.png){kind=icon}
返回
Recent advances in delivery systems optimization using machine learning approaches
.png){kind=icon}
PRE
AI
DOI：10.1016/j.cep.2023.109352.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Nowadays, the researchers delve into the intricacies of machine learning (Artificial Neural Network (ANN), Genetic algorithm (GA), support vector machines (SVM), K-means clustering, decision trees, etc) to uncover their potential for implementation in the development of optimized food delivery systems. They searched for a readyto-use machine learning approach to be applied. Thus, this review presents a comprehensive overview of the current machine learning approaches and algorithms available for optimizing food delivery systems. It is groundbreaking as it is the first of its kind to specifically concentrate on this topic. The latest advancements in this area have been carefully scrutinized, and the most viable techniques and methods have been identified to enhance food delivery systems. This review will be of great value to biological and chemical researchers as well as supply chain management professionals, as it offers a clear roadmap for employing different approaches of ANN, GA, SVM, K-means clustering, decision trees, in conjunction with response surface methodology (RSM), to enhance the accuracy and efficiency of nanoparticles and nanoemulsions in food delivery systems.
Keyword:
Delivery system
Artificial neural network
Genetic algorithm
Support vector machines
K-clustering
Decision trees
期刊
C
IF:
3.9
论文数:
5.7K
被引数:
1.4W
机构
引用论文
.png){kind=icon}
分享收藏分享收藏
分享收藏
查看更多内容