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共 4771 条记录，以下是281-290 订阅

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Association analysis of ordinal traits in generalized partial linear cumulative logistic models 4

Association analysis of ordinal traits in generalized partia...

引用

4th International conference on Biomedicine and Bioinformatics Engineering, ICBBE 2024

作者： Zhou, Yuhong Li, Xuewei Xie, Wanqiu School of Mathematical Sciences Heilongjiang University Harbin150080 China

ISBN: (纸本)9781510682443

The emergence of advanced gene sequencing technologies has transformed the study of structural and genetic variations within the human genome. This has spurred increased research into the genetic underpinnings of complex diseases. While medical research often utilizes ordinal response variables to reflect stages or severity of diseases, most existing methods focus on binary or continuous traits, neglecting vital ordinal data. To address this gap, this article introduces the Partial Linear Cumulative Logistic (PLCL) method within a mixed model framework, designed to accurately process ordinal traits and explore their genetic correlations with diseases. The PLCL method employs B-spline functions for modeling, uses Sieve maximum likelihood estimation for regression coefficients, and determines P-values via a likelihood ratio test. An aggregated Cauchy association test then combines these P-values for comprehensive analysis. Simulation results affirm PLCL's robustness, efficiency in P-value computation without permutations, strict Type I error control, and superior performance compared to traditional methods. © 2024 SPIE.

关键词： Gene encoding

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Resilience Assessment of Distributed Distribution Networks Taking into Account Sudden Natural Conditions 25

Resilience Assessment of Distributed Distribution Networks T...

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25th IEEE China conference on System Simulation Technology and its Application, CCSSTA 2024

作者： Song, Huayu Kong, Xiangyu Liu, Jiancun Song, Xudong Yang, Wenlong Liu, Hongbin Tianjin University of Technology Tianjin Key Laboratory of New Energy Power Conversion Transmission and Intelligent Control Tianjin China Tianjin Universityy Key Laboratory of Smart Grid Ministry of Education Tianjin China Electric Power Scientific Research Institute of Guangdong Power Grid Co. Guangzhou China

ISBN: (纸本)9798350366600

Understanding and improving the reliability of distribution grids under extreme weather conditions in the context of global climate change is important for ensuring stable socio-economic operation and improving the quality of life of the public. The current global assessment of distribution grid reliability under extreme weather conditions faces challenges such as insufficient data, modeling limitations, insufficient consideration of multidimensional impacts, technical resource constraints, and global change uncertainty. Through this paper, we hope to provide new perspectives and ideas for power system researchers and practitioners to jointly explore effective strategies for distribution network reliability and resilience under extreme weather. This study enhances the resilience assessment of distribution networks. It takes into account the generation and consumption dynamics of the network, the unpredictability of power supply and demand, as well as the impact of network topology and equipment on its reliability, and utilizes the Markov process to perform comprehensive reliability analysis of the network. In terms of assessment techniques, the Monte Carlo simulation method simulates the stochastic nature of severe weather, including its severity, timing, and duration, while quantifying its impact on the network through fault modeling. In terms of assessment metrics, the network is divided into two phases in order to establish a precise and clear system of network resilience assessment metrics. Through literature review, modeling analysis, and actual case studies, the current status of distributed generation application in extreme weather is explored, its impact on distribution network reliability is assessed, and future research directions and practical suggestions are proposed. © 2024 IEEE.

关键词： Power distribution networks

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Measuring the Behavioral Intention Toward the Implementation of Super Artificial Intelligence (Super-AI) in Healthcare Sector: An Empirical analysis with Structural Equation modeling (SEM) 8th

Measuring the Behavioral Intention Toward the Implementation...

引用

8th International conference on ICT for Sustainable Development, ICT4SD 2023

作者： Dhara, Soumya Kanti Giri, Arunangshu Santra, Adrinil Chakrabarty, Dipanwita Haldia Institute of Technology West Bengal Haldia721657 India

ISBN: (纸本)9789819949311

The rationale of the study is to measure the behavioral intention toward the implementation of super artificial intelligence in healthcare sector—a model designed with the conglomeration of ‘technology acceptance model (TAM)’ and ‘theory of planned behavior (TPB)’ with their respective theoretical constructs. The authors intend to examine the effect of the constructs viz. ‘perceived usefulness’ and ‘perceived ease of use’ on ‘attitude toward use’ which further influences ‘behavioral intention toward use’. The study also examines the influence of ‘subjective norms’ on ‘behavioral intention toward use’ and the effect of moderating construct ‘perceived behavioral control’ to gauge the influence of ‘attitude toward use’ on ‘behavioral intention’. To test the proposed hypothesis, data were collected through structured questionnaire from 285 respondents. Exploratory factor analysis and structural equation modeling using AMOS were conducted for the establishment of the hypothesized research model. Moderation analysis was conducted through SPSS process macros. The result proposes the positive significant influence of all the factors on behavioral intention toward use with the exception of subjective norms that have negative influence on behavioral intention. The moderation effect of perceived behavioral control stated in this research is also justified. This paper contributes to test the behavioral intention toward the implementation of Super AI, an emerging field of computer science and engineering in healthcare sector and no such existing literature of previous research conducted has been found to provide any secondary data. Hence, the analysis is based on primary data collected, which justifies originality of the study. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： Artificial intelligence

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analysis of the process of Public Opinion Dissemination Based on the SCT Model and Model Improvement —Take the topic of "Small Town Exam-oriented Students" on Weibo as an example 7th

Analysis of the Process of Public Opinion Dissemination Base...

