Results 211 to 220 of about 102,874 (306)

Convolutional neural networks in brain disease diagnosis: a unified review of Alzheimer's, Parkinson's, and brain tumor classification. [PDF]

Front Neurosci
Nathea R, Ghosh K, Nisha JS.
europepmc   +1 more source

Emerging Memory and Device Technologies for Hardware‐Accelerated Model Training and Inference

Advanced Electronic Materials, EarlyView.
This review investigates the suitability of various emerging memory technologies as compute‐in‐memory hardware for artificial intelligence (AI) applications. Distinct requirements for training‐ and inference‐centric computing are discussed, spanning device physics, materials, and system integration.
Yoonho Cho, Dae‐won Kim, Yujin Kim, Jung‐Heum Na, Yuwon Jeong, Dong‐Yeop Lee, Shinhyun Choi   +6 more
wiley   +1 more source

Confidence Is Detection-Like in High-Dimensional Spaces. [PDF]

Open Mind (Camb)
Kozyra W, O'Neill K, Fleming SM.
europepmc   +1 more source

People Counting and Positioning Using Low‐Resolution Infrared Images for FeFET‐Based In‐Memory Computing

Advanced Electronic Materials, EarlyView.
In this work, low‐resolution infrared imaging is combined with a 28 nm FeFET IMC architecture to enable compact, energy‐efficient edge inference. MLC FeFET devices are experimentally characterized, and controlled multi‐level current accumulation is validated at crossbar array level.
Alptekin Vardar, Li Zhang, Alexander Rehmer, Marc Lupp, Subhashree Baskaran, Osama Abdelaal, Franz Müller, Nandakishor Yadav, Bing Li, Thomas Kämpfe   +9 more
wiley   +1 more source

Beyond 2D and CNNs: Future Directions for Deep Learning in Periodontal Diagnostics. [PDF]

Int Dent J
Meng K, Lv S, Liu J.
europepmc   +1 more source

Experimental Demonstration of Temporally Aware Fault‐Tolerant Sensor Fusion Using Memristive Associative Learning

Advanced Electronic Materials, EarlyView.
In dynamic driving scenarios, the proposed approach ensures only temporally aligned sensor inputs to make driving decisions, preventing false activations. By enabling selective hardware‐level learning, it achieves fast, reliable responses under noisy conditions.
Kapil Bhardwaj, Ashfak Nehal, Dmitrii Semenov, Roman Sotner, Sayani Majumdar   +4 more
wiley   +1 more source

Tensor enhanced chest cancer classification via CNN and Vision Transformer models. [PDF]

PLoS One
Asim N, Sirshar M, Khan MZ, Ejaz S, Khalid S, Aljubayri I, Alahmadi A.   +6 more
europepmc   +1 more source

Physics Informed Generative Surrogate Learning for Full Wave Validated Real Time Beamforming in Programmable Metasurfaces

Advanced Electronic Materials, EarlyView.
A physics‐guided generative surrogate framework is developed for programmable metasurface beamforming. Mode‐conditioned binary state generation, aperture‐physics prediction, routed residual correction, NSGA‐II optimization, and CST validation are combined to support fast candidate screening and full‐wave beam refinement across single‐beam, dual‐beam ...
Wenqian Liu, Weidong Chen, Chang Chen, Xiang Zhang, Sensong An   +4 more
wiley   +1 more source

Role of Three-Dimensional Convolution Neural Networks (3D-CNN) in Image Processing and Recognition in Oncology: A Systematic Review and Meta-Analysis. [PDF]

Int J Hematol Oncol Stem Cell Res
Jain SK, Afaq S, Sharma S.
europepmc   +1 more source

CNN

, 2016
openaire   +1 more source