检索结果-内蒙古大学图书馆

5th International Conference on Telecommunications, Optics, and Computer Science, TOCS 2024

作者： Zheng, Qinyuan Shanghai Maritime University Shanghai 201306 China

ISBN: (纸本)9781510691667

With the rapid development of artificial intelligence technology, its application in the field of trademark registration applications can save a significant amount of time and human resources. However, research on predicting trademark opposition and review is still relatively limited. This paper proposes a trademark appeal outcome prediction model based on natural language processing and machine learning. The model extracts text features using the TF-IDF algorithm, calculates the similarity between trademark names using the Levenshtein distance, and models the comprehensive features based on the catboost algorithm to predict the results of trademark registration appeals. Experiments show that the model achieves an accuracy rate of 69% in predicting trademark opposition and review outcomes, effectively evaluating the similarity between trademark texts, providing auxiliary decision-making for trademark applicants, reducing application time costs, and at the same time, increasing the efficiency of relevant institutions. © 2025 SPIE.

关键词： catboost algorithm Levenshtein distance prediction model TF-IDF algorithm Trademark rejection review prediction

来源：评论

学校读者我要写书评

暂无评论

Financial Risk Early Warning for Enterprises Based on the catboost algorithm 25

Financial Risk Early Warning for Enterprises Based on the Ca...

引用

Proceedings of the 2025 4th International Conference on Big Data, Information and Computer Network

作者： Shuhong Xie Aba Teachers University Aba Tibetan and Qiang Autonomous Prefecture Sichuan China

ISBN: (纸本)9798400712425

As the complexity of corporate financial data increases, traditional financial risk assessment methods are difficult to cope with. Machine learning methods, especially catboost algorithms, have become emerging technologies for financial risk prediction due to their advantages in processing category features. This paper first selected 224 non-ST companies and 112 ST companies as samples, and constructed an early warning system including 16 key financial indicators such as profitability, debt repayment ability, operating ability, and capital structure. The significant indicators were screened out through the T test, and then the financial risk early warning model was trained based on the catboost algorithm. The experimental results show that the prediction accuracy of the model based on the catboost algorithm in T-2 and T-3 years is 93.75% and 96.43% respectively. Compared with other models, the indicators perform better, proving that the model can effectively distinguish between companies with higher financial risks and normal companies. The research conclusion shows that the application of the catboost algorithm in corporate financial risk early warning has high prediction accuracy and can provide effective support for the financial decision-making of enterprises.

关键词： catboost algorithm

来源：评论

学校读者我要写书评

暂无评论

In-process belt-image-based material removal rate monitoring for abrasive belt grinding using catboost algorithm

引用

INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2022年第7-8期123卷 2575-2591页

作者： Wang, Yuxiang Huang, Xiaokang Ren, Xukai Chai, Ze Chen, Xiaoqi Shanghai Jiao Tong Univ Sch Mat Sci & Engn Shanghai Key Lab Mat Laser Proc & Modificat Shanghai 200240 Peoples R China Shaoxing Key Lab Special Equipment Intelligent Te Shaoxing 312071 Peoples R China Swinburne Univ Technol Sch Engn Hawthorn Vic 3122 Australia

A reliable material removal rate (MRR) prediction method significantly optimizes the grinding surface quality and improves the processing efficiency for robotic abrasive belt grinding. Using worn-belt image features to predict MRR is a direct and reliable method;however, this method is rarely reported at present. This paper proposes an MRR prediction method for Inconel 718 grinding based on the abrasive belt image analysis and categorical boosting (catboost) algorithm. During belt grinding, four wear types of abrasive belts, namely fracture, adhesion, rubbing wear, and fall-off, are identified and analyzed. Under various grinding parameters, the experimental MRR rapidly decreases at first, then in a gradual manner. For an effective evaluation of belt wear severity, cutting grain area ratio, color moments, and texture features are extracted from belt images. MRR and abrasive belt image features are strongly correlated after normalization. All image features are taken into account for MRR prediction model training. Verification experiments indicate that the predicted data is in good agreement with the experimental data. The maximum absolute error, mean absolute error, root mean square error, and determination coefficient of the MRR prediction model are 0.17 mu m, 0.4 mu m, 0.2 mu m, and 99.42%, respectively, which are superior to those of other popular machine learning algorithms. In this study, we present a comprehensive understanding of the relationship between MRR and abrasive belt characteristics, as well as demonstrate the feasibility of accurately predicting MRR using the catboost algorithm.

关键词： Robotic abrasive belt grinding Inconel 718 Material removal rate Image feature catboost algorithm

来源：评论

学校读者我要写书评

暂无评论

Housing Rental Information Management and Prediction System Based on catboost algorithm - a Case Study of Halifax Region

Housing Rental Information Management and Prediction System ...

