… To extract the high quality of information from applications, we have to adopt suitable
algorithms such as machine/deep learning (DL) algorithms. The major contribution to the building …

… of video surveillance in smart cities, the existing schemes for efficient encoding and
transmission of surveillance videos in smart cities and then deep learning based salient region …

… , houses, city streets, and other facets of city life will … deep learning, machine learning, and
image processing techniques. To achieve this objective, a hybrid deep and machine learning …

… intelligence (AI) and deep learning (DL) approaches … deep learning (SDL)-based smart
health diagnosis of COVID-19 using Chest X-Ray Images. The SDL makes use of dual deep …

… Deep learning is also a machine learning approach that could be used to train the models …
using deep learning for traffic management in smart cities in disasters. Deep learning requires …

… Therefore, we propose deep learning-based forest fire classification in smart city applications
where AI-capable IoT devices (such as UAVs equipped with cameras) can be used. The …

… Artificial intelligence and namely its machine learning branch is expected to reinforce this
trend by making smart cities even smarter. However, smart cities can only be successful if they …

License Plate Recognition System (LPRS) plays a vital role in smart city initiatives such as
traffic control, smart parking, toll management and security. In this article, a cloud-based LPRS …

Citizen safety in modern urban environments is an important aspect of life quality. Implementation
of a smart city approach to video surveillance depends heavily on the capability of …

… We demonstrate the fundamentals of deep learning, which later help us understand the
techniques used for dimension reduction and how we can use them to compress data in IoT …