Results 221 to 230 of about 289,279 (277)

Biomarker‐Agnostic Detection of Ovarian Cancer from Blood Plasma Using a Machine Learning‐Driven Electronic Nose

Advanced Intelligent Systems, EarlyView.
This study introduces a biomarker‐agnostic diagnostic strategy for ovarian cancer, utilizing a machine learning‐enhanced electronic nose to analyze volatile organic compound signatures from blood plasma. By overcoming the dependence on specific biomarkers, this approach enables accurate detection, staging, and cancer type differentiation, offering a ...
Ivan Shtepliuk, Lingyin Meng, Christer Borgfeldt, Jens Eriksson, Donatella Puglisi   +4 more
wiley   +1 more source

Bayesian Optimization in Bioprocess Engineering-Where Do We Stand Today? [PDF]

Biotechnol Bioeng
Gisperg F, Klausser R, Elshazly M, Kopp J, Brichtová EP, Spadiut O.   +5 more
europepmc   +1 more source

Autonomous Robotic Colonoscopy: A Supervised Learning Approach for Enhanced Navigation and Collision Detection

Advanced Intelligent Systems, EarlyView.
A novel autonomous robotic colonoscopy is introduced through supervised learning approaches. The proposed system consists of 3 degrees of freedom motorized colonoscope with an integrated navigation module that can infer a target steering point and collision probability.
Bohyun Hwang, Dae Kyung Sohn, Seonggun Joe, Byungkyu Kim   +3 more
wiley   +1 more source

Feature Disentangling and Combination Implemented by Spin–Orbit Torque Magnetic Tunnel Junctions

Advanced Intelligent Systems, EarlyView.
Spin–orbit torque magnetic tunnel junctions (SOT‐MTJs) enable efficient feature disentangling and integration in image data. A proposed algorithm leverages SOT‐MTJs as true random number generators to disentangle and recombine features in real time, with experimental validation on emoji and facial datasets.
Xiaohan Li, Yingqian Xu, Caihua Wan, Ran Zhang, Meike Hindenberg, Raik Hoffmann, Shiqiang Liu, Tianyi Zhang, Shilong Xiong, Dehao Kong, Jihao Xia, Fangshuo Gao, Zhuyang Nie, Thomas Kämpfe, Guoqiang Yu, Xiufeng Han   +15 more
wiley   +1 more source

Bayesian Optimization with Gaussian Processes Assisted by Deep Learning for Material Designs. [PDF]

J Phys Chem Lett
Kiyohara S, Kumagai Y.
europepmc   +1 more source

Metalearning‐Driven Inverse Optimization for Precision Microstructure Fabrication in Digital Light Processing Three‐Dimensional Printing

Advanced Intelligent Systems, EarlyView.
Metalearning‐based inverse optimization enables precise microscale three‐dimensional printing using a DLP system. Distorted structures from conventional printing are analyzed via neural network regression, which predicts optimal exposure time and mask design.
Jae Won Choi, Suhyeong Lim, Jeongsu Lee, Cheol Woo Ha   +3 more
wiley   +1 more source

A Portfolio Approach to Massively Parallel Bayesian Optimization. [PDF]

J Artif Intell Res
Binois M, Collier N, Ozik J.
europepmc   +1 more source

Integrating Artificial Intelligence With Droplet‐Based Microfluidics: Advances, Challenges, and Emerging Opportunities

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, Sukrit Gaira, Rongfeng Wang, Qingtian Zhang, Ming Li, Liao Wu, Diangeng Li, Yi Li, Chengchen Zhang, Jinhong Guo, Shi‐Yang Tang   +10 more
wiley   +1 more source

Leveraging prior mean models for faster Bayesian optimization of particle accelerators. [PDF]

Sci Rep
Boltz T, Martinez JL, Xu C, Baker KRL, Zhu Z, Morgan J, Roussel R, Ratner D, Mustapha B, Edelen AL.   +9 more
europepmc   +1 more source

Bayesian Optimisation for the Experimental Sciences: A Practical Guide to Data‐Efficient Optimisation of Laboratory Workflows

Advanced Intelligent Systems, EarlyView.
This study provides an introduction to Bayesian optimisation targeted for experimentalists. It explains core concepts, surrogate modelling, and acquisition strategies, and addresses common real‐world challenges such as noise, constraints, mixed variables, scalability, and automation.
Chuan He, Martin Singull, T. Jesper Jacobsson   +2 more
wiley   +1 more source