• Ms. SailasreeAnchanala
• Mrs. G.Neelima

The cold chain logistics industry plays a crucial role in preserving the quality and safety of perishable products, including medicines, food, and specialty chemicals. Cold storage facilities are essential for maintaining these products between production and distribution, ensuring their freshness and integrity. In this study, we focus on product identification within the cold chain using the decision tree technique, a machine learning supervised learning algorithm known for its effectiveness in classification problems.The decision tree operates as a flow chart, classifying products based on various sets of variables. This method allows for accurate ranking of classes and efficient categorization of products. By analyzing tagged data, which includes information about freshness, quantity of loss, and other traits, we can quickly assess the condition of items in cold storage. This approach is particularly valuable for perishable goods, where degradation can occur rapidly.The study also emphasizes the importance of web-based support in cold chain logistics. Web-based platforms provide real-time information about the quantity and quality of stored products. This transparency is beneficial for clients, allowing them to access critical information about their products at any given time. Overall, our approach aims to enhance the effectiveness of cold chain product monitoring and ensure the preservation of quality throughout the supply chain.

Cold Chain,Machine Learning,Supervised Learning Algorithm,Logistics

"Cold Chain Product Monitoring with Web-Based Support", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 2, page no.e271-e274, February-2024, Available :http://www.jetir.org/papers/JETIR2402436.pdf

"Cold Chain Product Monitoring with Web-Based Support", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 2, page no. ppe271-e274, February-2024, Available at : http://www.jetir.org/papers/JETIR2402436.pdf