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- W4387617609 abstract "Abstract This article presents an investment recommender system based on an Adaptive Neuro-Fuzzy Inference System (ANFIS) and pre-trained weights from a Multimodal Neural Network. The system takes input from a web-based questionnaire that collects data on investors' preferences and investment goals. The data is then preprocessed and clustered using ETL tools, JMP, MATLAB, and Python. The ANFIS based recommender system is designed with three inputs and one output and trained using a hybrid approach over three epochs with 188 data pairs and 18 fuzzy rules. The system's performance is evaluated using metrics such as RMSE, accuracy, precision, recall, and F1-score. The system is also designed to incorporate expert feedback and opinions from investors to customize and improve investment recommendations. The article concludes that the proposed ANFIS-based investment recommender system is effective and accurate in generating investment recommendations that meet investors' preferences and goals." @default.
- W4387617609 created "2023-10-14" @default.
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- W4387617609 date "2023-10-13" @default.
- W4387617609 modified "2023-10-15" @default.
- W4387617609 title "Developing an ANFIS-based Investment Recommender System using Multimodal Neural Network Pretraining" @default.
- W4387617609 doi "https://doi.org/10.21203/rs.3.rs-3136619/v1" @default.
- W4387617609 hasPublicationYear "2023" @default.
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