Titlebook: Advances in Soft Computing; 23rd Mexican Interna Lourdes Martínez-Villaseñor,Gilberto Ochoa-Ruiz Conference proceedings 2025 The Editor(s)

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From EEG Signal Acquisition and Classification to Mobile Integration: A Comprehensive Framework proposed framework encompasses four core elements: data capture, preprocessing and feature extraction, model configuration, and integration and development. EEG signals are captured and preprocessed using signal filtering and the Common Spatial Pattern method, followed by classification with a Supp

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Leveraging Pre-trained Models for Robust Federated Learning for Kidney Stone Type Recognitionue to exist because of the requirement for huge datasets and legal limitations on data exchange. A solution is provided by Federated Learning (FL), which permits decentralized model training while maintaining data privacy. However, FL models are susceptible to data corruption, which may result in pe

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RESTful API for Intent Recognition Based on RASA purpose of a user’s input. There are several intent recognition solutions available, including Amazon Lex, DialogFlow, and RASA. RASA stands out among these solutions due to its customizable, open source nature and powerful deep learning integration. However, the official availability of RASA to th

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Conference proceedings 20252024, held in Tonantzintla, Mexico in October 21–25, 2024...The 37 full papers presented in these proceedings were carefully reviewed and selected from 141 submissions. The papers presented in these two volumes are organized in the following topical sections:..Part I - Machine Learning; Computer Vis

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https://doi.org/10.1007/978-3-8350-9497-0ting, possible, tax underreporting. The proposed strategy is evaluated on a data set associated with individual income tax statistics of the United States. The results achieved are considered to be useful in decision-making and preventive actions on cases reported as suspicious.

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Tax Underreporting Detection Using an Unsupervised Learning Approachting, possible, tax underreporting. The proposed strategy is evaluated on a data set associated with individual income tax statistics of the United States. The results achieved are considered to be useful in decision-making and preventive actions on cases reported as suspicious.

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