Results 101 to 110 of about 250,191 (314)

Hyperbolic Convolutional Neural Networks

CoRR, 2023
Deep Learning is mostly responsible for the surge of interest in Artificial Intelligence in the last decade. So far, deep learning researchers have been particularly successful in the domain of image processing, where Convolutional Neural Networks are used.
Andrii Skliar, Maurice Weiler
openaire   +2 more sources

Performance Enhancement of Tin Chloride‐Incorporated Ferroelectric Polymer‐Based Artificial Synapse for Hardware Neural Networks

Advanced Functional Materials, EarlyView.
This paper proposes a highly efficient ferroelectric artificial synapse device based on an oxide semiconductor and SnCl2‐inserted P(VDF‐TrFE) gate dielectric layer. This FeFET significantly improved the synaptic performance due to the ion‐dipole interaction.
Hyun‐Soo Kim, Hyunkyu Lee, Yeonho Choi, Won Young Jang, Hyungmin Kim, Krishna Moorthy Ponnusamy, Hongseung Lee, Jaewook Yoo, Ji Ye Lee, S. Chandramohan, Seongin Hong, Hagyoul Bae, Hyeon‐Sik Jang, Sang Yeol Lee, Keun Heo   +14 more
wiley   +1 more source

Deep Recognition of Chinese Herbal Medicines Based on a Caputo Fractional Order Convolutional Neural Network [PDF]

Chinese herbal medicines are the treasure of Chinese traditional medicine, which contains active ingredients that have significant therapeutic effects for many diseases.
Lulu Lv, Zhipeng Yuan, Yu, H., Kang Liu, Chenxi Li, Youyong Li, Tao Li, Jiawei Yang, Hongqing Yu   +8 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

Review of Node Classification Methods Based on Graph Convolutional Neural Networks [PDF]

Jisuanji kexue
Node classification is one of the important research tasks in graph field.In recent years,with the continuous deepening of research on graph convolutional neural network,significant progress has been made in the research and application of node ...
ZHANG Liying, SUN Haihang, SUN Yufa , SHI Bingbo
doaj   +1 more source

Intelligent Orthopedics: Machine Learning in Diagnosis of Bone Disease, Implants, and Bone Health Monitoring

Advanced Healthcare Materials, EarlyView.
Efficient recovery from traumatic or degenerative diseases is a great challenge, even after all the advancements in bone and cartilage regeneration. Machine learning (ML) algorithms have presented opportunities to enhance these aspects by accurately analyzing imaging data.
Maryam Kamaei, Hesam Mohammadi, Shamim Golafshan, Mojtaba Alehosseini, Mahsa Mohammadzadeh, Atefeh Golshirazi, Nasim Golafshan, Tatiane Eufrásio‐da‐Silva, Alireza Dolatshahi‐Pirouz, Gorka Orive   +9 more
wiley   +1 more source

DeepFeature: feature selection in nonimage data using convolutional neural network [PDF]

, 2021
Artificial intelligence methods offer exciting new capabilities for the discovery of biological mechanisms from raw data because they are able to detect vastly more complex patterns of association that cannot be captured by classical statistical tests ...
Lysenko, Artem, Boroevich, Keith A, Vans, Edwin, Sharma, Alok, Tsunoda, Tatsuhiko   +4 more
core   +1 more source

Forest fire image recognition based on convolutional neural network

Journal of Algorithms & Computational Technology, 2019
In order to detect fire automatically, a forest fire image recognition method based on convolutional neural networks is proposed in this paper. There are two main types of fire recognition algorithms.
Yuanbin Wang, Langfei Dang, Jieying Ren
doaj   +1 more source

INVESTIGATIONS ON THE POTENTIAL OF CONVOLUTIONAL NEURAL NETWORKS FOR VEHICLE CLASSIFICATION BASED ON RGB AND LIDAR DATA [PDF]

ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2017
In recent years, there has been a significant improvement in the detection, identification and classification of objects and images using Convolutional Neural Networks.
R. Niessner, H. Schilling, B. Jutzi
doaj   +1 more source

Understanding Convolutional Neural Networks

CoRR, 2016
Statistical Machine Learning Course Project at Carnegie Mellon ...
openaire   +2 more sources