Lin Wang

ORCID: 0000-0003-0881-9689
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About
Contact & Profiles
Research Areas
  • Energy Load and Power Forecasting
  • Supply Chain and Inventory Management
  • Stock Market Forecasting Methods
  • Scheduling and Optimization Algorithms
  • Metaheuristic Optimization Algorithms Research
  • Neural Networks and Applications
  • Optimization and Mathematical Programming
  • Market Dynamics and Volatility
  • Multi-Criteria Decision Making
  • Technology Adoption and User Behaviour
  • Vehicle Routing Optimization Methods
  • Forecasting Techniques and Applications
  • Grey System Theory Applications
  • Digital Marketing and Social Media
  • Electric Power System Optimization
  • Global trade and economics
  • Solar Radiation and Photovoltaics
  • Advanced Multi-Objective Optimization Algorithms
  • Advanced Manufacturing and Logistics Optimization
  • Diverse Aspects of Tourism Research
  • Evaluation and Optimization Models
  • Customer Service Quality and Loyalty
  • Advanced Algorithms and Applications
  • Innovation and Knowledge Management
  • Optimization and Packing Problems

Huazhong University of Science and Technology
2016-2025

Shanxi Medical University
1992-2024

Shenyang Ligong University
2024

Institute of Atmospheric Physics
2024

Chinese Academy of Sciences
2019-2024

Shanxi Eye Hospital
2024

Donghua University
2021-2024

Shanghai International Studies University
2020-2024

CHN Energy (China)
2024

Bengbu University
2024

A Monte Carlo simulation study was conducted to investigate the effects on structural equation modeling (SEM) fit indexes of sample size, estimation method, and model specification. Based a balanced experimental design, samples were generated from prespecified population covariance matrix fitted models with different degrees misspecification. Ten SEM studied. Two primary conclusions suggested: (a) some appear be noncomparable in terms information they provide about for misspecified (b)...

10.1080/10705519909540119 article EN Structural Equation Modeling A Multidisciplinary Journal 1999-01-01

With big data growth in biomedical and healthcare communities, accurate analysis of medical benefits early disease detection, patient care, community services. However, the accuracy is reduced when quality incomplete. Moreover, different regions exhibit unique characteristics certain regional diseases, which may weaken prediction outbreaks. In this paper, we streamline machine learning algorithms for effective chronic outbreak disease-frequent communities. We experiment modified models over...

10.1109/access.2017.2694446 article EN cc-by-nc-nd IEEE Access 2017-01-01
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