Predicting the Matching Possibility of Online Dating Youths Using Novel Machine Learning Algorithm - Karthikeyan Palanisamy; Muthumani Muralidharan (Journal of Artificial Intelligence and System Modelling)
In today's fast-paced society, many choose speed dating since it is efficient and convenient. Speed dating events are organized to allow busy singles to meet a variety of potential partners in a short timeframe, thereby maximizing their chances of making connections. It creates an organized setting that encourages brief but significant contacts, allowing people to quickly assess chemistry and compatibility. Furthermore, in the digital age, when online dating can be impersonal, speed dating provides a face-to-face connection, which increases authenticity and reduces the ambiguity of online profiles. In general, speed dating appeals to modern daters who want quick and tangible results in their search for romance. This research project aims to gain insights into forecasting the course of relationships created during initial meetings using cutting-edge Machine Learning (ML) approaches. Light Gradient Boosting Classification (LGBC) serves as a foundational framework, and an innovative approach is introduced by combining it with the Henry Gass Solubility Optimization Algorithm (HGSOA), Flying Fox Optimization (FFO), and Mayflies Optimization (MO), resulting in a hybrid model. Investigation reveals that throughout the training phase, the LGBC model achieved a small accuracy of 0.938, suggesting its comparative inferiority to the LGHS and LGMO models, which achieved accuracies of 0.945 and 0.956, respectively. Nonetheless, the hybrid HGFF model emerged as the most accurate, outperforming all other competitors with an astounding accuracy of 0.965. As a result, it is often regarded as the best model for anticipating relationship dynamics during early meetings, providing vital insights into the complexities of relationships on first dates.