Deepfake videos and images have increased in recent years at exponential rates, and refer to the transfer of features of interest from source image (or video) to target image (or video), such that the target modality appears to animate the source almost close to the real. The forensic professionals, policy makers, and the public alike are interested in the role of Deepfakes is playing to confuse viewers in spreading misinformation by undermining the truth. In the past decade, computer vision has made significant advances using the latest state-of-art-methods of image processing techniques. Supervised Deep learning models produce super-human results in a variety of computer vision and machine learning applications. The field of medical imaging is scarce in terms of a reliable data set that is extensive enough to train distinct models. One way to tackle this problem is to use a Generative Adversarial Network to synthesise DEEPFAKE images to augment the data. DEEPFAKE images can be a useful means in various applications like translating to different useful and sometimes malicious modalities, unbalanced datasets or increasing the amount of datasets. In this paper the data scarcity has been addressed by using Progressive Growing Generative Adversarial Networks (PGGAN). However, PGGAN consists of convolution layer that suffers from the training-related issues. PGGAN requires a large number of convolution layers in order to obtain highresolution image training, which makes training a difficult task. In this work, a subjective self-attention layer has been added with convolution layers for efficient feature learning and the use of spectral normalization for training stabilization -the two tasks resulting into what is referred to as Enhanced-GAN. The Enhanced-GAN performance is compared to PGGAN performance using the parameters of AM Score and Mode Score. In addition, the strength of Enhanced-GAN and PGGAN synthesis is evaluated using the U-net model for segmentation tasks. Dice Coefficient metrics show that U-net trained on Enhanced-GAN DEEPFAKE data optimized with real data performs better than PGGAN DEEPFAKE data with real data.

Researchers are becoming more interested in crowd surveillance because of its several potential applications. These applications may include detecting unusual activity for security purposes, monitoring reasons for archiving records, and conducting inventory for facility planning and extension. Detecting people and tracking them from a security viewpoint and understanding their behavior in places large crowds is highly important because unruly crowds in public spaces can lead to serious health and security concerns. Crowd related accidents happen to cause injuries and deaths, which often occur during events not properly planned. The planning of the organizers relies heavily on exploring the behavior of the few in a crowd of individuals and groups in thousands that create the crowds. It is this focus that provides the main reason for this research. This work proposes a model that can count people in crowds, automatically detect and track people, and then estimate their direction and speed. Deep learning networks have proven costly to run, needing memory and power to perform computations beyond what is possible on edge devices with limited resources. As a result, we propose the use of hybrid YOLOv4 consisting of detection method combined with the training phase pruning and the use the convolution attention module strategy. Accuracy of the Hybrid YOLOv4 is increased by 33%, whereas mAP reached 92.1%. While training on the JHU dataset, the suggested hybrid YOLOv4 strategy decreases the computational memory requirements, all of which closely meet the real-time application conditions. This work will help avoid the threatening situation of crowding gathering around to cause stampedes and thus risking crowds with disastrous consequences.

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Since December 2019, many unexplained viral pneumonia cases have been found in Wuhan City, Hubei Province, China. It was later confirmed that the outbreak's causative agent was a new coronavirus. The virus was temporarily named "2019-new coronavirus" (2019-nCoV) by the World Health Organization (WHO). The diseases caused by 2019-nCoV were called by the National Health and Health Commission of China "New coronavirus pneumonia" (Novel Coronavirus Pneumonia, NCP) and was named "Coronavirus disease 2019" (COVID-19) by the WHO. The outbreak of NCP seriously affected the lives of the public. This article focuses on the group of cancer patients, comprehensively considers the social, medical resources and family issues to analyze the possible impact of the epidemic on cancer patients' drug treatment and health and makes recommendations for cancer patients' management.