Omar Elbadry

An Intelligent Electronic Nose for the assistance of Anosmic-Patients IEEE Anosmic people's inability to detect any odors almost always results in unfavorable outcomes. Failure to identify gas leaks or dangerous substances is seen as a threat to their safety. However, as a result of the COVID-19 Pandemic, the number of anosmic patients is steadily increasing. In this paper we propose a system assists anosmic patients in recognizing hazards that they cannot smell. This revolutionary system detects gas leaks, smoke, and early fires, as well as dangerous substances, automatically. With the aid of an array of gas sensors and different machine learning algorithms the, E-nose can identify six distinct smells. At last, if any hazardous gas spread occurs, the system fires alert message specifics the identified gas or event. We succeeded in achieving F1-score of 98 % for Support Vector Machine (SVM), logistic regression, and Decision Tree. While K-nearest Neighbors and Random Forest scored 100%