Algorithm Selection and Optimization: A Comparative Analysis

It is widely acknowledged that various algorithms have distinct strengths and weaknesses, and their appropriateness depends on the nature of the problem being addressed. Therefore, careful selection and optimization of algorithms are crucial for achieving desired outcomes. While conventional algorithms are well-suited for problems characterized by limited data volume and clear features, deep learning algorithms are ideal for tackling complex nonlinear problems. On the other hand, transfer learning algorithms are recommended for addressing new problems that resemble previous ones. It is noteworthy that these algorithms are not mutually exclusive but rather complementary, as demonstrated in the BSUV-Net deep learning algorithm presented in literature [2], where traditional methods were employed to determine the background and facilitate object detection.