Abstract
Spinel ferrite recently attracted attention for possible application in magnetic refrigeration due to its noticeable high magnetocaloric effect and tunable magnetic ordering temperature around room temperature. Being a magnetic semiconductor, the material has enjoyed wider application in different practical domains such as drug delivery, humidity sensor, photo-catalyst, high density data storage, magnetic resonance imaging and magnetic cooling among others. However, simplicity of its preparation and excellent cost effectiveness as compared to the existing magnetocaloric-based materials further contribute to its suitability for attaining magnetic cooling. Effective utilization of this material for magnetic cooling requires precise measurement of its magnetic ordering temperature (MOT) which requires laborious experimental procedures and sophisticated equipment. This work addresses the challenges by employing elemental compositions of spine ferrite in developing hybrid models for predicting MOT using hybrid genetic-based support vector regression algorithm (GBSVRA) and extreme learning machine (ELM). The developed ELM-SN model with sine activation function performs better than hybrid GBSVRA and ELM-SG (with sigmoid activation function) model with performance improvement of 42.63% and 38.78%, respectively, through RMSE performance yardstick, while the ELM-SG model outperforms hybrid GBSVRA model with performance enhancement of 2.87% when validated using testing dataset. The developed ELM-SN model further outperforms other two developed models using other performance metrics. Harnessing the potentials of the presented models would strengthen precise, effective and quick tuning of spinel ferrite MOT for achieving magnetic cooling without experimental cost and difficulties.