Elias Hossain¹, Abdulwahab Alazeb^2,*, Naif Al Mudawi², Sultan Almakdi², Mohammed Alshehri², M. Gazi Golam Faruque³, Wahidur Rahman³

CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4945-4966, 2022, DOI:10.32604/cmc.2022.027058 - 21 April 2022

Abstract Depression is a crippling affliction and affects millions of individuals around the world. In general, the physicians screen patients for mental health disorders on a regular basis and treat patients in collaboration with psychologists and other mental health experts, which results in lower costs and improved patient outcomes. However, this strategy can necessitate a lot of buy-in from a large number of people, as well as additional training and logistical considerations. Thus, utilizing the machine learning algorithms, patients with depression based on information generally present in a medical file were analyzed and predicted. The methodology… More >

Ankur Dumka¹, Parag Verma², Rajesh Singh³, Anil Kumar Bisht⁴, Divya Anand^5,6,*, Hani Moaiteq Aljahdali⁷, Irene Delgado Noya^6,8, Silvia Aparicio Obregon^6,9

CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 6029-6044, 2022, DOI:10.32604/cmc.2022.024698 - 21 April 2022

Abstract Coronavirus (COVID-19) has impacted nearly every person across the globe either in terms of losses of life or as of lockdown. The current coronavirus (COVID-19) pandemic is a rare/special situation where people can express their feelings on Internet-based social networks. Social media is emerging as the biggest platform in recent years where people spend most of their time expressing themselves and their emotions. This research is based on gathering data from Twitter and analyzing the behavior of the people during the COVID-19 lockdown. The research is based on the logic expressed by people in this… More >