引用

International Joint Conference on Rough Sets (IJCRS)

作者： Shao, Shuangrun Zhao, Bingxi Cui, Xiangen Dai, Yihong Bao, Beining Beijing Jiaotong Univ Sch Econ & Management Beijing 100044 Peoples R China Beijing Jiaotong Univ Sch Elect & Informat Engn Beijing 100044 Peoples R China

ISBN: (纸本)9783031656675;9783031656682

Considering the growing demands for efficient information retrieval from house rental markets by non-professional users, we develop a comprehensive framework for house information management, visualization, and prediction based on the catboost algorithm. We aim to promote the digital transformation of house rental market management and drive innovation in management methods. The conception and ideas of the Housing Rental Information Management and Prediction System are initially proposed, with subsequent application in Halifax, Canada. Integrating the Tableau server, database, and prediction model, we build a seamless web system to harmonize management, visualization, and prediction functionalities for rental house data. The details and effects of the application of the catboost algorithm within this system are emphasized, highlighting its precision, adaptability, and business viability in forecasting the house rental market.

关键词： catboost algorithm House Price Prediction Information Management and Prediction System

来源：评论

学校读者我要写书评

暂无评论

Assessing seismic reliability & feature importance of steel jacket type offshore platforms using catboost algorithm

引用

SHIPS AND OFFSHORE STRUCTURES 2024年

作者： Shokrgozar, Hamed Rahman Rashid, Ali Univ Mohaghegh Ardabili Fac Engn POB *** Daneshgah St Ardebil Iran Univ Mohaghegh Ardabili Earthquake Engn Ardebil Iran

Predicting the vulnerability and failure probability of steel jacket-type offshore platforms (SJTOPs) during earthquakes is challenging due to uncertainties in structural, soil and loading parameters. This study used machine learning, specifically the catboost algorithm, to assess the seismic reliability of SJTOPs by analysing the feature importance of 17 variables related to the superstructure, pile and soil. The study simulated 150 models considering these uncertainties using the Latin Hypercube method and conducted nonlinear time history analyses in OpenSees for artificial earthquakes at two seismic hazard levels. The results indicated that leg diameter is the most critical superstructure parameter, while shear wave velocity and the shear modulus to maximum shear modulus ratio significantly influence the soil's impact on structural response. The study found that SJTOPs have a higher failure probability in the serviceability limit state than in the ultimate limit state, highlighting the importance of accurate seismic assessments for offshore platforms.

关键词： Feature importance seismic reliability steel jacket-type offshore platforms catboost algorithm Latin HyperCub sampling

来源：评论

学校读者我要写书评

暂无评论

Price Prediction Model of Building Materials based on catboost algorithm

International Core Journal of Engineering

引用

International Core Journal of Engineering 2023年第5期9卷 298-308页

作者： Jialin Wang

The price prediction of building materials is an important issue for the construction industry and related industries. At the same time, the price fluctuation of building materials has an important impact on the result of the project cost. catboost algorithm is a gradient lifting tree algorithm, with high efficiency and accuracy, suitable for various types of data. In this paper, catboost algorithm is used to establish the price prediction model of building materials. By collecting the data of Φ16‐25mm, HRB400 type rebar construction material price and related influencing factors, data cleaning and feature engineering are carried out, the data is divided into training set and test set, and catboost algorithm is used to train and optimize the model. The experimental results show that catboost algorithm has high accuracy and efficiency in predicting the price of building materials.

关键词： Price of Building Materials catboost algorithm Price Prediction

来源：评论

学校读者我要写书评

暂无评论

Building gender-specific sexually transmitted infection risk prediction models using catboost algorithm and NHANES data

引用

BMC MEDICAL INFORMATICS AND DECISION MAKING 2024年第1期24卷 1页

作者： Hu, Mengjie Peng, Han Zhang, Xuan Wang, Lefeng Ren, Jingjing Zhejiang Univ Sch Med Affiliated Hosp 1 Dept Gen Practice Hangzhou 310003 Peoples R China Hangzhou Med Coll Zhejiang Prov Peoples Hosp Affiliated Peoples Hosp Clin Res Inst Hangzhou Peoples R China Zhejiang Univ Sch Med Affiliated Hosp 1 Dept Cardiol Hangzhou 310003 Peoples R China Zhejiang Univ Coll Med Affiliated Hosp 1 Kidney Dis Ctr Hangzhou 310003 Peoples R China

