On Soft Computing Techniques in Various Areas

Das, Santosh Kumar; Kumar, Abhishek; Das, Bappaditya; Burnwal, Burnwal

doi:10.5121/csit.2013.3206

Cited by 39 publications

(27 citation statements)

References 35 publications

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“…Hence, algorithmically, there are a wide array of methods to include in our computational arsenal, and the reader is directed toward some of the references cited at the end of this article as a guide to the computational approaches that are available (Figure 4) (25)(26)(27)(28)(29)(30)(31)(32)(33)(34)(35)(36)(37)(38). In the following discussion, we provide some examples of how, by utilizing these soft computing methods, we can harness the multiple V's of Big Data for knowledge discovery by taking advantage of the tools of machine learning and inference to help build an unsupervised learning framework for knowledge discovery, even when the volume or data size is relatively small.…”

Section: Predictive Models And/or Classificationsmentioning

confidence: 99%

Materials Informatics: The Materials “Gene” and Big Data

Rajan

2015

Annu. Rev. Mater. Res.

255

150

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Materials informatics provides the foundations for a new paradigm of materials discovery. It shifts our emphasis from one of solely searching among large volumes of data that may be generated by experiment or computation to one of targeted materials discovery via high-throughput identification of the key factors (i.e., "genes") and via showing how these factors can be quantitatively integrated by statistical learning methods into design rules (i.e., "gene sequencing") governing targeted materials functionality. However, a critical challenge in discovering these materials genes is the difficulty in unraveling the complexity of the data associated with numerous factors including noise, uncertainty, and the complex diversity of data that one needs to consider (i.e., Big Data). In this article, we explore one aspect of materials informatics, namely how one can efficiently explore for new knowledge in regimes of structure-property space, especially when no reasonable selection pathways based on theory or clear trends in observations exist among an almost infinite set of possibilities. 153 Annu. Rev. Mater. Res. 2015.45:153-169. Downloaded from www.annualreviews.org Access provided by Yale University -Law Library on 07/04/15. For personal use only.

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Section: Predictive Models And/or Classificationsmentioning

confidence: 99%

Materials Informatics: The Materials “Gene” and Big Data

Rajan

2015

Annu. Rev. Mater. Res.

255

150

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“…the decision criteria such as goals, constraints, and restrictions may be modeled by FST. Bellman & Zadeh [1][2][3][4] applied the concept of FST for solving decision making problems. Zimmerman [5] proposed the first model of Linear programming in fuzzy environment.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Multi-Objective Linear Programming Approach for Solving Problem of Food Industry

Sinha

Burnwal²,

Singh

2018

ijsrtm

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Abstract. Enterprises and industrial centers need current decision for making products in fast changing market. Uncertainty and yield defined goals make decision making more difficult. In this situation fuzzy logic is used for coping surrounding environment. This paper deals with a fuzzy linear programming model for a problem of food industry. The different types of achievement function such as compensatory and weighted compensatory form associated with equal importance of objectives and non-importance of objectives both have been discussed.

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“…In SC, the tolerance for imprecision and uncertainty is exploited and applied to real world problems frequently to offer more robust, tractability, lower cost, high machine intelligence quotient (MIQ) and economy of communication than those obtained by most HC mathematical techniques [111]. This is capable of addressing several problems in different domains such as control, data mining, forecasting, modeling, optimization, planning, reliability [136] and also in the areas of application such as banking, energy, food industry, industrial production, logistics, medical industry, polymer extrusion process, software engineering, agricultural and environmental to mention a few [45] - [47]. It consists of several techniques and branches, still developing, new ideas are emerging every day and techniques inspired by the activities of the human brain, laws of nature and the behavior of animals.…”

Section: Soft Computing (Sc) and Techniquesmentioning

confidence: 99%

A Holistic Review of Soft Computing Techniques

Philip¹,

Mom²,

Igwue³

2017

ACM

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Due to notable technological convergence that brought about exponential growth in computer world, SoftComputing (SC) has played a vital role with automation capability features to new levels of complex applications. In this research paper, the authors reviewed journals related to the subject matter with the aim of striking a convincing balance between a system that is capable of tolerance to uncertainty, imprecision, approximate reasoning and partial truth to achieve tractability, robustness, economy of communication, high machine intelligence quotient (MIQ), low cost solution and better rapport with reality to conventional techniques. This paper gives an insight on four major consortiums of SC that sprang from the concept of cybernetics, explores and reviews the different techniques, methodologies; application areas and algorithms are formulated to give an idea on how these computing techniques are applied to create intelligent agents to solve a variety of problems. The mechanisms highlighted can serve as an inspiration platform and awareness to new and old researchers that are not or fully grounded in this unique area of research and to create avenue in order to fully embrace the techniques in research communities.

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On Soft Computing Techniques in Various Areas

Abstract: Soft Computing refers to the science of reasoning, thinking and deduction that recognizes and uses the real world phenomena of grouping, memberships, and classification of various

Cited by 39 publications

References 35 publications

Materials Informatics: The Materials “Gene” and Big Data

Materials Informatics: The Materials “Gene” and Big Data

Fuzzy Multi-Objective Linear Programming Approach for Solving Problem of Food Industry

A Holistic Review of Soft Computing Techniques

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