Pattern Recognition by IoT Systems of Machine Learning
Shyam R. Sihare · Advances in systems analysis, software engineering, and high performance computing book series · 2023
Today, it is feasible to observe how quickly electronic devices are becoming connected to the internet. Electronic devices that are connected to the internet can be managed or observed from any location in the world. Many of the challenges have been made easier by internet connectivity for technological gadgets. A good judgement may be made by spotting certain trends using IoT and machine learning (ML) technologies. Its application areas can be expanded much farther than they are now by combining IoT and ML algorithms. ML uses a variety of algorithms, and while analysing them to choose the best one for a certain electronic device, runtime complexity, memory needs, and accuracy are taken into consideration. In comparison to other ML algorithms, support vector machine, random forest, and k-nearest neighbour have higher runtime complexity, a smaller memory requirement, and higher accuracy. In this chapter, the aforementioned topics have all been covered. The different ML algorithms and IoT pattern recognition application areas are covered in this chapter.