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Artificial Intelligence has changed our outlook towards the whole world, and it is regularly used to better understand all the data and information that surround us in our everyday lives. One such application of Artificial Intelligence in a real-world scenario is the extraction of data from various images and interpreting them in different ways. This includes applications like object detection, image segmentation, image restoration, etc. While every technique has its own area of application, image segmentation has a variety of applications extending from the complex medical field to regular pattern identification. The aim of this paper is to research about several FCNN-based semantic segmentation techniques to develop a deep learning model that is able to segment tumours in brain MRI images to a high degree of precision and accuracy. The aim is to try several different architectures and experiments with several loss functions to improve the accuracy of our model and obtain the best model for our classification including newer loss functions like dice loss function, hierarchical dice loss function cross entropy, etc. [ABSTRACT FROM AUTHOR]

Electroencephalogram (EEG) can track the brain waves which contain the neural activity of the brain. EEG signals help to understand the physiological and functional details and activities of the brain. In the era of Artificial Intelligence (AI), machine learning algorithms were useful in brain disorder detection and classification. Recently, a rapid increase in using Deep Learning (DL) methods in various applications in EEG signals not only helps in the detection of brain disorders but also facilitates the recognition of human emotions and various psycho-neuro disorders. In order to offer a beneficial and broad perspective, a detailed survey on the application of deep learning architecture in EEG signals has been carried out in this paper. Different deep learning methods, using varied architecture in EEG signal analysis, offer an understanding to develop the next level of AI-based systems. This review will provide information about how deep learning methods are used in EEG signals and the challenges and limitations of each method in classification; moreover making it helpful for those who are exploring EEG signals using DL algorithms. [ABSTRACT FROM AUTHOR]