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Extracting drug utilization knowledge using self-organizing map and rough set theory

Hsin-Chuan Chou, Ching-Hsue Cheng, Jing-Rong Chang
2007 Expert systems with applications  
Therefore, it is critical and necessary to evaluate drug utilization and laboratory test in order to discover the knowledge that is beneath and can be extracted from those raw data.  ...  With 10-fold cross-verification, the proposed SOM-SOM-RST process successfully and effectively detects patients whose diagnosis codes have been changed during the period of investigation and attains an  ...  To reduce user manipulation, this paper utilize self-organizing map, one of neural network techniques, to perform the discovery.  ... 
doi:10.1016/j.eswa.2006.05.020 fatcat:d6vminppwrd4zasrdaa6p3vuli

Temporal data mining using genetic algorithm and neural network —A case study of air pollutant forecasts

Shine-Wei Lin, Chih-Hong Sun, Chin-Han Chen
2004 Geo-spatial Information Science  
Artificial intelligence technology like neural network and genetic algorithm can easily cope with highly complicated and non-linear combined spatial and temporal issues.  ...  These new GIS tools can be readily applied in a practical and appropriate manner in spatial and temporal research to patch the gaps in GIS data mining and knowledge discovery functions.  ...  The default filename in the back-propagation network (BPN) is "neurowgt.dat", and in self-organizing maps (SOM) they are "somwgt.dat" and "sommap.dat".  ... 
doi:10.1007/bf02826674 fatcat:ds4yyw7dk5fmfcnywqmav5xbne

Special Issue Editorial: Cognitively-Inspired Computing for Knowledge Discovery

Kaizhu Huang, Rui Zhang, Xiaobo Jin, Amir Hussain
2018 Cognitive Computation  
Cognitively-inspired neural networks.  ...  and new knowledge discovery methodologies.  ...  On the other hand, to solve the second limitation of existing neural networks, Wang et al. propose two neural network models based on the Lagrange programming neural network (LPNN), with application to  ... 
doi:10.1007/s12559-017-9532-y fatcat:64jcrljij5d6xgqjemgijftp7m

Page 2500 of Psychological Abstracts Vol. 86, Issue 6 [page]

1999 Psychological Abstracts  
Sensory neural nets self-organize on the basis of 5 sensory features. The system is then taught arbitrary symbolic iabels for a small number of similar stimuli.  ...  —Proposes a novel architecture and set of learning rules for cortical self-organization.  ... 

Data Mining Technology Based on Bayesian Network Structure Applied in Learning

Chunhua Wang, Dong Han
2016 International Journal of Database Theory and Application  
Abstract:Originated from Bayesian statistics, Bayesian network,with such characteristics as its unique expression form of uncertainty knowledge, rich probabilistic expression abilities, and the incremental  ...  of Bayesian network applied in in learning.  ...  The artificial neural network method can be divided into feedforward neural networks (BP algorithm), self-organizing neural network (self-organizing feature map, competitive learning, etc.)  ... 
doi:10.14257/ijdta.2016.9.5.27 fatcat:i2w4p22kwvayxo5kf6whwxdgzm

Growing recurrent self organizing map

Ozge Yeloglu, A. Nur Zincir-Heywood, Malcolm I. Heywood
2007 2007 IEEE International Conference on Systems, Man and Cybernetics  
The growing Recurrent Self-Organizing Map (GRSOM) is embedded into a standard Self-Organizing Map (SOM) hierarchy.  ...  To do so, the KDD benchmark dataset from the International Knowledge Discovery and Data Mining Tools Competition is employed.  ...  In this work, we are concerned with the representation of time under the unsupervised learning paradigm where a static neural network (Self-Organizing Map) is provided with dynamic properties.  ... 
doi:10.1109/icsmc.2007.4414001 dblp:conf/smc/YelogluZH07 fatcat:5oay34z25nhqhfnhocuntwe4yq

Alternative Visualization of Large Geospatial Datasets

Etien L. Koua, Menno-Jan Kraak
2004 The Cartographic Journal  
This approach is based on the effective application of computational algorithms, such as the Self-Organizing Map (SOM).  ...  graphical representations are applied to portray extracted patterns in a visual form that allows for better understanding of the derived structures and possible geographical processes, and should facilitate knowledge  ...  THE SELF-ORGANIZING MAP AND THE EXPLORATION OF LARGE GEOSPATIAL DATA The Self-Organizing Map algorithm The Self-Organizing Map (Kohonen, 1989) is an Artificial Neural Network used to map multidimensional  ... 
doi:10.1179/000870404x13283 fatcat:xfk45xuiy5hlrojcxxslfcwqjm

From Biological Synapses to "Intelligent" Robots

Birgitta Dresp-Langley
2022 Electronics  
Its potential for adaptive learning and control without supervision, the generation of functional complexity, and control architectures based on self-organization is brought forward.  ...  Hebbian synaptic learning is discussed as a functionally relevant model for machine learning and intelligence, as explained on the basis of examples from the highly plastic biological neural networks of  ...  Grip forces self-organize progressively in a way that is similar to the self-organization of neural oscillations during task learning, and, in surgical human-robot interaction, a self-organizing neural  ... 
doi:10.3390/electronics11050707 fatcat:fopi24ot2vdr7p6qqcn7prqpge

