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- research-articleMarch 2024
NLDyn - An open source MATLAB toolbox for the univariate and multivariate nonlinear dynamical analysis of physiological data
Computer Methods and Programs in Biomedicine (CBIO), Volume 243, Issue CJan 2024https://doi.org/10.1016/j.cmpb.2023.107941Highlights- NLDyn offers 80+ methods for intricate analysis of multi-channel physiological signal dynamics.
- NLDyn integrates advanced nonlinear and multivariate techniques.
- Developed in MATLAB, NLDyn features an intuitive interface with ...
We present NLDyn, an open-source MATLAB toolbox tailored for in-depth analysis of nonlinear dynamics in biomedical signals. Our objective is to offer a user-friendly yet comprehensive platform for researchers to explore ...
- review-articleJune 2023
Machine learning- and statistical-based voice analysis of Parkinson’s disease patients: A survey
Expert Systems with Applications: An International Journal (EXWA), Volume 219, Issue CJun 2023https://doi.org/10.1016/j.eswa.2023.119651AbstractThe preliminary diagnosis and evaluation of the presence and/or severity of Parkinson’s disease is crucial in controlling the progress of the disease. Real-time, non-invasive methodologies based on machine learning-enhanced voice ...
- research-articleFebruary 2023
Image perceptual hashing for content authentication based on Watson’s visual model and LLE
Journal of Real-Time Image Processing (SPJRTIP), Volume 20, Issue 1Feb 2023https://doi.org/10.1007/s11554-023-01269-9AbstractImage perceptual hashing has been widely used in image content authentication. In order to extract hashing sequences that are more consistent with the subjective feeling of human’s eyes, better express the nonlinear relationship and internal ...
- research-articleJanuary 2023
Texture feature dimensionality reduction-based mammography classification using Random Forest
Journal of Computational Methods in Sciences and Engineering (JOCMSE), Volume 23, Issue 32023, Pages 1537–1545https://doi.org/10.3233/JCM-226669Breast cancer is the most frequent cancer and the leading cause of death among females. Diagnosis mass from mammogram correctly can reduce the unnecessary biopsy to a large extent. In this paper, we present a novel mammogram classification method ...
- research-articleDecember 2020
Predicting virulent proteins in bacterial pathogens Using A Novel Method
CAIH2020: Proceedings of the 2020 Conference on Artificial Intelligence and HealthcareOctober 2020, Pages 234–238https://doi.org/10.1145/3433996.3434039Identifying whether the uncharacterized protein belongs to a virulent protein or not is important. If it is virulent protein, it is very useful for studying its virulence mechanisms in pathogens as well as designing antiviral drugs. Particularly, with a ...
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- ArticleSeptember 2020
A Long Short-Term Memory Neural Network Model for Predicting Air Pollution Index Based on Popular Learning
Database Systems for Advanced Applications. DASFAA 2020 International WorkshopsSep 2020, Pages 190–199https://doi.org/10.1007/978-3-030-59413-8_16AbstractWith the acceleration of industrialization and modernization, the problem of air pollution has become more and more prominent, which causing serious impact on people’s production and life. Therefore, it is of great practical significance and ...
- research-articleJanuary 2020
Dimensionality reduction of image feature based on geometric parameter adaptive LLE algorithm
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology (JIFS), Volume 38, Issue 22020, Pages 1569–1577https://doi.org/10.3233/JIFS-179520Locally linear embedding (LLE) is a classical nonlinear dimensionality reduction algorithm, and it has been widely used in image feature selection. LLE reduces the dimensions of a data set only by exploring the geometric structure, which is calculated by ...
- articleJune 2019
A comparison of dimension reduction techniques for support vector machine modeling of multi-parameter manufacturing quality prediction
Journal of Intelligent Manufacturing (SPJIM), Volume 30, Issue 5June 2019, Pages 2245–2256https://doi.org/10.1007/s10845-017-1388-1Manufacturing quality prediction model, as an effective measure to monitor the quality in advance, has been developed using various data-driven techniques. However, multi-parameter in multi-stage of the modern manufacturing industry brings about the ...
- articleApril 2018
Nonlinear Dimensionality Reduction for Data with Disconnected Neighborhood Graph
Neural Processing Letters (NPLE), Volume 47, Issue 2April 2018, Pages 697–716https://doi.org/10.1007/s11063-017-9676-5Neighborhood graph based nonlinear dimensionality reduction algorithms, such as Isomap and LLE, perform well under an assumption that the neighborhood graph is connected. However, for datasets consisting of multiple clusters or lying on multiple ...
- articleApril 2018
A random walk based multi-kernel graph learning framework
Multimedia Tools and Applications (MTAA), Volume 77, Issue 8Apr 2018, Pages 9943–9957https://doi.org/10.1007/s11042-017-4599-8Graph learning is an important approach for machine learning. Kernel method is efficient for constructing similarity graph. Single kernel isn't sufficient for complex problems. In this paper we propose a framework for multi-kernel learning. We give a ...
