scholarly journals Advances in applying soft computing techniques for big data and cloud computing

2018 ◽  
Vol 22 (23) ◽  
pp. 7679-7683 ◽  
Author(s):  
B. B. Gupta ◽  
Dharma P. Agrawal ◽  
Shingo Yamaguchi ◽  
Michael Sheng
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Soft Computing ◽  
Soft Computing Techniques

scholarly journals Soft computing techniques for big data and cloud computing

2020 ◽  
Vol 24 (8) ◽  
pp. 5483-5484 ◽  
Author(s):  
B. B. Gupta ◽  
Dharma P. Agrawal ◽  
Shingo Yamaguchi ◽  
Michael Sheng
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Soft Computing ◽  
Soft Computing Techniques

Deep Learning Based Sentiment Classification on User-Generated Big Data

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.

scholarly journals Satellite Orbit Prediction Using Big Data and Soft Computing Techniques to Avoid Space Collisions

Mathematics ◽  
2021 ◽  
Vol 9 (17) ◽  
pp. 2040
Author(s):  
Cristina Puente ◽  
Maria Ana Sáenz-Nuño ◽  
Augusto Villa-Monte ◽  
José Angel Olivas
Keyword(s):  
Big Data ◽  
Soft Computing ◽  
Satellite Orbit ◽  
Orbit Error ◽  
Preliminary Stage ◽  
Soft Computing Techniques ◽  
Full Study ◽  
Data Arrangement ◽  
Estimated Error ◽  
Do So

The number of satellites and debris in space is dangerously increasing through the years. For that reason, it is mandatory to design techniques to approach the position of a given object at a given time. In this paper, we present a system to do so based on a database of satellite positions according to their coordinates (x,y,z) for one month. We have paid special emphasis on the preliminary stage of data arrangement, since if we do not have consistent data, the results we will obtain will be useless, so the first stage of this work is a full study of the information gathered locating the missing gaps of data and covering them with a prediction. With that information, we are able to calculate an orbit error which will estimate the position of a satellite in time, even when the information is not accurate, by means of prediction of the satellite’s position. The comparison of two satellites over 26 days will serve to highlight the importance of the accuracy in the data, provoking in some cases an estimated error of 4% if the data are not well measured.

scholarly journals Benefits of using Big Data Sentiment Analysis and Soft Computing Techniques in E-Governance

2019 ◽  
Vol 8 (3) ◽  
pp. 3038-3044 ◽  
Keyword(s):  
Big Data ◽  
Sentiment Analysis ◽  
Soft Computing ◽  
Electronic Media ◽  
Citizen Involvement ◽  
Digital World ◽  
Soft Computing Techniques

In the current digital world, we tend to use electronic media to complete, each process and every work on time and quickly. In governance, we are benefited in a significant way from the use of sentiment posted electronically, called big data sentiment for e-governance. In this paper, we discuss the benefits of big data sentiment analysis and soft computing techniques for e-government with advancements of using big data. And its featured faster sentiment analysis using soft computing techniques and its framework. It helps to improve the goals of e-Governance that are Transparent, Trustworthy and Corruption free and quick action Governance as well as more citizen involvement in the country's development.

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