Optimized Deep Learning for Gas Sensor | ||||
| International Journal of Theoretical and Applied Research | ||||
| Volume 3, Issue 1, June 2024, Page 371-378 PDF (444.59 K) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/ijtar.2024.215279.1062 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
Mariem Mohamed Mahmoud  1; Asmaa A Ibrahim 2; Abeer S .Desuky 2; Hany M. Harb3
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| 1Faculty of Science, Al-Azhar University (Girls), School of Computer Science, Canadian International College (CIC), New Cairo, Egypt | ||||
| 2Faculty of Science, Al-Azhar University (Girls), | ||||
| 3Faculty Of Engineering Al-Azhar University (Boys) | ||||
| 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. Gas sensors are essential in a variety of applications, such as environmental monitoring, industrial safety, and healthcare. These sensors are intended to detect and measure the presence of certain gases in their surroundings. Significant progress has been achieved in the development of gas sensor technology in recent years, resulting in better sensitivity, selectivity, and miniaturization. In this paper, we propose an optimized deep-learning approach for gas sensor data analysis that improves gas prediction accuracy. The proposed approach includes advanced data preprocessing techniques, feature selection, and model optimization to increase gas prediction performance. The contribution of this research is the development of a novel deep learning-based approach that optimizes the accuracy of gas prediction, making it more trustworthy 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; SVM; Decision Tree; Feature Selection | ||||
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