Lecture notes in networks and systems, Год журнала: 2024, Номер unknown, С. 1 - 10
Опубликована: Янв. 1, 2024
Язык: Английский
Lecture notes in networks and systems, Год журнала: 2024, Номер unknown, С. 1 - 10
Опубликована: Янв. 1, 2024
Язык: Английский
Applied Sciences, Год журнала: 2024, Номер 14(19), С. 8884 - 8884
Опубликована: Окт. 2, 2024
This systematic literature review employs the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology to investigate recent applications of explainable AI (XAI) over past three years. From an initial pool 664 articles identified through Web Science database, 512 peer-reviewed journal met inclusion criteria—namely, being recent, high-quality XAI application published in English—and were analyzed detail. Both qualitative quantitative statistical techniques used analyze articles: qualitatively by summarizing characteristics included studies based on predefined codes, quantitatively analysis data. These categorized according their domains, techniques, evaluation methods. Health-related particularly prevalent, with a strong focus cancer diagnosis, COVID-19 management, medical imaging. Other significant areas environmental agricultural industrial optimization, cybersecurity, finance, transportation, entertainment. Additionally, emerging law, education, social care highlight XAI’s expanding impact. The reveals predominant use local explanation methods, SHAP LIME, favored its stability mathematical guarantees. However, critical gap results is identified, as most rely anecdotal evidence or expert opinion rather than robust metrics. underscores urgent need standardized frameworks ensure reliability effectiveness applications. Future research should developing comprehensive standards improving interpretability explanations. advancements are essential addressing diverse demands various domains while ensuring trust transparency systems.
Язык: Английский
Процитировано
4Bioengineering, Год журнала: 2025, Номер 12(2), С. 109 - 109
Опубликована: Янв. 24, 2025
Seizure prediction is a critical challenge in epilepsy management, offering the potential to improve patient outcomes through timely interventions. This study proposes novel framework combining convolutional neural network (CNN) based on EfficientNet-B0 and an ensemble of six Support Vector Machines (SVMs) with voting mechanism for robust seizure prediction. The leverages normalized Short-Time Fourier Transform (STFT) channel correlation features extracted from EEG signals capture both spectral spatial information. methodology was validated CHB-MIT dataset across preictal windows 10, 20, 30 min, achieving accuracies 96.12%, 94.89%, 94.21%, sensitivities 95.21%, 93.98%, 93.55%, respectively. Comparing results state-of-the-art methods, we highlight framework’s robustness adaptability. backbone ensures high accuracy computational efficiency, while SVM enhances reliability by mitigating noise variability data.
Язык: Английский
Процитировано
0Revue Neurologique, Год журнала: 2025, Номер unknown
Опубликована: Март 1, 2025
Язык: Английский
Процитировано
0Communications in computer and information science, Год журнала: 2025, Номер unknown, С. 183 - 194
Опубликована: Янв. 1, 2025
Язык: Английский
Процитировано
0Elsevier eBooks, Год журнала: 2025, Номер unknown, С. 175 - 211
Опубликована: Янв. 1, 2025
Язык: Английский
Процитировано
0Epilepsy Research, Год журнала: 2025, Номер unknown, С. 107593 - 107593
Опубликована: Май 1, 2025
Язык: Английский
Процитировано
0Epiliepsy currents/Epilepsy currents, Год журнала: 2024, Номер unknown
Опубликована: Март 31, 2024
Artificial intelligence, machine learning, and deep learning are increasingly being used in all medical fields including for epilepsy research clinical care. Already there have been resultant cutting-edge applications both the arenas of epileptology. Because is a need to disseminate knowledge about these approaches, how use them, their advantages, potential limitations, goal 2023 Merritt-Putnam Symposium this synopsis review that symposium has present background state art then draw conclusions on current future approaches through following: (1) Initially provide an explanation fundamental principles artificial learning. These presented first section by Dr Wesley Kerr. (2) Provide insights into screening medications neural organoids, general, particular. Sandra Acosta. (3) intelligence can predict response medication treatments. Patrick Kwan. (4) Finally, expanding detection analysis EEG signals intensive care, monitoring unit, intracranial situations, as below Gregory Worrell. The expectation that, coming decade beyond, increasing above will transform care supplement, but not replace, diligent work clinicians researchers.
Язык: Английский
Процитировано
0Lecture notes in networks and systems, Год журнала: 2024, Номер unknown, С. 1 - 10
Опубликована: Янв. 1, 2024
Язык: Английский
Процитировано
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