Results 31 to 40 of about 57,862 (263)

A Rapid Scalable Embedding and Mapping Model for Recommendation

IEEE Access, 2020
The recommendation systems based on deep neural networks show high accuracy and precision in advertising prediction and rating prediction tasks, but the performance declines when they encounter sparsity problems.
Suiyu Zhang, Cheng Yang, Wei Luo
doaj   +1 more source

Discovering Domain-Invariant Subspace for Depression Recognition by Jointly Exploiting Appearance and Dynamics Feature Representations

IEEE Access, 2019
Focusing on cross-dataset Automated Depression Recognition (ADR) by jointly exploring facial appearance and dynamics feature representations, we explore to propose a novel Latent Domain Adaptation Depression Recognition (latDADR) framework via ...
Jianwen Tao, Haote Xu
doaj   +1 more source

Network representation with clustering tree features [PDF]

Journal of Intelligent Information Systems, 2018
Representing and inferring interaction networks is a challenging and long-standing problem. Modern technological advances have led to a great increase in both volume and complexity of generated network data. The size of networks such as drug protein interaction networks or gene regulatory networks is constantly growing and multiple sources of ...
Pliakos, Konstantinos, Vens, Celine
openaire   +1 more source

iRNA5hmC: The First Predictor to Identify RNA 5-Hydroxymethylcytosine Modifications Using Machine Learning

Frontiers in Bioengineering and Biotechnology, 2020
RNA 5-hydroxymethylcytosine (5hmC) modification plays an important role in a series of biological processes. Characterization of its distributions in transcriptome is fundamentally important to reveal the biological functions of 5hmC.
Yuan Liu, Dasheng Chen, Ran Su, Wei Chen, Wei Chen, Leyi Wei, Leyi Wei   +6 more
doaj   +1 more source

A Local Discrete Feature Histogram for Point Cloud Feature Representation

Applied Sciences
Local feature descriptors are a critical problem in computer vision; the majority of current approaches find it difficult to achieve a balance between descriptiveness, robustness, compactness, and efficiency.
Linjing Jia, Cong Li, Guan Xi, Xuelian Liu, Da Xie, Chunyang Wang   +5 more
doaj   +1 more source

Multi-Features Refinement and Aggregation for Medical Brain Segmentation

IEEE Access, 2020
Due to the complexity of the anatomical structure for human organs, medical image segmentation is always a challenging computer vision task. The Convolutional Neural Network (CNN) requires a rich feature representation, which not only needs the ...
Dongyuan Wu, Yi Ding, Mingfeng Zhang, Qiqi Yang, Zhiguang Qin   +4 more
doaj   +1 more source

Feature Selection in Automatic Music Genre Classification [PDF]

, 2008
This paper presents the results of the application of a feature selection procedure to an automatic music genre classification system. The classification system is based on the use of multiple feature vectors and an ensemble approach, according to time ...
Celso A. A. Kaestner, Carlos N. Silla Jr., Alessandro L. Koerich, Silla Jr, Carlos N., Koerich, Alessandro L., Kaestner, Celso A.A.   +5 more
core   +1 more source

Causality-driven feature representation for connectivity prediction

Frontiers in Artificial Intelligence
Causal reasoning is essential for understanding relationships and guiding decision-making in different applications, as it allows for the identification of cause-and-effect relationships between variables. By uncovering the underlying process that drives
Bruno Souza, Manuel Castro, Ahmed Esmin, Ahmed Esmin, Leonardo Machado, Alexandre Ferreira, Anderson Rocha   +6 more
doaj   +1 more source

Deep Belief Network-Based Approaches for Link Prediction in Signed Social Networks

Entropy, 2015
In some online social network services (SNSs), the members are allowed to label their relationships with others, and such relationships can be represented as the links with signed values (positive or negative).
Feng Liu, Bingquan Liu, Chengjie Sun, Ming Liu, Xiaolong Wang   +4 more
doaj   +1 more source

Automatic large-scale classification of bird sounds is strongly improved by unsupervised feature learning [PDF]

, 2014
Automatic species classification of birds from their sound is a computational tool of increasing importance in ecology, conservation monitoring and vocal communication studies.
Plumbley, Mark D., Stowell, D, Plumbley, MD, Dan Stowell, Mark D. Plumbley, Stowell, Dan; id_orcid, Stowell, Dan   +6 more
core   +1 more source