Publication

Abstract : This paper explains how faults or anomalies might be indicated by changes in the initial condition of tissues, particularly the flow of potassium and sodium ions with leaks. Convolutional Neural Networks (CNNs) are becoming more and more capable of being used, even if these mistakes have been found in tissue systems. CNNs are trained to identify patterns in data, much like digital analysts. By providing information regarding both typical and exceptional motor function, we hope to help peo-ple comprehend that things aren't always correct. Our research examines how CNN s can be used to forecast changes in ion channel function, which could indicate important problems with neural properties. With this approach, we want to increase our capacity to detect and treat arthritis early on. By in-tegrating CNN s into fault detection algorithms, we want to pave the way for more ecologically friendly neural diagnostic and therapeutic approaches