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Deep Learning Based Approach for Milk Quality Prediction

  • Unique Paper ID: 164166
  • Volume: 10
  • Issue: 12
  • PageNo: 1126-1129
  • Abstract:
  • This study addresses the growing consumer demand for high-quality milk products in response to concerns over potential quality issues, such as unclear milk origin, altered fat and water content, and other composition discrepancies. To streamline the process of classifying, predicting, and monitoring milk quality, the paper proposes an innovative architecture using machine learning. Specifically, the study engages a novel support vector machine (SVM) method which is used to predict the milk quality and it compares its performance with the help of the J48 decision tree algorithm. The results demonstrate that the optimized SVM classifier outperforms the J48 approach in predicting milk quality grades. The study had used an dataset from the Kaggle, by taking an sample size of 20, it had been evenly split between the two groups. The statistical analysis was conducted with an alpha value of 0.05, a beta value of 0.2, and a 95% confidence interval, with a G-power setting of 0.8 for robustness. The algorithm SVM and J48 had been trained and tested individually with an equal number of sample sizes (N=10) for the milk quality prediction. The results of it describe that SVM algorithm had Achieved a victory. The results states that SVM is more effective tool in Analyzing the quality of the milk. This approach will help and ensure the consumer safety and this will help the milk industry's efforts to maintain the high-quality standards of the milk.
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Cite This Article

  • ISSN: 2349-6002
  • Volume: 10
  • Issue: 12
  • PageNo: 1126-1129

Deep Learning Based Approach for Milk Quality Prediction

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