Results 71 to 80 of about 2,085,409 (278)

Explainable Artificial Intelligence (XAI) for Material Design and Engineering Applications: A Quantitative Computational Framework

International Journal of Mechanical System Dynamics
The advancement of artificial intelligence (AI) in material design and engineering has led to significant improvements in predictive modeling of material properties.
Bo-Kai Liu, Pengju Liu, Weizhuo Lu, Thomas Olofsson   +3 more
semanticscholar   +1 more source

Closing the Empirical Loop: Autonomous AI Agents Conduct End‐to‐end Research With Human Participants

Advanced Science, EarlyView.
A multi‐agent AI system autonomously executes the complete scientific workflow, from hypothesis to manuscript, across three psychological studies involving 288 participants. The system designs experiments, collects real world data, develops analysis pipelines, and writes manuscripts with theoretical rigor comparable to experienced researchers.
Gabrielle Wehr, Reuben Rideaux, Amaya J. Fox, David R. Lightfoot, Jason Tangen, Jason B. Mattingley, Shane E. Ehrhardt   +6 more
wiley   +1 more source

eXplainable Artificial Intelligence in Process Engineering: Promises, Facts, and Current Limitations

Applied System Innovation
Artificial Intelligence (AI) has been swiftly incorporated into the industry to become a part of both customer services and manufacturing operations.
Luigi Piero Di Bonito, Lelio Campanile, Francesco Di Natale, Michele Mastroianni, Mauro Iacono   +4 more
doaj   +1 more source

XAITK: The explainable AI toolkit

Applied AI Letters, 2021
Recent advances in artificial intelligence (AI), driven mainly by deep neural networks, have yielded remarkable progress in fields, such as computer vision, natural language processing, and reinforcement learning.
Brian Hu, Paul Tunison, Bhavan Vasu, Nitesh Menon, Roddy Collins, Anthony Hoogs   +5 more
doaj   +1 more source

Capturing Users’ Reality: A Novel Approach to Generate Coherent Counterfactual Explanations [PDF]

, 2021
The opacity of Artificial Intelligence (AI) systems is a major impediment to their deployment. Explainable AI (XAI) methods that automatically generate counterfactual explanations for AI decisions can increase users’ trust in AI systems.
Klier, Mathias, Mathias Klier, Kluge, Kilian, Maximilian Förster, Philipp Hühn, Hühn, Philipp, Kilian Kluge, Förster, Maximilian   +7 more
core   +1 more source

Smart Exploration of Perovskite Photovoltaics: From AI Driven Discovery to Autonomous Laboratories

Advanced Energy Materials, EarlyView.
In this review, we summarize the fundamentals of AI in automated materials science, and review AI applications in perovskite solar cells. Then, we sum up recent progress in AI‐guided manufacturing optimization, and highlight AI‐driven high‐throughput and autonomous laboratories.
Wenning Chen, Zheyu Zhang, Kelvian T. Mularso, Bonghyun Jo, Hyun Suk Jung   +4 more
wiley   +1 more source

Explainable Artificial Intelligence Technology and Its Applications: Toward Transparent, Human-Centered, and Trustworthy AI

Applied Sciences
Explainable artificial intelligence (XAI) has become a central methodology for developing transparent, accountable, and human-centered AI systems.
Xue Sun, Lan Tian, Weidong Zhou, Yazhou Zhao, Guoyang Liu   +4 more
doaj   +1 more source

The Enlightening Role of Explainable Artificial Intelligence in Chronic Wound Classification [PDF]

, 2021
Artificial Intelligence (AI) has been among the most emerging research and industrial application fields, especially in the healthcare domain, but operated as a black-box model with a limited understanding of its inner working over the past decades.
Guler, Ozgur, Salih Sarp, Ozgur Guler, Umit Cali, Cali, Umit, Emmanuel Wilson, Kuzlu, Murat, Wilson, Emmanuel, Murat Kuzlu, Sarp, Salih   +9 more
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

Mapping the Research Landscape of Artificial Intelligence Innovation for Dropout Prevention and Learning Opportunity Loss Mitigation Policy

Jurnal Kependidikan
This study aims to map the research landscape of Explainable Artificial Intelligence (XAI) for dropout prevention and learning loss mitigation. The study employed bibliometric science mapping to identify publication trends, conceptual structures, and ...
Iik Nurulpaik, Ahmad Satibi, Encep Supriatna, Oki Suprianto   +3 more
doaj   +1 more source