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Automated Kernel Independent Component Analysis Based Two Variable Weighted Multi-view Clustering for Complete and Incomplete Dataset
2015
Research Journal of Applied Sciences Engineering and Technology
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Novel Subspace Two Variable Weighted Clustering (NSTVWC) is proposed that can function with apartial and complete mapping. ...
The NSTVWC framework is to bid a remedy for the mixed attributes multiview clustering problem, the potential of various mode of existing clustering algorithms and features of different types of complete ...
Every fish typically resides in the place with k k k calculate the optimized probability value for clustering a best objective function (21). New fuzzy based artificial process. ...
doi:10.19026/rjaset.9.2611
fatcat:e5ffpc5nfvgeniejisuyklhjje
Optimized Fuzzy Enabled Semi-Supervised Intrusion Detection System for Attack Prediction
2022
Intelligent Automation and Soft Computing
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This model increases the accuracy of intrusion detection using Machine Learning Methodologies and fuzziness has been used to identify various categories of hazards, and a machine learning approach has ...
The combined use of fuzziness-based and RNN-IDS is therefore highly suited to high-precision classification, and its efficiency is better compared to that of conventional machine learning approaches. ...
Their proposed approach is considered to be a Semi-Supervised Learning method in which the learning process requires some samples with unknown labelling with low fuzziness. ...
doi:10.32604/iasc.2022.022211
fatcat:u4jmyoynyzfv3mjvq23mneoywm
Economic Order Quantity Model-Based Optimized Fuzzy Nonlinear Dynamic Mathematical Schemes
2022
Computational Intelligence and Neuroscience
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The principal expenditures are treated as fuzzy numbers in this article, which includes a blurry categorial prototype with pattern-diverse stipulation and collapse with salvation worth. ...
nonlinear features of the complex dynamic and nonlinear systems as well as structures. ...
. e goal of K-means is to reduce the sum of the squared error over all clusters in line with J(C) � K k�1 x i ∈c k x i − μ k � � � � � � � � 2 . ( 2 ) In traditional clustering, each item is categorically ...
doi:10.1155/2022/3881265
fatcat:pko6644uczhj3awcu5njr7xama
Partitioning Clustering algorithms for handling numerical and categorical data: a review
[article]
2019
arXiv
pre-print
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In this paper, we review partitioning based algorithm such as K-prototype, Extension of K-prototype, K-histogram, Fuzzy approaches, genetic approaches, etc. ...
However, datasets with mixed types of attributes are common in real life data mining applications. ...
Rather than the weight of categorical attribute is taken form user, it is automated from data set itself. ...
arXiv:1311.7219v3
fatcat:odu6orzxfbgavaqdagjrnt4td4
An intelligent video categorization engine
2005
Kybernetes
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Purpose -We describe an intelligent video categorization engine (IVCE) that uses the learning capability of artificial neural networks (ANNs) to classify suitably preprocessed video segments into a predefined ...
We also compare the performance of two popular ANNs: Kohonen's self-organizing map (SOM) and fuzzy adaptive resonance theory (Fuzzy ART). ...
When the learning process ends, clusters containing coded input patterns are formed at all committed F 2 layer nodes. Like other ART networks, Fuzzy ART can operate between plastic and stable modes. ...
doi:10.1108/03684920510595490
fatcat:2jbxbctsrje35ididicbs3j5wy
A REVIEW STUDY OF METHODS UTILIZED FOR IDENTIFYING AND SEGMENTING THE BRAIN TUMOR FROM MR IMAGERIES
2019
Zenodo
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Also integrate a comparative study of the automated brain tumor coupled through the utilization of tuomr detection techniques. ...
will provide an analysis of the process integrated pertaining to the retrieval of brain images through the identification of the specific data sets selected in the process to identify the stipulated features ...
The techniques dealt here were, K-means clustering accompany with watershed segmentation procedure Optimized K-means clustering merged along with Genetic Algorithm (GA) Optimized C-means clustering ...
doi:10.5281/zenodo.3256441
fatcat:xiqd75juvbbhnjbwffgruujnbi
A Survey on Brain Tumor Detection and Classification System based on Artificial Neural Network
2014
International Journal of Computer Applications
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SMO when used with kmeans clustering provides a more accurate system. ...
Hence a brain tumour detection and classification system is required for early detection and categorization of tumour . ...
ANNs, like people, learn by example. The neuron has two modes operations The training/learning mode and the using/testing mode [16] . ...
doi:10.5120/15820-4651
fatcat:7xasiedkabgwlnxewuo4vb6a2y
Hyperbox based machine learning algorithms: A comprehensive survey
[article]
2019
arXiv
pre-print
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In general, according to the architecture and characteristic features of the resulting models, the existing hyperbox-based learning algorithms may be grouped into three major categories: fuzzy min-max ...
