… The need for deep learning approaches stems from the need to extract high-level abstractions
from the raw data. Each layer of a DNN generates an abstract representation of its input …

… Both attacks lead to physical disruption to smart city infrastructure, which may even lead …
deep learning model is developed for detecting replay and DDoS attacks in a real life smart city …

… Image classifier is basically an algorithm created using neural network and deep learning
to detect … In this project, we are integrating Image Classifier with IoT to create a Smart City …

… buildings, 3D reconstruction can quickly obtain cities' information and improve the efficiency
… for smart cities based on deep learning. First, we propose a fine transmission image deep …

… Abstract—Collecting the massive internet of things data produced in a large smart city is quite
… Hence, this paper proposes a novel deep learning algorithm called DeepVDD to facilitate …

… smart cities. In this context, the electronic part of the robot in this study was supported with
a deep learning-… formation was shaped for a Deep Learning process. During this process, …

… This research contributes to improving the smart city application widely and to enhancing …
section of Deep learning neural network model. This is designed for smart city application-…

… by using federated learning trusted authority principle. In this paper, we investigate a novel
federated deep learning approach to urban traffic flow forecasting that graph learning, and …

… Hence, it can have a wide range of smart city applications such as person intrusion … , smart
home security, etc. Index Terms—Deep learning, intrusion detection system, transfer learning, …