Abstract
In recent years, the concept of crowdsourcing becomes more popular and on demand due to the capability of solving many real-world problems in less time. The crowd-sourcing has several ben- efits such as increased speed, flexibility and efficiency in terms of collecting and processing the data. One of the major demands in this crowdsourcing is to find the truthfulness of the obtained data and to ascertain trustworthiness of the sources. Several existing approaches are used for truth discovery among crowd-sourced-based applications. But they work efficiently only for structured data and the accuracy levels are dependent on the availability of ground truth values. To over- come the above-mentioned issues a novel truth discovery model is proposed. In this work it uses knowledge extraction and truth discovery phases to process the data. Reliability scores are assigned to trustworthy users based on the frequency with which they provide true values over a period of time. It works on both categorical and continues data. So, finally the proposed model is tested on three real-valued data sets. Experimental results prove to provide better performance than the conventional approaches.
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Communicated by Meng Joo.
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Vadavalli, A., Subhashini, R. A novel truth prediction algorithm for ascertaining the truthfulness of the data and reliability of the users in crowdsourcing application. Soft Comput 27, 1685–1698 (2023). https://doi.org/10.1007/s00500-022-07095-8
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DOI: https://doi.org/10.1007/s00500-022-07095-8