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检索条件"任意字段=5th International Conference on Machine Learning and Computer Application, ICMLCA 2024"

共 2900 条记录，以下是41-50 订阅

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Research on Car Insurance Compensation Based on machine learning 24

Research on Car Insurance Compensation Based on Machine Lear...

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5th international conference on computer Science and Management Technology, ICCSMT 2024

作者： Han, Yichen Jinan University-University of Birmingham Joint Institute Jinan University Guangdong Guangzhou China

ISBN: (纸本)9798400709999

In the field of insurance claims, it is important to understand the impact of each variable on the amount of the claim. Accurate prediction of whether a car insurance claim will be paid out and the amount of the claim will be paid out is of great significance to increase the company's profit, optimize the premium pricing, reduce the financial risk of the insurance company, and enhance the competitiveness of the insurance company in the market. In this study, we analyze industry reports, market data and relevant literature to study the key factors affecting the amount of insurance claims, firstly, we construct the 'Insurance Claims Prediction Model' and select five variables that have the greatest impact on the amount of car insurance claims, and then we predict the amount of car insurance claims through the selected variables. By analyzing the intrinsic connection of the data, based on the random forest model, it predicts whether the insurance is paid or not, and selects the variables that affect the insurance compensation amount, and predicts whether the insurance is paid or not;in addition, in order to accurately predict the amount of the insurance compensation, based on the theory of neural network, it constructs a large-data neural network prediction model, and achieves good experimental results, and obtains a low error. this study conducted the preliminary research on the prediction of car insurance compensation amount, which provides an important reference value for the subsequent formulation of the premium amount of car insurance. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Decision trees

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Rigorous Engineering of Collective Adaptive Systems Introduction to the 5^th Track Edition 12th

Rigorous Engineering of Collective Adaptive Systems Introduc...

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12th international Symposium on Leveraging applications of Formal Methods, Verification and Validation

作者： Wirsing, Martin De Nicola, Rocco Jaehnichen, Stefan Tribastone, Mirco Ludwig Maximilians Univ Munchen Munich Germany IMT Sch Adv Studies Lucca Lucca Italy TU Berlin Berlin Germany FZI Forschungszentrum Informat Berlin Berlin Germany

ISBN: (纸本)9783031751066;9783031751073

A collective adaptive system is made up of cooperating entities that can adjust in real time to evolving, open environments and shifting requirements. To ensure such a system meets its intended goals, rigorous engineering must employ suitable methods and tools. this introduction offers a short overview of the 5(th) edition of the track "Rigorous Engineering of Collective Adaptive Systems" and briefly presents the 20 scientific contributions, organised into seven thematic sections. Large Ensembles and Collective Dynamics, Knowledge, Consciousness and Emergence, Automated Reasoning for Better Interaction, Modelling and Engineering Collective Adaptive Systems, Analysing Collective Adaptive Systems.

关键词： adaptive system collective system ensemble software engineering formal method rigorous method machine learning large language model

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application of deep transfer learning on UAV-based aerial images for forest fire detection 5

Application of deep transfer learning on UAV-based aerial im...

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5th international conference on Signal Processing and computer Science, SPCS 2024

作者： Yu, Feng International Education College Beijing University of Chemical Technology Beijing China

ISBN: (数字)9781510686731

ISBN: (纸本)9781510686724

the prompt detection and timely response to forest fires are crucial for effectively protecting the ecological environment. Despite the progress of deep learning fire image recognition models, in UAV aerial image processing, traditional models face challenges such as small fire points, smoke interference, and complex backgrounds, resulting in unsatisfactory detection accuracy. To cope with such problems, this paper proposes a forest fire detection method for UAV aerial photography based on a pre-trained DenseNet201 model, which is trained by using the FLAME dataset and combined with a channel and spatial attention mechanism module (CBAM). this model fire detection accuracy reaches 88.63% and the F-score can reach 90.02%. the model's performance was further validated by visual analysis of the confusion matrix, classification report, ROC curve, and precision-recall curve. the method therefore has a broad application prospect in real forest fire monitoring. © 2025 SPIE.

关键词： Aerial photography

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Can We Trust the machine learning Based Geotechnical Model? 5th

Can We Trust the Machine Learning Based Geotechnical Model?

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5th international conference on Information Technology in Geo-Engineering, ICITG 2024

作者： Hansen, Tom F. Norwegian Geotechnical Institute Sandakerveien 140 Oslo0484 Norway Informatics Institute University of Oslo Blindern Oslo0316 Norway

ISBN: (纸本)9783031765278

the study investigates the interpretability and explainability of machine learning models in geotechnical engineering, focusing on case studies on rock mass quality and rock type predictions from drilling data. By applying permutation feature importance, Shapley values, direct model interpretation, and partial dependency plots, features such as overburden thickness, tunnel width, feeder pressure, and penetration rate were identified as significant influencers on model predictions. these insights ensure the models’ predictions are consistent with geotechnical physical expectations, thus improving their practical applicability. the research underscores the necessity for transparent and interpretable machine learning solutions in geotechnical engineering, enhancing the reliability and application of such models in critical construction operations. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Geotechnical engineering

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Deep learning improvement and optimization of incremental Kernel fuzzy clustering algorithm for financial time series analysis 24

Deep learning improvement and optimization of incremental Ke...

