Hybrid Deep Learning Models for Smart Cities Data

Till know it is difficult to find one deep learning method that can analysis different data type in smart cities this is because data captured in smart cities could be 1D as text and numbers for example air data or 2D or 3D as images and videos for example health care. Current deep learning models like: Recurrent Neural Networks (RNN), Convolutional Neural Networks (CNN) and Deep Belief Network (DBN) are used in smart cities systems. Hybrid deep learning models is defines as using more than one type of deep learning models for smart cities applications. The seminar explains hybrid deep learning models in smart cities and gives an overview of researches using hybrid deep learning models for example human mobility, medical sensing, traffic congestion, etc. and their effects and benefits in developing smart cities applications.