Performance Analysis of DE over K-Means Proposed Model of Soft Computing

In real world data increased periodically, huge amount of data is called Big data. It is a well-known term used to define the exponential growth of data, both in structured and unstructured format. Data analysis is a method of cleaning, altering, learning valuable statistics, decision making and advising assumption with the help of many algorithm and procedure such as classification and clustering. In this chapter we discuss about big data analysis using soft computing technique and propose how to pair two different approaches like evolutionary algorithm and machine learning approach and try to find better cause.

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Unsupervised Machine Learning Approach for Gene Expression Microarray Data Using Soft Computing Technique

Proceedings of 3rd International Conference on Advanced Computing, Networking and Informatics - Smart Innovation, Systems and Technologies ◽

10.1007/978-81-322-2538-6_51 ◽

2015 ◽

pp. 497-506

Author(s):

Madhurima Rana ◽

Prachi Vijayeeta ◽

Utsav Kar ◽

Madhabananda Das ◽

B. S. P. Mishra

Keyword(s):

Gene Expression ◽

Machine Learning ◽

Soft Computing ◽

Microarray Data ◽

Learning Approach ◽

Gene Expression Microarray ◽

Computing Technique ◽

Gene Expression Microarray Data ◽

Machine Learning Approach ◽

Soft Computing Technique

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Method of Modeling of Big Data Analysis for Korean Medical Tourism: Machine Learning Approach

Asia-Pacific Journal of Neural Networks and Its Applications ◽

10.21742/ajnnia.2018.2.2.02 ◽

2018 ◽

Vol 2 (2) ◽

pp. 7-12

Author(s):

Jun-Ho Huh ◽

Keyword(s):

Machine Learning ◽

Big Data ◽

Data Analysis ◽

Medical Tourism ◽

Big Data Analysis ◽

Learning Approach ◽

Machine Learning Approach

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A Method of Modeling of Basic Big Data Analysis for Korean Medical Tourism: A Machine Learning Approach Using Apriori Algorithm

Information Science and Applications 2017 - Lecture Notes in Electrical Engineering ◽

10.1007/978-981-10-4154-9_91 ◽

2017 ◽

pp. 784-790 ◽

Cited By ~ 2

Author(s):

Jun-Ho Huh ◽

Han-Byul Kim ◽

Jinmo Kim

Keyword(s):

Machine Learning ◽

Big Data ◽

Data Analysis ◽

Medical Tourism ◽

Big Data Analysis ◽

Learning Approach ◽

Apriori Algorithm ◽

Machine Learning Approach

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A Survey Big data Analysis in Healthcare using machine Learning Approach

International Journal of Computer Sciences and Engineering ◽

10.26438/ijcse/v7i2.300307 ◽

2019 ◽

Vol 7 (2) ◽

pp. 300-307

Author(s):

A. Thammi Reddy ◽

M. Nagendra

Keyword(s):

Machine Learning ◽

Big Data ◽

Data Analysis ◽

Big Data Analysis ◽

Learning Approach ◽

Machine Learning Approach

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Track geometry big data analysis: A machine learning approach

2017 IEEE International Conference on Big Data (Big Data) ◽

10.1109/bigdata.2017.8258381 ◽

2017 ◽

Cited By ~ 2

Author(s):

Emmanuel Nii Martey ◽

Lasisi Ahmed ◽

Nii Attoh-Okine

Keyword(s):

Machine Learning ◽

Big Data ◽

Data Analysis ◽

Big Data Analysis ◽

Learning Approach ◽

Track Geometry ◽

Machine Learning Approach

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Deep Learning Based Sentiment Classification on User-Generated Big Data

Recent Advances in Computer Science and Communications ◽

10.2174/2213275912666190409152308 ◽

2020 ◽

Vol 13 (5) ◽

pp. 1047-1056

Author(s):

Akshi Kumar ◽

Arunima Jaiswal

Keyword(s):

Neural Network ◽

Big Data ◽

Deep Learning ◽

Soft Computing ◽

Sentiment Classification ◽

Convolution Neural Network ◽

Support Vector ◽

Computing Technique ◽

Proposed Model ◽

Soft Computing Techniques

Background: Sentiment analysis of big data such as Twitter primarily aids the organizations with the potential of surveying public opinions or emotions for the products and events associated with them. Objective: In this paper, we propose the application of a deep learning architecture namely the Convolution Neural Network. The proposed model is implemented on benchmark Twitter corpus (SemEval 2016 and SemEval 2017) and empirically analyzed with other baseline supervised soft computing techniques. The pragmatics of the work includes modelling the behavior of trained Convolution Neural Network on wellknown Twitter datasets for sentiment classification. The performance efficacy of the proposed model has been compared and contrasted with the existing soft computing techniques like Naïve Bayesian, Support Vector Machines, k-Nearest Neighbor, Multilayer Perceptron and Decision Tree using precision, accuracy, recall, and F-measure as key performance indicators. Methods: Majority of the studies emphasize on the utilization of feature mining using lexical or syntactic feature extraction that are often unequivocally articulated through words, emoticons and exclamation marks. Subsequently, CNN, a deep learning based soft computing technique is used to improve the sentiment classifier’s performance. Results: The empirical analysis validates that the proposed implementation of the CNN model outperforms the baseline supervised learning algorithms with an accuracy of around 87% to 88%. Conclusion: Statistical analysis validates that the proposed CNN model outperforms the existing techniques and thus can enhance the performance of sentiment classification viability and coherency.

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