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5th international conference on computer Science and Management Technology, ICCSMT 2024

作者： Yue, Dazhi Business School Xiamen Institute of Technology Fujian Xiamen China

ISBN: (纸本)9798400709999

this study proposes an improved incremental kernel fuzzy clustering algorithm aimed at optimizing financial time series analysis. the algorithm not only extracts advanced features through artificial intelligence deep learning, but also introduces time decay function, which effectively improves the integration and analysis ability of financial time series data. the traditional kernel fuzzy clustering algorithm has limitations in dealing with dynamically changing financial data, and it is difficult to capture the long-term dependence and short-term fluctuation characteristics in time series. To solve this problem, this study introduces an exponential time decay function into the algorithm, which enables the clustering process to give higher weight to recent data while retaining the influence of historical information. the double improved algorithm shows significant advantages in the following aspects: first, through the extraction of advanced features and the introduction of time decay function, the algorithm can more accurately reflect the latest trends of the financial market, and improve the timeliness and accuracy of the clustering results. Secondly, the incremental learning strategy enables the algorithm to process large-scale streaming data efficiently and adapt to the real-time requirements of the financial market. Moreover, the application of kernel method enables the algorithm to deal with nonlinear relationships and better capture complex patterns in financial data. Empirical studies show that the improved algorithm performs well in financial applications such as stock market classification, risk assessment and portfolio optimization. Compared with the traditional method, the new algorithm has significant improvement in clustering quality, computational efficiency and prediction accuracy. this research provides a new methodological perspective for financial time series analysis, which is of great significance for improving the accuracy and efficiency of fina

关键词： Financial markets

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SVM-based malware classification through deep learning method 5

SVM-based malware classification through deep learning metho...

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5th international conference on Signal Processing and computer Science, SPCS 2024

作者： Shi, Jiawei Wang, Wancheng Zhang, Meng Yao, Qinbo Cheng, Shiyuan Zhan, Zhifei College of Artificial Intelligence and Automation Hohai University Nanjing China

ISBN: (数字)9781510686731

ISBN: (纸本)9781510686724

Various daily applications, including image categorization, natural language comprehension, and voice identification, heavily rely on fully connected and convolutional neural networks. When tackling classification problems, conventional deep learning architectures typically employ softmax activation functions to normalize outputs and minimize the model's cross-entropy loss. In this study, we present a novel malware classification model that integrates hybrid support vector machines with neural networks. Notably, this model substitutes the conventional softmax layer in neural networks with a support vector machine, thereby shifting the learning process towards minimizing margin losses instead of cross-entropy losses. this modification enhances the precision of malware classification for both standard machine learning algorithms and several prevalent deep learning models. To validate our proposed model, we employed the Malimg dataset, comprising malware images derived from binary malware samples. Subsequently, we trained an DL-SVM model to assign scores to each distinct malware family. Experimental outcomes revealed that the malware classification model achieved an accuracy of 96.31%, a precision rate of 97.00%, a recall rate of 97.35%, and an F1 score of 97.16%, respectively. © 2025 SPIE.

关键词： Convolutional neural networks

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Fifth international conference on Signal Processing and computer Science, SPCS 2024

Fifth International Conference on Signal Processing and Comp...

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5th international conference on Signal Processing and computer Science, SPCS 2024

ISBN: (纸本)9781510686724

the proceedings contain 88 papers. the topics discussed include: DFCRN: deep-learning-based audio denoising for bird monitoring;emotion classification based on EEG wavelet features and LSTM network;facial expression recognition algorithm based on deep learning;an aircraft recovery method based on ALNS;vehicle classification method based on deep learning;the impact of ephemeral data sharing on e-commerce supply chain product quality and service strategies under channel power change;application of conditional DDPM on the MNIST dataset;interpretability analysis of flight delay prediction based on KernelSHAP;MVDR algorithm based on sample selection strategy;fast video stitching algorithm based on adaptive key frames extraction;and based on structure and texture dual-stream network for ancient mural restoration.

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A Lightweight Feature Fusion and machine learning Method for Effective Home Monitoring and Early Detection of Sleep Apnea 5th

A Lightweight Feature Fusion and Machine Learning Method fo...

