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Multiple Sclerosis (MS), an autoimmune condition in which the immune cells of the body destroys the covering (myelin sheath) surrounding the nerves, hinders the brain from effectively communicating with the body. If progressed to the later stages, the condition can lead to irreparable deterioration of the nerves. Neurologists use Magnetic Resonance Images (MRI) to classify them. Due to the complexity of the brain and MRI scan images, manual examination might be time consuming. To aid doctors across the world and reduce the time taken to diagnose this disease, Convolutional Neural Networks (CNNs) have been suggested. Moreover, this paper is a comparative study using two CNN Architectures done on MRI scan images for MS diagnosis. The dataset constitutes MRI scan images of MS patients in 2 different orientations and healthy brain MRI images. Expeditious diagnosis leads to an earlier medicament which can help contain the spread of the disease. Metrics like accuracy and other evaluation criteria are considered for the comparison of the CNN models.

In this project smart traffic control system using wireless communication is implemented. By using Arduino microcontroller entire system is controlled. When an emergency vehicle approaches the crossing, the RFID Reader reads the information and this information is passed to the poles of nearest junction. When the sensing device nearest to the traffic light finally receives the signal from its preceding pole, the traffic light changes to green. In the same way when green light is on then buzzer also give indication. This facilitates the smooth and quick passage of the emergency vehicle.