The domain of Brain-Computer Interface (BCI) explores how humans can interact with the computer without giving direct instruction. Recognizing the activity from brain signals affiliated with an electronic device might (i.e. MEG) provide a stepping stone in the field of BCI. The intended algorithm in the paper aimed at presenting a statistical strategy to classify the brain signal from the MEG signal data, provided by BCI competition IV dataset III. The algorithm is compartmentalized in three levels: preprocessing, feature extraction, and classification. Autoregressive features have been extracted from the signals to classify using UNEQ, KNN and SIMCA, discuss the data distribution and asses how well the algorithm performs on unknown yet similar distribution. 

In this paper, we have explored different strategies to find out the saliency of the features from different pretrained models in detecting violence in videos. A dataset has been created which consists of violent and non-violent videos of different settings. Three ImageNet models; VGG16, VGG19, ResNet50 are being used to extract features from the frames of the videos. In one of the experiments, the extracted features have been feed into a fully connected network which detects violence in frame level. Moreover, in another experiment, we have fed the extracted features of 30 frames to a long short-term memory (LSTM) network at a time. 

This research aims in detecting violent crowd flows in the context of Bangladesh. For this purpose, we have collected a dataset which includes both violent and non-violent crowd flows. Different deep learning algorithms and approaches have been applied on this dataset to detect scenarios which contain violence. Convolutional neural networks (CNN) and long short-term memory network (LSTM) based architectures have been experimented separately on this dataset and in combination as well.  

This article provides a comprehensive review of deep learning-based blood vessel segmentation of the brain. Cerebrovascular disease develops when blood arteries in the brain are compromised, resulting in severe brain injuries such as ischemic stroke, brain hemorrhages, and many more. Early detection enables patients to obtain more effective treatment before becoming critically unwell. Due to the superior efficiency and accuracy compared to manual segmentation and other computer-assisted diagnosis procedures, deep learning algorithms have been extensively deployed in brain vascular segmentation. This study examined current articles on deep learning-based brain vascular segmentation, which examined the proposed methodologies, particularly the network architectures, and determined the model trend. We evaluated challenges and crucial factors associated with the application of deep learning to brain vascular segmentation, as well as future research prospects. This paper will assist researchers in developing more sophisticated and robust models in the future to develop deep learning solutions. 

The brain tumor is recognized as one of the most frequent tumors, with a significant mortality rate associated with its development. Segmentation of brain tumors involves distinguishing normal brain tissue from malignant tissue. When evaluating brain tumors, it is possible to determine the existence of tumor tissue quickly. However, accurate and reproducible segmentation and characterization of anomalies are not readily achievable. Consequently, several researchers have proposed various biomedical image segmentation methods to distinguish between tumor and normal brain tissue reliably. However, state-of-the-art segmentation has not been achieved by the existing brain tumor segmentation models, and they often come with high model complexity. Att-Sharp-U-net, a model influenced by the actual U-net model utilized in various medical image segmentation research, is presented as a contribution by this study. Two critical alterations to the underlying U-net model have been incorporated into the model: a grid-based attention block and a sharp block. By doing this, we were able to address the constraints of the U-net model while simultaneously enhancing segmentation performance with increasing negligible computational complexity. Experiments on the Brats2020 dataset, a recent publicly available benchmark dataset in brain tumor segmentation, showed that the proposed model improved segmentation performance with a dice score of 0.9275 and Jaccard score of 0.8684 when compared to the baselines.