Granulation-Based Data Fusion Approach for a Critical Thinking Worldview Information Processing

A. Madhuri, Veerapaneni Esther .., S. Praveen, M. Altaee, Ibrahim N. Abdullah
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引用次数: 0

Abstract

Natural computation, motivated by the organic game arrangement, is a knowledge base field that formalizes the measurements seen in living organic entities to plan machine techniques to tackle complicated issues or to plan artificial structures with additional traditional behaviors. Seeable corporations disconnected from natural wonders, reminiscent of mind demonstration, self-association, self-redundancy, Darwinian resistance, self-evaluation, discernment, and granulation, nature is crammed as a supply of motivation to advance competition. Computational devices or frameworks accustomed solve complex problems. The ideal, nature-motivated primary computation models used for such sweetening incorporate artificial neural organizations, spongy reasoning, arduous set, biological process calculations, shape mathematics, DNA registration, artificial life, And granular or insight-based processing. The granulation of information within the granular register is an innate attribute of human thought and therefore the life of thought acted call at regular daily existence. This paper illustrates the importance of normal recording in terms of granulation-based data preparation models, for example, neural organizations, soft and ugly sets, and their hybridization. we have a tendency to emphasize the bio-sensitive inspiration, designing standards, application zones, open scan problems, and testing issues for these models.
基于颗粒的批判性思维世界观信息处理数据融合方法
自然计算是由有机博弈安排驱动的,是一个知识库领域,它将有机生命体中的测量形式化,以规划机器技术来解决复杂问题或规划具有额外传统行为的人工结构。可见的公司与自然奇观脱节,让人联想到心灵展示、自我联想、自我冗余、达尔文式抵抗、自我评价、洞察力和颗粒化,大自然被塞满了,成为推动竞争的动力来源。用于解决复杂问题的计算设备或框架。理想的、自然驱动的初级计算模型用于这种甜味,包括人工神经组织、海绵状推理、艰巨的集合、生物过程计算、形状数学、DNA注册、人工生命,以及颗粒或基于洞察力的处理。粒状寄存器内的信息粒化是人类思想的固有属性,因此思想的生命在有规律的日常存在中发挥着作用。本文从基于颗粒的数据准备模型(如神经组织、软集和丑集)及其杂交等方面说明了正常记录的重要性。我们倾向于强调这些模型的生物敏感灵感、设计标准、应用区域、开放扫描问题和测试问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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CiteScore
1.70
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