- research-articleJanuary 2018
Robust data representation using locally linear embedding guided PCA
Neurocomputing (NEUROC), Volume 275, Issue CJanuary 2018, Pages 523–532https://doi.org/10.1016/j.neucom.2017.08.053Locally Linear Embedding (LLE) is widely used for embedding data on a nonlinear manifold. It aims to preserve the local neighborhood structure on the data manifold. Our work begins with a new observation that LLE has a natural robustness property. ...
- research-articleJanuary 2016
Low-rank image tag completion with dual reconstruction structure preserved
Neurocomputing (NEUROC), Volume 173, Issue P2January 2016, Pages 425–433https://doi.org/10.1016/j.neucom.2014.12.121User provided tags, albeit play an essential role in image annotation, may inhibit accurate annotation as well since they are potentially incomplete. To address this problem, a novel tag completion method is proposed in this paper. In order to exploit ...
- research-articleJanuary 2016
A Convex Matrix Optimization for the Additive Constant Problem in Multidimensional Scaling with Application to Locally Linear Embedding
SIAM Journal on Optimization (SIOPT), Volume 26, Issue 42016, Pages 2564–2590https://doi.org/10.1137/15M1043133The additive constant problem has a long history in multidimensional scaling and it has recently been used to resolve the issue of indefiniteness of the geodesic distance matrix in ISOMAP. But it would lead to a large positive constant being added to all ...
- abstractSeptember 2015
Modeling of Liquid-Liquid Equilibrium Data and Estimation of New Binary Interaction Parameters for NRTL Model for the Quaternary System Water/ Ethanol/1-Butanol / KCL at 298.15K
ICEMIS '15: Proceedings of the The International Conference on Engineering & MIS 2015September 2015, Article No.: 2, Page 1https://doi.org/10.1145/2832987.2832992The present study concerns experimental measurements of the salting-out effect on liquid-liquid phase equilibrium (LLE) of partially miscible systems such as water/ ethanol/1- butanol /Potassium chloride at 298.15K. The salt KCl was used at different ...
- ArticleDecember 2014
A Multi-exposure Fusion Method Based on Locality Properties
Proceedings of the 15th Pacific-Rim Conference on Advances in Multimedia Information Processing --- PCM 2014 - Volume 8879December 2014, Pages 333–342https://doi.org/10.1007/978-3-319-13168-9_37A new method is proposed for fusing a multi-exposure sequence of images into a high quality image based on the locality properties of the sequence. We divide the images into uniform blocks and use variance to represent the information of blocks. The ...
- research-articleJuly 2014
Local Geometry Feature Extraction for Face Sketch Recognition
ICIMCS '14: Proceedings of International Conference on Internet Multimedia Computing and ServiceJuly 2014, Pages 205–208https://doi.org/10.1145/2632856.2632929In this paper, we present a new feature extraction approach for face sketch recognition. Based on the assumption that small image patches in the photo and sketch images form manifold with similar local geometry in two different image spaces, we propose ...
- ArticleJuly 2013
Tumor gene expressive data classification based on locally linear representation fisher criterion
ICIC'13: Proceedings of the 9th international conference on Intelligent Computing Theories and TechnologyJuly 2013, Pages 443–449https://doi.org/10.1007/978-3-642-39482-9_51In this paper, a discriminant manifold learning method based on Locally Linear Embedding (LLE), which is named Locally Linear Representation Fisher Criterion (LLRFC), is proposed for the classification of tumor gene expressive data. In the proposed ...
- articleNovember 2012
Optimized keyframe extraction for 3D character animations
Computer Animation and Virtual Worlds (CAVW), Volume 23, Issue 6November 2012, Pages 559–568https://doi.org/10.1002/cav.1471In this paper, we propose a new method to automatically extract keyframes from animation sequences. Our method can be applied equally to both skeletal and mesh animations. It uses animation saliency computed on the original data to help select the group ...
- ArticleOctober 2011
Super-Resolution Using Manifold Learning
CICN '11: Proceedings of the 2011 International Conference on Computational Intelligence and Communication NetworksOctober 2011, Pages 707–710https://doi.org/10.1109/CICN.2011.154In this paper, a novel method for solving single image super-resolution problem is proposed. Objective is to recover a high resolution version of the given low resolution image. This method takes into account, a popular method of dimensionality ...
- ArticleAugust 2011
The connections between principal component analysis and dimensionality reduction methods of manifolds
ICIC'11: Proceedings of the 7th international conference on Advanced Intelligent Computing Theories and Applications: with aspects of artificial intelligenceAugust 2011, Pages 638–643https://doi.org/10.1007/978-3-642-25944-9_83Isometric feature mapping (ISOMAP), locally linear embedding (LLE) and Laplacian eigenmaps (LE) are recently proposed nonlinear dimensionality reduction methods of manifolds. When these methods are satisfied with some specific constraints, some hidden ...