parameters of a circular cross-section for concrete-filled steel tubular (CFST) columns under the combined action of bending moments and compressive axial forces. This problem has not been solved by machine learning methods before. The main focus is on automating the design process of CFST columns using the catboost algorithm and artificial neural networks. Three machine learning models were developed to solve the problem. The first and second models are based on the catboost algorithm. They predict the column diameter at minimum and maximum wall thicknesses, respectively. The third model is an artificial neural network, which is designed to determine the wall thickness of a CFST column. The models were trained on synthetic data generated in accordance with Russian design codes. The first and second models demonstrated high accuracy in predicting the column diameter (RMSE = 3.86 mm and 4.12 mm, respectively). The third model showed high efficiency over the entire range of wall thicknesses (correlation coefficient R = 0.99974). Feature importance analysis using SHAP values confirmed the key role of bending moment and axial force in predicting geometric parameters.

关键词： concrete-filled steel tubular columns machine learning catboost algorithm artificial neural networks bearing capacity

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Analyzing the Determinants of US Residential Energy Usage and Spending: A Machine Learning Approach

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ENERGY JOURNAL 2025年第1期46卷 147 - 177页

作者： Chidmi, Benaissa Ettoumi, Youssef Aljohani, Emad S. Texas Tech Univ Dept Agr & Appl Econ MS 42132 Lubbock TX 79409 USA Natl Sch Architecture Tetouan Tetouan Morocco King Saud Univ Dept Agr Econ Riyadh Saudi Arabia

This study explores the factors that impact residential energy usage and spending in the United States. Using data from the 2020 Residential Energy Consumption Survey (RECS), we investigate the significance of different energy consumption determinants at various analysis levels. Our analysis covers residential energy usage, electricity, natural gas, propane, and fuel oil consumption. We also examine energy usage for space heating, cooling, and water heating. To leverage the extensive RECS data, which includes over 180 variables, we utilized machine learning (ML) techniques for feature selection and determined their Shapley contribution for different target outcomes. Our results indicate that the catboost algorithm outperforms other ML techniques on the 2020 Residential Energy Consumption Survey sample. Our findings demonstrate that it is not appropriate to aggregate consumption and expenditure, as each level has distinct important *** Classification: D12, Q41, R21

关键词： residential energy consumption and expenditure space heating space cooling water heating catboost algorithm residential energy consumption survey

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A novel study on forecasting the airfoil self-noise, using a hybrid model based on the combination of catboost and Arithmetic Optimization algorithm

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EXPERT SYSTEMS WITH APPLICATIONS 2023年第PartA期229卷

作者： Rastgoo, Amir Khajavi, Hamed KN Toosi Univ Technol Dept Civil Engn Tehran Iran

One of the important challenges in aircraft design is the light, inexpensive, and self-noise-free design of airfoil. Since the self-noise generated by rotor blades interacting with turbulent flow produced in their boundary layer can impact how well the system works. Predicting aerofoil self-noise can help shorten computational times and improve cost-effectiveness because traditional computational fluid dynamics methods flow has more time con-sumption. Therefore, this paper proposes a forecasting approach using the hybrid model of the catboost algo-rithm. In this way, the hyperparameters of the catboost are tuned by twelve optimizers to increase precision and speed up forecasting. The precision of the hybrid models is evaluated by statistical evaluation indices such as Coefficient of determination (R2), Root Mean Square Error (RMSE), Mean Absolute Error (MSE), and relative absolute error (MAE). The obtained results show that the catboost combination with the Arithmetic Optimi-zation algorithm better operates optimally and depicts lower error values compared to other optimizers. Also, the values of the coefficient of determination for training and testing data with 0.9999 and 0.9706, respectively, demonstrate that Arithmetic Optimizer is the most precise optimizer among studied metaheuristic algorithms.

