Machine Learning ( 4) and Deep learning approaches play an essential role in health care ( 5) and industrial applications ( 6, 7) for prediction and optimization. As of late, with the guide of electronic stethoscopes combined with pattern recognition and artificial intelligence, the mechanized respiratory sound examination has drawn much consideration since it conquers the confinements of normal auscultation and gives an effective technique to clinical conclusion ( 3). However, non-stationary signs are hard to examine and challenging to recognize if not done by a well-prepared doctor this may prompt wrong analysis. Later on, the auscultation by stethoscope is whimsical because it relies upon the doctor's capacity and the low affectability of the human ear hearing. Furthermore, it gives much data about the respiratory organ and the indications of the sicknesses that influence it ( 1, 2).

The typical stethoscope is usually considered a cheap and secure method for examining the patients, other than setting aside less effort required for the conclusion. A couple of decades back, doctors depended on their hearing to distinguish symptomatic signs in lung audios through utilizing the standard stethoscope equipment.

Lung sound is produced when air flows during the process of respiration. Respiratory sound (RS) attributes and their analyses structure a fundamental piece of pneumonic pathology such as COVID-19 pneumonia, and it gives symptomatic data about a patient's lung. The obtained results using the proposed framework are superior to previous techniques since we simultaneously considered the seven different abnormal respiratory sound classes. ANA makes sound features enough reach to be identified more accurately than the respiratory sounds without ANA. The proposed framework contains an adaptive mechanism of adding a similar type of noise to unhealthy respiratory sounds. Spectrum analysis was done through Artificial Noise Addition (ANA) in conjunction with different deep convolutional neural networks (CNN) to classify the seven abnormal respiratory sounds-both continuous (CAS) and discontinuous (DAS). In this research, we have applied Fourier analysis for the visual inspection of abnormal respiratory sounds. Lung disease is the third most ordinary cause of death worldwide, so it is essential to classify the RS abnormality accurately to overcome the death rate. A couple of decades back, doctors depended on their hearing to distinguish symptomatic signs in lung audios by utilizing the typical stethoscope, which is usually considered a cheap and secure method for examining the patients. Respiratory sound (RS) attributes and their analyses structure a fundamental piece of pneumonic pathology, and it gives symptomatic data regarding a patient's lung. 5Department of Computer and Network Engineering, College of Information Technology, UAE University, Al Ain, United Arab Emirates.4Cloud Computing and Applications Reseach Lab, Staffordshire University, Stoke-on-Trent, United Kingdom.3Centre for Smart Systems, AI and Cybersecurity, Staffordshire University, Stoke-on-Trent, United Kingdom.

2Department of Information Technology, College of Computing and Information Technology at Khulais, University of Jeddah, Jeddah, Saudi Arabia.1Faculty of Computing and Information Technology, University of Gujrat, Gujrat, Pakistan.Rizwana Zulfiqar 1 Fiaz Majeed 1 Rizwana Irfan 2 Hafiz Tayyab Rauf 3 Elhadj Benkhelifa 4 Abdelkader Nasreddine Belkacem 5 *

Rhonchi lung sound