Results 81 to 90 of about 2,085,409 (278)

Explainable AI Over the Internet of Things (IoT): Overview, State-of-the-Art and Future Directions

IEEE Open Journal of the Communications Society, 2022
Explainable Artificial Intelligence (XAI) is transforming the field of Artificial Intelligence (AI) by enhancing the trust of end-users in machines. As the number of connected devices keeps on growing, the Internet of Things (IoT) market needs to be ...
Senthil Kumar Jagatheesaperumal, Quoc-Viet Pham, Rukhsana Ruby, Zhaohui Yang, Chunmei Xu, Zhaoyang Zhang   +5 more
doaj   +1 more source

Advances in Thermal Modeling and Simulation of Lithium‐Ion Batteries with Machine Learning Approaches

Advanced Intelligent Discovery, EarlyView.
Heat generation in lithium‐ion batteries affects performance, aging, and safety, requiring accurate thermal modeling. Traditional methods face efficiency and adaptability challenges. This article reviews machine learning‐based and hybrid modeling approaches, integrating data and physics to improve parameter estimation and temperature prediction ...
Qi Lin, Kang Fu, Junjie Ding, Kai Sun, Peng Tan   +4 more
wiley   +1 more source

Explainable AI Frameworks: Navigating the Present Challenges and Unveiling Innovative Applications

Algorithms
This study delves into the realm of Explainable Artificial Intelligence (XAI) frameworks, aiming to empower researchers and practitioners with a deeper understanding of these tools. We establish a comprehensive knowledge base by classifying and analyzing
Neeraj Anand Sharma, Rishal Ravikesh Chand, Zain Buksh, A. B. M. Shawkat Ali, Ambreen Hanif, Amin Beheshti   +5 more
doaj   +1 more source

Explainable Artificial Intelligence in Medical Imaging for Tumor and Alzheimer's Diagnosis :A Review [PDF]

JES: Journal of Engineering Sciences
Recently, incorporating artificial intelligence (AI) into healthcare has shown considerable promise. Despite this progress, the limited interpretability of AI systems presents challenges for their implementation in clinical environments.
Nourhan Ibrahim, Hala Badr, Eman Abdel-Ghaffar, Lamiaa Elrefaei   +3 more
doaj   +1 more source

Explainable Artificial Intelligence for Patient Safety: A Review of Application in Pharmacovigilance

IEEE Access, 2023
Explainable AI (XAI) is a methodology that complements the black box of artificial intelligence, and its necessity has recently been highlighted in various fields.
Seunghee Lee, Seonyoung Kim, Jieun Lee, Jong-Yeup Kim, Mi-Hwa Song, Suehyun Lee   +5 more
doaj   +1 more source

Explaining the Origin of Negative Poisson's Ratio in Amorphous Networks With Machine Learning

Advanced Intelligent Discovery, EarlyView.
This review summarizes how machine learning (ML) breaks the “vicious cycle” in designing auxetic amorphous networks. By transitioning from traditional “black‐box” optimization to an interpretable “AI‐Physics” closed‐loop paradigm, ML is shown to not only discover highly optimized structures—such as all‐convex polygon networks—but also unveil hidden ...
Shengyu Lu, Xiangying Shen
wiley   +1 more source

Investigating Human-Centered Perspectives in Explainable Artificial Intelligence

, 2023
The widespread use of Artificial Intelligence (AI) in various domains has led to a growing demand for algorithmic understanding, transparency, and trustworthiness.
Muhammad Suffian, Jose Maria Alonso-Moral, Ilia Stepin, Alessandro Bogliolo   +3 more
core   +1 more source

Explainable Artificial Intelligence for Resilient Security Applications in the Internet of Things

IEEE Open Journal of the Communications Society
The performance of Artificial Intelligence (AI) systems reaches or even exceeds that of humans in an increasing number of complicated tasks. Highly effective non-linear AI models are generally employed in a black-box form nested in their complex ...
Mohammed Tanvir Masud, Marwa Keshk, Nour Moustafa, Igor Linkov, Darren K. Emge   +4 more
doaj   +1 more source

xxAI - Beyond Explainable Artificial Intelligence

, 2022
310The success of statistical machine learning from big data, especially of deep learning, has made artificial intelligence (AI) very popular. Unfortunately, especially with the most successful methods, the results are very difficult to comprehend by ...
Moon, T., Fong, R., Samek, Wojciech, Müller, K.-R., Holzinger, A., Goebel, R.   +5 more
core   +1 more source

AI‐Driven Cancer Multi‐Omics: A Review From the Data Pipeline Perspective

Advanced Intelligent Discovery, EarlyView.
The exponential growth of cancer multi‐omics data brings opportunities and challenges for precision oncology. This review systematically examines AI's role in addressing these challenges, covering generative models, integration architectures, Explainable AI for clinical trust, clinical applications, and key directions for clinical translation.
Shilong Liu, Shunxiang Li, Kun Qian
wiley   +1 more source