IOTEML: An Internet of Things (IoT)-Based Enhanced Machine Learning Model for Tumour Investigation

Abstract

In the current age of technology, various diseases in the body are also on the rise. Tumours that cause more discomfort in the body are set to increase the discomfort of most patients. Patients experience different effects depending on the tumour size and type. Future developments in the medical field are moving towards the development of tools based on IoT devices. These advances will in the future follow special features designed based on multiple machine learning developed by artificial intelligence. In that order, an improved algorithm named Internet of Things-based enhanced machine learning is proposed in this paper. What makes it special is that it involves separate functions to diagnose each type of tumour. It analyzes and calculates things like the size, shape, and location of the tumour. Cure from cancer is determined by the stage at which we find cancer. Early detection of cancer has the potential to cure quickly. At a saturation point, the proposed Internet of Things-based enhanced machine learning model achieved 94.56% of accuracy, 94.12% of precision, 94.98% of recall, 95.12% of F1-score, and 1856 ms of execution time. The simulation is conducted to test the efficacy of the model, and the results of the simulation show that the proposed Internet of Things-based enhanced machine learning obtains a higher rate of intelligence than other methods.

SCSO-MHEF: Sand Cat Swarm Optimization based MHEF for Nonlinear LTI-IoT Sensor Data Enhancement

“…This can involve techniques such as data cleaning, normalization, and filtering, as well as leveraging machine learning algorithms to identify patterns and anomalies in the data [4,5]. The goal of sensor data enhancement is to ensure that the data collected from IoT devices is reliable, accurate, and actionable, which can help organizations make better decisions and improve their operations [6,7].…”