Results 111 to 120 of about 56,159 (288)

Machine Learning Approach Enables Highly Accurate Identification of At‐Risk Metabolic Dysfunction‐Associated Steatohepatitis

Hepatology Research, EarlyView.
ABSTRACT Aim At‐risk metabolic dysfunction‐associated steatohepatitis (MASH), characterized by significant activity and fibrosis, increases the risk of liver complications. Liver stiffness measurement (LSM), commonly used to detect significant fibrosis, has limitations in terms of accessibility and performance in certain populations.
Masaya Sato, Takuma Nakatsuka, Tatsuya Minami, Yotaro Kudo, Rie Irie, Hanako Tsujikawa, Takumi Kawaguchi, Hirokazu Takahashi, Yuichiro Eguchi, Eisuke Murakami, Kazuaki Chayama, Takeshi Okanoue, Michiie Sakamoto, Mitsuhiro Fujishiro, Kazuhiko Koike, Ryosuke Tateishi   +15 more
wiley   +1 more source

Overreliance on Orthographic Similarity in L2‐Japanese Conceptual Processing by L1‐Chinese Learners

International Journal of Applied Linguistics, EarlyView.
ABSTRACT Orthographic and phonological similarities between first (L1) and second (L2) languages can facilitate L2 processing. Particularly, L1‐Chinese learners of L2‐Japanese can benefit from the shared morphosyllabic Chinese characters (Japanese kanji/Chinese hanzi) because of their similar orthographies.
Xuehan Zhao, Kexin Xiong, Sachiko Kiyama
wiley   +1 more source

Dermatologist-like explainable AI enhances trust and confidence in diagnosing melanoma

Nature Communications
Artificial intelligence (AI) systems have been shown to help dermatologists diagnose melanoma more accurately, however they lack transparency, hindering user acceptance.
Tirtha Chanda, Katja Hauser, Sarah Hobelsberger, Tabea-Clara Bucher, Carina Nogueira Garcia, Christoph Wies, Harald Kittler, Philipp Tschandl, Cristian Navarrete-Dechent, Sebastian Podlipnik, Emmanouil Chousakos, Iva Crnaric, Jovana Majstorovic, Linda Alhajwan, Tanya Foreman, Sandra Peternel, Sergei Sarap, İrem Özdemir, Raymond L. Barnhill, Mar Llamas-Velasco, Gabriela Poch, Sören Korsing, Wiebke Sondermann, Frank Friedrich Gellrich, Markus V. Heppt, Michael Erdmann, Sebastian Haferkamp, Konstantin Drexler, Matthias Goebeler, Bastian Schilling, Jochen S. Utikal, Kamran Ghoreschi, Stefan Fröhling, Eva Krieghoff-Henning, Reader Study Consortium, Titus J. Brinker   +35 more
doaj   +1 more source

Towards an MLOps Architecture for XAI in Industrial Applications [PDF]

, 2023
Leonhard Faubel, Thomas Woudsma, Leila Methnani, Amir Ghorbani Ghezeljhemeidan, Fabian Buelow, Klaus Schmid, W.D. van Driel, Benjamin Kloepper, Andreas Theodorou, Mohsen Nosratinia, Magnus Bång   +10 more
openalex   +1 more source

Bridging the implementation gap in MCABC inventory management: from a taxonomy to practical archetypes

International Transactions in Operational Research, EarlyView.
Abstract Despite increasing demands for resilient and sustainable supply chains, inventory management often relies on outdated single‐criterion analyses. While multi‐criteria ABC (MCABC) analyses provide a theoretically mature assessment of resilience‐sustainability‐benefit trade‐offs in inventory, their adoption remains limited due to fragmented ...
Lukas Grützner, Michael H. Breitner
wiley   +1 more source

Explainable machine learning for breast cancer diagnosis from mammography and ultrasound images: a systematic review

BMJ Health & Care Informatics
Background Breast cancer is the most common disease in women. Recently, explainable artificial intelligence (XAI) approaches have been dedicated to investigate breast cancer. An overwhelming study has been done on XAI for breast cancer.
Worku Jimma, Daraje kaba Gurmessa
doaj   +1 more source

Dating Apps and the Right to an Explanation

Journal of Applied Philosophy, EarlyView.
ABSTRACT This article argues that in countries where dating apps have become the primary means of meeting romantic partners and promise to help users find love, individuals should be entitled to access certain information about how their algorithms function. Specifically, we advocate for a legal right to an explanation that addresses the following, not
Bouke de Vries, Kristian González Barman   +1 more
wiley   +1 more source

Explaining Explanations in AI [PDF]

Recent work on interpretability in machine learning and AI has focused on the building of simplified models that approximate the true criteria used to make decisions.
Mittelstadt, Brent
core  

Breeding 5.0: Artificial intelligence (AI)‐decoded germplasm for accelerated crop innovation

Journal of Integrative Plant Biology, EarlyView.
ABSTRACT Crop breeding technologies are vital for global food security. While traditional methods have improved yield, stress tolerance, and nutrition, rising challenges such as climate instability, land loss, and pest pressure now demand new solutions.
Jiayi Fu, Shouzhi Zheng, Longjiang Fan, Xiaoming Zheng, Qian Qian   +4 more
wiley   +1 more source

The role of IoT and XAI convergence in the prediction, explanation, and decision of customer perceived value (CPV) in SMEs: a theoretical framework and research proposition perspective

Discover Internet of Things
The goal of this study is to look at how the convergence of IoT and XAI (IoT-XAI) effects the explanation, prediction, and decision-making on customer perceived value (CPV) in SMEs, utilising CPV and IoT-XAI convergence theories.
Kwabena Abrokwah-Larbi
doaj   +1 more source