scholarly journals An algorithmic implementation of entropic ternary reduct soft sentiment set (ETRSSS) using soft computing technique on big data sentiment analysis (BDSA) for optimal selection of a decision based on real-time update in online reviews

2019 ◽  
Author(s):  
Akhilesh Dwivedi ◽  
RP Pant
Keyword(s):  
Big Data ◽  
Sentiment Analysis ◽  
Real Time ◽  
Soft Computing ◽  
Online Reviews ◽  
Optimal Selection ◽  
Computing Technique ◽  
Soft Computing Technique ◽  
Time Update ◽  
Selection Of

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

2016 ◽  
pp. 211-224
Author(s):  
Kapil Patidar ◽  
Manoj Kumar ◽  
Sushil Kumar
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Soft Computing ◽  
Big Data Analysis ◽  
Learning Approach ◽  
Real World Data ◽  
Computing Technique ◽  
Proposed Model ◽  
Machine Learning Approach ◽  
Soft Computing Technique

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.

Utilization of Soft Computing Techniques in Sputtering Processes: A Review

2013 ◽  
Vol 832 ◽  
pp. 260-265
Author(s):  
Norlina M. Sabri ◽  
Mazidah Puteh ◽  
Mohamad Rusop Mahmood
Keyword(s):  
Soft Computing ◽  
Process Parameters ◽  
Optimization Techniques ◽  
Future Research ◽  
Film Properties ◽  
Reliable Technique ◽  
Computing Technique ◽  
Optimization Of Process Parameters ◽  
Soft Computing Techniques ◽  
Soft Computing Technique

This paper presents an overview of research works on the utilizing of soft computing in the optimization of process parameters and in the prediction of thin film properties in sputtering processes. The papers from this review were obtained from relevant databases and from various scientific journals. The papers collected were published from 2008 to 2012. The focus of the review is to provide an outlook on the utilization of soft computing techniques in sputtering processes. Based on the review, the soft computing techniques which have been applied so far are ANN, GA and Fuzzy Logic. The first finding of this review is that soft computing technique is a promising and more reliable approach to optimize and predict process parameters compared to the traditional methods. The second finding is that the utilizing of soft computing techniques in sputtering processes are still limited and still in exploratory phase as they have not yet been extensively and stably applied. The techniques applied are also limited to ANN, GA and Fuzzy, whereas the exploration into other techniques is also necessary to be conducted in order to seek the most reliable technique and so as to expand the application of soft computing approach. Future research could focus on the exploration of other soft computing techniques for optimization in order to find the best optimization techniques based on the specific processes.

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