An intelligent optimization framework to predict the vulnerable range of tumor cells using Internet of things
Venkata Ashok K Gorantla, Shiva Kumar Sriramulugari, Amit Hasmukhbhai Mewada, J. Logeshwaran
Abstract
The intelligent optimization framework for predicting the vulnerable range of tumor cells using Internet of Things (IoT) provides a robust and efficient solution for monitoring, assessing, and managing cancer treatment. This framework is based on a combination of deep learning algorithms, big data analytics, cloud computing, and IoT technology. The main purpose of this framework is to develop an optimal solution for detecting the vulnerable range of tumor cells using the latest advances in image processing, machine learning and natural language processing. This framework collects data from various medical or health-related sources such as patient records, imaging results, and medical reports and stores them on the cloud. It filters real-time data at micro-levels to generate a more accurate analysis of the vulnerable range of tumor cells in order to make better proactive healthcare decisions. In this framework, the deep learning algorithms are used to develop predictive models that can accurately detect the vulnerable range of tumor cells. The big data analytics platform is used to analyze the collected data patterns for more refined predictions. Finally, the cloud computing technology provides a distributed computing platform to take advantage of the data from different sources. By integrating the latest advances in machine learning, natural language processing, and cloud computing, the intelligent optimization framework for predicting the vulnerable range of tumor cells using IoT provides an efficient and accurate solution for optimizing the diagnosis and treatment of cancer, enabling doctors to provide better proactive care to their patients.