引用

7th International conference on Information Science, Communication and Computing, ISCC 2023

作者： Zhang, Xuexiao Heng, Wanshun Lei, Ming Xu, Li Xu, Siwen College of computer Science and Technology Harbin Engineering University Harbin150001 China Modeling and Emulation in E-Goverment National Engineering Laboratory Harbin China Université Paul Sabatier-Toulouse Toulouse France

ISBN: (纸本)9789819971602

The SCT model, which was created based on the WD model and describes the diffusion of public opinion, is unable to fully capture the evolution of public opinion. We examined the development and affecting aspects of "Small Town Exam-oriented Students" using the preprocessing of crawler data. Based on this fundamental concept, we suggested an enhanced SCT model. Initially, using the interaction amplitude control parameters, we described the impact of official media on public opinion. The value of the parameters determining how attractive a topic is was then the subject of a classified conversation. The experiment shown that, when compared to the conventional SCT model, our improved SCT model has a faster rate of convergence with no differences in the evolving results. The final evolving outcome typically agrees with the microblog content LDA model analysis result. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Social aspects

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Mathematical modeling of the Interest Rates Formation on Consumer Loans in Russia 23rd

Mathematical Modeling of the Interest Rates Formation on ...

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23rd International conference on Mathematical Optimization Theory and Operations Research, MOTOR 2024

作者： Shananin, Alexander A. Trusov, Nikolai V. Federal Research Center "Computer Science and Control" of RAS Vavilova Street 40 Moscow119333 Russia Moscow Institute of Physics and Technology National Research University 9 Institutsky pereulok Moscow Region Dolgoprudny141701 Russia

ISBN: (纸本)9783031627910

We present the model for the formation of interest rates on consumer loans based on an analysis of commercial interests and the logic of behavior of commercial banks. From the position of the commercial banks, the borrowers’ income is a stochastic process described by a geometric Brownian motion. The commercial banks assess the default risk of borrowers. The consumer loan demand is described with the help of representative rational consumer behavior based on Ramsey-type model. The historical data of the interest rates on loans are identified. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Consumer behavior

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A Comprehensive Study on Time Series analysis in Healthcare 14

A Comprehensive Study on Time Series Analysis in Healthcare

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14th International conference on Advances in Computing, control, and Telecommunication Technologies, ACT 2023

作者： Myilvahanan, J. Karthick Nivetha, K. Krishnaveni, A. Mohana Sundaram, N. Santosh, R. New Horizon College of Engineering ISE Bengaluru India Karpagam Academy of Higher Education CSE Coimbatore India

There has been a lot of interest in time series forecasting in recent years. Deep neural networks have shown their effectiveness and accuracy in various industries. It is currently one of the most extensively used machine-learning algorithms for dealing with massive volumes of data due to the reasons stated above. Statistical modeling includes forecasting, which is used for decision-making in various fields. Time-varying variables may be forecasted based on their past values, which is the goal of forecasting. Developing models and techniques for trustworthy forecasting is an important part of the forecasting process. As part of this study, a systematic mapping investigation and a literature review are used. Time-series researchers have relied on ARIMA approaches for decades, notably the autoregressive integrated moving average model. But the need for stationary makes this method somewhat rigid. Forecasting methods have improved and expanded with the introduction of computers, ranging from stochastic models to soft computers. Conventional approaches may not be as accurate as soft computing. In addition, the volume of data that can be analyzed and the efficiency of the process are two of the many benefits of using soft computing. © Grenze Scientific Society, 2023.

关键词： Forecasting

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Next-Generation Artificial Intelligence Solutions for Comprehensive analysis and Improvement of Heat Dissipation in High-Intensity Boiling Environments

Next-Generation Artificial Intelligence Solutions for Compre...

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2024 OPJU International Technology conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Gadiyar, H. Manoj T. Kumar, S Vinod Raj, Vijilius Helena Yadav, Dinesh Kumar Hasoni, Mortada Kareem Sabi Lakhanpal, Sorabh Canara Engineering College Department of Computer Science and Business System India Institute of Aeronautical Engineering Hyderabad Dundigal India New Horizon College of Engineering Department of Applied Sciences Bangalore India Lloyd Institute of Engineering & Technology Knowledge Park II Uttar Pradesh Greater Noida201306 India National University of Science and Technology AlTalb Collage of Nursing Dhi Qar64001 Iraq Lovely Professional University Phagwara India