Background and aimsSexually transmitted infections (STIs) are a significant global public health challenge due to their high incidence rate and potential for severe consequences when early intervention is neglected. Research shows an upward trend in absolute cases and DALY numbers of STIs, with syphilis, chlamydia, trichomoniasis, and genital herpes exhibiting an increasing trend in age-standardized rate (ASR) from 2010 to 2019. Machine learning (ML) presents significant advantages in disease prediction, with several studies exploring its potential for STI prediction. The objective of this study is to build males-based and females-based STI risk prediction models based on the catboost algorithm using data from the National Health and Nutrition Examination Survey (NHANES) for training and validation, with sub-group analysis performed on each STI. The female sub-group also includes human papilloma virus (HPV) *** study utilized data from the National Health and Nutrition Examination Survey (NHANES) program to build males-based and females-based STI risk prediction models using the catboost algorithm. Data was collected from 12,053 participants aged 18 to 59 years old, with general demographic characteristics and sexual behavior questionnaire responses included as features. The Adaptive Synthetic Sampling Approach (ADASYN) algorithm was used to address data imbalance, and 15 machine learning algorithms were evaluated before ultimately selecting the catboost algorithm. The SHAP method was employed to enhance interpretability by identifying feature importance in the model's STIs risk *** catboost classifier achieved AUC values of 0.9995, 0.9948, 0.9923, and 0.9996 and 0.9769 for predicting chlamydia, genital herpes, genital warts, gonorrhea, and overall STIs infections among males. The catboost classifier achieved AUC values of 0.9971, 0.972, 0.9765, 1, 0.9485 and 0.8819 for predicting chlamydia, genital herpes, genital warts, gonorrhea

关键词： Sexually transmitted infections catboost algorithm NHANES data SHAP algorithm

来源：评论

学校读者我要写书评

暂无评论

Mechanical Characteristic Fault Diagnosis of High Voltage Circuit Breaker Based on Multi-Signal Fusion and catboost

引用

IETE JOURNAL OF RESEARCH 2025年

作者： Dong, Huajun Hu, Shixin Li, Jinjin Li, DongHeng Ku, Zhaoyu Dalian Jiaotong Univ Sch Mech Engn CO-116000 Dalian Peoples R China Dalian Jiaotong Univ Sch Automat & Elect Engn CO-116000 Dalian Peoples R China

The mechanical characteristics of the high-voltage circuit breaker (HVCB) determine whether it can operate smoothly. In order to efficiently and accurately identify the mechanical state of HVCBs, this paper proposes a fault identification method for the mechanical characteristics of HVCBs based on multi-signal fusion and the optimization of the catboost algorithm by the Firefly algorithm. Firstly, the closing coil current, the conversion time of the two contacts in the arc extinguishing chamber and the moving contact stroke are collected and transmitted to the oscilloscope during the closing process of HVCBs. Then, using wavelet function to decompose the current, extracting current, displacement, and time information and integrating the extracted information from multiple sources. Next, using Principal Component Analysis to construct a fault diagnosis dataset. Finally, train the dataset using catboost. The training results show: the method improves the efficiency of feature extraction in the current signal by segmenting the signal source, improves the diagnostic accuracy by 0.4%, and reduces the training mean square error by 0.0079 and 0.064s. The method proposed in this paper can identify the mechanical characteristics of circuit breakers, and provide a new idea for online diagnosis and fault warning of high-voltage circuit breakers.

关键词： catboost algorithm fault diagnosis HVCBs mechanical characteristic multi-signal fusion

来源：评论

学校读者我要写书评

暂无评论

Hyper tuning hybrid MLP-catboost classifier using Mayfly optimization for detection of heart disease

引用

HEALTH SERVICES AND OUTCOMES RESEARCH METHODOLOGY 2025年 1-23页

作者： MahaLakshmi, N. Venkata Rout, Ranjeet Kumar Natl Inst Technol Hazratbal Srinagar 190006 Jammu & Kashmir India DR BR Ambedkar Natl Inst Technol Jalandhar Dept IT Jalandhar 144001 Punjab India

Diagnosing heart disease is considered a difficult task as it provides a digital estimation of the seriousness of the disease. As a result, the quickest treatment can be done. So, heart diagnosis has attracted a lot more attention in the medical industry throughout the globe, and along with excellence in efficacy, the optimization algorithm plays a crucial role in the detection of heart disease. Here, to predict the cardiac illness an improvised catboost algorithm and Multi-layer Perceptron classifier are used. Also, proper hyperparameter tweaking is needed for the successful implementation of the classifier. In order to optimize the hybrid model's hyperparameters, the Mayfly optimization algorithm is deployed for effective hyperparameter optimization. In order to increase prediction accuracy, the Harris-Hawks optimization technique is used to choose the essential features from the dataset. Z-Alizadeh Sani and Cleveland heart disease datasets are utilized to detect heart disease. Also, it is compared with the existing models. To validate the efficiency of a model, six various measures are used: precision, accuracy, recall, the F- 1 measure, specificity, and loss. Here, when compared to the previous studies, the proposed model yields better performance, i.e., 98.7% accuracy with Cleveland and 99.2% with Alizadeh Sani Datasets.

关键词： Machine learning Hyper-parameters tuning Optimization algorithm Mayfly optimization algorithm Multi-layer perceptron Classification catboost algorithm

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：