COVID’19 is an emerging disease and the precise epidemiological profile does not exist in the world. Hence, the COVID’19 outbreak is treated as a Public Health Emergency of the International Concern by the World Health Organization (WHO). Hence, an effective and optimal prediction of COVID’19 mechanism, named Jaya Spider Monkey Optimization-based Deep Convolutional long short-term classifier (JayaSMO-based Deep ConvLSTM) is proposed in this research to predict the rate of confirmed, death, and recovered cases from the time series data. The proposed COVID’19 prediction method uses the COVID’19 data, which is the trending domain of research at the current era of fighting the COVID’19 attacks thereby, to reduce the death toll. However, the proposed JayaSMO algorithm is designed by integrating the Spider Monkey Optimization (SMO) with the Jaya algorithm, respectively. The Deep ConvLSTM classifier facilitates to predict the COVID’19 from the time series data based on the fitness function. Besides, the technical indicators, such as Relative Strength Index (RSI), Rate of Change (ROCR), Exponential Moving Average (EMA), Williams %R, Double Exponential Moving Average (DEMA), and Stochastic %K, are extracted effectively for further processing. Thus, the resulted output of the proposed JayaSMO-based Deep ConvLSTM is employed for COVID’19 prediction. Moreover, the developed model obtained the better performance using the metrics, like Mean Square Error (MSE), and Root Mean Square Error (RMSE) by considering confirmed, death, and the recovered cases of COVID’19 for China and Oman. Thus, the proposed JayaSMO-based Deep ConvLSTM showed improved results with a minimal MSE of 1.791, and the minimal RMSE of 1.338 based on confirmed cases in Oman. In addition, the developed model achieved the death cases with the values of 1.609, and 1.268 for MSE and RMSE, whereas the MSE and the RMSE value of 1.945, and 1.394 is achieved by the developed model using recovered cases in China.

The application of Deep Learning Techniques in biometrics has grown significantly during the last decade. The use of deep learning models in ear biometrics is restricted due to the lack of large ear datasets. Researchers employ transfer learning based on several pretrained models to overcome the limitations. For the unconstrained AWE ear dataset, traditional Machine Learning (ML) techniques and hand-crafted features fall short of providing a good recognition accuracy. This paper evaluates the influence of separating left and right ears and the effect of occlusion on the recognition accuracy in AWE dataset. The left and right ear of a person need not be identical. A study by separating the left and right ear into two different datasets is carried out with the pretrained ResNet50 based model. There is a remarkable increase in accuracy when the left and right ear images are independently considered. A new data augmentation technique, incorporating occlusion, is also proposed and experimented with the ResNet50 based model.

Commonly, a USB flash drive is utilized for storing, transferring and backing up data like personal files, software, media files, etc. But users might not have much knowledge about its other hidden characteristics. It could also act as a replacement of CD/hard disk/DVD media as OSs handler resources and as a plug and play portable system. However, security is a major concern for external boot system and to fix this issue, numerous solutions were proposed and implemented. Out of all the existing security provisioning schemes, biometric based security solutions are always reliable and hassle free to process. The USB drives now available with fingerprint protection and to come out of the box, this article secures the USB drives with the combination of fingerprint and finger vein. On successful authentication, the user can boot OS from USB. The performance of the work is analysed in terms of FAR, FRR, accuracy and time consumption rates and observed that it achieves greater accuracy rate when compared with other classifiers.

Virtual reality is a computer-generated three-dimensional environment where seemingly real graphics are used to simulate an imaginary world. It is generally accessed by using a special VR helmet or spectacles which enable you to access this imaginary world. Virtual reality uses the concept of split-screen to project to different images to our eyes in a selected angle which makes our brain believe that we are viewing a three-dimensional image. This tricks the brain into thinking that the human is standing in a three-dimensional environment where they can move around. Over the years, virtual reality has been included in a lot of traditional fields to challenge the endless possibilities in those fields. It has been used in medical sciences to train doctors, the aerospace industry to train the pilots and astronauts, the architecture industry to obtain maximum efficiency in designing the structures, and many more fields. VR gaming is also becoming a huge market where people can interact with the game components to get a realistic experience of being in a game. VR is also being used by counselors and psychiatrists around the world to treat people with mental health problems. In this chapter, the authors use the concept of virtual reality in the live music industry to simulate realistic music concerts by designing and developing a platform to host virtual concerts using virtual reality.

No abstract