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共 1144 条记录，以下是261-270 订阅

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Research on User Demand mining and Repurchase Behavior Prediction Algorithm Selection Based on Big data

Research on User Demand Mining and Repurchase Behavior Predi...

引用

Electrical Engineering, Big data and Algorithms (EEBDA), IEEE international conference on

作者： Tingwei Zhou Shanghai University Shanghai China

Based on the text data of authoritative skin care community, this study analyzes the user's needs and summarizes six topics on the premise that it has the option of repurchase intention. We need to calculate the emotional scores of six topics of each text data and contact the repurchase intention, thus constructing a feature data set, and then exploring which machine learning method is used to predict the repurchase behavior of online community review texts, which is relatively more accurate and reliable.

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Research on Hybrid Remaining Usable Life Prediction Method Based on Battery Aging Model and data-Driven 3rd

Research on Hybrid Remaining Usable Life Prediction Method B...

引用

3rd international conference on machine learning, Cloud Computing and Intelligent mining, MLCCIM 2024

作者： Zhang, Yu Xu, Zhenyu Li, Dongqi Han, Shunjie Changchun University of Technology Changchun130012 China

ISBN: (纸本)9789819624676

Reliable prediction of the remaining useful life (RUL) of the battery is capable of providing a critical reference decision for users to use and replace batteries. Although there are many methods for battery RUL prediction, these methods suffer from insufficient training, ignoring local regeneration of capacity degradation curves, complex structure and a wide range of optimization seeking parameters, so this paper proposes a hybrid battery RUL prediction method. In the data processing stage, adaptive noise complete ensemble empirical modal decomposition (CEEMDAN) decomposes the battery capacity sequence, then calculates the fuzzy entropy value of each component and reconstructs the components with similar entropy values. In the model training stage, two data and model class methods, particle filtering to update the battery aging model parameters and Gaussian process regression (GPR) to fit the reconstructed components, are used respectively to further learn the long-term and local fluctuation parts of the battery capacity series. In the prediction stage, the prediction results of the two components are combined to obtain the predicted value of the future capacity of the battery and the uncertainty quantification of its RUL. Finally, the proposed hybrid model is validated on a battery dataset at different temperatures and experimental conditions, as a result, the RUL prediction method is shown to possess favorable prediction accuracy, good stability, and reliable uncertainty management capability. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Prediction models

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Combining the Clinical and Operational Perspectives in Heterogeneous Treatment Effect Inference in Healthcare Processes 3rd

Combining the Clinical and Operational Perspectives in Heter...

引用

3rd international conference on Process mining (ICPM)

作者： Verboven, Sam Martin, Niels Vrije Univ Brussel Data Analyt Lab Pleinlaan 2 B-1050 Brussels Belgium Hasselt Univ Res Grp Business Informat Martelarenlaan 42 B-3500 Hasselt Belgium Res Fdn Flanders FWO Egmontstr 5 B-1000 Brussels Belgium

ISBN: (纸本)9783030985813;9783030985806

Recent developments in causal machine learning open perspectives for new approaches that support decision-making in healthcare processes using causal models. In particular, Heterogeneous Treatment Effect (HTE) inference enables the estimation of causal treatment effects for individual cases, offering great potential in a process mining context. At the same time, HTE literature typically focuses on clinical outcome measures, disregarding process efficiency. This paper shows the potential of jointly considering the clinical and operational effects of treatments in the context of healthcare processes. Moreover, we present a simple pipeline that makes existing HTE machine learning techniques directly applicable to event logs. Besides these conceptual contributions, a proof-of-concept application starting from the publicly available sepsis event log is outlined, forming the basis for a critical reflection regarding HTE estimation in a process mining context.

关键词： Heterogeneous Treatment Effect Process mining machine learning Event Log

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Based on the Application of machine learning Algorithms in Business data Analysis

Based on the Application of Machine Learning Algorithms in B...

引用

Smart Generation Computing, Communication and Networking (SMART GENCON), international conference on

作者： Yiwen Zhang The University of Auckland Auckland CBD Auckland

With the increasing complexity of business environment, the importance of data analysis in business decision-making has become increasingly prominent. As a powerful data analysis tool, machine learning algorithm has been widely used in the field of business data analysis. This paper will introduce the application of machine learning algorithm in business data analysis, including classification, clustering, prediction and association rule mining.

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Ecological Vulnerability Assessment of Helan Mountain in Ningxia Based on SPR Model 3rd

Ecological Vulnerability Assessment of Helan Mountain in Nin...

