| Optimized Deep Learning For Gas Sensor |
| Paper ID : 1064-ISCBAS |
| Authors |
|
Mariem M. Abdellatif *1, Asmaa A. Ibrahim2, Abeer S. Desuky3, Hany M. Harb4 1Al-Azhar University , Canadian International College (CIC) 2Instructor of Al-Azhar University 3Prof. of Al-Azhar University 4Prof.Dr. Al-Azhar University |
| Abstract |
| Gas sensors are widely used to detect the presence of hazardous gases in our daily lives, and their accuracy is crucial for ensuring the safety of individuals and environments. In this paper, we propose an optimized deep learning approach for gas sensor data analysis that enhances the accuracy of gas prediction. The proposed method involves advanced data preprocessing techniques, feature selection, and model optimization, which improves the performance of gas prediction. Our paper's contribution is the development of a novel deep learning-based approach that optimizes the accuracy of gas prediction, making it more reliable and practical for real-world applications. The proposed method has significant implications for gas detection and can potentially save lives by providing early warning of dangerous gas levels. |
| Keywords |
| Gas Detection, Deep Learning, Optimization, Prediction, SVM, Decision Tree, Feature Selection . |
| Status: Abstract Accepted (Oral Presentation) |