Authors :

Nor Azura Husin and Balasubramaniyan Divager

Address :

Department of Computer science, Universiti Putra Malaysia, 43400 Serdang Selangor, Malaysia

Faculty of computer science and information technology, University putra Malaysia

Abstract :

Systems for social computing use environmental data to provide interactive applications that integrate software and computer power. Social computing systems with multi-input management have enhanced processing power and application speed because of their audible and readable characteristics. The multi-variant language processing problem in social computing that leads to computational failures is discussed in this article. A Variation-Centric Processing Model (VCPM) is used to mitigate breakdown errors caused by overlapping speech inputs in the aforementioned situation. By enhancing the similarity feature, the suggested model seeks to maximize the text input processing rate while accounting for different pronunciations and tones. The first input is regarded as the baseline upon which subsequent categorization and correlation are based. Based on the high processing rate, this initial reference is updated on a regular basis, after which additional classifications are carried out. Regressive mistakes, which are recognized as discrepancies in the processing of text and speech inputs, are used to reduce the variances in a regressive categorization pattern. By modifying the choices for regularized efficiency, this improves the efficiency of the social computing system. Processing rate, latency, error, accuracy, and dispersion are the measures used to validate the performance of this model.

Keywords :

NLP, Regression Learning, Social Computing System, Speech Processing.