Fog Big Data Analysis for IoT Sensor Application Using Fusion Deep Learning

Algorithm 2

FBEBDBA-based analysis algorithm.

	Input: algorithm, β, λ, training dataset, T, L, Q, P, Z = store the classified records.
	Output: classified dataset labeled dataset
	Labeled dataset LD, pre-eminent model
	For completely dataset L occurrence ∈ novel dataset L event LD to do
	Z ⟵ { smart city IoT Sensor Applications datasets (parking, transportation, pollution, Security and sensor IoT dataset) dataset type1..., IoT Sensor Applications (Usage Datasets) dataset type}
	The condition of {|Z| = I}
	Z ⟵ Z∪T perform the analysis in terms of prediction for recommendation and use the backpropagation learning
	LD ⟵Random sample consensus (Z, perfect α, β, λ) {predict LD}
	New LD ⟵ LD \ { IoT Sensor Applications (Usage Datasets) dataset classification event}
	Perfect ⟵ FLD (New LD)
	Preeminent model LD ⟵ preeminent model LD∪ New LD
	The end for return preeminent model LD