Results 131 to 140 of about 86,091 (310)

PancDS in Real‐World Practice: A Prospective Multicenter Validation of a Clinical Decision‐Support System Bridging Experience Gaps in Pancreatic Lesion Diagnosis

Advanced Science, EarlyView.
A biomimetic artificial intelligence system, PancDS, has been developed to distinguish pancreatic ductal adenocarcinoma from mass‐forming pancreatitis by adaptively integrating clinical data, radiomics, and deep learning features. Validated across multicenter, reader‐study, and prospective settings, PancDS improves diagnostic accuracy, particularly for
Zhibo Wang, Weinuo Qu, Wenwen Cai, Chuhuai Wang, Chenxi Lyu, Qingguo Xie, Qian Chu, Yaqi Shen, Peng Xiao, Feng Li, Qingpeng Zhang, Jiali Li, Jeong Min Lee, Zhen Li   +13 more
wiley   +1 more source

State Classification with CNN

CoRR, 2018
There is a plenty of research going on in field of object recognition, but object state recognition has not been addressed as much. There are many important applications which can utilize object state recognition, such as, in robotics, to decide for how to grab an object.
openaire   +2 more sources

PlantGFM: A Genomic Foundation Model for Discovery and Creation of Plant Genes

Advanced Science, EarlyView.
A plant genomic foundation model pre‐trained on 12 species enables both accurate gene prediction and de novo gene design. Through AI‐human knowledge screening, seven designed sequences showed transcriptional activity in plants, with two expressing stable proteins—demonstrating the first DNA‐RNA‐protein expression of LLM‐generated genes in plants and ...
Changhao Li, Qizhe Zhang, Hanchen Chen, Kepeng Lin, Chengfang Luo, Mengying Yang, Wei Xu, Fan Yao, Jianbing Yan, Qing‐Yong Yang, Xuehai Hu   +10 more
wiley   +1 more source

Comparing the Wavelet Scattering Transform and 1-D Convolutional Neural Networks for Drone Propeller Fault Classification

IEEE Access
Unmanned Aerial Vehicles (UAVs) are widely used in transportation, telecommunications, scientific mapping, and remote sensing. However, their diverse applications make them susceptible to propulsion system faults, particularly in the propellers.
Erick Axel Martinez-Rios, Riccardo Cespi, Stefano Di Gennaro, Martin Rogelio Bustamante-Bello   +3 more
doaj   +1 more source

FPGA Acceleration of CNNs Using OpenCL

, 2020
: Convolutional Neural Network (CNN) has achieved state-of-the-art performance in numerous applications like computer vision, natural language processing, robotics etc.

core  

CNNs VERSUS LSTMs FOR TIME SERIES FORECASTING

, 2021
The goal of this thesis is to compare the performances of long short-term memory (LSTM) recurrent neural networks and feedforward convolution neural networks (CNNs) in time series forecasting.
Bhurtel, Bidur Prasad
core  

How Advanced Artificial Intelligence Technologies Shape Drug–Drug and Drug–Target Interaction Modeling

Advanced Science, EarlyView.
This review explores the convergence of artificial intelligence technologies in modeling drug–drug and drug–target interactions. By evaluating advanced feature engineering, architectural innovations, and learning paradigms reveals shared evolutionary trends and critical challenges, such as cold‐start settings and shortcut learning.
Xin Sun, Tong Wang
wiley   +1 more source

Hardware Automated Datafow Deployment of CNNs

, 2017
Deep Convolutional Neural Networks (CNNs) are the state of the art systems for image classification and scene understating. However, such techniques are computationally intensive and involve highly regular parallel computation. CNNs can thus benefit from
Berry, François, Pelcat, Maxime, Quinton, Jean-Charles, Abdelouahab, Kamel, Sérot, Jocelyn, Bourrasset, Cédric   +5 more
core   +2 more sources

Outcomes and antimicrobial usage in preterm neonates < 34 weeks gestation with culture-negative neonatal sepsis: a prospective observational study

BMC Infectious Diseases
Background Culture-negative sepsis (CNNS) constitutes a significant proportion of neonates admitted to the NICU. However, the outcomes and factors influencing antimicrobial therapy in this group remain understudied.
J Girisha Rao, Rachita Sarangi, Ratan Das, Debashish Nanda, Pragnya Paramita Jena, Vijay Kumar Krishnegowda   +5 more
doaj   +1 more source

Permutohedral Lattice CNNs

, 2014
This paper presents a convolutional layer that is able to process sparse input features. As an example, for image recognition problems this allows an efficient filtering of signals that do not lie on a dense grid (like pixel position), but of more general features (such as color values).
Kiefel, M., Jampani, V., Gehler, P.
openaire   +3 more sources