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Accurate Identification and Classification of User Needs: Based on the Combination of BP Neural Network and Kano
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2 of the 43 references listed on this page are in the Retraction Watch database as retracted (export 2026-11-11): 10.1007/s00500-023-08437-w (notice 2024-08-27), 10.1155/2022/7123079 (notice 2023-10-04).
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dataset doi:10.7910/DVN/WT8VOA (CC BY 4.0).
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inferredcites →https://doi.org/10.1007/s00500-023-08437-wopenalex-extractedRETRACTED ARTICLE: Transparency in healthcare and e-commerce: detecting online fake reviews using a dense neural network model with relevance mapping
inferredcites →https://doi.org/10.1016/j.cej.2020.127998openalex-extractedMolecular image-convolutional neural network (CNN) assisted QSAR models for predicting contaminant reactivity toward OH radicals: Transfer learning, data augmentation and model interpretation
inferredcites →https://doi.org/10.1016/j.eswa.2011.11.026openalex-extractedAccounting for dynamics in attribute-importance and for competitor performance to enhance reliability of BPNN-based importance–performance analysis
inferredcites →https://doi.org/10.1016/j.scs.2020.102679openalex-extractedHybrid ensemble intelligent model based on wavelet transform, swarm intelligence and artificial neural network for electricity demand forecasting
inferredcites →https://doi.org/10.3390/app10020431openalex-extractedInnovative Approaches in Sports Science—Lexicon-Based Sentiment Analysis as a Tool to Analyze Sports-Related Twitter Communication
inferredcites →https://doi.org/10.3390/info12110486openalex-extractedAnalysis of Unsatisfying User Experiences and Unmet Psychological Needs for Virtual Reality Exergames Using Deep Learning Approach
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