ISBN: (纸本)9798350373783

This research delves into the realm of AI-driven heat dissipation, offering a suite of algorithms to address the challenges posed by high-intensity boiling environments. The proposed predictive modeling algorithm, rooted in linear regression, provides a systematic and robust approach through 20 meticulously designed steps. This algorithm, along with neural networks, reinforcement learning, generative adversarial networks (GANs), and genetic algorithms, forms a versatile toolbox for thermal management. Linear regression, through its step-by-step predictive modeling process, ensures accurate heat dissipation predictions. Neural networks, employing backpropagation, showcase adaptability and complexity, excelling in real-time adaptation. Reinforcement learning, specifically Q-Learning, introduces dynamic decision-making strategies, vital for navigating complex thermal scenarios. GANs contribute by generating realistic synthetic data, an invaluable asset for simulating high intensity boiling environments. Genetic algorithms, resembling natural selection, prove versatile and scalable in evolving optimal solutions. The research evaluates these algorithms comprehensively, demonstrating the proposed method's superiority in prediction accuracy, energy efficiency, adaptability, and overall performance. The proposed predictive modeling algorithm stands out as a benchmark, showcasing its potential for practical implementation and optimization in diverse scenarios. This comprehensive exploration sets the stage for innovative solutions to the intricate challenges of thermal management, as continuous adaptation and fine-tuning remain pivotal in this dynamic landscape. © 2024 IEEE.

关键词： Generative adversarial networks

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A Context-Based Approach for Real-Time Adaptation Need Detection 20

A Context-Based Approach for Real-Time Adaptation Need Detec...

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20th International conference on Smart Business Technologies, ICSBT 2023

作者： Oukharijane, Jamila Said, Imen Ben Chaâbane, Mohamed Amine Andonoff, Eric Bouaziz, Rafik MIRACL Laboratory University of Sfax Tunisia IRIT University Toulouse 1-Capitole 2 Rue du Doyen Gabriel Marty Toulouse31042 Cedex France

ISBN: (纸本)9789897586675

The increasingly dynamic and changing environment in which business processes are evolving requires companies to adapt them frequently. Thus, we propose in this paper a new context-based approach recommending to analyze well the environmental changes. This approach recommends to structure knowledge required for process adaptation need in accordance with our BPMN4V-Context meta-model, which supports the modeling of the operating environment, using the context along with the versions of the model of each BPMN process and their use conditions. It also advocates (i) a filtering activity to retain only significant context changes in the monitored data, as low-level context parameters, (ii) a reasoning activity to deduce high-level context parameters from filtered low-level ones, enhancing the current situation of running processes and (iii) examining the current situation before its analysis in order to resolve problems related to the used units and synonym values. Finally, the feasibility and applicability of this approach is demonstrated by a case study from the crisis domain and two performance tests. © 2023 ICSBT International conference on Smart Business Technologies. All rights reserved.

关键词： Adaptation Need Detection Context Context analysis Real-Time Monitoring Situation Filtering Situation Reasoning

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Monitoring the Linear Part of the Kernel Semi-Parametric Model Based on EWMA control Charts

Monitoring the Linear Part of the Kernel Semi-Parametric Mod...

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Advanced Algorithms and control Engineering (ICAACE), International conference on

作者： Zimeng Pan Xuemin Zi Tianjin University of Technology and Education China

ISBN: (数字)9798331535087

ISBN: (纸本)9798331535094

This paper focuses on monitoring the coefficient estimates of kernel semi-parametric models using exponentially weighted moving average (EWMA) control charts. The kernel semi-parametric model can adapt to the complexity of the data and is widely used in the fields of survival analysis and biostatistics, etc. Using EWMA control charts to monitor it, we can monitor the stability of the process and detect small drifts in the process. This allows the operator to detect and take appropriate measures to adjust and improve the process during the actual production.

关键词： Adaptation models Biological system modeling process control control charts Production Stability analysis data models Kernel Biomedical monitoring Monitoring

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Multiple Machine Learning Methods with Correlation analysis for Short-Term River Water Quality Prediction 15th

Multiple Machine Learning Methods with Correlation Analysis ...

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15th International conference on Advances in Swarm Intelligence (ICSI)

作者： Chen, Ming Liu, Guanliang Lv, Ting Nanjing Res Inst Ecol & Environm Protect Nanjing Peoples R China Southeast Univ Coll Software Engn Nanjing Peoples R China

ISBN: (纸本)9789819771837;9789819771844

Water quality prediction and early warning are crucial aspects of water quality monitoring and management. These predictions play a pivotal role in river pollution control. In this study, we focus on a typical river watershed in Nanjing, China. We collect and process historical monitoring data from multiple automated water quality stations within the basin. To predict water quality, we employ three time series models: Support Vector Machine (SVM), Random Forest (RF), and Long Short-Term Memory Network (LSTM). The primary water quality parameters exceeding standards in the basin are ammonia nitrogen and total phosphorus. Notably, the predicted values of ammonia nitrogen and total phosphorus exhibit a strong correlation with their historical counterparts, while showing weaker associations with other water indicators. Among the three machine learning models, LSTM demonstrates superior predictive performance for total phosphorus, while RF excels in predicting ammonia nitrogen.

关键词： Water quality prediction Time series prediction Machine learning Neural networks Correlation analysis

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