A Novel Analytical Model of Brain Tumor Based on Swarm Robotics

Abstract

A tumor is referred to as “intracranial hard neoplasm” if it grows near the brain or central spinal vessel (neoplasm). In certain cases, it is possible that the responsible cells are neurons situated deep inside the brain’s structure. This article discusses a strategy for halting the progression of brain tumor. A precise and accurate analytical model of brain tumors is the foundation of this strategy. It is based on an algorithm known as kill chain interior point (KCIP), which is the result of a merger of kill chain and interior point algorithms, as well as a precise and accurate analytical model of brain tumors. The inability to obtain a clear picture of tumor cell activity is the biggest challenge in this endeavor. Based on the motion of swarm robots, which are considered a subset of artificial intelligence, this article proposes a new notion of this kind of behavior, which may be used in various situations. The KCIP algorithm that follows is used in the analytical model to limit the development of certain cell types. According to the findings, it seems that different KCIP speed ratios are beneficial in preventing the development of brain tumors. It is hoped that this study will help researchers better understand the behavior of brain tumors, so as to develop a new drug that is effective in eliminating the tumor cells.