Detection and Prediction of HMS from Drinking Water by Analysing the Adsorbents from Residuals Using Deep Learning
Contamination HM is an important issue associated with the environment, and it requires suitable steps for the reduction of HMs in water at an acceptable ratio. With modern technologies, this could be possible by enabling the carbon adsorbents to adsorb the pollutions via deep learning strategies. I...
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| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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SAGE Publishing
2022-01-01
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| Series: | Adsorption Science & Technology |
| Online Access: | http://dx.doi.org/10.1155/2022/3265366 |
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| author | Sanjay Kumar Suman N. Arivazhagan L. Bhagyalakshmi Himanshu Shekhar P. Shanmuga Priya T. Helan Vidhya Sushma S. Jagtap Gouse Baig Mohammad Shubhangi Digamber Chikte S. Chandragandhi Alazar Yeshitla |
| author_facet | Sanjay Kumar Suman N. Arivazhagan L. Bhagyalakshmi Himanshu Shekhar P. Shanmuga Priya T. Helan Vidhya Sushma S. Jagtap Gouse Baig Mohammad Shubhangi Digamber Chikte S. Chandragandhi Alazar Yeshitla |
| author_sort | Sanjay Kumar Suman |
| collection | DOAJ |
| description | Contamination HM is an important issue associated with the environment, and it requires suitable steps for the reduction of HMs in water at an acceptable ratio. With modern technologies, this could be possible by enabling the carbon adsorbents to adsorb the pollutions via deep learning strategies. In this paper, we develop a model on detection and prediction of presence of HMs from drinking water by analysing the adsorbents from residuals using deep learning. The study uses dense neural networks or DenseNets to analyse the microscopic images of the residual adsorbents. The study initially preprocesses and extracts features using standardised procedure. The DenseNets are used finally for detection purpose, and it is trained and tested with standard set of microscopic images. The experimental results are conducted to test the efficacy of the deep learning model on detecting the HM composition. The results of simulation show that the proposed deep learning model achieves 95% higher rate of detecting the HM composition from the adsorption residuals than other methods. |
| format | Article |
| id | doaj-art-b212f41a6ba046c0a691a16a0ea6d14c |
| institution | Kabale University |
| issn | 2048-4038 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Adsorption Science & Technology |
| spelling | doaj-art-b212f41a6ba046c0a691a16a0ea6d14c2025-01-02T22:37:30ZengSAGE PublishingAdsorption Science & Technology2048-40382022-01-01202210.1155/2022/3265366Detection and Prediction of HMS from Drinking Water by Analysing the Adsorbents from Residuals Using Deep LearningSanjay Kumar Suman0N. Arivazhagan1L. Bhagyalakshmi2Himanshu Shekhar3P. Shanmuga Priya4T. Helan Vidhya5Sushma S. Jagtap6Gouse Baig Mohammad7Shubhangi Digamber Chikte8S. Chandragandhi9Alazar Yeshitla10St. Martin's Engineering CollegeDepartment of Computational IntelligenceDepartment of Electronics and Communication EngineeringDepartment of Electronics and Communication EngineeringRajalakshmi Engineering CollegeRajalakshmi Engineering CollegeRajalakshmi Engineering CollegeDepartment of Computer Science and EngineeringDepartment of Computer Science and EngineeringDepartment of Computer Science EngineeringDepartment of BiotechnologyContamination HM is an important issue associated with the environment, and it requires suitable steps for the reduction of HMs in water at an acceptable ratio. With modern technologies, this could be possible by enabling the carbon adsorbents to adsorb the pollutions via deep learning strategies. In this paper, we develop a model on detection and prediction of presence of HMs from drinking water by analysing the adsorbents from residuals using deep learning. The study uses dense neural networks or DenseNets to analyse the microscopic images of the residual adsorbents. The study initially preprocesses and extracts features using standardised procedure. The DenseNets are used finally for detection purpose, and it is trained and tested with standard set of microscopic images. The experimental results are conducted to test the efficacy of the deep learning model on detecting the HM composition. The results of simulation show that the proposed deep learning model achieves 95% higher rate of detecting the HM composition from the adsorption residuals than other methods.http://dx.doi.org/10.1155/2022/3265366 |
| spellingShingle | Sanjay Kumar Suman N. Arivazhagan L. Bhagyalakshmi Himanshu Shekhar P. Shanmuga Priya T. Helan Vidhya Sushma S. Jagtap Gouse Baig Mohammad Shubhangi Digamber Chikte S. Chandragandhi Alazar Yeshitla Detection and Prediction of HMS from Drinking Water by Analysing the Adsorbents from Residuals Using Deep Learning Adsorption Science & Technology |
| title | Detection and Prediction of HMS from Drinking Water by Analysing the Adsorbents from Residuals Using Deep Learning |
| title_full | Detection and Prediction of HMS from Drinking Water by Analysing the Adsorbents from Residuals Using Deep Learning |
| title_fullStr | Detection and Prediction of HMS from Drinking Water by Analysing the Adsorbents from Residuals Using Deep Learning |
| title_full_unstemmed | Detection and Prediction of HMS from Drinking Water by Analysing the Adsorbents from Residuals Using Deep Learning |
| title_short | Detection and Prediction of HMS from Drinking Water by Analysing the Adsorbents from Residuals Using Deep Learning |
| title_sort | detection and prediction of hms from drinking water by analysing the adsorbents from residuals using deep learning |
| url | http://dx.doi.org/10.1155/2022/3265366 |
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