Along with this trend, machine learning algorithms have been formed and evolved continuously to discover new information and knowledge from different data sources. ...
They used the weighted fuzzy min-max neural network for considering the weight of each feature to choose interesting features for each sample class. ...
arXiv:1901.11303v3
fatcat:ak66d74lxvbp7j3hjg4mzvqlr4
Applied machine learning and management of volatility, uncertainty, complexity & ambiguity (V.U.C.A)
2020
Journal of Intelligent & Fuzzy Systems
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using fuzzy clustering discrete optimization. ...
The author has used multivariate linear regression to assign weights to state variables, and k-nearest neighbour algorithm and Kalman filter to update the weights to adapt the uncertainties of traffic ...
model by combining the naive Bayesian classification algorithm with the fuzzy model and validated their model in teaching practice. ...
doi:10.3233/jifs-179915
fatcat:njwjogoperg2jggdkyheiibbhi
Machine Learning in Efficient and Effective Web Service Discovery
2015
Journal of Web Engineering
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Machine learning is a promising area for researchers to produce accurate estimates consistently. Machine learning system effectively "learns" how to estimate from training set of completed projects. ...
This paper discusses various eminent researchers' work in this direction using machine learning based techniques. ...
They have introduced a weighted fuzzy clustering algorithm that detected implicit service capability groups. ...
dblp:journals/jwe/BhardwajS15
fatcat:hjpemsjbnvavbfohuw63d4zoke
Assimilated Strong Fuzzy C-means in MR Images for Glioblastoma Multiforme
2015
Indian Journal of Science and Technology
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Methods/Statistical Analysis: This automated process implemented by a robust Fuzzy C-Means (FCM). This FCM needs novel objective function. This is obtained by performing replacement. ...
It results in membership partition matrices and successive prototypes with equation. The initial cluster reduces both the running time and computational complexity. ...
Rqc with vk = (v1k , v2k , ...., vqk) T ∈Rq is the cluster center of kth cluster, 1 ≤ k ≤ c. • xi − vk is Euclidean distance between the object xi and center vk. • m is the weighting exponent (also called ...
doi:10.17485/ijst/2015/v8i1/79281
fatcat:5khggotu7jffrnhlnuwrhs7mri
Components of Soft Computing for Epileptic Seizure Prediction and Detection
[chapter]
2019
Epilepsy - Advances in Diagnosis and Therapy [Working Title]
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The classifier with a suitable learning method can perform well for automated epileptic seizure detection systems. ...
Components of soft computing include machine learning, fuzzy logic, evolutionary computation, and probabilistic theory. These components have the cognitive ability to learn effectively. ...
In machine learning, classification is the process of categorizing the data by training the machine with the class label. ...
doi:10.5772/intechopen.83413
fatcat:rjixze4mbrawpjl2btuajpzose
Feature-weighted clustering with inner product induced norm based dissimilarity measures: an optimization perspective
2017
Machine Learning
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We develop the general algorithms to solve the hard (k-means) and fuzzy (fuzzy c-means) partitional clustering problems and undertake indepth analyses of the convergence of the algorithms as well. ...
feature-weighted clustering algorithms using such measures. ...
Recently Saha and Das (2015b) extended the weight learning strategy to fuzzy k-modes clustering of categorical data. ...
doi:10.1007/s10994-016-5623-3
fatcat:yis524prw5bwphnynjqn2kujv4
A Review of Evolutionary Multi-objective Clustering Approaches
[article]
2022
arXiv
pre-print
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2021 pre-print arXiv:2110.08100v1 fatcat:oqjlcwyw3vhe7dj2dkpj4hvsaq
Also, we categorized the EMOC algorithms based on shared characteristics that highlight the main features or application fields. ...
For that, we introduce a general architecture of EMOC to describe the main elements applied in designing EMOC algorithms and we correlate them with the main features found in the literature. ...
[98, 99] considered the K-Mode internal distance (Km id ) and K-Mode weighted internal distance (Km wid ) as objective functions. ...
arXiv:2110.08100v2
fatcat:k2lxfbfjwrdmpoer4kb2cyohty
Classification of DNA Sequence Using Soft Computing Techniques: A Survey
2016
Indian Journal of Science and Technology
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knowledge in it had been referred to as Fuzzy C-Means method with Empirical Mode Decomposition (FCM-EMD). ...
Membership perform is most vital in fuzzy model for calculative the membership value. Initially features are extracted using 6-letter switch over cluster methodology. ...
doi:10.17485/ijst/2015/v8i1/89343
fatcat:3nxqk2efv5bkdmhilhmyup7noe
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