Applications of the self-organising feature map neural network in community data analysis

This paper presents a software tool called AVID (A VIsualization and Design) which is particularly useful for data mining with an artificial neural network known as the self-organising feature map (SOM). AVID supports network training in both the i) selection of network inputs and ii) visualisation of the trained SOM. Both these features are novel aids to SOM network training and are particularly important when consideration is given to using the SOM for data mining. Once trained the SOM produces a 2-dimensional topological ordering of the input training data and it is particularly useful for representing the relationships within multi-dimensional data. The main classes within the data can be identified from the output map. AVID is an important software tool which enables data mining with the SOM by the selection of network inputs and the subsequent visualisation of the classes within these input vectors.

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Consumer’s Purchasing Behavior Analysis Based on the Self-Organizing Feature Map Neural Network Algorithm in E-Supply Chain

Neural Information Processing - Lecture Notes in Computer Science ◽

10.1007/11893028_114 ◽

2006 ◽

pp. 1022-1029

Author(s):

Lijuan Huang

Keyword(s):

Neural Network ◽

Supply Chain ◽

Behavior Analysis ◽

The Self ◽

Purchasing Behavior ◽

Feature Map ◽

Network Algorithm ◽

Neural Network Algorithm ◽

Self Organizing

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CHEMOMETRIC DATA ANALYSIS WITH AUTOENCODER NEURAL NETWORK

Electronic Imaging ◽

10.2352/issn.2470-1173.2019.1.vda-679 ◽

2019 ◽

Vol 2019 (1) ◽

pp. 679-1-679-6 ◽

Cited By ~ 1

Author(s):

Muhammad Bilal ◽

Mohib Ullah ◽

Habib Ullah

Keyword(s):

Neural Network ◽

Data Analysis

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Disequilibrium and Divinity Salience as Invariant Structures in The Halal Executives’ Experience of Eudaimonia

ADVANCES IN BUSINESS RESEARCH INTERNATIONAL JOURNAL ◽

10.24191/abrij.v5i1.9995 ◽

2019 ◽

Vol 5 (1) ◽

pp. 109

Author(s):

Jalilah Ahmad ◽

Rosmimah Mohd. Roslin ◽

Mohd Ali Bahari Abdul Kadir

Keyword(s):

Data Analysis ◽

Lived Experience ◽

Status Quo ◽

The Self ◽

Phenomenological Method ◽

Sustainable Practices ◽

Muslim Population ◽

Living Well ◽

Hard Skills ◽

Invariant Structures

The global Halal industry is large and continues to grow as the global Muslim population increases in size and dispersion. There are 1.84 billion Muslims today spread over 200 countries and is expected to increase to 2.2 billion by 2030. The industry will be worth USD6.4 trillion by the end of 2018 with more non-traditional players and emergent markets. The stakes are high with pressures to generate novel and sustainable practices. This goes beyond systems and hard skills as it needs to cut into the self – the person of virtues in virtuous acts, not because they “have to” but because it is the purpose of humankind or his telos - to be “living well” and “acting well” or eudaimonia. This study seek to explore Halal executives’ lived experience of “eudaimonia.”. Using Giorgi’s descriptive psychological phenomenological method for data analysis, the study elicits two distinct invariant structures – ‘disequilibrium in status quo’ and ‘divinity salience’.

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Model of artificial neural network for complex data analysis in slag glass-ceramic

Zeitschrift für Metallkunde ◽

10.3139/146.017915 ◽

2004 ◽

Vol 95 (2) ◽

pp. 97-101 ◽

Cited By ~ 1

Author(s):

Hongyuan Sun ◽

Qiye Wen ◽

Peixin Zhang ◽

Jianhong Liu ◽

Qianling Zhang ◽

...

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Data Analysis ◽

Glass Ceramic ◽

Complex Data ◽

Slag Glass ◽

Artificial Neural

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An Intelligent Evaluation Method to Analyze the Competitiveness of Airlines

Mathematical Problems in Engineering ◽

10.1155/2020/8589346 ◽

2020 ◽

Vol 2020 ◽

pp. 1-9

Author(s):

Jun Zhao ◽

Xumei Chen

Keyword(s):

Neural Network ◽

Prior Knowledge ◽

Evaluation Method ◽

Standard Sample ◽

Reference Value ◽

The Self ◽

Index Data ◽

Som Neural Network ◽

Simulation Results ◽

Self Organizing

An intelligent evaluation method is presented to analyze the competitiveness of airlines. From the perspective of safety, service, and normality, we establish the competitiveness indexes of traffic rights and the standard sample base. The self-organizing mapping (SOM) neural network is utilized to self-organize and self-learn the samples in the state of no supervision and prior knowledge. The training steps of high convergence speed and high clustering accuracy are determined based on the multistep setting. The typical airlines index data are utilized to verify the effect of the self-organizing mapping neural network on the airline competitiveness analysis. The simulation results show that the self-organizing mapping neural network can accurately and effectively classify and evaluate the competitiveness of airlines, and the results have important reference value for the allocation of traffic rights resources.

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