Automatic Incremental Clustering Using Bat-Grey Wolf Optimizer-Based MapReduce Framework for Effective Management of High-Dimensional Data

2020 ◽  
Vol 11 (4) ◽  
pp. 72-92
Author(s):  
Ch. Vidyadhari ◽  
N. Sandhya ◽  
P. Premchand
Keyword(s):  
Feature Extraction ◽  
Bat Algorithm ◽  
Grey Wolf Optimizer ◽  
Jaccard Coefficient ◽  
Grey Wolf ◽  
Incremental Clustering ◽  
Mapreduce Framework ◽  
Text Documents ◽  
Two Phases ◽  
Point Distance

In this research paper, an incremental clustering approach-enabled MapReduce framework is implemented that include two phases, mapper and reducer phase. In the mapper phase, there are two processes, pre-processing and feature extraction. Once the input data is pre-processed, the feature extraction is done using wordnet features. Then, the features are fed to the reducer phase, where the features are selected using entropy function. Then, the automatic incremental clustering is done using bat-grey wolf optimizer (BAGWO). BAGWO is the integration of bat algorithm (BA) into grey wolf optimization (GWO) for generating various clusters of text documents. Upon the arrival of the incremental data, the mapping of the new data with respect to the centroids is done to obtain the effective cluster. For mapping, kernel-based deep point distance and for centroid update, fuzzy concept is used. The performance of the proposed framework outperformed the existing techniques using rand coefficient, Jaccard coefficient, and clustering accuracy with maximal values 0.921, 0.920, and 0.95, respectively.

Bat-Grey Wolf Optimizer and kernel mapping for automatic incremental clustering

2020 ◽  
Vol 11 (04) ◽  
pp. 2050032
Author(s):  
C. Vidyadhari ◽  
N. Sandhya ◽  
P. Premchand
Keyword(s):  
Feature Extraction ◽  
Bat Algorithm ◽  
Grey Wolf Optimizer ◽  
Jaccard Coefficient ◽  
Grey Wolf ◽  
Incremental Clustering ◽  
Text Documents ◽  
Deep Point ◽  
Processing Module ◽  
Point Distance

The technical advancement in information systems contributes towards the massive availability of the documents stored in the electronic databases such as e-mails, internet and web pages. Therefore, it becomes a complex task for arranging and browsing the required document. This paper proposes an approach for incremental clustering using the Bat-Grey Wolf Optimizer (BAGWO). The input documents are initially subjected to the pre-processing module to obtain useful keywords, and then the feature extraction is performed based on wordnet features. After feature extraction, feature selection is carried out using entropy function. Subsequently, the clustering is done using the proposed BAGWO algorithm. The BAGWO algorithm is designed by integrating the Bat Algorithm (BA) and Grey Wolf Optimizer (GWO) for generating the different clusters of text documents. Hence, the clustering is determined using the BAGWO algorithm, yielding the group of clusters. On the other side, upon the arrival of a new document, the same steps of pre-processing and feature extraction are performed. Based on the features of the test document, the mapping is done between the features of the test document, and the clusters obtained by the proposed BAGWO approach. The mapping is performed using the kernel-based deep point distance and once the mapping terminated, the representatives are updated based on the fuzzy-based representative update. The performance of the developed BAGWO outperformed the existing techniques in terms of clustering accuracy, Jaccard coefficient, and rand coefficient with maximal values 0.948, 0.968, and 0.969, respectively.

Particle Grey Wolf Optimizer (PGWO) Algorithm and Semantic Word Processing for Automatic Text Clustering

2019 ◽  
Vol 27 (02) ◽  
pp. 201-223 ◽  
Author(s):  
Ch. Vidyadhari ◽  
N. Sandhya ◽  
P. Premchand
Keyword(s):  
Word Processing ◽  
Text Categorization ◽  
Text Clustering ◽  
Significant Feature ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Text Documents ◽  
Text Document ◽  
Text Feature ◽  
Automatic Text

Text mining refers to the process of extracting the high-quality information from the text. It is broadly used in applications, like text clustering, text categorization, text classification, etc. Recently, the text clustering becomes the facilitating and challenging task used to group the text document. Due to some irrelevant terms and large dimension, the accuracy of text clustering is reduced. In this paper, the semantic word processing and novel Particle Grey Wolf Optimizer (PGWO) is proposed for automatic text clustering. Initially, the text documents are given as input to the pre-processing step which caters the useful keyword for feature extraction and clustering. Then, the resultant keyword is applied to wordnet ontology to find out the synonyms and hyponyms of every keyword. Subsequently, the frequency is determined for every keyword which is used to build the text feature library. Since the text feature library contains the larger dimension, the entropy is utilized to select the most significant feature. Finally, the new algorithm Particle Grey Wolf Optimizer (PGWO) is developed by integrating the particle swarm optimization (PSO) into the grey wolf optimizer (GWO). Thus, the proposed algorithm is used to assign the class labels to generate the different clusters of text documents. The simulation is performed to analyze the performance of the proposed algorithm, and the proposed algorithm is compared with existing algorithms. The proposed method attains the clustering accuracy of 80.36% for 20 Newsgroup dataset and the clustering accuracy of 79.63% for Reuter which ensures the better automatic text clustering.

