Results 131 to 140 of about 249,220 (290)

Smarter Sensors Through Machine Learning: Historical Insights and Emerging Trends across Sensor Technologies

Advanced Functional Materials, EarlyView.
This review highlights how machine learning (ML) algorithms are employed to enhance sensor performance, focusing on gas and physical sensors such as haptic and strain devices. By addressing current bottlenecks and enabling simultaneous improvement of multiple metrics, these approaches pave the way toward next‐generation, real‐world sensor applications.
Kichul Lee, Osman Gul, Yeongjae Kwon, Jaeseok Jeong, Seokjoo Cho, Jihyeon Ahn, Jehee Yu, Cheolmin Kim, Donho Lee, Hyeonseok Han, Byeongju Lee, Jungrak Choi, Ji‐Hwan Ha, Yongrok Jeong, Kyungnam Kang, Ali Javey, Junseong Ahn, Inkyu Park   +17 more
wiley   +1 more source

A study protocol for a systematic review and meta-analysis on macroscopic body movements as a marker for acute stress

Systematic Reviews
Background Experiencing stress is a normative part of human life. Due to its major impact on health and longevity, effort has been put into understanding underlying determinants and consequences. For stress measurement, a vast number of different methods,
Miriam Kurz, Veronika Ringgold, Luca Abel, Felicitas Burkhardt, Robert Richer, Lena Schindler-Gmelch, Anne I. Turner, Bjoern M. Eskofier, Nicolas Rohleder   +8 more
doaj   +1 more source

An Improved Big Data Analytics Architecture for Intruder Classification Using Machine Learning [PDF]

, 2023
Muhammad Ali Babar, Sarah Kaleem, Adnan Sohail, Muhammad Asim, Muhammad Usman Tariq   +4 more
openalex   +1 more source

In Materia Shaping of Randomness with a Standard Complementary Metal‐Oxide‐Semiconductor Transistor for Task‐Adaptive Entropy Generation

Advanced Functional Materials, EarlyView.
This study establishes a materials‐driven framework for entropy generation within standard CMOS technology. By electrically rebalancing gate‐oxide traps and Si‐channel defects in foundry‐fabricated FDSOI transistors, the work realizes in‐materia control of temporal correlation – achieving task adaptive entropy optimization for reinforcement learning ...
Been Kwak, Ryun‐Han Koo, Youngchan Cho, Changhyeon Han, Dongbin Kim, Soi Jeong, Yunho Shin, Joonhyeok Choi, Jiseong Im, Jonghyun Ko, Jong‐Ho Lee, Jangsaeng Kim, Youngho Kang, Wonjun Shin, Daewoong Kwon   +14 more
wiley   +1 more source

WITHDRAWN: Advanced machine learning-based analytics on COVID-19 data using generative adversarial networks

, 2020
Janga Vijay Kumar, A. Harshavardhan, Hanumanthu Bhukya, A.V. Krishna Prasad   +3 more
openalex   +2 more sources

Toward Scalable Solutions for Silver‐Based Gas Diffusion Electrode Fabrication for the Electrochemical Conversion of CO2 – A Perspective

Advanced Functional Materials, EarlyView.
In this study, the preparation techniques for silver‐based gas diffusion electrodes used for the electrochemical reduction of carbon dioxide (eCO2R) are systematically reviewed and compared with respect to their scalability. In addition, physics‐based and data‐driven modeling approaches are discussed, and a perspective is given on how modeling can aid ...
Simon Emken, K. Ashoke Raman, Alexander Bauer, Violeta Karyofylli, Eva Jodat, André Karl, Rüdiger‐A. Eichel   +6 more
wiley   +1 more source

A robust machine learning approach to predicting remission and stratifying risk in rheumatoid arthritis patients treated with bDMARDs

Scientific Reports
Rheumatoid arthritis (RA) is a chronic autoimmune disease affecting millions worldwide, leading to inflammation, joint damage, and reduced quality of life. Although biological disease-modifying antirheumatic drugs (bDMARDs) are effective, they are costly,
Fatemeh Salehi, Emmanuelle Salin, Benjamin Smarr, Sara Bayat, Arnd Kleyer, Georg Schett, Ruth Fritsch-Stork, Bjoern M. Eskofier   +7 more
doaj   +1 more source

PRELIVE: A Framework for Predicting Lipid Nanoparticles In Vivo Efficacy and Reducing Reliance on Animal Testing

Advanced Functional Materials, EarlyView.
PREdicting LNP In Vivo Efficacy (PRELIVE) framework enables the prediction of lipid nanoparticle (LNPs) organ‐specific delivery through dual modeling approaches. Composition‐based models using formulation parameters and protein corona‐based models using biological fingerprints both achieve high predictive accuracy across multiple organs.
Belal I. Hanafy, Chuan‐En Lu, Kai Liu, Audrey Gallud   +3 more
wiley   +1 more source

Universal Electronic‐Structure Relationship Governing Intrinsic Magnetic Properties in Permanent Magnets

Advanced Functional Materials, EarlyView.
Permanent magnets derive their extraordinary strength from deep, universal electronic‐structure principles that control magnetization, anisotropy, and intrinsic performance. This work uncovers those governing rules, examines modern modeling and AI‐driven discovery methods, identifies critical bottlenecks, and reveals electronic fingerprints shared ...
Prashant Singh
wiley   +1 more source

Bio‐Inspired Molecular Events in Poly(Ionic Liquids)

Advanced Functional Materials, EarlyView.
Originating from dipolar and polar inter‐ and intra‐chain interactions of the building blocks, the topologies and morphologies of poly(ionic liquids) (PIL) govern their nano‐ and micro‐processibility. Modulating the interactions of cation‐anion pairs with aliphatic dipolar components enables the tunability of properties, facilitated by “bottom‐up ...
Jiahui Liu, Marek W. Urban
wiley   +1 more source