Pre-determination of power density and application time in laser applications using PSONN hybrid algorithm

This article aimed to automatically determine the power density and application time values of the coagulation state obtained as a result of this application according to the relevant texture properties. For this purpose, these target values of the laser system were estimated by using some properties obtained from the examined tissue. Particle swarm optimization based artificial neural networks hybrid algorithm was used for this pre-determination process. As a result of the applications, test accuracy rates of mean squared error percentages were obtained as 99.88% and 98.47% for the power density and application time targets, respectively. Furthermore, the correlation coefficients between actual and predicted data for both targets were calculated as 0.99. According to the literature search, as a result of giving some laser measurements for coagulation state as input to the proposed system or the other artificial intelligence algorithms, any study has not been encountered in which the power density and application time of the laser system are detected automatically in advance. When the research is evaluated from this novel perspective, it is thought that it will contribute to the literature and present ideas with different innovations to other researchers.

Automated summary

This article aimed to automatically determine the power density and application time values of the coagulation state obtained as a result of this application according to the relevant texture properties. Using a hybrid algorithm based on particle swarm optimization and artificial neural networks, the target values of the laser system were estimated. Test accuracy rates of 99.88% and 98.47% were achieved for the power density and application time targets, with correlation coefficients between actual and predicted data calculated as 0.99 for both targets.