Fuzzy deep learning for modeling uncertainty in character recognition using EEG signals

Latifi, Farzaneh; Hosseini, Rahil; Sharifi, Arash

doi:10.1016/j.asoc.2024.111575

Cited by 2 publications

(1 citation statement)

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“…Integrating fuzzy systems with deep learning techniques can lead to significant improvements in this area. Fuzzy systems, with their ability to handle uncertainty and produce decisions based on rules that can simulate human reasoning, can optimize the classification of signal features in the presence of ambiguous labels or highly variable signals that might confuse conventional deep learning models [114][115][116][117].…”

Section: Machine Learning Basics: Unlocking Ai's Core Principlesmentioning

confidence: 99%

EEG Data Analysis Techniques for Precision Removal and Enhanced Alzheimer’s Diagnosis: Focusing on Fuzzy and Intuitionistic Fuzzy Logic Techniques

Versaci,

La Foresta

2024

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Effective management of EEG artifacts is pivotal for accurate neurological diagnostics, particularly in detecting early stages of Alzheimer’s disease. This review delves into the cutting-edge domain of fuzzy logic techniques, emphasizing intuitionistic fuzzy systems, which offer refined handling of uncertainties inherent in EEG data. These methods not only enhance artifact identification and removal but also integrate seamlessly with other AI technologies to push the boundaries of EEG analysis. By exploring a range of approaches from standard protocols to advanced machine learning models, this paper provides a comprehensive overview of current strategies and emerging technologies in EEG artifact management. Notably, the fusion of fuzzy logic with neural network models illustrates significant advancements in distinguishing between genuine neurological activity and noise. This synthesis of technologies not only improves diagnostic accuracy but also enriches the toolset available to researchers and clinicians alike, facilitating earlier and more precise identification of neurodegenerative diseases. The review ultimately underscores the transformative potential of integrating diverse computational techniques, setting a new standard in EEG analysis and paving the way for future innovations in medical diagnostics.

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Section: Machine Learning Basics: Unlocking Ai's Core Principlesmentioning

confidence: 99%