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Wuhan Province in China reported the first case of novel corona virus as pneumonia outbreak during December 2019. The novel coronavirus was soon declared a pandemic by the World Health Organization. On 16th of July 2021, the number of COVID-19 confirmed cases was 188,128,952 globally, out of which 4,059,339 individuals succumbed to this deadly virus. In a short span of time, eight vaccines were approval for emergency use in different nations. The selection of vaccine depends upon many criteria. Concepts from multi-criteria decision making (MCDM) are appropriate to compare and rank them. The paper proposes analytical network processing (ANP) method to rank the eight vaccines according to seven criteria. The study proposes a decision tool to select the best vaccine among the candidate vaccines. A mathematical model based on ANP approach with three clusters having interrelationships within and among the clusters is proposed. [ABSTRACT FROM AUTHOR]

Pulmonary nodule is one of the lung diseases and its early diagnosis and treatment are essential to cure the patient. This paper introduces a deep learning framework to support the automated detection of lung nodules in computed tomography (CT) images. The proposed framework employs VGG-SegNet supported nodule mining and pre-trained DL-based classification to support automated lung nodule detection. The classification of lung CT images is implemented using the attained deep features, and then these features are serially concatenated with the handcrafted features, such as the Grey Level Co-Occurrence Matrix (GLCM), Local-Binary-Pattern (LBP) and Pyramid Histogram of Oriented Gradients (PHOG) to enhance the disease detection accuracy. The images used for experiments are collected from the LIDC-IDRI and Lung-PET-CT-Dx datasets. The experimental results attained show that the VGG19 architecture with concatenated deep and handcrafted features can achieve an accuracy of 97.83% with the SVM-RBF classifier. [ABSTRACT FROM AUTHOR]