关键词： Airfoil self-noise prediction Arithmetic Optimization algorithm catboost algorithm Machine learning Metaheuristic algorithms

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A novel feature engineered-catboost-based supervised machine learning framework for electricity theft detection

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ENERGY REPORTS 2021年 7卷 4425-4436页

作者： Hussain, Saddam Mustafa, Mohd Wazir Jumani, Touqeer A. Baloch, Shadi Khan Alotaibi, Hammad Khan, Ilyas Khan, Afrasyab Univ Teknol Malaysia Sch Elect Engn Johor Baharu 81310 Malaysia Mehran Univ Engn & Technol Dept Elect Engn SZAB Campus Khairpur Mirs 66020 Pakistan Mehran Univ Engn & Technol Jamshoro Dept Mechatron Engn Sindh 76062 Pakistan Taif Univ Coll Sci Dept Math POB 11099 At Taif 21944 Saudi Arabia Majmaah Univ Coll Sci Al Zulfi Dept Math Al Majmaah 11952 Saudi Arabia South Ural State Univ Inst Engn & Technol Dept Hydraul & Hydraul & Pneumat Syst Lenin Prospect 76 Chelyabinsk 454080 Russia

This paper presents a novel supervised machine learning-based electric theft detection approach using the feature engineered-catboost algorithm in conjunction with the SMOTETomek algorithm. Contrary to the previous literature, where the missing observations in data are either ignored or imputed with average values, this work utilizes k-Nearest neighbor technique for missing data imputation;thus, an accurate and realistic estimation of the missing data is achieved. To mitigate the biasness to the majority data class, the proposed model utilizes the SMOTETomek algorithm, which neutralizes the mentioned effect by managing a proper balance between over-sampling and under-sampling techniques. Feature Extraction and Scalable Hypothesis (FRESH) algorithm is utilized at the later stage of the proposed NTL detection framework to extract and select the most relevant data features from the provided dataset. Afterward, the model is trained using the catboost algorithm to classify the consumers into two distinct categories, i.e., genuine and theft. Finally, to interpret the model's decision for the corresponding predictions, the tree-SHAP algorithm is utilized. To validate the efficacy of the proposed ML based theft detection approach, its performance is compared with that of the traditional gradient boosting ML algorithms such as XGBoost, lightGBM, Ensemble bagging, boosting ML models, and other conventional ML models using five of the most widely used performance metrics, i.e., precision, accuracy, F1score Kappa and MCC. The proposed technique achieved an accuracy of 93% and a detection rate of 92%, which is significantly higher than all the considered competing algorithms under identical dataset and hyperparameters. (C) 2021 Published by Elsevier Ltd.

关键词： catboost algorithm NTL detection Smart meters Feature engineering Machine learning model interpretation

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Improved estimation of dark fermentation biohydrogen production utilizing a robust categorical boosting machine-learning algorithm

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INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2024年第PartA期52卷 190-199页

作者： Bandpey, Atieh Fahimi Abdi, Jafar Firozjaee, Tahere Taghizade Lappeenranta Lahti Univ Technol LUT Sch Engn Sci Ind Engn & Management IEM POB 20 FI-53851 Lappeenranta Finland Shahrood Univ Technol Fac Chem & Mat Engn Shahrood *** Iran Shahrood Univ Technol Fac Civil Engn Shahrood *** Iran

Dark fermentation is attracting great attention due to some capabilities such as wastewater treatment and biofuel production. In this study, the performance of different machine learning algorithms, including Bagging-Extreme Gradient Boosting (BagXGBoost), Categorical Boosting (catboost), K Nearest Neighborhood (KNN), Light Gradient Boosting Machine (LightGBM), and Decision Trees (DT) in the prediction of biohydrogen production from wastewater during dark fermentation is investigated. Effect of parameters Ni, Fe, biomass, pH, chemical oxygen demand (COD), HRT, acetate, ethanol, butyrate, and acetate/butyrate ratio as inputs were considered for the model development. A databank with 208 data points was culled from the literature. For the validation of the proposed models, various statistical criteria were determined. catboost and BagXGBoost exhibited superior performance in predicting fermentative hydrogen (H2) production. Shapley additive explanations were used for relative analysis, revealing the highest absolute SHAP values of 1.1, 1, and 0.07 for COD, nickel, and butyrate, respectively, highlighting their significance in H2 production. The insights gained from this study can contribute to further optimization of the dark biohydrogen fermentation process.

