Results 51 to 60 of about 86,722 (264)

C-Net: A Method for Generating Non-deterministic and Dynamic Multivariate Decision Trees

Knowledge and Information Systems, 2001
Despite the fact that artificial neural networks (ANNs) are universal function approximators, their black box nature (that is, their lack of direct interpretability or expressive power) limits their utility. In contrast, univariate decision trees (UDTs) have expressive power, although usually they are not as accurate as ANNs. We propose an improvement,
Abbass, Hussein, Towsey, Michael, Finn, G   +2 more
openaire   +3 more sources

Multimodal Wearable Biosensing Meets Multidomain AI: A Pathway to Decentralized Healthcare

Advanced Science, EarlyView.
Multimodal biosensing meets multidomain AI. Wearable biosensors capture complementary biochemical and physiological signals, while cross‐device, population‐aware learning aligns noisy, heterogeneous streams. This Review distills key sensing modalities, fusion and calibration strategies, and privacy‐preserving deployment pathways that transform ...
Chenshu Liu, Haolin Fan, Minwoo Kim, Tong Zhou, Pinyi Yang, Lingdi Zhao, Yiran Wang, Ziyuan Che, Chia‐Wei Liu, Bingbing Li, Yangzhi Zhu   +10 more
wiley   +1 more source

Functional Disorder at the Neural Interface: How Disordered Nanostructures Promote Proper Growth and Differentiation in In Vitro Neural Cultures

Advanced Science, EarlyView.
This work provides a practical guide for neuroengineers to design advanced neural interfaces, embracing and tailoring the concept of functional disorder. By bridging 2D and 3D in vitro models, this work highlights how non‐periodic, spatially heterogeneous, multiscale nanotopography can enable more physiologically relevant platforms for studying neural ...
F. Maita, E. Palmieri, I. Lucarini, L. Montaina, L. Maiolo   +4 more
wiley   +1 more source

Shadow‐Calibrated Stereo Vision for Colorimetric Sweat Analysis

Advanced Science, EarlyView.
By establishing a mathematical model that reconstructs 3D structures through geometric features of object shadows under controlled illumination, and combining it with Convolutional Neural Network‐based 2D image analysis for volumetric calibration, this work enables highly accurate 3D morphological reconstruction.
Ting Xiao, Yuwen Yan, Miaorong Lin, Jiahui Chen, Jianxin Meng, Xiang Cui, Peng Zhang, Fengyu Li   +7 more
wiley   +1 more source

optRF: Optimising random forest stability by determining the optimal number of trees

BMC Bioinformatics
Machine learning is frequently used to make decisions based on big data. Among these techniques, random forest is particularly prominent. Although random forest is known to have many advantages, one aspect that is often overseen is that it is a non ...
Thomas M. Lange, Mehmet Gültas, Armin O. Schmitt, Felix Heinrich   +3 more
doaj   +1 more source

Subexponential convergence for information aggregation on regular trees

, 2011
We consider the decentralized binary hypothesis testing problem on trees of bounded degree and increasing depth. For a regular tree of depth t and branching factor k>=2, we assume that the leaves have access to independent and identically distributed ...
Kanoria, Yashodhan, Montanari, Andrea
core   +1 more source

Data‐Driven Modeling of Composition–Processing–Microstructure Relations for Recycled Aluminum Cast Alloys

Advanced Science, EarlyView.
Interpretable machine learning reveals how composition and processing govern the formation and microstructural burden of Fe‐rich intermetallic compounds in recycled Al–Si–Fe–Mn alloys. By separating morphology selection from morphology‐conditioned burden partitioning, this framework shows that identical Fe contents can yield different intermetallic ...
Jaemin Wang, Waleed Mohammed, Dierk Raabe   +2 more
wiley   +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

AI in chemical engineering: From promise to practice

AIChE Journal, EarlyView.
Abstract Artificial intelligence (AI) in chemical engineering has moved from promise to practice: physics‐aware (gray‐box) models are gaining traction, reinforcement learning complements model predictive control (MPC), and generative AI powers documentation, digitization, and safety workflows.
Jia Wei Chew, Ronnie Andersson, Thomas Bierweiler, Patrik Ryttestal, Torsten Wik   +4 more
wiley   +1 more source

Permutation Games for the Weakly Aconjunctive $\mu$-Calculus

, 2018
We introduce a natural notion of limit-deterministic parity automata and present a method that uses such automata to construct satisfiability games for the weakly aconjunctive fragment of the $\mu$-calculus.
Deifel, Hans-Peter, Hausmann, Daniel, Schröder, Lutz   +2 more
core   +2 more sources