Optimization of CART Decision Tree Algorithm inTiny Deep Learning System to Improve IOTSustainability

Tiny deep learning is deployed on local or edge IoTdevices instead of processing in the cloud, and uses machinelearning by embedding artificial intelligence in the hardware.One of the growing areas of deep learning is small and is a subsetof machine learning, algorithms, hardware and software. It aimsto enable low-latency interfaces in edge devices that consumeonly a few milliwatts of battery power. Such reductions inconsumption enable machine learning devices to last for weeks,months, or even years while the machine learning application isrunning continuously at the edge or endpoint. In this article, weintroduce a Tiny deep learning system using the CART decisiontree and describe the applications of this system in Internet ofThings networks. Machine learning also solves data security,privacy, latency, storage and energy efficiency issues.Considering the use of decision trees in the layers of neuralnetworks in this article, the performance of these networks hasincreased, and this performance, along with small deep learningalgorithms, has reduced network load and energy consumptionin Internet of Things environments

Keywords:

Deep Learning , Artificial intelligence , Internetof things , CART