Results 81 to 90 of about 55,142 (282)
Forms of understanding for XAI-Explanations
Cognitive Systems Researchrevised ...
Hendrik Buschmeier +19 more
openaire +2 more sources
Advanced Intelligent Systems, EarlyView.Droplet‐based microfluidics enables precise, high‐throughput microscale reactions but continues to face challenges in scalability, reproducibility, and data complexity. This review examines how artificial intelligence enhances droplet generation, detection, sorting, and adaptive control and discusses emerging opportunities for clinical and industrial ...
Junyan Lai +10 more
wiley +1 more source
Solving the explainable AI conundrum by bridging clinicians’ needs and developers’ goals
npj Digital Medicine, 2023 Explainable artificial intelligence (XAI) has emerged as a promising solution for addressing the implementation challenges of AI/ML in healthcare. However, little is known about how developers and clinicians interpret XAI and what conflicting goals and ...
Nadine Bienefeld +7 more
doaj +1 more source
Advanced Intelligent Systems, EarlyView.This review explores the transformative impact of artificial intelligence on multiscale modeling in materials research. It highlights advancements such as machine learning force fields and graph neural networks, which enhance predictive capabilities while reducing computational costs in various applications.
Artem Maevskiy +2 more
wiley +1 more source
The Machine as an Autonomous Explanatory Agent
Felsefe DünyasıThe holy grail of Artificial Intelligence (AI) is to transform the machine into an agent that can decide, make inferences, cluster the contents, predict, recommend, and exhibit similar higher cognitive faculties.
Dilek Yargan
doaj +1 more source
The evolution of Explainable Artificial Intelligence (XAI) within healthcare represents a crucial turn towards more transparent, understandable, and patient-centric AI applications. The main objective is not only to increase the accuracy of AI models but also, and more importantly, to establish user trust in decision support systems through improving ...
Gezici, Gizem +6 more
openaire +2 more sources
Advanced Intelligent Systems, EarlyView.Four decades of retinal vessel segmentation research (1982–2025) are synthesized, spanning classical image processing, machine learning, and deep learning paradigms. A meta‐analysis of 428 studies establishes a unified taxonomy and highlights performance trends, generalization capabilities, and clinical relevance.
Avinash Bansal +6 more
wiley +1 more source
Open-world Learning and Application to Product Classification
, 2019 Classic supervised learning makes the closed-world assumption, meaning that classes seen in testing must have been seen in training. However, in the dynamic world, new or unseen class examples may appear constantly. A model working in such an environment
Liu, Bing, Shu, Lei, Xu, Hu, Yu, P.
core +1 more source
Advanced Intelligent Systems, EarlyView.Explainable artificial intelligence (XAI) guides selective electrode activation in retinal prostheses by emphasizing visually informative regions. XAI‐assisted phosphene generation maintains object recognition performance while significantly reducing stimulation power.
Sein Kim, Hamin Shim, Maesoon Im
wiley +1 more source
Improving speaker turn embedding by crossmodal transfer learning from face embedding
, 2017 Learning speaker turn embeddings has shown considerable improvement in situations where conventional speaker modeling approaches fail. However, this improvement is relatively limited when compared to the gain observed in face embedding learning, which ...
Le, Nam, Odobez, Jean-Marc
core +1 more source