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dc.contributor.authorTripathi, Ashok Kumar-
dc.contributor.authorSaini, Hemraj-
dc.contributor.authorRathee, Geetanjali-
dc.description.abstractMissing data is a universal complexity for most of the research fields, which introduces uncertainty into data analysis. This can take place due to many mishandling, inability to collect an observation, measurement errors, aberrant value deleted, or merely being short of study. The nourishment area is not an exemption to the difficulty of missing data. Most frequently, this difficulty is determined by manipulative means or medians from the existing datasets which need improvements. The paper proposes hybrid schemes of MICE and ANN known as extended ANN to search and analyze the missing values and perform imputations in the given dataset. The proposed mechanism is efficiently able to analyze the blank entries and fill them with proper examination of their neighboring records in order to improve the accuracy of the dataset. In order to validate the proposed scheme, the extended ANN is further compared against various recent algorithms or mechanisms to analyze the efficiency as well as the accuracy of the results.en_US
dc.publisherJaypee University of Information Technology, Solan, H.P.en_US
dc.subjectExtended KNNen_US
dc.subjectFood Analysisen_US
dc.subjectFood Consumptionen_US
dc.subjectK-Nearest Neighboren_US
dc.titleFuturistic Prediction of Missing Value Imputation Methods Using Extended ANNen_US
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