关键词： Dark fermentation Machine learning catboost algorithm SHAP values Relevancy analysis

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A novel scheme for employee churn problem using multi-attribute decision making approach and machine learning

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JOURNAL OF INTELLIGENT INFORMATION SYSTEMS 2021年第2期56卷 279-302页

作者： Jain, Nishant Tomar, Abhinav Jana, Prasanta K. Indian Inst Technol ISM Dept Comp Sci & Engn Dhanbad 826004 Bihar India

Employee churn (ECn) is a crucial problem for any organization that adversely affects its overall revenue and brand image. Many machine learning (ML) based systems have been developed to solve the ECn problem. However, they miss out on some essential issues such as employee categorization, category-wise churn prediction, and retention policy for effectively addressing the ECn problem. By considering all these issues, we propose, in this paper, a multi-attribute decision making (MADM) based scheme coupled with ML algorithms. The proposed scheme is referred as employee churn prediction and retention (ECPR). We first design an accomplishment-based employee importance model (AEIM) that utilizes a two-stage MADM approach for grouping the employees in various categories. Preliminarily, we formulate an improved version of the entropy weight method (IEWM) for assigning relative weights to the employee accomplishments. Then, we utilize the technique for order preference by similarity to ideal solution (TOPSIS) for quantifying the importance of the employees to perform their class-based categorization. The catboost algorithm is then applied for predicting class-wise employee churn. Finally, we propose a retention policy based on the prediction results and ranking of the features. The proposed ECPR scheme is tested on a benchmark dataset of the human resource information system (HRIS), and the results are compared with other ML algorithms using various performance metrics. We show that the system using the catboost algorithm outperforms other ML algorithms.

关键词： Employee churn Employee importance model Retention policy catboost algorithm MADM method TOPSIS

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Renewable power source energy consumption by hybrid machine learning model

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ALEXANDRIA ENGINEERING JOURNAL 2022年第12期61卷 9447-9455页

作者： Abd El-Aziz, Rasha M. Jouf Univ Coll Sci & Arts Qurayyat Dept Comp Sci Sakakah Saudi Arabia

Today, electricity is in high demand in a variety of places, including hospitals, industry, households, transportation, and communication, among others. Renewable energy is a revolutionary type of energy that is increasingly being used to replace electricity demand because it has been regenerated and reused several times. Renewable energy is an intermediate and unpredictable natural resource, so it is difficult for many research studies to estimate its rate. To address this problem, this study uses a hybrid machine learning technique to precisely predict the energy level of natural resources. The hybrid machine learning is the combination of Multilayer Perceptron (MLP), Support Vector Regression (SVR) and catboost algorithm that increases the performance and predictability of renewable energy consumption. The proposed system dataset's results are evaluated at the train and test levels, and the results are then compared to other current approaches. The end results reveal that the proposed hybrid machine learning technique has a high prediction level when compared to others, as well as a lower cost rate and improved overall system performance.(c) 2022 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Alexandria University This is an open access article under the CC BY-NC-ND license (http://***/ licenses/by-nc-nd/4.0/).

关键词： Machine Learning Renewable Power Source Multilayer Perception catboost algorithm

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Segmentation and classification of white blood cancer cells from bone marrow microscopic images using duplet-convolutional neural network design

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MULTIMEDIA TOOLS AND APPLICATIONS 2023年第23期82卷 35277-35299页

作者： Devi, Tulasi Gayatri Patil, Nagamma Rai, Sharada Sarah, Cheryl Philipose Natl Inst Technol Karnataka Dept Informat Technol Mangalore Karnataka India Kasturba Med Coll & Hosp Dept Pathol Mangalore Karnataka India

Cancer is a disease linked to the untamed and rapid division of cells in the body. Cancer detection through conventional methods like complete blood count is a tedious and time-consuming task prone to human errors. The introduction of image processing techniques and computer-aided diagnostics is beneficial to this field as the results obtained by utilizing these methods are quick and accurate. The proposed method in this paper uses a design Convolutional Leaky RELU with catboost and XGBoost (CLR-CXG) to segment the images and extract the important features that help in classification. The binary classification algorithm and gradient boosting algorithm catboost (Categorical Boost) and XGBoost (Extreme Gradient Boost) are implemented individually. Moreover, Convolutional Leaky RELU with catboost (CLRC) is designed to decrease bias and provide high accuracy, while Convolutional Leaky RELU with XGBoost (CLRXG) is designed for classification or regression prediction problems which will increase the speed of executing the algorithm and improve its performance. Thus the CLR-CXG classifies the test images into Acute Lymphoblastic Leukemia (ALL) or Multiple Myeloma (MM). Finally, the CLRC algorithm achieved 100% accuracy in classifying cancer cells, and the recorded run time is 10s. Moreover, the CLRXG algorithm has gained an accuracy of 97.12% for classifying cancer cells and 12 s for executing the process.

关键词： Acute Lymphoblastic Leukemia Adamoptimizer Binary Classification Blood Cancer catboost algorithm Convolutional Neural Network Extreme Gradient Boost algorithm Multiple Myeloma

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