Covid-19 has become a deadly pandemic claiming more than three million lives worldwide. SARS-CoV-2 causes distinct pathomorphological alterations in the respiratory system, thereby acting as a biomarker to aid its diagnosis. A multimodal framework (Ai-CovScan) for Covid-19 detection using breathing sounds, chest X-ray (CXR) images, and rapid antigen test (RAnT) is proposed. Transfer Learning approach using existing deep-learning Convolutional Neural Network (CNN) based on Inception-v3 is combined with Multi-Layered Perceptron (MLP) to develop the CovScanNet model for reducing false-negatives. This model reports a preliminary accuracy of 80% for the breathing sound analysis, and 99.66% Covid-19 detection accuracy for the curated CXR image dataset. Based on Ai-CovScan, a smartphone app is conceptualised as a mass-deployable screening tool, which could alter the course of this pandemic. This app ’s deployment could minimise the number of people accessing the limited and expensive confirmatory tests, thereby reducing the burden on the severely stressed healthcare infrastructure.

Pneumonia is a respiratory infection resulting in inflammation of the lungs. The causes of this infectious disease could be attributed to viruses, bacteria or fungi. One of the many ways of detecting the disease is by a chest X-ray of the patient. The rural population in developing nations have limited access to doctors, medical diagnostic facilities, and hospitals. Hence, diagnosis is delayed resulting in adverse consequences. This paper is an attempt to design and develop a smartphone-based application (app) for the preliminary detection of pneumonia using X-ray images. The app is based on machine learning which identifies pneumonia, using a chest X-ray image of a patient with a 'MobileNets' model, trained on thousands of X-ray images of known cases of pneumonia. The app has been developed on Android Studio, incorporating TensorFlow library. The patient's chest X-ray is scanned and uploaded to the app using the smartphone camera. Additionally, an e-diagnosis facility is integrated into the app where qualified medical practitioners' advice is taken on the obtained results. A breathing pattern recorder module is developed, which, in future, could be integrated into the smartphone app to increase its accuracy in prediction.

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COVID-19 has now been declared a 'Global Pandemic' by WHO. The pandemic has affected more than 200 countries since its first outbreak in December 2019. The spread of COVID-19 resulted in a state of lockdown globally. India too, closed its borders to contain the virus. Those worst affected by the pandemic are migrant workers at the 'Bottom of Pyramid' (BoP) due to unemployment and lack of monetary aid. Family sustenance has been difficult for them, with children impacted physically and psychologically. This paper proposes a Product-Service System (PSS) that provides essential emergency kits to infants (6-12 months), children (1-6 years), and their mothers during such emergencies. This PSS scheme strives to fulfil their basic hygiene, nutritional and psychological requirements. Three types of kits are distributed to the migrant families using an online service platform. The entire system operates on a sustainable, single-use plastic-free design. The case study of this humanitarian scheme is specific to India but is also valid for other developing nations. Reaching out to the communities is achieved through a smartphone app and website. The system uses ICT infrastructure to connect various stakeholders and can be admirably adapted to the framework of an inclusive smart city.

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