Shekure Tewodros, Worku Hussien Seid, Mohapatra Sudhir Kumar, Das Tapan Kumar
Artificial Intelligent and Robotics, Department of Software Engineering, Addis Ababa Science and Technology University, Addis Ababa, Ethiopia.
Artificial Intelligence and Robotics, Department of Software Engineering, Addis Ababa Science and Technology University, Addis Ababa, Ethiopia.
Sci Rep. 2024 Dec 28;14(1):30703. doi: 10.1038/s41598-024-79626-2.
Dairy product requirement and the demand-supply gap of milk in Ethiopia have been increasing at an alarming rate due to various factors such as shortage of animal's feeds, feed staffs, feed costs, and poor genetic merits of the local breeds of the country. This problem can be lessened by selecting best breed and modern animal breeding facilities, which require technologies like big data analysis and machine learning. In this study, a prediction model that can predict age at first calving of weaned calves based on their pre-weaning and weaning parameters, including dam's parity number, season of calving, birth weight, pre-weaning health status, pre-weaning average daily weight gain (ADG), weaning age and weaning weight is developed. Primary data collected by Ardayta Dairy Research Centre; Ethiopia is used for this research. First, different pre-trained models developed using support vector regression (SVR), Linear support vector regression (LSVR) and Nu support vector regression (NuSVR) techniques with their default hyperparameter values in which SVR performed best. Second, a model was developed by tuning hyperparameters of SVR including kernel function, regularization (C-parameter) and gamma parameters, and it resulted in an accuracy of 96.46%. Next, Whale optimization technique is used to select the optimized features of the dataset. Furthermore, an ensemble of SVR, LSVR, NuSVR is designed, and the framework is trained by optimized features of data. The designed model achieved an accuracy of 98.3% superseding the other combinations.
由于动物饲料短缺、饲料工作人员、饲料成本以及该国本地品种遗传优势不佳等多种因素，埃塞俄比亚乳制品需求和牛奶供需缺口一直在以惊人的速度增长。通过选择最佳品种和现代动物育种设施可以缓解这个问题，而这需要大数据分析和机器学习等技术。在本研究中，开发了一种预测模型，该模型可以根据断奶前和断奶时的参数，包括母牛的胎次、产犊季节、出生体重、断奶前健康状况、断奶前平均日增重（ADG）、断奶年龄和断奶体重，预测断奶犊牛首次产犊的年龄。本研究使用了埃塞俄比亚Ardayta乳制品研究中心收集的原始数据。首先，使用支持向量回归（SVR）、线性支持向量回归（LSVR）和Nu支持向量回归（NuSVR）技术开发了不同的预训练模型，这些模型使用其默认超参数值，其中SVR表现最佳。其次，通过调整SVR的超参数，包括核函数、正则化（C参数）和伽马参数，开发了一个模型，其准确率达到了96.46%。接下来，使用鲸鱼优化技术选择数据集的优化特征。此外，设计了SVR、LSVR、NuSVR的集成模型，并通过数据的优化特征对该框架进行训练。所设计的模型实现了98.3%的准确率，超过了其他组合。