引用

3rd international conference on machine learning, Cloud Computing and Intelligent mining, MLCCIM 2024

作者： He, Yuanrong Chen, Yuhang Song, Yongfei Zhang, Liheng Big Data Institute of Digital Natural Disaster Monitoring in Fujian Xiamen University of Technology Xiamen361024 China Ningxia Survey and Monitor Institute of Land and Resources Yinchuan China

ISBN: (纸本)9789819624676

In order to explore the ecological vulnerability of Helan Mountain in the context of the ecological protection project, this paper, based on the SRP model, selects 15 evaluation indicators to construct an ecological vulnerability evaluation system for the Helan Mountain, and utilizes the fuzzy hierarchical analysis method to reveal the ecological vulnerability of the study area in 2021 from the distribution of spatial characteristics, different land types, and the relationship between key ecological restoration areas and vulnerability in three dimensions. The results showed that (1) the overall spatial distribution of ecological vulnerability in the study area is dominated by slight and mild vulnerability in the southwest and severe vulnerability in the northeast, and the hinterland of the more developed cities is dominated by moderate and severe vulnerability due to the impacts of industrialization and urbanization as well as the population density at the county scale. (2) Ecological vulnerability is significantly affected by the type of land use, with the lowest vulnerability in paddy fields and forested land, and relatively higher in urban land and rocky land. (3) The analysis of 169 key restoration areas in Helan Mountain of Ningxia showed that 140 of them were severely fragile and 21 were extremely fragile, including Rujigou mine area where coal fire spontaneous combustion was serious. The research can provide data support and decision support for ecological protection and coal fire rectification of Helan Mountain in Ningxia Province. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Forest ecology

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Investigation on Smart Campus Management Platform Based on Digital Twin 3

Investigation on Smart Campus Management Platform Based on D...

引用

3rd international conference on machine learning and Big data Analytics for IoT Security and Privacy, SPIoT2023

作者： Hu, Pan DianChi College Yunnan University Yunnan Kunming China

With the improvement of digitalization and the development of information technology, intelligent campus management has become an important research direction in the current education field. Based on digital twin technology, this article proposed a design scheme for a smart campus management platform to address the current problems in campus management. This could manage and serve students, teachers, parents, and other aspects, and achieve the mining and analysis of school big data. Through the implementation and application of the platform, the efficiency and accuracy of campus management could be improved, and the modernization of education could be further promoted. The smart campus management platform of digital twin utilized digital twin technology to digitize the campus environment, equipment, students, teaching resources, and other information of the school, thus achieving three-dimensional visualization, intelligent analysis, and feedback. The experimental results showed that the security of the smart campus management platform designed in this article exceeded 90% and performed well in system testing. © 2023 Elsevier B.V.. All rights reserved.

关键词： Big data

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Application of Big data in the Research of Intelligent Information Management System

Application of Big Data in the Research of Intelligent Infor...

引用

Advancement in Technology (ICONAT), international conference for

作者： Qianqian Sun Kunlun Xue Huangdao District People’s Hospital Qingdao Shandong China Qingdao Municipal Public Security Bureau Huangdao Branch network police Brigade Shandong China

ISBN: (数字)9798350354171

ISBN: (纸本)9798350354188

data analysis and mining play an important role in the research of intelligent information management system, but there is a problem of inaccurate information management. Traditional machine learning cannot solve the information management problem in the research of intelligent information management system, and the effect is not satisfactory. Therefore, this paper proposes the application of intelligent information management system based on big data, and the application of intelligent information management system research an analysis was carried out. Firstly, The key is your guiding theory, es, to reduce the interference factors in data analysis and mining. Then, the dataset theory is used to form a big data analysis and mining scheme, and the data analysis and mining results are comprehensively analyzed. The MATLAB simulation results show that under certain evaluation criteria, big data is better than traditional machine learning in terms of data analysis and mining accuracy, data analysis and mining influencing factor time.

关键词： data analysis Accuracy Law enforcement Simulation machine learning Interference Big data Information management data mining MATLAB

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Stockwell Transform and data mining based Fault Diagnosis Method to protect Microgrid 3

Stockwell Transform and Data Mining based Fault Diagnosis Me...