Diabetic Retinopathy Detection Using Optimization Assisted Deep Learning Model: Outlook on Improved Grey Wolf Algorithm

2021 ◽  
pp. 2150035
Author(s):  
Nitin Shivsharan ◽  
Sanjay Ganorkar
Keyword(s):  
Feature Extraction ◽  
Activation Function ◽  
Extraction Process ◽  
Identification Accuracy ◽  
Grey Wolf ◽  
Significant Area ◽  
Major Shortcoming ◽  
Conventional Models ◽  
Two Phases ◽  
Hidden Neurons

In recent days, study on retinal image remains a significant area for analysis. Several retinal diseases are identified by examining the differences occurring in the retina. Anyhow, the major shortcoming between these analyses was that the identification accuracy is not satisfactory. The adopted framework includes two phases namely; (i) feature extraction and (ii) classification. Initially, the input fundus image is subjected to the feature extraction process, where the features like Local Binary Pattern (LBP), Local Vector Pattern (LVP) and Local Tetra Patterns (LTrP) are extracted. These extracted features are subjected to the classification process, where the Deep Belief Network (DBN) is used as the classifier. In addition, to improve the accuracy, the activation function and hidden neurons of DBN are optimally tuned by means of the Self Improved Grey Wolf Optimization (SI-GWO). Finally, the performance of implemented work is compared and proved over the conventional models.

scholarly journals Montagem de panorâmicas 360 através de Multi-band Blending com ajuste ótimo via meta-heurísticas

2021 ◽  
Author(s):  
◽  
Maria Júlia Rosa Aguiar
Keyword(s):  
Bat Algorithm ◽  
Bundle Adjustment ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
De Se ◽  
Multi Band

O Stitching de imagens é o alinhamento de múltiplas imagens em composições maiores que representam partes de uma cena 3D. A construção automática de panoramas a partir de múltiplas imagens digitais é uma área de grande importância, encontrando aplicações em diferentes setores como sensoriamento remoto, inspeção e manutenção em ambientes de trabalho e medicina. Diversos algoritmos de mosaico de imagens foram propostos nos últimos anos. Ao mesmo tempo, o advento contínuo de novos métodos de mosaico torna muito difícil escolher um algoritmo apropriado para uma finalidade específica. Este trabalho apresenta técnicas para a montagem de panorâmicas 360° a partir de imagens tiradas por um sistema robótico desenvolvido. Foram utilizados os algoritmos de otimização bioinspirados Grey Wolf Optimizer e Bat Algorithm com intuito de se obter um ajuste ótimo no posicionamento das imagens sendo responsáveis por um Bundle adjustment. Após, o ajustamento das imagens para se corrigir possíveis diferenças de coloração e discrepâncias nas imagens utiliza-se a metodologia Multi-band Blending para se obter, ao final, uma imagem uniforme. A comparação entre os algoritmos envolverá análise da variabilidade das soluções e características de convergência.

scholarly journals Optimal Sizing of an Island Hybrid Microgrid Based on Improved Multi-Objective Grey Wolf Optimizer

Processes ◽  
2020 ◽  
Vol 8 (12) ◽  
pp. 1581
Author(s):  
Wenqiang Zhu ◽  
Jiang Guo ◽  
Guo Zhao ◽  
Bing Zeng
Keyword(s):  
Power Supply ◽  
Tidal Current ◽  
System Optimization ◽  
Energy System ◽  
Energy Sources ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Hybrid Energy System ◽  
Hybrid Energy ◽  
Multi Objective

The hybrid renewable energy system is a promising and significant technology for clean and sustainable island power supply. Among the abundant ocean energy sources, tidal current energy appears to be very valuable due to its excellent predictability and stability, particularly compared with the intermittent wind and solar energy. In this paper, an island hybrid energy microgrid composed of photovoltaic, wind, tidal current, battery and diesel is constructed according to the actual energy sources. A sizing optimization method based on improved multi-objective grey wolf optimizer (IMOGWO) is presented to optimize the hybrid energy system. The proposed method is applied to determine the optimal system size, which is a multi-objective problem including the minimization of annualized cost of system (CACS) and deficiency of power supply probability (DPSP). MATLAB software is utilized to program and simulate the hybrid energy system. Optimization results confirm that IMOGWO is feasible to optimally size the system, and the energy management strategy effectively matches the requirements of system operation. Furthermore, comparison of hybrid systems with and without tidal current turbines is undertaken to confirm that the utilization of tidal current turbines can contribute to enhancing system reliability and reducing system investment, especially in areas with abundant tidal energy sources.

Optimizing cetane improver concentration in biodiesel-diesel blend via grey wolf optimizer algorithm

Fuel ◽  
2020 ◽  
Vol 273 ◽  
pp. 117784 ◽  
Author(s):  
Erol Ileri ◽  
Aslan Deniz Karaoglan ◽  
Sener Akpinar
Keyword(s):  
Grey Wolf Optimizer ◽  
Grey Wolf
Sign in / Sign up

Export Citation Format

Share Document