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14 papers · ranked by Valyu relevance

01
PPubMed9 May 2023
Topological feature generation for link prediction in biological networks
Mustafa Temiz, Burcu Bakir-Gungor, Pınar Güner Şahan, Mustafa Coskun + 1 more
'Rajesh Bhardwaj'] Graph or network embedding is a powerful method for extracting missing or potential information from interactions between nodes in biological networks. Graph embedding methods learn representations of nodes and interactions in a graph with low-dimensional vectors, which facilitates research to…
83relevance
02
PPubMed29 Mar 2020
CDE++: Learning Categorical Data Embedding by Enhancing Heterogeneous Feature Value Coupling Relationships
Bin Dong, Songlei Jian, Ke Zuo
Categorical data are ubiquitous in machine learning tasks, and the representation of categorical data plays an important role in the learning performance. The heterogeneous coupling relationships between features and feature values reflect the characteristics of the real-world categorical data which need to be captured…
83relevance
03
PPubMed4 Feb 2021
Survey on graph embeddings and their applications to machine learning problems on graphs
Ilya Makarov, Dmitrii Kiselev, Nikita Nikitinsky, Lovro Subelj + 1 more
Dealing with relational data always required significant computational resources, domain expertise and task-dependent feature engineering to incorporate structural information into a predictive model. Nowadays, a family of automated graph feature engineering techniques has been proposed in different streams of…
83relevance
04
PPubMed19 May 2020
Generalized EmbedSOM on quadtree-structured self-organizing maps [version 2; peer review: 2 approved]
Miroslav Kratochvíl, Abhishek Koladiya, Jiří Vondrášek
EmbedSOM is a simple and fast dimensionality reduction algorithm, originally developed for its applications in single-cell cytometry data analysis. We present an updated version of EmbedSOM, viewed as an algorithm for landmark-directed embedding enrichment, and demonstrate that it works well even with manifold-learning…
80relevance
05
PPubMed28 Jun 2020
Propositionalization and embeddings: two sides of the same coin
Nada Lavrač, Blaž Škrlj, Marko Robnik-Šikonja
Data preprocessing is an important component of machine learning pipelines, which requires ample time and resources. An integral part of preprocessing is data transformation into the format required by a given learning algorithm. This paper outlines some of the modern data processing techniques used in relational…
80relevance
06
PPubMed15 Feb 2023
Design and Application of Deep Hash Embedding Algorithm with Fusion Entity Attribute Information
Xiaoli Huang, Haibo Chen, Zheng Zhang, Donald J. Jacobs + 1 more
'Sotiris Kotsiantis'] Hash is one of the most widely used methods for computing efficiency and storage efficiency. With the development of deep learning, the deep hash method shows more advantages than traditional methods. This paper proposes a method to convert entities with attribute information into embedded vectors…
80relevance
07
PPubMed5 Sep 2022
HyperQuaternionE: A hyperbolic embedding model for qualitative spatial and temporal reasoning
Ling Cai, Krzysztof Janowicz, Rui Zhu, Gengchen Mai + 2 more
Qualitative spatial/temporal reasoning (QSR/QTR) plays a key role in research on human cognition, e.g., as it relates to navigation, as well as in work on robotics and artificial intelligence. Although previous work has mainly focused on various spatial and temporal calculi, more recently representation learning…
78relevance
08
PPubMed16 Nov 2023
Graph embedding and geometric deep learning relevance to network biology and structural chemistry
Paola Lecca, Michela Lecca
Graphs are used as a model of complex relationships among data in biological science since the advent of systems biology in the early 2000. In particular, graph data analysis and graph data mining play an important role in biology interaction networks, where recent techniques of artificial intelligence, usually…
78relevance
09
PPubMed9 Feb 2023
Random embedded calibrated statistical blind steganalysis using cross validated support vector machine and support vector machine with particle swarm optimization
Deepa D. Shankar, Adresya Suresh Azhakath
The evolvement in digital media and information technology over the past decades have purveyed the internet to be an effectual medium for the exchange of data and communication. With the advent of technology, the data has become susceptible to mismanagement and exploitation. This led to the emergence of Internet…
76relevance
10
PPubMed29 May 2020
Reversible data hiding techniques with high message embedding capacity in images RDH techniques with high message embedding capacity
Furqan Aziz, Taeeb Ahmad, Abdul Haseeb Malik, M. Irfan Uddin + 3 more
'Shafiq Ahmad' 'Mohamed Sharaf' 'Gandharba Swain'] Reversible Data Hiding (RDH) techniques have gained popularity over the last two decades, where data is embedded in an image in such a way that the original image can be restored. Earlier works on RDH was based on the Image Histogram Modification that uses the peak…
74relevance
11
PPubMed27 Apr 2019
Visual IoT Security: Data Hiding in AMBTC Images Using Block-Wise Embedding Strategy †
Yu-Hsiu Lin, Chih-Hsien Hsia, Bo-Yan Chen, Yung-Yao Chen
This study investigates combining the property of human vision system and a 2-phase data hiding strategy to improve the visual quality of data-embedded compressed images. The visual Internet of Things (IoT) is indispensable in smart cities, where different sources of visual data are collected for more efficient…
74relevance
12
PPubMed19 Jul 2022
High-Quality Video Watermarking Based on Deep Neural Networks and Adjustable Subsquares Properties Algorithm
Maciej Kaczyński, Zbigniew Piotrowski, Naveen Chilamkurti
This paper presents a method of high-capacity and transparent watermarking based on the usage of deep neural networks with the adjustable subsquares properties algorithm to encode the data of a watermark in high-quality video using the H.265/HEVC (High-Efficiency Video Coding) codec. The aim of the article is to…
74relevance
13
PPubMed30 Sep 2020
Clustering and Recognition of Spatiotemporal Features Through Interpretable Embedding of Sequence to Sequence Recurrent Neural Networks
Kun Su, Eli Shlizerman
The generic goal of RNN Seq2Seq model is to continue (predict) the evolution of each given input sequence chosen from a (test) dataset which includes K different types of multidimensional time series. Such a goal is challenging as it requires the network to generate a sequence which superimposes the typical dynamics of…
74relevance
14
PPubMed9 Apr 2026
Reversible data hiding for 3D mesh models based on spatial polygon prediction and dual sorting
Qingjun Wang, Qilong Zhang, Xiaoying Song, Yiwen Liu
Reversible data hiding (RDH) is a crucial information hiding technique for copyright protection and integrity verification of digital media, as it ensures the original content can be perfectly restored. However, current research on RDH remains predominantly focused on images, and relatively little attention has been…
68relevance

