Results 181 to 190 of about 2,085,409 (278)

Developing Predictive and Explainable Models for Cryptocurrency Delistings: A Case Study of Binance Exchange

Asia-Pacific Journal of Financial Studies, EarlyView.
Abstract This study develops an explainable machine learning model to predict cryptocurrency delistings using Binance data. It combines quantitative indicators (price, volume) with qualitative data from real‐time news and Reddit. Latent Dirichlet Allocation (LDA) is used to extract topic trends and community reactions, which are transformed into time ...
Sungju Yang, Hunyeong Kwon
wiley   +1 more source

Beyond transparency: why Traditional Chinese Medicine (TCM) need explainable artificial intelligence (XAI). [PDF]

Chin Med
Zheng W, Tong Y, Huang J, Zhu L, Chai J.
europepmc   +1 more source

Explainable artificial intelligence (XAI) detects wildfire occurrence in the Mediterranean countries of Southern Europe. [PDF]

Sci Rep, 2022
Cilli R, Elia M, D'Este M, Giannico V, Amoroso N, Lombardi A, Pantaleo E, Monaco A, Sanesi G, Tangaro S, Bellotti R, Lafortezza R.   +11 more
europepmc   +1 more source

Spatio-temporal feature attribution of European summer wildfires with Explainable Artificial Intelligence (XAI)

SPATIO-TEMPORAL FEATURE ATTRIBUTION OF EUROPEAN SUMMER WILDFIRES WITH EXPLAINABLE ARTIFICIAL INTELLIGENCE (XAI) Spatio-temporal feature attribution of European summer wildfires with Explainable Artificial Intelligence (XAI) / Li, Hanyu (Rights reserved)
Rocha, Alby Duarte, Vulova, Stenka, Kleinschmit, Birgit, Li, Hanyu   +3 more
core  

Artificial intelligence prediction algorithms for refractive error onset and progression in children and adolescents: A systematic review and meta‐analysis

Acta Ophthalmologica, EarlyView.
Abstract This systematic review and meta‐analysis evaluates the performance of artificial intelligence (AI)‐based models for predicting the onset and progression of refractive error (RE) in children and adolescents and quantitatively synthesizes their prediction accuracy.
Athanasia Sandali, Anna Nikolaidou, Theodora Gianni, Andreas Katsimpris, Ioannis D. Apostolopoulos, Lampros Lamprogiannis, Eirini Maliagkani   +6 more
wiley   +1 more source

Explainable Artificial Intelligence (xAI) for 5-HT_2A Receptor Binding Affinity of New Psychoactive Substances. [PDF]

Molecules
Schöning V, Grafinger KE, Pasin D, Stove CP, Weinmann W, Hammann F.   +5 more
europepmc   +1 more source

Editorial: Explainable Artificial Intelligence (XAI) in Systems Neuroscience. [PDF]

Front Syst Neurosci, 2021
Lombardi A, Tavares JMRS, Tangaro S.
europepmc   +1 more source

Using multilabel classification neural network to detect intersectional DIF with small sample sizes

British Journal of Mathematical and Statistical Psychology, EarlyView.
Abstract This study introduces InterDIFNet, a multilabel classification neural network for detecting intersectional differential item functioning (DIF) in educational and psychological assessments, with a focus on small sample sizes. Unlike traditional marginal DIF methods, which often fail to capture the effects of intersecting identities and require ...
Yale Quan, Chun Wang
wiley   +1 more source

Explainable Artificial Intelligence (XAI) in Pain Research: Understanding the Role of Electrodermal Activity for Automated Pain Recognition. [PDF]

Sensors (Basel), 2023
Gouverneur P, Li F, Shirahama K, Luebke L, Adamczyk WM, Szikszay TM, Luedtke K, Grzegorzek M.   +7 more
europepmc   +1 more source

Visualizing Image Segmentation Network Behavior Through the Lens of Scale Space Analysis

Computer Graphics Forum, EarlyView.
Abstract Deep neural networks are widely used for image segmentation, also in sensitive applications such as medical imaging or autonomous driving. However, few explainable AI methods are available that help developers understand such networks beyond classification.
A. C. Mikliss, T. Schultz
wiley   +1 more source