Routing algorithms in internet of things complex network with the role of machine learning

Abstract

In recent years, the growth of internet-based technologies increased at a rapid pace. The development of technologies such as the Internet of Things (IoT) influenced the enormous increase in the use of data and internet services. IoT devices use different algorithms for facilitating connectivity between devices and control them. However, ensuring smooth connectivity using protocols across a shared medium of network resources is challenging. IoT ecosystems utilize several routing algorithms to deliver the best path/ route for network traffic to control cyber physical systems. These routing algorithms allow IoT networks to use network routes, thereby increasing network traffic mobility effectively. So, a comprehensive survey is needed, which paves the path for researchers. Specifically, this survey investigates and compares the routing solutions in the IoT environment from different perspectives than current surveys such as safety, flow control of data and other essential parameters in IoT physical and nonphysical systems.

Comprehensive analysis of machine learning models for predicting concrete compressive strength

“…The core idea is to give more weight to the misclassified or mispredicted data points from the previous tree, ensuring that the next tree in the sequence addresses these errors. The final prediction is obtained by aggregating the predictions of all the trees [36,37]. Gradient boosting is particularly effective in scenarios where there's a high bias, as it focuses on reducing bias iteratively.…”