引用

3rd international conference for Emerging Technology, INCET 2022

作者： Kumar Joga, S Ramana Sinha, Pampa Maharana, Manoj Kumar Jena, Chitralekha Mishra, Alok Roy, Ashutosh Kiit Deemed to Be University School of Electrical Engineering Bhubaneswar India

ISBN: (纸本)9781665494991

Increase in industrial and residential loads gives arise to the need of local power generation. The concept of local power generation to local loads in a small area made a new local power grid known as Microgrid. The monitoring and control of Microgrid is the important concern of electrical engineers and power engineering researchers. Current fluctuations are common in Microgrid due to various factors. In literature many methods are there to diagnose the fault occur in Microgrid. machine learning techniques are one of the future method to detect and island the fault in Microgrid. Signal Processing techniques helps to analyze the signal condition for further diagnosis. combination of these two methods for fault diagnosis in Microgrid yields better result compared to other previous methods that present in the literature. In this paper, a Microgrid consisting of PV Distributed Energy Resources (DER) and Wind System with local loads is simulated and all the simulations are carried out in MATLAB Simulink. © 2022 IEEE.

关键词： Failure analysis

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Length of Stay Prediction for Diabetes Mellitus Patients on Indonesia Health Insurance data using Logistic Regression and Process mining 3

Length of Stay Prediction for Diabetes Mellitus Patients on ...

引用

3rd international conference on Intelligent Cybernetics Technology and Applications, ICICyTA 2023

作者： Cahyanti, Fitri Eka Kurniati, Angelina Prima Alharbi, Amirah Adiwijaya Telkom University School of Computing Bandung Indonesia Umm Al-Qura University Computer Science Department Makkah Saudi Arabia

ISBN: (纸本)9798350394559

Diabetes Mellitus has a substantial impact on Indonesians, affecting 88.51% of those aged 44-94, as revealed by the Indonesia Health Insurance program. The urgency of this health concern is underscored by the international Diabetes Federations alarming statistic: one person succumbs to Diabetes Mellitus every five seconds, demanding immediate attention to diagnosis and risk reduction. This study focuses on predicting the Length of Stay (LOS) for Diabetes Mellitus patients, utilizing Logistic Regression and various machine learning models, particularly Random Forest. Concurrently, Process mining, encompassing Process Discovery and Conformance Checking, elevates model quality. Key factors influencing patient LOS, including the healthcare facilitys location, patient age, and arrival time, are unveiled. Logistic Regression exhibits commendable accuracy at 0.76 with rapid prediction times of 0.00082 seconds, while Random Forest excels with an impressive accuracy of 0.88 and a prediction time of 0.07 seconds. Specific medical procedures, such as those involving 'Diabetes and mild Nutritional/Metabolic Disorders,' reveal prolonged sojourn times, averaging 41.2 days. Best conformance-checking results emerge with a dependency threshold of 0.6 and a loop-two threshold of 32, yielding a Precision of 0.97, Trace Fitness of 0.66, and Generalization of 0.60. The finding of this study underscores the pivotal role of model selection and class distribution in LOS prediction. Despite Random Forest superior performance, Logistic Regression demonstrates notable accuracy and efficiency. Analysis of sojourn times underscores the importance of efficient healthcare services, particularly in cases involving 'Diabetes and Mild Nutritional/Metabolic Disorders.' Adjusting threshold values significantly impacts model accuracy, with lower threshold values potentially leading to overfitting. © 2023 IEEE.

关键词： Health insurance

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Research on fast extraction algorithm of big data information based on machine learning 22

Research on fast extraction algorithm of big data informatio...

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3rd Asia-Pacific conference on Image Processing, Electronics and Computers, IPEC 2022

作者： Wu, Wenchen Yang, Xuliang Guangzhou Institute of Applied Science and Technology Guangdong Guangzhou511300 China Dongguan City College Guangdong Dongguan523000 China

ISBN: (纸本)9781450395786

With the development of the information industry, people are faced with an increasing amount of data. In order to obtain the intrinsic relationship and implicit information from these large-scale data, data mining as an important method has been paid more and more attention by people, and cluster analysis is an important research direction of data mining. In the era of big data, massive multi-modal data exists widely. How to mine the huge hidden value of data through complementary learning between modal data is the main problem of big data research at this stage, and it is also the task of big data and traditional data learning. Mixed attribute datasets are the most common type of datasets in the real world, especially in commercial financial databases, but there are very few clustering algorithms suitable for such datasets. The method used in this paper is machine learning, which enables computers to simulate human learning behavior, automatically acquire knowledge and skills through learning, continuously improve performance, and achieve self-improvement. © 2022 ACM.

关键词： Big data

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