Results 51 to 60 of about 5,846,406 (312)

Medical imaging analysis with artificial neural networks [PDF]

, 2010
Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection ...
Jiang, J., Ren, Jinchang, Trundle, P.
core   +4 more sources

CONVOLUTIONAL DEEP LEARNING NEURAL NETWORK FOR STROKE IMAGE RECOGNITION: REVIEW

Вестник КазНУ. Серия математика, механика, информатика, 2021
Deep learning is one of the developing area of articial intelligence research. It includes machine learning methods based on articial neural networks. One method that has been widely used and researched in recent years is convolution neural networks (CNN)
Azhar Toilybaikyzy Tursynova, B. S. Omarov, O. A. Postolache, M. Zh. Sakypbekova   +3 more
doaj   +1 more source

Deep Morphological Neural Networks

CoRR, 2019
Mathematical morphology is a theory and technique to collect features like geometric and topological structures in digital images. Given a target image, determining suitable morphological operations and structuring elements is a cumbersome and time-consuming task.
Yucong Shen, Xin Zhong 0001, Frank Y. Shih   +2 more
openaire   +3 more sources

Compact and Computationally Efficient Representation of Deep Neural Networks

, 2022
S.772-785At the core of any inference procedure, deep neural networks are dot product operations, which are the component that requires the highest computational resources. For instance, deep neural networks, such as VGG-16, require up to 15-G operations
Wiedemann, S., Samek, W., Müller, K.-R.
core   +1 more source

Deep Sparse Learning for Automatic Modulation Classification Using Recurrent Neural Networks

Sensors, 2021
Deep learning models, especially recurrent neural networks (RNNs), have been successfully applied to automatic modulation classification (AMC) problems recently.
Ke Zang, Wenqi Wu, Wei Luo
doaj   +1 more source

Are Modern Deep Learning Models for Sentiment Analysis Brittleƒ An Examination on Part-of-Speech

, 2020
Part of IEEE WCCI 2020 is the world’s largest technical event on computational intelligence, featuring the three flagship conferences of the IEEE Computational Intelligence Society (CIS) under one roof: The 2020 International Joint Conference on Neural ...
Wei Emma Zhang, Sheng, Q.Z., Abdulwahab Aljubairy, Alhazmi, A., Ahoud Alhazmi, Quan Z Sheng, Zhang, W.E., Aljubairy, A.   +7 more
core   +1 more source

Deep Learning and Music Adversaries [PDF]

, 2015
OA Monitor ExerciseOA Monitor ExerciseAn {\em adversary} is essentially an algorithm intent on making a classification system perform in some particular way given an input, e.g., increase the probability of a false negative.
STURM, BLT, Kereliuk, C, Kereliuk, Corey Mose, Sturm, Bob L., Larsen, Jan, Larsen, J   +5 more
core   +1 more source

Deep Learning-Based Intrusion Detection With Adversaries

IEEE Access, 2018
Deep neural networks have demonstrated their effectiveness in most machine learning tasks, with intrusion detection included. Unfortunately, recent research found that deep neural networks are vulnerable to adversarial examples in the image ...
Zheng Wang
doaj   +1 more source

Deep oscillatory neural network

Scientific Reports
We propose a novel, brain-inspired deep neural network model known as the Deep Oscillatory Neural Network (DONN). Deep neural networks like the Recurrent Neural Networks indeed possess sequence processing capabilities but the internal states of the network are not designed to exhibit brain-like oscillatory activity.
Nurani Rajagopal Rohan, Vigneswaran C, Sayan Ghosh, Kishore Rajendran, Gaurav A, V. Srinivasa Chakravarthy   +5 more
openaire   +5 more sources

Performance analysis of different DCNN models in remote sensing image object detection

EURASIP Journal on Image and Video Processing, 2022
In recent years, deep learning, especially deep convolutional neural networks (DCNN), has made great progress. Many researchers use different DCNN models to detect remote sensing targets. Different DCNN models have different advantages and disadvantages.
Huaijin Liu, Jixiang Du, Yong Zhang, Hongbo Zhang   +3 more
doaj   +1 more source