Application‐Based Review of Soft Computational Methods to Enhance Industrial Practices Abetted by the Patent Landscape Analysis

ABSTRACT Soft computing is a collective methodology that touches all engineering and technology fields owing to its easiness in solving various problems while comparing the conventional methods. Many analytical methods are taken over by this soft computing technique and resolve it accurately and the soft computing has given a paradigm shift. The flexibility in soft computing results in swift knowledge acquisition processing and the information supply renders versatile and affordable technological system. Besides, the accuracy with which the soft computing technique predicts the parameters has transformed the industrial productivity to a whole new level. The interest of this article focuses on versatile applications of SC methods to forecast the technological changes which intend to reorient the progress of various industries, and this is ascertained by a patent landscape analysis. The patent landscape revealed the players who are in the segment consistently and this also provides how this field moves on in the future and who could be a dominant country for a specific technology. Alongside, the accuracy of the soft computing method for a particular practice has also been mentioned indicating the feasibility of the technique. The novel part of this article lies in patent landscape analysis compared with the other data while the other part is the discussion of application of computational techniques to various industrial practices. The progress of various engineering applications integrating them with the patent landscape analysis must be envisaged for a better understanding of the future of all these applications resulting in an improved productivity.

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Publication Details

Journal
Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery
Published
2024-10-31
DOI
https://doi.org/10.1002/widm.1564
Citations
4
Primary Topic
Machine Learning and Data Classification
Type
article
Field-Weighted Citation Impact
1.39
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article

Application‐Based Review of Soft Computational Methods to Enhance Industrial Practices Abetted by the Patent Landscape Analysis

G. Dhanalakshmi, L. Rajeshkumar, D. Balaji, S. Tamilselvan
4 citations
Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery
Machine Learning and Data Classification
1.39
article

Application‐Based Review of Soft Computational Methods to Enhance Industrial Practices Abetted by the Patent Landscape Analysis

G. Dhanalakshmi, L. Rajeshkumar, D. Balaji, S. Tamilselvan
article en
4 citations

Abstract

ABSTRACT Soft computing is a collective methodology that touches all engineering and technology fields owing to its easiness in solving various problems while comparing the conventional methods. Many analytical methods are taken over by this soft computing technique and resolve it accurately and the soft computing has given a paradigm shift. The flexibility in soft computing results in swift knowledge acquisition processing and the information supply renders versatile and affordable technological system. Besides, the accuracy with which the soft computing technique predicts the parameters has transformed the industrial productivity to a whole new level. The interest of this article focuses on versatile applications of SC methods to forecast the technological changes which intend to reorient the progress of various industries, and this is ascertained by a patent landscape analysis. The patent landscape revealed the players who are in the segment consistently and this also provides how this field moves on in the future and who could be a dominant country for a specific technology. Alongside, the accuracy of the soft computing method for a particular practice has also been mentioned indicating the feasibility of the technique. The novel part of this article lies in patent landscape analysis compared with the other data while the other part is the discussion of application of computational techniques to various industrial practices. The progress of various engineering applications integrating them with the patent landscape analysis must be envisaged for a better understanding of the future of all these applications resulting in an improved productivity.

Wiley Interdisciplinary Reviews Data Mining and Knowledge DiscoveryVol. 15(1)
Alliance University (IN), KPR Institute of Engineering and Technology (IN)
Decent work and economic growth
Openalex Percentile: Top 15%
Machine Learning and Data Classification
1.39
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