부산대학교 도서관

Generally, the procedure of blood flow velocity computation contains gathered data on the movement of red blood cells or other indicators of blood flow and utilizes that data to compute the velocity of blood flows with the blood vessels. The procedure of blood flows velocity computation contains evaluating the speed at which blood moves through a blood vessel. It is done utilizing several approaches like magnetic resonance imaging (MRI), Doppler flowmetry, ultrasound, particle image velocimetry (PIV), or computed tomography (CT) scans. This manuscript involves the design of Intelligent Blood Flow Velocity Calculation using Deep Belief Network with Harmony Search Algorithm (BFV-DBNHSA) technique. The proposed BFV-DBNHSA technique computes the velocity of the blood flow accurately and timely. In the presented BFV-DBNHSA technique, the major aim is to determine the interior blood flow velocity. To accomplish this, the BFV-DBNHSA technique employs DBN model to produce the features of the blood flow velocity. Moreover, the BFV-DBNHSA technique uses HSA algorithm for optimal hyperparameter selection of the DBN model. The experimental outcome investigation of the BFV-DBNHSA system is well studied under different measures. The comprehensive comparison analysis revealed the improvement of the BFV-DBNHSA technique over recent algorithms.