Volume 18, No. 6, 2021

An Efficient Data Recovery Technique For Prediction Of Milk Adulteration Using Dairy Logistics Prediction (Dlp21) Algorithm

K.Radhika , Dr.A.Shaik Abdul Khadir


Data Science is worried about investigating Big Data to separate connections with appraisals of probability and error. Adulteration in milk is a typical situation for acquiring additional benefit, which might cause extreme hurtful impacts on humans.The subjective compound examination procedure gives a superior answer for identifying the harmful substance of milk and foodstuffs. Crude cow milk might be adulterated water and pseudo proteins, for example, melamine either purposefully or coincidentally for getting more profit. The paper likewise talks about milk run logistics in the obtainment framework with a unique accentuation on the car business. Milk run framework is about logistics support for the store network. Milk run framework brings about decrease in cost of transportation, voyaging way and fuel utilization. The proposed technique uses a DLP-21 prediction technique to improve the quality of milk and to maintain a purity in milk in longer transportation. Here we uses a logistics regression method to show the statistical result of milk purity. In all the existing techniques it is shown as 87% and in our proposed technique we will improve it to 94.75% of purity with high Lacto value without any chemical when transportation distance ,time and quantity of milk is same as existing. An Enhancing DLP-21(Dairy Logistics Prediction 21) with logistic regression method is implemented for improving the purity of milk without preserving.

Pages: 5695-5704

Keywords: Milk run logistics, Adulteration, Purity, Data science.

Full Text