A semantic software development based on random probability distribution model
by Qiang Mei; Yanmei Huang; Prathik Anandhan; R. Parthasarathy
International Journal of Technology Intelligence and Planning (IJTIP), Vol. 13, No. 1, 2021

Abstract: Machine learning strategies to automate data processing are urgently needed with the increasing accumulation of biological datasets. In recent years, software development has received considerable attention because of its interpretation of the model topics originating from natural language processing. In this article, the application and development of semantic software development emphasise the understanding of semantic software development. Furthermore, literature was reviewed and evaluated in detail based on semantic software development models to the clustering-based random probability distribution model (CRPDM). However, the related studies offered an outlook for the use of topical models to create semantic software applications according to the types of models based on the random probability entity. The simulation of CRPDM improves the accuracy and performance of analysing semantic software applications. Furthermore, the theme models are a promising method in semantic software development research for different applications that have been firmly researched in this paper.

Online publication date: Wed, 06-Oct-2021

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