Results for embedding methods

Topological feature generation for link prediction in biological networks

CDE++: Learning Categorical Data Embedding by Enhancing Heterogeneous Feature Value Coupling Relationships

Survey on graph embeddings and their applications to machine learning problems on graphs

Generalized EmbedSOM on quadtree-structured self-organizing maps [version 2; peer review: 2 approved]

Propositionalization and embeddings: two sides of the same coin

Design and Application of Deep Hash Embedding Algorithm with Fusion Entity Attribute Information

HyperQuaternionE: A hyperbolic embedding model for qualitative spatial and temporal reasoning

Graph embedding and geometric deep learning relevance to network biology and structural chemistry

Random embedded calibrated statistical blind steganalysis using cross validated support vector machine and support vector machine with particle swarm optimization

Reversible data hiding techniques with high message embedding capacity in images RDH techniques with high message embedding capacity

Visual IoT Security: Data Hiding in AMBTC Images Using Block-Wise Embedding Strategy †

High-Quality Video Watermarking Based on Deep Neural Networks and Adjustable Subsquares Properties Algorithm

Clustering and Recognition of Spatiotemporal Features Through Interpretable Embedding of Sequence to Sequence Recurrent Neural Networks

Reversible data hiding for 3D mesh models based on spatial polygon prediction and dual sorting

Topological feature generation for link prediction in biological networks

CDE++: Learning Categorical Data Embedding by Enhancing Heterogeneous Feature Value Coupling Relationships

Survey on graph embeddings and their applications to machine learning problems on graphs

Generalized EmbedSOM on quadtree-structured self-organizing maps [version 2; peer review: 2 approved]

Propositionalization and embeddings: two sides of the same coin

Design and Application of Deep Hash Embedding Algorithm with Fusion Entity Attribute Information

HyperQuaternionE: A hyperbolic embedding model for qualitative spatial and temporal reasoning

Graph embedding and geometric deep learning relevance to network biology and structural chemistry

Random embedded calibrated statistical blind steganalysis using cross validated support vector machine and support vector machine with particle swarm optimization

Reversible data hiding techniques with high message embedding capacity in images RDH techniques with high message embedding capacity

Visual IoT Security: Data Hiding in AMBTC Images Using Block-Wise Embedding Strategy †

High-Quality Video Watermarking Based on Deep Neural Networks and Adjustable Subsquares Properties Algorithm

Clustering and Recognition of Spatiotemporal Features Through Interpretable Embedding of Sequence to Sequence Recurrent Neural Networks

Reversible data hiding for 3D mesh models based on spatial polygon prediction and dual sorting