ABSTRACT
The sudden impact of Covid19 in India coincided with the NAAC assessment submission decision at Sreyas Institute of Engineering & Technology at Hyderabad, India. Institute decided to document the upgraded National Assessment and Accreditation Council (NAAC) version and submit it to the NAAC board during February 2020;Covid19 started spreading in India. By March first week, a total lockdown started, and all educational institutions were closed. Along with quickly adapting to online classes, online examinations for the benefit of student's the staff also adopted the online media to complete the NAAC documentation as per the new standards laid by NAAC. To motivate the staff, who were technically knowledgeable, a lateral thinking approach was adopted. The criteria aspects were mapped to the socio, spiritual, scientific, and management aspects to bring the group together to focus on achieving their common goal for the institute of achieving the assessment and approval of the upgraded NAAC procedures and standards. Within the Covid locked down period of about five months, the required five years of data of all aspects of all criteria were collected with co-operation among the staff, and the submission was completed in September of 2020. This report is to focus on the mind mapping used initially to bring the faculty team together for preparing the NAAC Document. All seven criteria of the NAAC system were mapped to socio, scientific and spiritual practices through which all humans were brought up. The relationship is communicated via zoom meetings to all staff and the criteria in charge. Commitment improved as they related the known imbibed knowledge they possessed. © 2022, Rajarambapu Institute Of Technology. All rights reserved.
ABSTRACT
Medical imaging techniques are often used in treatment and follow-ups for diagnosed diseases. Image scans provide quick acquisition of images and clear and precise information, along with a magnified view of a particular portion of the body. Chest images can demonstrate various lung disorders, such as, COVID-19, Interstitial Lung Diseases (ILD) and Chronic lung disease, Pneumonia, Bronchiectasis, Cystic Fibrosis, etc. However, subtle changes in the volume and character of lung abnormalities can be difficult to assess even by expert radiologists. This is where Artificial Intelligence (AI) comes in. AI can aid traditional medical imaging technology by offering computational prowess that process images with greater speed and precision. This work presents a solution that performs AI-empowered analysis of Chest image scans for diagnosis, tracking and prognosis of various lung diseases. © 2021 IEEE