This paper proposes an IoT based solution to automate smart lighting in large public spaces. The proposed solution aims to optimize power efficiency and cost effectiveness while providing an automated solution. The system makes use of devices that can detect Wi-Fi signals (Wi-Fi sniffers) from mobile devices. Continuously monitoring the smart devices (connected to a wireless network) in the vicinity makes it possible to estimate the position and number of people in an area based on the range of the device and use this to control the lighting.

Brain tumor classification is a challenging task in the field of medical image processing. Technology has now enabled medical doctors to have additional aid for diagnosis. We aim to classify brain tumors using MRI images, which were collected from anonymous patients and artificial brain simulators. In this article, we carry out a comparative study between Simple Artificial Neural Networks with dropout, Basic Convolutional Neural Networks (CNN), and Dilated Convolutional Neural Networks.

The Particle Swarm Optimization (PSO) algorithm updates each individual’s velocity and position using its own prior best position and the best position found so far by any particle. Effective search for global optima can benefit if each particle may utilize additional historical information regarding the quality of previously visited positions in its proximity. We present an approach for doing so, using an archive containing some prior particle positions, analogous to the archive used in some multi-objective optimization algorithms.