ArticleSensors (Basel, Switzerland)2022
An Improved Entropy-Weighted Topsis Method for Decision-Level Fusion Evaluation System of Multi-Source Data.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 25 citations in OpenAlex.
- Lung cancer multimodal auxiliary diagnosis based on entropy weight decision fusion.Biomedical engineering online · 2026Article
- Development of an entropy-weighted evaluation system for optometric education quality in vocational colleges of Yunnan Province.BMC medical education · 2026Article
- Dynamic evaluation and continuous improvement model of pharmaceutical administration based on DPSIR-TOPSIS.BMC health services research · 2026Article
- Road safety measurement with reliability using an advanced hybrid decision model.Scientific reports · 2025Article
- Class-weighted Dempster-Shafer in dual-level fusion for multimodal fake real estate listings detection.PeerJ. Computer science · 2025Article
- Evaluating and enhancing the service capacity of secondary public hospitals in urban China: a multi-method empirical analysis based on Guangzhou (2019-2023).Frontiers in health services · 2025Article
- Simultaneous Quantitative Determination of Low-Concentration Preservatives and Heavy Metals in Tricholoma Matsutakes Based on SERS and FLU Spectral Data Fusion.Foods (Basel, Switzerland) · 2023Article
- Field validation of different intervention modes for the potential transmission risk of schistosomiasis during post-transmission interruption phase.PLoS neglected tropical diseases · 2023Article
- A procedure for risk assessment of check dam systems: A case study of Wangmaogou watershed.PloS one · 2023Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
Due to the rapid development of industrial internet technology, the traditional manufacturing industry is in urgent need of digital transformation, and one of the key technologies to achieve this is multi-source data fusion. For this problem, this paper proposes an improved entropy-weighted topsis method for a multi-source data fusion evaluation system. It adds a fusion evaluation system based on the decision-level fusion algorithm and proposes a dynamic fusion strategy. The fusion evaluation system effectively solves the problem of data scale inconsistency among multi-source data, which leads to difficulties in fusing models and low fusion accuracy, and obtains optimal fusion results. The paper then verifies the effectiveness of the fusion evaluation system through experiments on the multilayer feature fusion of single-source data and the decision-level fusion of multi-source data, respectively. The results of this paper can be used in intelligent production and assembly plants in the discrete industry and provide the corresponding management and decision support with a certain practical value.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.