Results 221 to 230 of about 240,959 (297)

Early Tuberculosis Detection via Privacy-Preserving, Adaptive-Weighted Deep Models. [PDF]

Diagnostics (Basel)
Gasmi K, Alanazi A, Ben Aoun N, Altaieb MO, Abdalrahman AEM, Hamid O, Almenwer S, Ben Ammar L, Yahyaoui S, Mrabet M.   +9 more
europepmc   +1 more source

Roadmap on Artificial Intelligence‐Augmented Additive Manufacturing

Advanced Intelligent Systems, EarlyView.
This Roadmap outlines the transformative role of artificial intelligence‐augmented additive manufacturing, highlighting advances in design, monitoring, and product development. By integrating tools such as generative design, computer vision, digital twins, and closed‐loop control, it presents pathways toward smart, scalable, and autonomous additive ...
Ali Zolfagharian, Liuchao Jin, Qi Ge, Wei‐Hsin Liao, Andrés Díaz Lantada, Francisco Franco Martínez, Tianyu Zhang, Tao Liu, Charlie C. L. Wang, Mohammad Hossein Mosallanejad, Reza Ghanavati, Abdollah Saboori, Alejandro De Blas De Miguel, William Solórzano‐Requejo, Yi Cai, Xiangyang Dong, Huangyi Qu, Najmeh Samadiani, Guangyan Huang, Austin Downey, Yanzhou Fu, Lang Yuan, Tsz‐Kwan (Glory) Lee, Arbind Agrahari Baniya, Eisha Waseem, Abdul Rahman Sani, Abbas Z. Kouzani, Yijia Wu, Markus P. Nemitz, Masoud Shirzad, Dageon Oh, Seung Yun Nam, Amedeo Franco Bonatti, Irene Chiesa, Gabriele Maria Fortunato, Giovanni Vozzi, Carmelo De Maria, Mahdi Bodaghi   +37 more
wiley   +1 more source

Enhanced optical visible light positioning via RSS-based evolutionary optimization under LOS and NLOS conditions. [PDF]

Sci Rep
Pathak P, Sharavanan S, Karthikeyan M, Mubarakali A, Arun Kumar U.   +4 more
europepmc   +1 more source

Optisense: Computational Optimization for Strain Sensor Placement in Wearable Motion Tracking Systems

Advanced Intelligent Systems, EarlyView.
A computational framework for optimizing strain sensor placement in wearable motion tracking systems is presented. By combining dense strain mapping with a genetic algorithm, the method discovers counterintuitive yet highly effective configurations that reduce joint angle error by 32%.
Minu Kim, Juhyung Ahn, Jieun Seo, Steve Park, Joo Yong Sim   +4 more
wiley   +1 more source

QL-HIT2F: A Q-Learning-Aided Adaptive Fuzzy Path Planning Algorithm with Enhanced Obstacle Avoidance. [PDF]

Sensors (Basel)
Zhou N, Zhou F, Li C, Zhong J.
europepmc   +1 more source

Iterative Data Curation for Machine Learning‐Based Inverse Design of Active Composite Plates for Four‐Dimensional Printing

Advanced Intelligent Systems, EarlyView.
A machine learning framework is developed for the inverse design of 4D‐printed active composite plates. It utilizes a forward model to predict shapes from patterns and an inverse model to suggest initial patterns for desired shapes. This framework integrates a genetic algorithm to refine the predicted patterns, ensuring higher accuracy in achieving ...
Teerapong Poltue, Chao Zhang, Frédéric Demoly, Kun Zhou, H. Jerry Qi   +4 more
wiley   +1 more source

Evolutionary algorithm-optimized feature fusion for accurate classification of shredded tobacco using multi-sensor data. [PDF]

Front Plant Sci
Chen L, Tang N, Wu X, Wang Y, He C, He Z, Xie L, Zhang X, Chen X, Zhou T.   +9 more
europepmc   +1 more source

A Fully Soft Sensing Suit With Optimal Sensor Placement for Real‐Time Motion Tracking

Advanced Intelligent Systems, EarlyView.
A fully soft, skin‐conformable sensing suit integrating stretchable sensors, liquid metal wiring, and soft electrodes was developed using direct ink writing, with sensor placement optimized through an automated algorithmic pipeline. This system enables accurate and unobtrusive real‐time motion tracking, providing a scalable, material‐based solution to ...
Jinhyeok Oh, Joonbum Bae
wiley   +1 more source

QSAR machine learning-guided discovery of renieramycin right-half derivatives with cytotoxic activity against NSCLC cells. [PDF]

Sci Rep
Putra ID, Maitarad P, Yokoya M, Ei ZZ, Hotta D, Shi L, Rungrotmongkol T, Chanvorachote P.   +7 more
europepmc   +1 more source

Genotypes and Phenotypes of Patients With TSPEAR‐Related Disorder: Evidence of a Predominant Dental Phenotype

American Journal of Medical Genetics Part A, EarlyView.
ABSTRACT TSPEAR (chr. 21q22.3) encodes a protein involved in tooth development and is predominantly expressed in the enamel knot. Biallelic loss of function variants in TSPEAR cause ectodermal dysplasia, tooth agenesis and sensorineural hearing loss. However, the role of TSPEAR in auditory processes is unclear.
Debora Vergani, Lucia Tiberi, Annarita Giliberti, Elia Dirupo, Laila Zaroili, Francesco Brancati, Michela Brena, Stefano Caraffi, Chiara De Luca, Livia Garavelli, Anna Virginia Gulino, Milena Mariani, Marzia Pollazzon, Angelo Selicorni, Samuela Landini, Ilaria Sani, Rosangela Artuso, Angela Peron   +17 more
wiley   +1 more source