Results 61 to 70 of about 2,085,409 (278)

eXplainable Artificial Intelligence (XAI) in aging clock models [PDF]

, 2023
eXplainable Artificial Intelligence (XAI) is a rapidly progressing field of machine learning, aiming to unravel the predictions of complex models. XAI is especially required in sensitive applications, e.g.
Franceschi, Claudio, Moskalev, Alexey, Ivanchenko, Mikhail, Yusipov, Igor, Kalyakulina, Alena   +4 more
core   +1 more source

Intelligent Orthopedics: Machine Learning in Diagnosis of Bone Disease, Implants, and Bone Health Monitoring

Advanced Healthcare Materials, EarlyView.
Efficient recovery from traumatic or degenerative diseases is a great challenge, even after all the advancements in bone and cartilage regeneration. Machine learning (ML) algorithms have presented opportunities to enhance these aspects by accurately analyzing imaging data.
Maryam Kamaei, Hesam Mohammadi, Shamim Golafshan, Mojtaba Alehosseini, Mahsa Mohammadzadeh, Atefeh Golshirazi, Nasim Golafshan, Tatiane Eufrásio‐da‐Silva, Alireza Dolatshahi‐Pirouz, Gorka Orive   +9 more
wiley   +1 more source

Explainable Artificial Intelligence (XAI) for Intrusion Detection and Mitigation in Intelligent Connected Vehicles: A Review

Applied Sciences, 2023
The potential for an intelligent transportation system (ITS) has been made possible by the growth of the Internet of things (IoT) and artificial intelligence (AI), resulting in the integration of IoT and ITS—known as the Internet of vehicles (IoV).
C. I. Nwakanma, Love Allen Chijioke Ahakonye, J. Njoku, J. C. Odirichukwu, S. A. Okolie, C. Uzondu, Christiana Chidimma Ndubuisi Nweke, Dong-Seong Kim   +7 more
semanticscholar   +1 more source

“Smelltronics”—From Gas to Smell Sensing

Advanced Materials, EarlyView.
The emerging field of smelltronics, encompassing sensing technologies for complex volatile organic compounds, holds significant potential for extracting valuable chemical information. It facilitates the noninvasive, real‐time monitoring of humans, food, and the environment.
Takeshi Ono, Tsunaki Takahashi, Jiangyang Liu, Haruka Honda, Zeng Jing, Wataru Tanaka, Takuro Hosomi, Takeshi Yanagida   +7 more
wiley   +1 more source

Unlocking the Black Box: Explainable Artificial Intelligence (XAI) for Trust and Transparency in AI Systems

Journal of Digital Art & Humanities, 2023
Explainable Artificial Intelligence (XAI) has emerged as a critical field in AI research, addressing the lack of transparency and interpretability in complex AI models.
Nipuna Thalpage
semanticscholar   +1 more source

Intelligent Maintenance Review for Robots: Multimodal Information, Deep Diagnosis and Embodied Artificial Intelligence

Advanced Robotics Research, EarlyView.
This review maps the methods to monitor robots’ health by fusing vibration, sound, control signals, vision, force, and oil information with artificial intelligence. It identifies deep learning, transfer learning, digital twins, and physics‐informed models as key methodological pathways enabling earlier diagnosis, safer human–robot collaboration, and ...
Yuting Qiao, Hongbo Wang, Naipeng Li, Shuo Wang, Yaguo Lei, Tonghai Wu, Junyi Cao   +6 more
wiley   +1 more source

Explainable AI for Cyber-Physical Systems: Issues and Challenges

IEEE Access
Artificial intelligence and cyber-physical systems (CPS) are two of the key technologies of the future that are enabling major global shifts. However, most of the current implementations of AI in CPS are not explainable, which creates serious problems in
Amber Hoenig, Kaushik Roy, Yaa Takyiwaa Acquaah, Sun Yi, Salil S. Desai   +4 more
doaj   +1 more source

A Literature Review on Applications of Explainable Artificial Intelligence (XAI)

IEEE Access
As AI technologies, particularly deep learning models, have advanced, their inherent “black box” nature has raised significant concerns regarding accountability, fairness, and trust, especially in critical domains such as healthcare, finance, and ...
Khushi Kalasampath, K. N. Spoorthi, S. Sajeev, Sahil Sarma Kuppa, Kavya Ajay, Angulakshmi Maruthamuthu   +5 more
semanticscholar   +1 more source

Sustainable Materials Design With Multi‐Modal Artificial Intelligence

Advanced Science, EarlyView.
Critical mineral scarcity, high embodied carbon, and persistent pollution from materials processing intensify the need for sustainable materials design. This review frames the problem as multi‐objective optimization under heterogeneous, high‐dimensional evidence and highlights multi‐modal AI as an enabling pathway.
Tianyi Xu, Tianshuo Wei, Yan Ge, Bo Peng, Yue Li, Maolin Wang, Peng Wen, Chao Yang, Ye Wei   +8 more
wiley   +1 more source

Explainable artificial intelligence systems for predicting mental health problems in autistics

Alexandria Engineering Journal
The recognition of mental disorder symptoms is crucial for timely management and reduction of recurring symptoms and disabilities. The ability to predict and explain mental health challenges can enable earlier intervention and more effective ...
El-Sayed Atlam, M. Rokaya, M. Masud, H. Meshref, Rakan Alotaibi, Abdulqader M. Almars, Mohammed Assiri, Ibrahim Gad   +7 more
doaj   +1 more source