Self-Organizing Maps and its Applications in Sleep Apnea Research and Molecular Genetics [chapter]

G. Guimarães, W. Urfer
2003 Studies in Classification, Data Analysis, and Knowledge Organization  
This paper presents the application of special unsupervised neural networks (self-organizing maps) to different domains, as sleep apnea discovery, protein sequences analysis and tumor classification.  ...  Furthermore, an integration of unsupervised neural networks with hidden markov models is proposed.  ...  Self-Organizing Maps for Exploratory Data Analysis Artificial Neural Networks (ANNs) may be classified according to their learning principles mainly into two different types: ANNs with supervised learning  ... 
doi:10.1007/978-3-642-55721-7_34 fatcat:3hqvjxu7pzfnhohtuoeaurdkse

Spatial data mining on remote sensing perspective

2016 International Journal of Latest Trends in Engineering and Technology  
Various kinds of networks ranging from multi layer perceptrons to self organizing feature maps have been developed for the spatial knowledge extraction from remote sensing images.  ...  Limin Jiao and Yaolin Liu [41] proposed a spatial clustering model based on self-organizing feature map and a composite distance measure, for the knowledge discovery from spatial database of spatial objects  ... 
doi:10.21172/1.74.036 fatcat:mdrfpf5gefg7lojxpmknvcloym

Self-Organizing Map and Multi-Layer Perceptron Neural Network Based Data Mining to Envisage Agriculture Cultivation

E.T. Venkatesh, Dr. P. Thangaraj
2008 Journal of Computer Science  
With the tested techniques available for calibrating the quality of soil and the crops suitable for cultivation in it, it is possible to determine the exact crop, irrigation patterns and even the cycle  ...  This paper dealt with the application of SOM based clustering and Artificial Intelligence techniques, to analyze the patterns of soils distributed across huge geographical area and identify the suitable  ...  Self Organizing Maps for clustering has been chosen for the solution after a pervasive scrutiny of all neural network algorithms.  ... 
doi:10.3844/jcssp.2008.494.502 fatcat:otwctxz54bgovj6rdacml2inwe

Concept Discovery Innovations in Law Enforcement: A Perspective

Jonas Poelmans, Paul Elzinga, Stijn Viaene, Guido Dedene
2010 2010 International Conference on Intelligent Networking and Collaborative Systems  
FCA is combined with statistical techniques such as Hidden Markov Models (HMM) and Emergent Self Organizing Maps (ESOM).  ...  The combination of this concept discovery and refinement technique with statistical techniques for analyzing high-dimensional data not only resulted in new insights but often in actual improvements of  ...  We combined FCA with Emergent Self Organizing Maps to discover emergent structures in the high-dimensional data space.  ... 
doi:10.1109/incos.2010.18 dblp:conf/incos/PoelmansEVD10 fatcat:b47gxcypvbb7zo7hh4j23btfyq

Knowledge extracted from recurrent deep belief network for real time deterministic control

Shin Kamada, Takumi Ichimura
2017 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC)  
The knowledge that can realize faster inference of pre-trained deep network is extracted as IF-THEN rules from the network signal flow given input data.  ...  We can success the knowledge extraction from the trained deep learning with high classification capability.  ...  We proposed the adaptive learning method of RNN-RBM with self-organization function of network structure according to a given input data [8] .  ... 
doi:10.1109/smc.2017.8122711 dblp:conf/smc/KamadaI17 fatcat:jf7fn5jiqjffxacz6752nrjp5a

A review on data clustering using spiking neural network (SNN) models

Siti Aisyah Mohamed, Muhaini Othman, Mohd Hafizul Afifi
2019 Indonesian Journal of Electrical Engineering and Computer Science  
The evolution of Artificial Neural Network recently gives researchers an interest to explore deep learning evolved by Spiking Neural Network clustering methods.  ...  Spiking Neural Network (SNN) models captured neuronal behaviour more precisely than a traditional neural network as it contains the theory of time into their functioning model [1].  ...  As a result of extensive studies in ANNs, has led to innovation of more advance type of networks such as self-organizing maps, recurrent networks, probabilistic neural networks, dynamic neural networks  ... 
doi:10.11591/ijeecs.v15.i3.pp1392-1400 fatcat:r767f32kcva7leu3zk65hv2rxy

A brain basis of dynamical intelligence for AI and computational neuroscience [article]

Joseph D. Monaco, Kanaka Rajan, Grace M. Hwang
2021 arXiv   pre-print
To motivate a brain basis of neural computation, we present a dynamical view of intelligence from which we elaborate concepts of sparsity in network structure, temporal dynamics, and interactive learning  ...  models distributed in long-term memory networks.  ...  For instance, a recent model from two of the authors demonstrated self-organized swarm control with phase-coupling and attractor dynamics 140 .  ... 
arXiv:2105.07284v2 fatcat:ble5h45pk5fczn72dwco2m3rkm
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