MilkSafe: A Hardware-Enabled Milk Quality Prediction using Machine Learning

T., H.; S, Sarveswaran; Jha, Somesh; Soundarya, B

doi:10.1109/vitecon58111.2023.10157863

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Book1

Other1

Article1

Relationship

Self Cite0

Independent3

Authors

Journals

Cited by 3 publications

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Utilizing Wavelet Transform in the Analysis of Scaling Dynamics for Milk Quality Evaluation

Maywald,

Vimalajeewa

2024

Time Series and Wavelet Analysis

View full text Add to dashboard Cite

Utilizing Wavelet Transform in the Analysis of Scaling Dynamics for Milk Quality Evaluation

Maywald,

Vimalajeewa

2024

Time Series and Wavelet Analysis

View full text Add to dashboard Cite

An Optimized Neural Network Model to Predict Milk Quality

Kumar,

Mudepalli,

Sathish

et al. 2024

2024 14th International Conference on Cloud Computing, Data Science &Amp;amp; Engineering (Confluence)

View full text Add to dashboard Cite

Enhancing Milk Quality Detection with Machine Learning: A Comparative Analysis of KNN and Distance-Weighted KNN Algorithms

Samad,

TAZE,

Kürsad UÇAR

2024

International Journal of Innovative Science and Research Techno

View full text Add to dashboard Cite

Ensuring the quality of milk is paramount for consumer health and industry standards. This study introduces a comparative analysis of two machine learning approaches, the k-Nearest Neighbors (KNN) algorithm and its variant, the Distance-Weighted KNN (DW-KNN), for the detection of milk quality. While the traditional KNN algorithm has been widely applied across various sectors for its simplicity and effectiveness, our research proposes an enhanced methodology through the implementation of the DW-KNN algorithm, which incorporates distance weighting to improve prediction accuracy. Through the analysis of a comprehensive dataset encompassing multiple milk quality indicators, we demonstrate that the DW-KNN algorithm significantly outperforms the standard KNN approach, achieving an exceptional accuracy of 99.53% compared to 98.58% by KNN. This substantial improvement highlights the potential of distance weighting in enhancing classification performance, particularly in applications requiring high precision in quality assessment. Our findings advocate for the adoption of the DW-KNN algorithm in the dairy industry and related fields, offering a robust tool for ensuring product quality and safety.

show abstract

MilkSafe: A Hardware-Enabled Milk Quality Prediction using Machine Learning

Cited by 3 publications

References 4 publications

Utilizing Wavelet Transform in the Analysis of Scaling Dynamics for Milk Quality Evaluation

Utilizing Wavelet Transform in the Analysis of Scaling Dynamics for Milk Quality Evaluation

An Optimized Neural Network Model to Predict Milk Quality

Enhancing Milk Quality Detection with Machine Learning: A Comparative Analysis of KNN and Distance-Weighted KNN Algorithms

Contact Info

Product

Resources

About