Journal of Soft Computing Paradigm

10.36548/jscp ◽  
2021 ◽  
Keyword(s):  
Soft Computing ◽  
Computing Paradigm

Soft Computing and its Applications

2010 ◽  
pp. 1-30 ◽  
Author(s):  
Siddhartha Bhattacharyya ◽  
Ujjwal Maulik ◽  
Sanghamitra Bandyopadhyay
Keyword(s):  
Neural Networks ◽  
Fuzzy Logic ◽  
Evolutionary Computation ◽  
Soft Computing ◽  
Real World ◽  
Industrial Applications ◽  
Approximation Techniques ◽  
Computing Paradigm ◽  
Numeric Data ◽  
Main Components

Soft Computing is a relatively new computing paradigm bestowed with tools and techniques for handling real world problems. The main components of this computing paradigm are neural networks, fuzzy logic and evolutionary computation. Each and every component of the soft computing paradigm operates either independently or in coalition with the other components for addressing problems related to modeling, analysis and processing of data. An overview of the essentials and applications of the soft computing paradigm is presented in this chapter with reference to the functionalities and operations of its constituent components. Neural networks are made up of interconnected processing nodes/neurons, which operate on numeric data. These networks posses the capabilities of adaptation and approximation. The varied amount of uncertainty and ambiguity in real world data are handled in a linguistic framework by means of fuzzy sets and fuzzy logic. Hence, this component is efficient in understanding vagueness and imprecision in real world knowledge bases. Genetic algorithms, simulated annealing algorithm and ant colony optimization algorithm are representative evolutionary computation techniques, which are efficient in deducing an optimum solution to a problem, thanks to the inherent exhaustive search methodologies adopted. Of late, rough sets have evolved to improve upon the performances of either of these components by way of approximation techniques. These soft computing techniques have been put to use in wide variety of problems ranging from scientific to industrial applications. Notable among these applications include image processing, pattern recognition, Kansei information processing, data mining, web intelligence etc.

Evolutionary Computing

2012 ◽  
pp. 271-295
Author(s):  
Paramartha Dutta ◽  
Paramita Bhattacharya ◽  
Siddhartha Bhattacharyya
Keyword(s):  
Evolutionary Algorithms ◽  
Soft Computing ◽  
Portfolio Management ◽  
Human Intelligence ◽  
Optimal Solutions ◽  
Computing Paradigm ◽  
Intelligent Management ◽  
Data Explosion ◽  
Global Optimal ◽  
Industrial Databases

The field of evolutionary computation forms one of the tenets of the soft computing paradigm, which aims at deriving at some possible global optimal solutions to search problems. The field of industrial informatics, being an emergent field, faces tremendous data explosion and associated challenges of data redundancies and inconsistencies. Different evolutionary algorithms have been put to use to evolve intelligence out of redundancies immanent in industrial databases. Industrial portfolio management has been a much-talked affair nowadays, thanks to the evolving fields of data intelligent management and archival techniques. An overview of the different facets of evolutionary algorithms and their role in imbibing human intelligence in data management and retrieval is presented with regards to its application in the optimization of a collection of financial portfolio instruments.

Handwritten Bangla character recognition using a soft computing paradigm embedded in two pass approach

2015 ◽  
Vol 48 (6) ◽  
pp. 2054-2071 ◽  
Author(s):  
Nibaran Das ◽  
Ram Sarkar ◽  
Subhadip Basu ◽  
Punam K. Saha ◽  
Mahantapas Kundu ◽  
...  
Keyword(s):  
Soft Computing ◽  
Character Recognition ◽  
Computing Paradigm

Soft Computing and its Applications

Data Mining ◽  
2013 ◽  
pp. 366-394
Author(s):  
Siddhartha Bhattacharyya ◽  
Ujjwal Maulik ◽  
Sanghamitra Bandyopadhyay
Keyword(s):  
Neural Networks ◽  
Fuzzy Logic ◽  
Evolutionary Computation ◽  
Soft Computing ◽  
Real World ◽  
Industrial Applications ◽  
Approximation Techniques ◽  
Computing Paradigm ◽  
Numeric Data ◽  
Main Components

Soft Computing is a relatively new computing paradigm bestowed with tools and techniques for handling real world problems. The main components of this computing paradigm are neural networks, fuzzy logic and evolutionary computation. Each and every component of the soft computing paradigm operates either independently or in coalition with the other components for addressing problems related to modeling, analysis and processing of data. An overview of the essentials and applications of the soft computing paradigm is presented in this chapter with reference to the functionalities and operations of its constituent components. Neural networks are made up of interconnected processing nodes/neurons, which operate on numeric data. These networks posses the capabilities of adaptation and approximation. The varied amount of uncertainty and ambiguity in real world data are handled in a linguistic framework by means of fuzzy sets and fuzzy logic. Hence, this component is efficient in understanding vagueness and imprecision in real world knowledge bases. Genetic algorithms, simulated annealing algorithm and ant colony optimization algorithm are representative evolutionary computation techniques, which are efficient in deducing an optimum solution to a problem, thanks to the inherent exhaustive search methodologies adopted. Of late, rough sets have evolved to improve upon the performances of either of these components by way of approximation techniques. These soft computing techniques have been put to use in wide variety of problems ranging from scientific to industrial applications. Notable among these applications include image processing, pattern recognition, Kansei information processing, data mining, web intelligence etc.

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