Worker Selection in Crowd-sourced Platforms using Non-dominated Sorting

Mishra, Sumit; Yadav, Akash; Sairam, Ashok Singh

doi:10.1109/tencon.2019.8929564

Cited by 2 publications

(1 citation statement)

References 9 publications

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“…Sourced platforms [13]. The purpose of the work is to function a non-dominated sorting of the employees primarily based on the requirement.…”

Section: Sumit Mishra Et Al Proposed a Non-dominated Sorting For Workmentioning

confidence: 99%

Online Purchasing Platform Using Crowd Sourcing With Improvisation of Classification Accuracy

Gokulakrishnan¹

2021

ITII

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Crowd-sourcing is a prototype where persons cum organisations acquire facts such as ideas, micro-tasks, financial, vote casting associated to items and offerings from individuals of large, open and rapidly-evolving nature. It entails utilization of web acquired and distribute work between members to get a collective result. The software of classification tasks in crowd-sourcing is a counter step due to the inclined reputation of crowd-sourcing market. Dynamic Label Acquisition and Answer Aggregation (DLTA) crowd-sourcing framework accomplishes the classification assignment in a promising manner. But most of the current works are now not in a position to supply an budget allocation for labels due to the fact they do not make the most the Label inference and acquisition phase. In addition, label mismatch and multi-label tasks are the different issues encountered in the current works. To overcome, it is proposed to undertake Random Forest Algorithm (RFA) for classification in crowd-sourcing. The goal of this work is to enhance the crowd-sourcing classification task efficiency with Dynamic Resource Algorithm. RFA is activated by means of developing a multitude of decision tree at training time and consequences with the training and it applies a bagging approach to produce the last end result with more accuracy.

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“…Sourced platforms [13]. The purpose of the work is to function a non-dominated sorting of the employees primarily based on the requirement.…”

Section: Sumit Mishra Et Al Proposed a Non-dominated Sorting For Workmentioning

confidence: 99%