Deep convolutional neural network-based enhanced crowd density monitoring for intelligent urban planning on smart cities.

The concept of a smart city has spread as a solution ensuring wider availability of data and services to citizens, apart from as a means to lower the environmental footprint of cities. Crowd density monitoring is a cutting-edge technology that enables smart cities to monitor and effectively manage crowd movements in real time. By utilizing advanced artificial intelligence and video analytics, valuable insights are accumulated from crowd behaviour, assisting cities in improving operational efficiency, improving public safety, and urban planning.

A multi-model feature fusion based transfer learning with heuristic search for copy-move video forgery detection.

Protecting data from management is a significant task at present. Digital images are the most general data representation. Images might be employed in many areas like social media, the military, evidence in courts, intelligence fields, security purposes, and newspapers.

Enhancing cybersecurity via attribute reduction with deep learning model for false data injection attack recognition.

Cyberattacks have given rise to several phenomena and have raised concerns among users and power system operators. When they are built to bypass state estimation bad data recognition methods executed in the conventional grid system control room, False Data Injection Attacks (FDIA) pose a significant security threat to the operation of power systems. Therefore, real-time monitoring becomes inevitable with the quick implementation of renewables within the grid operator.