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@article{163211,
        author = {Shrihari Sharad Shinde  and Jayesh Ravindra Banda and Nisarg Sunil Wani and Aditya Dinesh Nimse and Prof. S. B. Ghawate},
        title = {Milk Quality Prediction and Yogurt Fermentation Analysis Using Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {10},
        number = {11},
        pages = {2030-2035},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=163211},
        abstract = {The dairy industry plays a vital role in the global economy, with milk and dairy products being essential for human nutrition. Ensuring the quality of milk and its products is paramount for both consumers and producers. This study presents a novel approach to enhance milk quality prediction and optimize yoghurt fermentation processes using advanced Machine Learning (ML) techniques. An integrated system was developed, incorporating sensors, data processing units, and ML models, enabling automatic adjustments to the fermentation process in response to real-time data inputs. This not only enhanced the quality of the yoghurt but also increased production efficiency and reduced wastage significantly. The findings of this research demonstrate the potential of Machine Learning in revolutionizing the dairy industry. By leveraging predictive analytics and real-time data analysis, this approach ensures higher milk quality and more efficient yoghurt fermentation processes. Implementing these techniques at scale could lead to substantial improvements in dairy product quality, cost-effectiveness, and overall sustainability in the dairy industry.},
        keywords = {yogurt; Model-train; consumer preference; autoencoder; support vector Machine.},
        month = {},
        }