Results 21 to 30 of about 56,159 (288)
ISPRS International Journal of Geo-Information, 2023 Explainable Artificial Intelligence (XAI) has the potential to open up black-box machine learning models. XAI can be used to optimize machine learning models, to search for scientific findings, or to improve the understandability of the AI system for the
Cédric Roussel, Klaus Böhm
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A Model for Classical Space-time Co-ordinates [PDF]
, 1996 Field equations with general covariance are interpreted as equations for a target space describing physical space time co-ordinates, in terms of an underlying base space with conformal invariance.
Bateman H +4 more
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CAAI Transactions on Intelligence Technology, EarlyView., 2023 Abstract Diseases of the Gastrointestinal (GI) tract significantly affect the quality of human life and have a high fatality rate. Accurate diagnosis of GI diseases plays a pivotal role in healthcare systems. However, processing large amounts of medical image data can be challenging for radiologists and other medical professionals, increasing the risk ...
Muhammad Nouman Noor +5 more
wiley +1 more source
Explainable human‐in‐the‐loop healthcare image information quality assessment and selection
CAAI Transactions on Intelligence Technology, EarlyView., 2023 Abstract Smart healthcare applications cannot be separated from healthcare data analysis and the interactive interpretability between data and model. A human‐in‐the‐loop active learning approach is introduced to reduce the cost of healthcare data labelling by evaluating the information quality of unlabelled medical data and then screening the high ...
Yang Li, Sezai Ercisli
wiley +1 more source
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 AI: A Review of Machine Learning Interpretability Methods
Entropy, 2020 Recent advances in artificial intelligence (AI) have led to its widespread industrial adoption, with machine learning systems demonstrating superhuman performance in a significant number of tasks.
Pantelis Linardatos +2 more
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Sensors, 2022 The emerging field of eXplainable AI (XAI) in the medical domain is considered to be of utmost importance. Meanwhile, incorporating explanations in the medical domain with respect to legal and ethical AI is necessary to understand detailed decisions ...
Ruey-Kai Sheu, Mayuresh Sunil Pardeshi
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Recent Advances in Explainable Artificial Intelligence for Magnetic Resonance Imaging
Diagnostics, 2023 Advances in artificial intelligence (AI), especially deep learning (DL), have facilitated magnetic resonance imaging (MRI) data analysis, enabling AI-assisted medical image diagnoses and prognoses.
Jinzhao Qian +3 more
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XAI-KG: Knowledge Graph to Support XAI and Decision-Making in Manufacturing [PDF]
, 2021 The increasing adoption of artificial intelligence requires accurate forecasts and means to understand the reasoning of artificial intelligence models behind such a forecast. Explainable Artificial Intelligence (XAI) aims to provide cues for why a model issued a certain prediction.
Jože M. Rožanec +5 more
openaire +2 more sources
Applications of Explainable Artificial Intelligence in Diagnosis and Surgery
Diagnostics, 2022 In recent years, artificial intelligence (AI) has shown great promise in medicine. However, explainability issues make AI applications in clinical usages difficult.
Yiming Zhang, Ying Weng, Jonathan Lund
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