Results 171 to 180 of about 8,368,743 (296)

Role of Machine Learning (ML)-Based Classification Using Conventional ¹⁸F-FDG PET Parameters in Predicting Postsurgical Features of Endometrial Cancer Aggressiveness. [PDF]

Cancers (Basel), 2023
Bezzi C, Bergamini A, Mathoux G, Ghezzo S, Monaco L, Candotti G, Fallanca F, Gajate AMS, Rabaiotti E, Cioffi R, Bocciolone L, Gianolli L, Taccagni G, Candiani M, Mangili G, Mapelli P, Picchio M.   +16 more
europepmc   +1 more source

Leaftronics: Bio‐Fractal Scaffolds From Leaf Venation for Low‐Waste Electronics

Advanced Materials, EarlyView.
“Leaftronics” transforms naturally evolved leaf venation into quasi‐fractal scaffolds for sustainable electronics. Polymer‐infiltrated leaf skeletons can be used to fabricate ultra‐smooth, reflow‐ and thin‐film‐compatible decomposable substrates, while making the same lignocellulose networks conducting results in flexible transparent electrodes.
Rakesh Rajendran Nair, Hrisheekesh Thachoth Chandran, Karl Leo, Hans Kleemann   +3 more
wiley   +1 more source

New Analytical Strategies for Quality Control and Classification of Apple Juices Using Digital Image Processing (DIP) Combined with Machine Learning (ML). [PDF]

ACS Omega
Kaczala S, de Lima VA, Felsner ML.
europepmc   +1 more source

Organic Materials of Tomorrow: Horizons of Artificial Intelligence

Advanced Materials, EarlyView.
This review examines machine learning techniques accelerating the discovery of organic semiconductors by linking molecular structure to properties. Key methods include graph neural networks, generative models, and active learning. Applications to organic photovoltaics demonstrate practical impact.
Harold Mena, J. Terence Blaskovits, Kun‐Han Lin, Denis Andrienko   +3 more
wiley   +1 more source

Applications of Machine Learning (ML) in the context of marketing: a bibliometric approach. [PDF]

F1000Res
Cardona-Acevedo S, Agudelo-Ceballos E, Arango-Botero D, Valencia-Arias A, Ramírez Dávila JC, Jimenez Garcia JA, Flores Goycochea C, Martínez Rojas E.   +7 more
europepmc   +1 more source

Why machine learning (ML) has failed physical activity research and how we can improve. [PDF]

BMJ Open Sport Exerc Med, 2022
Fuller D, Ferber R, Stanley K.
europepmc   +1 more source

“Smelltronics”—From Gas to Smell Sensing

Advanced Materials, EarlyView.
The emerging field of smelltronics, encompassing sensing technologies for complex volatile organic compounds, holds significant potential for extracting valuable chemical information. It facilitates the noninvasive, real‐time monitoring of humans, food, and the environment.
Takeshi Ono, Tsunaki Takahashi, Jiangyang Liu, Haruka Honda, Zeng Jing, Wataru Tanaka, Takuro Hosomi, Takeshi Yanagida   +7 more
wiley   +1 more source

Gender and racial bias unveiled: clinical artificial intelligence (AI) and machine learning (ML) algorithms are fanning the flames of inequity. [PDF]

Oxf Open Digit Health
Otokiti AU, Shih HJ, Williams KS.
europepmc   +1 more source

Radiomics-based machine learning (ML) classifier for detection of type 2 diabetes on standard-of-care abdomen CTs: a proof-of-concept study. [PDF]

Abdom Radiol (NY), 2022
Wright DE, Mukherjee S, Patra A, Khasawneh H, Korfiatis P, Suman G, Chari ST, Kudva YC, Kline TL, Goenka AH.   +9 more
europepmc   +1 more source

Recent Progress in the Phase‐Controlled Synthesis of Ruthenium Nanocrystals for Catalytic Applications

Advanced Materials, EarlyView.
Template effect and kinetic control enable crystal‐phase engineering of Ru nanocrystals, granting access to either metastable fcc‐Ru or stable hcp‐Ru with distinct surface structures, thermal stabilities, and catalytic behaviors. Moreover, the hcp‐Ru can further serve as an epitaxial template to direct Pd and Rh nanocrystals into the metastable hcp ...
Jianlong He, Zhiqi Wang, Jiaqi Guan, Younan Xia   +3 more
wiley   +1 more source