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Contribution of Artificial Neural Network in Predicting Completeness Through the Impact and Complexity of its Improvement

Titre	Contribution of Artificial Neural Network in Predicting Completeness Through the Impact and Complexity of its Improvement
Publication Type	Conference Paper
Year of Publication	2020
Authors	Maqboul, J, Jaouad, BB
Conference Name	ACM International Conference Proceeding Series
Mots-clés	Decision makers, Decision making, Erroneous datum, Information systems, Information use, Neural networks, Process Improvement, Qualitative and quantitative analysis, Qualitative study, Survey data, Surveys, Technological evolution
Abstract	The technological evolution and the immensity of the data produced, circulated into company makes these data, the real capital of the companies to the detriment of the customers. The erroneous data put the knockout to relationships with customers, the company must address this problem and identify the quality projects on which it must make an effort. In this article, we will present an approach based on qualitative and quantitative analysis to help the decision-makers to target data by its impacts and complexities of process improvement. The Qualitative study will be a survey and a quantitative to learn from survey data to decide the prediction and the completeness of data. © 2020 ACM.
URL	https://www.scopus.com/inward/record.uri?eid=2-s2.0-85123040935&doi=10.1145%2f3386723.3387850&partnerID=40&md5=222e383506f6fed754588124c4a4be60
DOI	10.1145/3386723.3387850