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Article

Nermeen El Kashef, Yasser Fouad Hassan, Khaled Mahar and Mustafa H. Fahmy

Nature is the single and most complex system that has been always studied, and no one can compete Mother Nature, but we can learn from her, by many new methodologies…

Abstract

Purpose

Nature is the single and most complex system that has been always studied, and no one can compete Mother Nature, but we can learn from her, by many new methodologies through biology. The paper aims to discuss this issue.

Design/methodology/approach

In this paper, being inspired by the mechanism through which our Mother Nature handling human taste, a proposed model for clustering and classification hand gesture is introduced based on human taste controlling strategy.

Findings

The model can extract information from measurement data and handling it as the structure of tongue and the nervous systems of human taste recognition.

Originality/value

The efficiency of proposed model is demonstrated experimentally on classifying the sign language data set; in the high recognition accuracy obtained for numbers of ASL was 95.52 percent.

Details

Kybernetes, vol. 44 no. 3
Type: Research Article
ISSN: 0368-492X

Keywords

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Article

Yasser F. Hassan

This paper aims to utilize machine learning and soft computing to propose a new method of rough sets using deep learning architecture for many real-world applications.

Abstract

Purpose

This paper aims to utilize machine learning and soft computing to propose a new method of rough sets using deep learning architecture for many real-world applications.

Design/methodology/approach

The objective of this work is to propose a model for deep rough set theory that uses more than decision table and approximating these tables to a classification system, i.e. the paper propose a novel framework of deep learning based on multi-decision tables.

Findings

The paper tries to coordinate the local properties of individual decision table to provide an appropriate global decision from the system.

Research limitations/implications

The rough set learning assumes the existence of a single decision table, whereas real-world decision problem implies several decisions with several different decision tables. The new proposed model can handle multi-decision tables.

Practical implications

The proposed classification model is implemented on social networks with preferred features which are freely distribute as social entities with accuracy around 91 per cent.

Social implications

The deep learning using rough sets theory simulate the way of brain thinking and can solve the problem of existence of different information about same problem in different decision systems

Originality/value

This paper utilizes machine learning and soft computing to propose a new method of rough sets using deep learning architecture for many real-world applications.

Details

Kybernetes, vol. 46 no. 4
Type: Research Article
ISSN: 0368-492X

Keywords

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