As it is said history repeats itself, in 2020, the world witnessed another epidemic with the name of novel corona virus (2019-nCoV ). The dataset has been prepared through the information from John Hopkins University, WHO source, along with the data of Indian Government. In this paper, we will take a deeper view through the epidemiological data on corona virus affected patients. We have assessed various trends and discovered patterns through the data. We have studied the outbreak progression of the 2019-nCoV in India through exponential growth model. Various patterns are analyzed like gender affected, age group of people being affected the most and percentage people survived based on assessing delays between symptom onset and initialization of treatment by the professionals. Through modeling of maximum likelihood function, we have drawn an approximate conclusion on number of unreported cases of 2019-nCoV patients (initially as per data). Serial interval taken for other known corona-viruses (nCoV), Severe Acute Respiratory Syndrome (SARS), and Middle East Respiratory Syndrome (MERS) are used as an approximate measure to predict the number of unreported cases. The counts of unreported cases are more vital than its counterpart as it can cause severe outbreak if not checked. Along with that, we have proposed a LSTM-based architecture to predict the number of cases in India.

In this paper, we propose an ensemble-based transfer learning method to predict the X-ray image of a COVID-19 affected person. We have used a weighted Euclidean distance average as the parameter to ensemble the transfer learning model viz. ResNet50, VGG16, VGG19, Xception, and InceptionV3. Image augmentations have been carried out using generative adversarial network modelling. We took 784 training images, and 278 test images to validate our model accuracy, and the accuracy of our proposed model was around 98.67% for the training data set and 95.52% for the test data set. Along with that, we also propose a genetic algorithm optimized classification algorithm, to analyze the symptoms of COVID-19 for low, medium, and high-risk patients. The accuracy for the optimized set overshadowed the accuracy of un-optimized classification, and the optimized accuracy is as high as 88.96% for the optimized model. The novelty of this paper lies in the bi-sided model of the paper, i.e., we propose two major models, and one is the genetic algorithm optimized model to analyze the symptoms for a patient of varied risk and the other is to classify the X-ray image using an ensemble-based transfer learning model.

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In this chapter, the authors take a walkthrough in BCI technology. At first, they took a closer look into the kind of waves that are being generated by our brain (i.e., the EEG and ECoG waves). In the next section, they have discussed about patients affected by CLIS and ALS-CLIS and how they can be treated or be benefitted using BCI technology. Visually evoked potential-based BCI technology has also been thoroughly discussed in this chapter. The application of machine learning and deep learning in this field are also being discussed with the need for feature engineering in this paradigm also been said. In the final section, they have done a thorough literature survey on various research-related to this field with proposed methodology and results.

The most talked about disease of our era, cancer, has taken many lives, and most of them are due to late prognosis. Statistical data shows around 10 million people lose their lives per year due to cancer globally. With every passing year, the malignant cancer cells are evolving at a rapid pace. The cancer cells are mutating with time, and it's becoming much more dangerous than before. In the chapter, the authors propose a DCGAN-based neural net architecture that will generate synthetic blood cancer cell images from fed data. The images, which will be generated, don't exist but can be formed in the near future due to constant mutation of the virus. Afterwards, the synthetic image is passes through a CNN net architecture which will predict the output class of the synthetic image. The novelty in this chapter is that it will generate some cancer cell images that can be generated after mutation, and it will predict the class of the image, whether it's malignant or benign through the proposed CNN architecture.