Results 171 to 180 of about 1,888,792 (317)

Machine learning in artificial intelligence

, 2020
75
openaire   +1 more source

Transducers Across Scales and Frequencies: A System‐Level Framework for Multiphysics Integration and Co‐Design

Advanced Materials Technologies, EarlyView.
Transducers convert physical signals into electrical and optical representations, yet each mechanism is bounded by intrinsic trade‐offs across bandwidth, sensitivity, speed, and energy. This review maps transduction mechanisms across physical scale and frequency, showing how heterogeneous integration and multiphysics co‐design transform isolated ...
Aolei Xu, Mengyao Xiao, Zhouli Sui, Yinpeng Wang, Daoye Zheng, Yuxin Liu, Luwei Wang, Huajun Liu, Chengkuo Lee   +8 more
wiley   +1 more source

An Ensemble Learning Artificial Intelligence Model for Alzheimer's Disease Detection Using OCT. [PDF]

Ophthalmol Sci
Ran AR, Hu X, Hui HYH, Dai J, Chan VTT, Ho K, Au LWC, Ng CF, Sham K, Zheng C, Liu X, He Q, Tham CC, Kwok TCK, Hilal S, Cheng CY, Chua J, Schmetterer L, Liu TYA, Tham YC, Li-Hsian Chen C, Wong TY, Mok VCT, Cheung CY.   +23 more
europepmc   +1 more source

Deep Learning Artificial Intelligence Model for Assessment of Hip Dislocation Risk Following Primary Total Hip Arthroplasty From Postoperative Radiographs. [PDF]

J Arthroplasty, 2021
Rouzrokh P, Ramazanian T, Wyles CC, Philbrick KA, Cai JC, Taunton MJ, Maradit Kremers H, Lewallen DG, Erickson BJ.   +8 more
europepmc   +1 more source

Chemically Doped Conductive Polymers for Wearable Health Monitoring

Advanced Materials Technologies, EarlyView.
Among conductive polymers, poly(3,4‐ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS), polyaniline (PANI), and polypyrrole (PPy) are the most studied and applied. Chemical doping significantly boosts intrinsic conductivity and mechanical robustness.
Mengdi Zuo, Jian Song, Hong Hu, Le Zheng, Lei Zhang, Charles H. Lawrie   +5 more
wiley   +1 more source

Can algorithms come to the rescue of a failing heart? Machine learning, artificial intelligence, and decision-making in cardiogenic shock. [PDF]

J Anesth Analg Crit Care
Bottussi A, Wieruszewski PM, Bignami EG, Swol J, Buda KG, Cheungpasitporn W, Elmadhoun O, D'Andria Ursoleo J.   +7 more
europepmc   +1 more source

Recent Advances of Slip Sensors for Smart Robotics

Advanced Materials Technologies, EarlyView.
This review summarizes recent progress in robotic slip sensors across mechanical, electrical, thermal, optical, magnetic, and acoustic mechanisms, offering a comprehensive reference for the selection of slip sensors in robotic applications. In addition, current challenges and emerging trends are identified to advance the development of robust, adaptive,
Xingyu Zhang, Liao Wu, Shuai He, Zhao Sha, Yuwan Huang, Shuying Wu, Dewei Chu, Chun H. Wang, Shuhua Peng   +8 more
wiley   +1 more source

Tumor cell- and infiltrating immune cell-based supervised learning artificial intelligence multimodal platform for tumor prognosis. [PDF]

NPJ Precis Oncol
Cai XJ, Peng CR, Ding CY, Cui YY, Gao L, Xu ZX, Li L, Zhang JY, Li TJ.   +8 more
europepmc   +1 more source

Smart Closed‐Loop Systems in Personalized Healthcare: Advances and Outlook

Advanced Materials Technologies, EarlyView.
A smart closed‐loop e‐textile integrates multimodal sensing, onboard processing, wireless communication, and wearable power to enable real‐time physiological/biochemical monitoring and feedback‐controlled therapy. ABSTRACT Smart textiles represent a revolutionary frontier in healthcare, seamlessly blending fabric and advanced technologies to create ...
Safoora Khosravi, Sang Won Lee, Mohammad Abrar Uddin, Sina Hassanpoor, Neha Suvindran, Katherine Le, Harun Arkaz, Harishkumar Narayana, Saeid Soltanian, Danial Khorsandi, Negar Hosseinzadeh Kouchehbaghi, Taeil Kim, Yangzhi Zhu   +12 more
wiley   +1 more source

Deep Learning Artificial Intelligence and Restriction Spectrum Imaging for Patient-level Detection of Clinically Significant Prostate Cancer on Biparametric Magnetic Resonance Imaging. [PDF]

Eur Urol Open Sci
Song Y, Domingo MR, Conlin CC, Do DD, Baxter MT, Dornisch A, Xu G, Bagrodia A, Barrett T, Harisinghani M, Hollenberg G, Kamran S, Kane CJ, Kessler DA, Kuperman J, Lee K, Liss MA, Margolis DJA, Murphy PM, Nakrour N, Ngyuen T, Osinski TL, Rakow-Penner R, Roychowdhury S, Shabik AS, Trecarten S, Wehrli N, Weinberg EP, Woolen SA, Dale AM, Seibert TM.   +30 more
europepmc   +1 more source