Application of the Kohonen map analysis (KMA) on chromatographic datasets to achieve unsupervised classification of olive and non-olive oil samples: a novel approach

2017 ◽  
Vol 9 (45) ◽  
pp. 6386-6393 ◽  
Author(s):  
Keshav Kumar
Keyword(s):  
Olive Oil ◽  
Unsupervised Classification ◽  
Quality Monitoring ◽  
Novel Approach ◽  
Kohonen Map ◽  
Map Analysis

A novel procedure that involves application of the Kohonen map analysis (KMA) algorithm on the chromatographic datasets is introduced for quality monitoring of olive oil samples.

Adaptive Median Filtering Based on Unsupervised Classification of Pixels

2013 ◽  
pp. 273-296 ◽  
Author(s):  
J. K. Mandal ◽  
Somnath Mukhopadhyay
Keyword(s):  
Clustering Algorithm ◽  
Digital Images ◽  
Unsupervised Classification ◽  
Median Filtering ◽  
Adaptive Window ◽  
Novel Approach ◽  
Weighted Median ◽  
Window Selection ◽  
Filtering Approach

This chapter deals with a novel approach which aims at detection and filtering of impulses in digital images through unsupervised classification of pixels. This approach coagulates directional weighted median filtering with unsupervised pixel classification based adaptive window selection toward detection and filtering of impulses in digital images. K-means based clustering algorithm has been utilized to detect the noisy pixels based adaptive window selection to restore the impulses. Adaptive median filtering approach has been proposed to obtain best possible restoration results. Results demonstrating the effectiveness of the proposed technique are provided for numeric intensity values described in terms of feature vectors. Various benchmark digital images are used to show the restoration results in terms of PSNR (dB) and visual effects which conform better restoration of images through proposed technique.

Chromatographic unsupervised classification of olive and non-olive oil samples with the aid of graph theory

2017 ◽  
Vol 9 (44) ◽  
pp. 6267-6272 ◽  
Author(s):  
Keshav Kumar
Keyword(s):  
Graph Theory ◽  
Olive Oil ◽  
Unsupervised Classification ◽  
Chemometric Approach

Graph theory is introduced as a novel chemometric approach for classifying the samples in an unsupervised manner.

scholarly journals Genomic signal processing for DNA sequence clustering

PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e4264 ◽  
Author(s):  
Gerardo Mendizabal-Ruiz ◽  
Israel Román-Godínez ◽  
Sulema Torres-Ramos ◽  
Ricardo A. Salido-Ruiz ◽  
Hugo Vélez-Pérez ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Signal Processing ◽  
Dna Sequences ◽  
Digital Signal ◽  
Unsupervised Classification ◽  
Genomic Signal Processing ◽  
Novel Approach ◽  
Signal Processing Methods ◽  
Genomic Signal

Genomic signal processing (GSP) methods which convert DNA data to numerical values have recently been proposed, which would offer the opportunity of employing existing digital signal processing methods for genomic data. One of the most used methods for exploring data is cluster analysis which refers to the unsupervised classification of patterns in data. In this paper, we propose a novel approach for performing cluster analysis of DNA sequences that is based on the use of GSP methods and the K-means algorithm. We also propose a visualization method that facilitates the easy inspection and analysis of the results and possible hidden behaviors. Our results support the feasibility of employing the proposed method to find and easily visualize interesting features of sets of DNA data.

Unsupervised classification of street architectures based on InfoGAN

2019 ◽  
Author(s):  
Ning Wang ◽  
Xianhan Zeng ◽  
Renjie Xie ◽  
Zefei Gao ◽  
Yi Zheng ◽  
...  
Keyword(s):  
Unsupervised Classification

scholarly journals Classification of Greek Olive Oils from Different Regions by Machine Learning-Aided Laser-Induced Breakdown Spectroscopy and Absorption Spectroscopy

Molecules ◽  
2021 ◽  
Vol 26 (5) ◽  
pp. 1241
Author(s):  
Nikolaos Gyftokostas ◽  
Eleni Nanou ◽  
Dimitrios Stefas ◽  
Vasileios Kokkinos ◽  
Christos Bouras ◽  
...  
Keyword(s):  
Machine Learning ◽  
Olive Oil ◽  
Absorption Spectra ◽  
Absorption Spectroscopy ◽  
Laser Induced Breakdown Spectroscopy ◽  
Spectroscopic Techniques ◽  
Breakdown Spectroscopy ◽  
Olive Oils ◽  
Laser Induced Breakdown

In the present work, the emission and the absorption spectra of numerous Greek olive oil samples and mixtures of them, obtained by two spectroscopic techniques, namely Laser-Induced Breakdown Spectroscopy (LIBS) and Absorption Spectroscopy, and aided by machine learning algorithms, were employed for the discrimination/classification of olive oils regarding their geographical origin. Both emission and absorption spectra were initially preprocessed by means of Principal Component Analysis (PCA) and were subsequently used for the construction of predictive models, employing Linear Discriminant Analysis (LDA) and Support Vector Machines (SVM). All data analysis methodologies were validated by both “k-fold” cross-validation and external validation methods. In all cases, very high classification accuracies were found, up to 100%. The present results demonstrate the advantages of machine learning implementation for improving the capabilities of these spectroscopic techniques as tools for efficient olive oil quality monitoring and control.

scholarly journals Phenotypic homogeneity in childhood epilepsies evolves in gene-specific patterns across 3251 patient-years of clinical data

2021 ◽  
Author(s):  
David Lewis-Smith ◽  
Shiva Ganesan ◽  
Peter D. Galer ◽  
Katherine L. Helbig ◽  
Sarah E. McKeown ◽  
...  
Keyword(s):  
Clinical Data ◽  
Similarity Analysis ◽  
Genetic Studies ◽  
Human Phenotype ◽  
Phenotypic Similarity ◽  
Complex Phenotypes ◽  
Cognitive Framework ◽  
Novel Approach ◽  
Genetic Epilepsies

AbstractWhile genetic studies of epilepsies can be performed in thousands of individuals, phenotyping remains a manual, non-scalable task. A particular challenge is capturing the evolution of complex phenotypes with age. Here, we present a novel approach, applying phenotypic similarity analysis to a total of 3251 patient-years of longitudinal electronic medical record data from a previously reported cohort of 658 individuals with genetic epilepsies. After mapping clinical data to the Human Phenotype Ontology, we determined the phenotypic similarity of individuals sharing each genetic etiology within each 3-month age interval from birth up to a maximum age of 25 years. 140 of 600 (23%) of all 27 genes and 3-month age intervals with sufficient data for calculation of phenotypic similarity were significantly higher than expect by chance. 11 of 27 genetic etiologies had significant overall phenotypic similarity trajectories. These do not simply reflect strong statistical associations with single phenotypic features but appear to emerge from complex clinical constellations of features that may not be strongly associated individually. As an attempt to reconstruct the cognitive framework of syndrome recognition in clinical practice, longitudinal phenotypic similarity analysis extends the traditional phenotyping approach by utilizing data from electronic medical records at a scale that is far beyond the capabilities of manual phenotyping. Delineation of how the phenotypic homogeneity of genetic epilepsies varies with age could improve the phenotypic classification of these disorders, the accuracy of prognostic counseling, and by providing historical control data, the design and interpretation of precision clinical trials in rare diseases.

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