Results 131 to 140 of about 3,605,315 (303)

Effectiveness of Learning Systems from Common Image File Types to Detect Osteosarcoma Based on Convolutional Neural Networks (CNNs) Models. [PDF]

J Imaging, 2021
Loraksa C, Mongkolsomlit S, Nimsuk N, Uscharapong M, Kiatisevi P.   +4 more
europepmc   +1 more source

A novel online ensemble convolutional neural networks for streaming data

, 2019
In this study, we introduce an online ensemble method based on convolutional neural networks (CNNs) for streaming data. Recent work has shown that a convolution operation has been an effective way to extract features.
Pham, XC, Liew, AWC, Nguyen, TTT
core   +1 more source

A Closed‐Loop Framework for Inverse Design: Dynamic Training and Intelligent Optimization for Heterostructured Materials

Advanced Science, EarlyView.
To accelerate the inverse design of heterostructured metal matrix composites, a closed‐loop scientific machine learning framework integrates continual learning prediction with NSGA‐II‐PMCP optimization. The framework maps microstructural descriptors to strength, toughness, and modulus, expands high‐quality Pareto solutions, and guides experimentally ...
Zhiyan Zhong, Xueru Zheng, Xiao Zhou, Zhengheng Tao, Wan Han, Jingyi Pan, Dian Wu, Ning Gao, Lei Liu, Zhongyang Wang, Fanchao Meng, Tongxiang Fan   +11 more
wiley   +1 more source

A Hybrid Model Composed of Two Convolutional Neural Networks (CNNs) for Automatic Retinal Layer Segmentation of OCT Images in Retinitis Pigmentosa (RP). [PDF]

Transl Vis Sci Technol, 2021
Wang YZ, Wu W, Birch DG.
europepmc   +1 more source

Strategy of Triple‐Gradient in Binary Pixels for Flexible Pressure Sensing with High Sensitivity and Wide‐Range Linearity

Advanced Science, EarlyView.
A flexible pressure sensor with triple‐gradient design of conductivity, modulus, and dimension in binary micro‐dome pixels is proposed. Based on precisely‐designed CNT/PDMS matrix, the device exhibits a linear sensitivity of 974.1 kPa−1 across range up to 1.8 MPa (R2 > 0.99), offering an effective strategy for potential applications in healthcare ...
Yifan Liu, Xiao Xu, Fengming Hu, Yuanzhe Liang, Zidong Yin, Biao Qi, Ruolin Liu, Ziyi Dai, Jianyi Luo, Bingpu Zhou   +9 more
wiley   +1 more source

Application of Convolutional Neural Networks (CNNs) in Agriculture

DMPedia Lecture Notes in Computer Science & Engineering
AI and machine learning applications are on the rise. Especially, deep learning is used very frequently in research these days. Convolutional Neural Networks (CNNs) are one such popularly used deep learning approach in the agricultural domain. In smart agriculture, CNNs find its use in identification, classification and mapping problems. It is used for
Akanksha Joshi, Rajeev Singh
openaire   +1 more source

Musical Instrument Recognition in Polyphonic Audio Through Convolutional Neural Networks and Spectrograms

This study investigates the task of identifying musical instruments in polyphonic compositions using Convolutional Neural Networks (CNNs) from spectrogram inputs, focusing on binary classification. The model showed promising results, with an accuracy of
Ghobakhlou, Ali, Rujia, Chen, Narayanan, Ajit   +2 more
core  

A Unified Flash Memory Platform for Mode‐Adaptive and Robust AI Computation

Advanced Science, EarlyView.
A unified AND‐type flash memory platform enables both transistor‐mode and capacitor‐mode computing‐in‐memory operations within the same device structure. By selectively switching the sensing mode through peripheral reconfiguration, the platform provides adaptable trade‐offs between computational accuracy, robustness, and energy efficiency for AI ...
Dayeon Yu, Hwiho Hwang, Byeongchan Oh, Junmo Lee, Shimeng Yu, Tae‐Hyeon Kim, Hyungjin Kim   +6 more
wiley   +1 more source

CFAT: Convolutional Fieldy Attention Transformer for Polarimetric SAR Image Classification

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Classification of polarimetric synthetic aperture radar (PolSAR) images is one of the most prominent topics in the field of PolSAR and is crucial for its applications.
Xiandai Cui
doaj   +1 more source

Deep Learning and Convolutional Neural Networks (CNNs)

Deep Learning (DL), a subfield of Artificial Intelligence (AI) and Machine Learning (ML), has revolutionized computational intelligence by enabling machines to automatically learn hierarchical representations from large datasets. Among the various deep learning architectures, Convolutional Neural Networks have emerged as a dominant framework ...
V. V. Virginia, U.M. Mbanaso, E.S. Chaku, I.O. Clementina, Gilbert Aimufua   +4 more
openaire   +1 more source