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On utilizing the transitivity pursuit-enhanced object partitioning to optimize self-organizing lists-on-lists
In this paper, the Transitivity Pursuit-Enhanced Object Migration Automata (TPEOMA) is used to capture the dependence of elements in a hierarchical...
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Look and Feel What and How Recurrent Self-Organizing Maps Learn
This paper introduces representations and measurements for revealing the inner self-organization that occurs in a 1D recurrent self-organizing map....
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Signal-sensing dynamic S-box image encryption with 2D Griewank–sin map
Chaos systems serve as crucial tools in information security, particularly in applications such as image encryption and multimedia encryption....
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Comparison Between Self-organizing Maps and Principal Component Analysis for Assessment of Temporal Variations of Air Pollutants
This paper presents a comparison between self-organizing maps (SOMs) and principal component analysis (PCA) on investigating the temporal variation...
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Self-Organizing Maps with Convolutional Layers
Self-organizing maps (SOMs) are well appropriate for visualizing high-dimensional data sets. Training SOMs on raw high-dimensional data with classic...
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Electrofacies Estimation of Carbonate Reservoir in the Scotian Offshore Basin, Canada Using the Multi-resolution Graph-Based Clustering (MRGC) to Develop the Rock Property Models
Rock properties in geomechanical models depend on electrofacies. Electrofacies classification is a crucial task for generating accurate rock property...
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Advances in Self-Organizing Maps, Learning Vector Quantization, Clustering and Data Visualization Proceedings of the 13th International Workshop, WSOM+ 2019, Barcelona, Spain, June 26-28, 2019
This book gathers papers presented at the 13th International Workshop on Self-Organizing Maps, Learning Vector Quantization, Clustering and Data...
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A New Optimized Approach to Resolve a Combinatorial Problem: CoronaVirus Optimization Algorithm and Self-organizing Maps
The optimization provides resolutions to complex combinatorial problems that generally deal within large data size and expensive operating processes....
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Automatic Brain Tumor Detection and Segmentation from MRI Using Fractional Sobel Filter and SOM Neural Network
In the presented paper, the narrative automatic brain tumor detection and segmentation technique utilizing T1-weighted MR image is presented. Sobel...
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Comparing SONN Types for Efficient Robot Motion Planning in the Configuration Space
Motion planning in the configuration space (C-space) induces benefits, such as smooth trajectories. It becomes more complex as the degrees of freedom...
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Structural Health Monitoring with Self-Organizing Maps and Artificial Neural Networks
The use of self-organizing maps and artificial neural networks for structural health monitoring is presented in this paper. The authors recently...
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Applying Knowledge Map System for Sharing Knowledge in an Organization
Knowledge management (KM) is one of the most important systems that need to be applied in an organization. It has benefits for improving efficiency...
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Self-Organizing Map**s on the Flag Manifold
A flag is a nested sequence of vector spaces. The type of the flag is determined by the sequence of dimensions of the vector spaces making up the...
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A Comprehensive Study of SOMs, iSOMs, and Hybrid SOMs for Complex Data
With the help of data-driven insights and decision-making, machine learning has emerged as a transformational force across various areas....
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Bearing condition monitoring via an unsupervised and enhanced stacked auto-encoder
Supervised deep learning models have been widely used in the construction of bearing health indicators (HIs) for performance degradation. Such models...
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Hybrid Forecasting Model Based on Nonlinear Auto-Regressive Exogenous Network, Fourier Transform, Self-organizing Map and Pattern Recognition Model for Hour Ahead Electricity Load Forecasting
Unlike other goods, electricity generated cannot be stored on an industrial scale. Adding to that, the supply and demand keeps on fluctuating in the...
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Novelty Detection with Self-Organizing Maps for Autonomous Extraction of Salient Tracking Features
In the image processing field, many tracking algorithms rely on prior knowledge like color, shape or even need a database of the objects to be...
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VSOM: Efficient, Stochastic Self-organizing Map Training
Here we introduce VSOM, an efficient implementation of stochastic training for self-organizing maps. We derive VSOM from the standard stochastic...
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On the Self-organizing Migrating Algorithm Comparison by Means of Centrality Measures
In this article we continue in our research which combines three different areas - swarm and evolutionary algorithms, networks and coupled map...
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Optimizing Self-Organizing Maps Parameters Using Genetic Algorithm: A Simple Case Study
A Self-Organizing Map (SOM) is a powerful tool for data analysis, clustering, and dimensionality reduction. It is an unsupervised artificial neural...