Results 61 to 70 of about 33,125 (301)

Computational approaches to Explainable Artificial Intelligence: Advances in theory, applications and trends [PDF]

, 2023
Deep Learning (DL), a groundbreaking branch of Machine Learning (ML), has emerged as a driving force in both theoretical and applied Artificial Intelligence (AI).
Psarrou, A., Gorritz, J.M
core   +1 more source

Toward Accountable and Explainable Artificial Intelligence Part One: Theory and Examples

IEEE Access, 2022
Like other Artificial Intelligence (AI) systems, Machine Learning (ML) applications cannot explain decisions, are marred with training-caused biases, and suffer from algorithmic limitations.
Masood M. Khan, Jordan Vice
doaj   +1 more source

Risk of Non‐Arteritic Anterior Ischemic Optic Neuropathy in Idiopathic Intracranial Hypertension Patients Treated with GLP‐1 Receptor Agonists

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Introduction Glucagon‐like peptide‐1 receptor agonists (GLP‐1 RAs) have demonstrated significant weight‐reducing effects and may offer benefits in idiopathic intracranial hypertension (IIH); however, recent concerns about the risk of non‐arteritic anterior ischemic optic neuropathy (NAION) have emerged.
Faisal A. Al‐Harbi, Mohanad A. Alkuwaiti, Yazeed B. Alaql, Ahmed K. Alsaif, Ahmed A. Alessa, Meshari Ayed Alharbi, Mohammed Alfalah, Saud A. Alnaaim, Sajjad M. AlHaddad, Ahmed Y. Azzam   +9 more
wiley   +1 more source

Towards eXplainable Artificial Intelligence (XAI) in tax law : the need for a minimum legal standard [PDF]

, 2022
Published online: 02 September 2022Tax administrations globally increasingly rely on artificial intelligence (AI) systems for automation. However, automation has a huge potential impact on the rights of taxpayers subject to algorithmic assessments, which
ZELDENRUST, Reza, TYLIŃSKI, Kamil, GÓRSKI, Łukasz, WINOGRADSKA, Beata, ALMADA, Marco   +4 more
core  

Parameter-Efficiently Leveraging Session Information in Deep Learning-Based Session-Aware Sequential Recommendation

IEEE Access
In recommender systems, leveraging user interaction history as sequential information has recently led to significant performance improvements. However, in many online services, user interactions are often grouped into sessions that inherently share user
Jinseok Seol, Youngrok Ko, Sang-Goo Lee
doaj   +1 more source

Multiple Explainable Approaches to Predict the Risk of Stroke Using Artificial Intelligence

Information, 2023
Stroke occurs when a brain’s blood artery ruptures or the brain’s blood supply is interrupted. Due to rupture or obstruction, the brain’s tissues cannot receive enough blood and oxygen. Stroke is a common cause of mortality among older people.
Susmita S, Krishnaraj Chadaga, Niranjana Sampathila, Srikanth Prabhu, Rajagopala Chadaga, Swathi Katta S   +5 more
doaj   +1 more source

Explainable and responsible artificial intelligence [PDF]

Electronic Markets, 2022
Christian Meske, Babak Abedin, Mathias Klier, Fethi Rabhi   +3 more
openaire   +2 more sources

A manifesto on explainability for artificial intelligence in medicine

Artificial Intelligence in Medicine, 2022
The rapid increase of interest in, and use of, artificial intelligence (AI) in computer applications has raised a parallel concern about its ability (or lack thereof) to provide understandable, or explainable, output to users. This concern is especially legitimate in biomedical contexts, where patient safety is of paramount importance.
Carlo Combi, Beatrice Amico, Riccardo Bellazzi, Andreas Holzinger, Jason H. Moore, Marinka Zitnik, John H. Holmes   +6 more
openaire   +3 more sources

Spatial and Volumetric Characteristics of Glioblastoma: Associations With Clinical Presentation and Survival

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective We aim to comprehensively analyze how regional tumor and edema characteristics are associated with clinical presentations and survival outcomes in a large cohort of glioblastoma patients. Methods Patients with IDH‐wildtype glioblastoma who received brain MRI from 2010 to 2023 were included.
Daniel J. Zhou, Marc Jaskir, Julian Gal, Yombe Fonkeu, Christos Davatzikos, MacLean P. Nasrallah, Jose Garcia, Rubo Xing, Stephen J. Bagley, Arati Desai, Richard E. Phillips, Steven Tobochnik, Colin A. Ellis, James J. Gugger, Nishant Sinha, Joel M. Stein, Kathryn A. Davis   +16 more
wiley   +1 more source

Controllers (Expert Accountants) and Technologies: Artificial Intelligence and Explainable Artificial Intelligence

CECCAR Business Review
The demands of controlling when meeting cutting-edge technology are quite high given its underlying principles, its prospective character, flexibility, but also the desire for transparency, ethics, and responsibility.
Luana COSĂCESCU
doaj   +1 more source