A systematic review of trustworthy and explainable artificial intelligence in healthcare: Assessment of quality, bias risk, and data fusion

AS Albahri, AM Duhaim, MA Fadhel, A Alnoor… - Information …, 2023 - Elsevier
In the last few years, the trend in health care of embracing artificial intelligence (AI) has
dramatically changed the medical landscape. Medical centres have adopted AI applications …

Machine learning for electrocatalyst and photocatalyst design and discovery

H Mai, TC Le, D Chen, DA Winkler… - Chemical …, 2022 - ACS Publications
Electrocatalysts and photocatalysts are key to a sustainable future, generating clean fuels,
reducing the impact of global warming, and providing solutions to environmental pollution …

Machine learning and deep learning: A review of methods and applications

K Sharifani, M Amini - World Information Technology and …, 2023 - papers.ssrn.com
Abstract Machine learning and deep learning have rapidly emerged as powerful tools in
many fields, including image and speech recognition, natural language processing, and …

Edge artificial intelligence for 6G: Vision, enabling technologies, and applications

KB Letaief, Y Shi, J Lu, J Lu - IEEE Journal on Selected Areas …, 2021 - ieeexplore.ieee.org
The thriving of artificial intelligence (AI) applications is driving the further evolution of
wireless networks. It has been envisioned that 6G will be transformative and will …

Quo vadis artificial intelligence?

Y Jiang, X Li, H Luo, S Yin, O Kaynak - Discover Artificial Intelligence, 2022 - Springer
The study of artificial intelligence (AI) has been a continuous endeavor of scientists and
engineers for over 65 years. The simple contention is that human-created machines can do …

From attribution maps to human-understandable explanations through concept relevance propagation

R Achtibat, M Dreyer, I Eisenbraun, S Bosse… - Nature Machine …, 2023 - nature.com
The field of explainable artificial intelligence (XAI) aims to bring transparency to today's
powerful but opaque deep learning models. While local XAI methods explain individual …

[HTML][HTML] Explainable AI (XAI): A systematic meta-survey of current challenges and future opportunities

W Saeed, C Omlin - Knowledge-Based Systems, 2023 - Elsevier
The past decade has seen significant progress in artificial intelligence (AI), which has
resulted in algorithms being adopted for resolving a variety of problems. However, this …

[HTML][HTML] The role of deep learning in urban water management: A critical review

G Fu, Y Jin, S Sun, Z Yuan, D Butler - Water Research, 2022 - Elsevier
Deep learning techniques and algorithms are emerging as a disruptive technology with the
potential to transform global economies, environments and societies. They have been …

Combining machine learning and computational chemistry for predictive insights into chemical systems

JA Keith, V Vassilev-Galindo, B Cheng… - Chemical …, 2021 - ACS Publications
Machine learning models are poised to make a transformative impact on chemical sciences
by dramatically accelerating computational algorithms and amplifying insights available from …

[HTML][HTML] Explainable Artificial Intelligence (XAI) 2.0: A manifesto of open challenges and interdisciplinary research directions

L Longo, M Brcic, F Cabitza, J Choi, R Confalonieri… - Information …, 2024 - Elsevier
Understanding black box models has become paramount as systems based on opaque
Artificial Intelligence (AI) continue to flourish in diverse real-world applications. In response …