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5th international conference on Medical Imaging and computer Aided Diagnosis, MICAD 2024

作者： Sarkar, Subrata Dhar, Debjit Sarkar, Rajib Saha, Sanjoy K. Chakraborti, Tapabrata Computer Science and Engineering Department Jadavpur University Kolkata India Computer Science Department Derozio Memorial College Kolkata India The Alan Turing Institute University College London London United Kingdom

ISBN: (纸本)9789819638628

According to the American National Heart, Lung, and Blood Institute (NHLBI), undiagnosed Sleep Apnea (SA) may raise the risk of high blood pressure, diabetes, heart disease, and stroke. A lot of these complications can be managed effectively or even prevented through early diagnosis of SA, which often goes undetected since it occurs mostly during the natural daily sleep cycle. thus a low cost and non-invasive solution is urgently needed that may be used for home monitoring over longer periods, instead of high cost and obtrusive clinical methods such as polysomnography. Towards this goal, we propose a simple yet effective computational technique that combines feature fusion with machine learning (ML) in an interpretable manner from electrocardiogram (ECG) signals with high fidelity demonstrated through strong experimental results on benchmark public datasets. We have deliberately chosen a non deep learning based ML method that is transparent and fast, because this work is meant to be a proof of concept for future translation into practical application using smart portable ECG monitors, thereby facilitating early detection of SA and improve patient health. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Sleep research

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Ensemble learning with Feature Fusion for Well-Overflow Detection 5th

Ensemble Learning with Feature Fusion for Well-Overflow Dete...

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5th international conference on Neural Computing for Advanced applications (NCAA)

作者： Cui, Ziliang Liu, Li Xiong, Yinzhou Liu, Yinguo Su, Yu Man, Zhimin Wang, Ye Chongqing Univ Posts & Telecommun Chongqing Peoples R China Chongqing Energy Big Data Ctr Co Ltd Chongqing Peoples R China Shandong Zibo Polytech Vocat Sch Chongqing Peoples R China

ISBN: (纸本)9789819770069;9789819770076

Traditional methods for kick prediction rely on experience and basic physical models, but they struggle to maintain high accuracy under complex well conditions and varying geological environments. Moreover, conventional approaches often fail to fully leverage big data resources. therefore, this study aims to improve the accuracy of kick prediction through machine learning models, especially by adopting an innovative feature fusion strategy to optimize the prediction model. We evaluated the comprehensive performance of six different machine learning models: GBM, LightGBM, CatBoost, XGBoost, Random Forest, and AdaBoost. Based on these evaluations, we proposed a feature score weighted fusion method. this method weights the importance scores of features based on the accuracy of each model, selecting the features that have the most significant impact on the prediction results. Experimental results show that the model utilizing the feature fusion strategy performs better on the test set than any single base model. Specifically, the GBM model, optimized through feature fusion, achieved better values in accuracy, F1 score, and F2 score, proving the effectiveness of this feature fusion strategy in enhancing the accuracy of kick predictions during well control operations. Furthermore, this study compared the performance of different models, providing valuable insights for kick prediction in well control operations. this research not only demonstrates the potential of feature fusion strategies in improving the accuracy of kick prediction but also offers new insights for the future application of machine learning technologies in the field of oilfield data analysis.

关键词： machine learning feature fusion kill well overflow

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machine learning Tool for Yield Maximization in Cream Cheese Production 5th

Machine Learning Tool for Yield Maximization in Cream Cheese...

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5th international conference on Innovative Intelligent Industrial Production and Logistics

作者： Parrenin, Loic Dupuis, Ambre Danjou, Christophe Agard, Bruno Lab Intelligence Donnees Montreal PQ Canada Ecole Polytech Montreal Dept Math & Genie Ind Montreal PQ Canada Univ Quebec Trois Rivieres Dept Genie Ind Trois Rivieres PQ Canada

ISBN: (纸本)9783031807596;9783031807602

Artificial intelligence tools and data collection on the shop floor are enhancing flexibility and productivity in industry, addressing labor shortages and skills attrition by leveraging the tacit knowledge of workers. this study focuses on the cream cheese production sector, where operator expertise is essential for controlling the ultrafiltration concentration factor, a critical parameter affecting product moisture content. To ensure continuous and flexible production despite workforce challenges, a machine-learning tool was developed using the CRISP-DM approach to maximize cream cheese yield on a Canadian production line. A decision tree algorithm applied to real production and quality data yielded promising results, with an RMSE of 0.061 and an R.2 of 0.91 when predicting the ultrafiltration concentration factor used by an experienced operator to maximize yield while complying with quality standards. the implementation saw positive operator acceptance due to comprehensive training and an inclusive approach. this research marks a pioneering effort to harness tacit knowledge in the dairy industry for machine parameter control, highlighting data acquisition and quality as key areas for further investigation to enhance tool performance and adaptability.

关键词： machine learning Tacit knowledge Production flexibility Operator expertise Dairy industry

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