Results 31 to 40 of about 29,455 (191)
Current Directions in Biomedical Engineering, 2022 Deep Learning-based tissue classification may support pathologists in analyzing digitized whole slide images. However, in such critical tasks, only approaches that can be validated by medical experts in advance to deployment, are suitable.
Mohammed Aliya +8 more
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Adversarial XAI Methods in Cybersecurity [PDF]
IEEE Transactions on Information Forensics and Security, 2021 Machine Learning methods are playing a vital role in combating ever-evolving threats in the cybersecurity domain. Explanation methods that shed light on the decision process of black-box classifiers are one of the biggest drivers in the successful adoption of these models.
Aditya Kuppa, Nhien-An Le-Khac
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, 2022 Scripts for visualizing (rows, columns, channels) outputs of XAI ...
Evan Krell
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Frontiers in Reproductive HealthIntroductionMale participation in maternal and child healthcare (MCH) is essential for improving health outcomes for women and children. Despite evidence that male involvement enhances adherence to medical recommendations and reduces maternal and infant ...
Maudy Simão Muchanga +11 more
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Understanding Food Security and Hunger in Xai-Xai, Mozambique
, 2022 AbstractThe cyclical alternation of drought, cyclones and floods threaten food security for households in rapidly growing coastal cities such as Xai-Xai, Mozambique. Inhabitants of Xai-Xai are highly dependent on urban subsistence agriculture and informal markets in order to guarantee food for their households.
Inês Macamo Raimundo, Mary Caesar
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AI MagazineAbstractResearchers focusing on how artificial intelligence (AI) methods explain their decisions often discuss controversies and limitations. Some even assert that most publications offer little to no valuable contributions. In this article, we substantiate the claim that explainable AI (XAI) is in trouble by describing and illustrating four problems ...
Rosina O. Weber +3 more
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Proceedings of the International Conference on Industrial Engineering and Operations ManagementThe evolution of Explainable Artificial Intelligence (XAI) within healthcare represents a crucial turn towards more transparent, understandable, and patient-centric AI applications. The main objective is not only to increase the accuracy of AI models but also, and more importantly, to establish user trust in decision support systems through improving ...
Gezici, Gizem +6 more
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Feature Importance in Machine Learning with Explainable Artificial Intelligence (XAI) for Rainfall Prediction [PDF]
ITM Web of ConferencesPrecipitation expectation is a pivotal subject for the administration of water assets and counteraction of hydrological calamities. To make a precipitation forecast and find the essential elements influencing precipitation, this study presents a logical ...
Patel Mehul, Shah Ankit
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Explainable Artificial Intelligence (XAI) in Insurance
Risks, 2022 Explainable Artificial Intelligence (XAI) models allow for a more transparent and understandable relationship between humans and machines. The insurance industry represents a fundamental opportunity to demonstrate the potential of XAI, with the industry ...
Emer Owens +5 more
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Evaluation of Similarity of Image Explanations Produced by SHAP, LIME and Grad-CAM
Кібернетика та комп'ютерні технологіїIntroduction. Convolutional neural networks (CNNs) are a subtype of neural networks developed specifically to work with images [1]. They have achieved great success both in research and in practical applications in recent years, however, one of the major
Vladyslav Yavtukhovskyi +1 more
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