Background: Novel corona virus (2019-nCoV) has spread in the world since its first human infection in December 2019. India has also witnessed a rising number of infections since March 2020. The Indian government imposed lockdowns in the nation to control the movement of its citizens thereby confining the spread of the virus. Tweeters resorted to usage of social media platform to express their mind. Aim: Through this article, an attempt has been made to understand the mind-set of Indian people using Python and R statistical software, during the recent lockdown 2.0 (15 April 2020 to 3 May 2020) and lockdown 3.0 (4 May 2020 to 17 May 2020) through their tweets on the social media platform Twitter. Also, opinion on e-commerce during this pandemic has been analysed. Method: Analysis has been performed using Python and R statistical software. Also, recent articles related to COVID-19 have been considered and reviewed. Result: Although the country had a positive approach in lockdown 2.0 with only few instances of sadness, disgust and others, the majority of the people had a negative approach in lockdown 3.0. Conclusion: This analysis can help the health specialists to understand people’s mind-set, the authorities to take further corresponding measures in washing out the virus and the e-commerce stakeholders to adapt to the changing attitudes by adjusting demand and supply plans accordingly.

No abstract

No abstract

Employment of various sensors used in IoT can help create a sustainable urban life. Rapid advancements in IoT have made human existence smarter. Smartness may be associated to office, home, networks, energy consumption, agriculture, education, retail, and even healthcare. Smart health management addresses its populations, such as tracking routine activities, obesity, nutrition intake, heart rate, glucose level, oxygen level, body temperature, or even stress level monitoring. Stress is a condition which is being faced by people irrespective of their age, gender, or profession. But its identification at an early stage can help in preventing the consequences. This work presents nine machine learning techniques for identifying stress using the SWELL dataset. Hence, automated classifiers were utilised to predict working circumstances and stress-related mental states and were compared for accuracy for three stress conditions (no stress, interruption, and time pressure).

No abstract