Results 61 to 70 of about 20,583 (298)

Explainable Artificial Intelligence for Diabetes Diagnosis [PDF]

E3S Web of Conferences
Whether young, old, type 1, type 2, gestational, newly diagnosed, long-time sufferer, caretaker or loved one, millions of people are afflicted and affected by diabetes.
Lamri Mohamed, Sahour Abdelhakim, Boumehrez Farouk, Bekhouche Abdelaali   +3 more
doaj   +1 more source

Explainable artificial intelligence (XAI) in radiology and nuclear medicine: a literature review

Frontiers in Medicine, 2023
RationalDeep learning (DL) has demonstrated a remarkable performance in diagnostic imaging for various diseases and modalities and therefore has a high potential to be used as a clinical tool.
Bart M. de Vries, Gerben J. C. Zwezerijnen, George L. Burchell, Floris H. P. van Velden, Catharina Willemien Menke-van der Houven van Oordt, Ronald Boellaard   +5 more
doaj   +1 more source

Forms of understanding for XAI-Explanations

Cognitive Systems Research
revised ...
Hendrik Buschmeier, Heike M. Buhl, Friederike Kern, Angela Grimminger, Helen Beierling, Josephine Fisher, André Groß, Ilona Horwath, Nils Klowait, Stefan Lazarov, Michael Lenke, Vivien Lohmer, Katharina Rohlfing, Ingrid Scharlau, Amit Singh, Lutz Terfloth, Anna-Lisa Vollmer, Yu Wang, Annedore Wilmes, Britta Wrede   +19 more
openaire   +2 more sources

Araucana XAI

, 2023
<p>Open-source implementation for <strong>Araucana XAI</strong>, a novel XAI method for local, model-agnostic, post-hoc explanations presented in <a href="https://www.sciencedirect.com/science/article/pii/S0933365722002238"> ...
Buonocore, Tommaso Mario
core   +1 more source

Data‐Guided Photocatalysis: Supervised Machine Learning in Water Splitting and CO2 Conversion

Advanced Intelligent Discovery, EarlyView.
This review highlights recent advances in supervised machine learning (ML) for photocatalysis, emphasizing methods to optimize photocatalyst properties and design materials for solar‐driven water splitting and CO2 reduction. Key applications, challenges, and future directions are discussed, offering a practical framework for integrating ML into the ...
Paul Rossener Regonia, Christian Mark Pelicano   +1 more
wiley   +1 more source

Actionable Learning Analytics: Predicting University Performance Levels with Interpretable Machine Learning

Journal on Efficiency and Responsibility in Education and Science
Higher education institutions need timely, explainable tools to identify students at risk of low performance on large-scale examinations and to guide targeted academic support strategies.
Enrique De La Hoz, Carlos Garcia-Yerena, Ingrid Torres-Rojas   +2 more
doaj   +1 more source

Advances in Thermal Modeling and Simulation of Lithium‐Ion Batteries with Machine Learning Approaches

Advanced Intelligent Discovery, EarlyView.
Heat generation in lithium‐ion batteries affects performance, aging, and safety, requiring accurate thermal modeling. Traditional methods face efficiency and adaptability challenges. This article reviews machine learning‐based and hybrid modeling approaches, integrating data and physics to improve parameter estimation and temperature prediction ...
Qi Lin, Kang Fu, Junjie Ding, Kai Sun, Peng Tan   +4 more
wiley   +1 more source

DEEP LEARNING-BASED MELANOMA CLASSIFICATION ENHANCED BY FRACTAL DIMENSION ANALYSIS

KPI Science News
Background. Melanoma is a malignant skin lesion that is prone to metastasize aggressively, leading to an almost guaranteed lethal outcome if left unchecked.
Vladyslav Nikitin, Valerii Danilov
doaj   +1 more source

Some Marine Algae from Xai-xai

Journal of South African Botany, 1957
(Uploaded by Plazi from the Biodiversity Heritage Library) No abstract provided.
openaire   +1 more source

Toward Predictable Nanomedicine: Current Forecasting Frameworks for Nanoparticle–Biology Interactions

Advanced Intelligent Discovery, EarlyView.
Predictive models successfully screen nanoparticles for toxicity and cellular uptake. Yet, complex biological dynamics and sparse, nonstandardized data limit their accuracy. The field urgently needs integrated artificial intelligence/machine learning, systems biology, and open‐access data protocols to bridge the gap between materials science and safe ...
Mariya L. Ivanova, Michael Nicholls, Nicola Russo, Gueorgui Mihaylov, Konstantin Nikolic   +4 more
wiley   +1 more source