Intelligent CO2 Monitoring for Diagnosis of Sleep Apnea Using Neural Cryptography Techniques
In biomass wastage, carbon is one of the adsorbent materials. Biomass wastage contains complex materials, and pressure, various temperatures, and presence of various chemical components which are subjected to the adsorption of carbon are a tedious task, and it is used in the sustainable waste manage...
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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/10.1155/2022/6349335 |
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| _version_ | 1841563145588441088 |
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| author | Manar Ahmed Hamza Maha M. Althobaiti Ola Abdelgney Omer Ali Souad Larabi-Marie-Sainte Majdy M. Eltahir Anwer Mustafa Hilal Mesfer Al Duhayyim Ishfaq Yaseen |
| author_facet | Manar Ahmed Hamza Maha M. Althobaiti Ola Abdelgney Omer Ali Souad Larabi-Marie-Sainte Majdy M. Eltahir Anwer Mustafa Hilal Mesfer Al Duhayyim Ishfaq Yaseen |
| author_sort | Manar Ahmed Hamza |
| collection | DOAJ |
| description | In biomass wastage, carbon is one of the adsorbent materials. Biomass wastage contains complex materials, and pressure, various temperatures, and presence of various chemical components which are subjected to the adsorption of carbon are a tedious task, and it is used in the sustainable waste management system. While screening the biomass wastage management system, prediction of activated carbon’s quality and understanding of the mechanism of adsorption of CO2 are a complicated task. Many research works have been developed; the main issues are inaccurate and inefficient prediction of carbon available in the various feedstock of biomass wastage. To overcome these issues, this paper proposed gene expression programming (GEP) with K-nearest neighbour (GEP-KNN). The key advantage of the proposed work shows excellent performance in the prediction of adsorbing carbon and accuracy. The accuracy of the GEP-KNN algorithm with different K values produced the highest accuracy at K=9 and k=10 of 95.12% and 95.67%; the lowest accuracy is K=1 of 65.34%. |
| format | Article |
| id | doaj-art-6798888c23364c89bf5d85c435e1cfcc |
| 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-6798888c23364c89bf5d85c435e1cfcc2025-01-03T00:11:07ZengSAGE PublishingAdsorption Science & Technology2048-40382022-01-01202210.1155/2022/6349335Intelligent CO2 Monitoring for Diagnosis of Sleep Apnea Using Neural Cryptography TechniquesManar Ahmed Hamza0Maha M. Althobaiti1Ola Abdelgney Omer Ali2Souad Larabi-Marie-Sainte3Majdy M. Eltahir4Anwer Mustafa Hilal5Mesfer Al Duhayyim6Ishfaq Yaseen7Department of Computer and Self DevelopmentDepartment of Computer ScienceDepartment of Information TechnologyDepartment of Computer ScienceDepartment of Information SystemsDepartment of Computer and Self DevelopmentDepartment of Natural and Applied SciencesDepartment of Computer and Self DevelopmentIn biomass wastage, carbon is one of the adsorbent materials. Biomass wastage contains complex materials, and pressure, various temperatures, and presence of various chemical components which are subjected to the adsorption of carbon are a tedious task, and it is used in the sustainable waste management system. While screening the biomass wastage management system, prediction of activated carbon’s quality and understanding of the mechanism of adsorption of CO2 are a complicated task. Many research works have been developed; the main issues are inaccurate and inefficient prediction of carbon available in the various feedstock of biomass wastage. To overcome these issues, this paper proposed gene expression programming (GEP) with K-nearest neighbour (GEP-KNN). The key advantage of the proposed work shows excellent performance in the prediction of adsorbing carbon and accuracy. The accuracy of the GEP-KNN algorithm with different K values produced the highest accuracy at K=9 and k=10 of 95.12% and 95.67%; the lowest accuracy is K=1 of 65.34%.http://dx.doi.org/10.1155/2022/6349335 |
| spellingShingle | Manar Ahmed Hamza Maha M. Althobaiti Ola Abdelgney Omer Ali Souad Larabi-Marie-Sainte Majdy M. Eltahir Anwer Mustafa Hilal Mesfer Al Duhayyim Ishfaq Yaseen Intelligent CO2 Monitoring for Diagnosis of Sleep Apnea Using Neural Cryptography Techniques Adsorption Science & Technology |
| title | Intelligent CO2 Monitoring for Diagnosis of Sleep Apnea Using Neural Cryptography Techniques |
| title_full | Intelligent CO2 Monitoring for Diagnosis of Sleep Apnea Using Neural Cryptography Techniques |
| title_fullStr | Intelligent CO2 Monitoring for Diagnosis of Sleep Apnea Using Neural Cryptography Techniques |
| title_full_unstemmed | Intelligent CO2 Monitoring for Diagnosis of Sleep Apnea Using Neural Cryptography Techniques |
| title_short | Intelligent CO2 Monitoring for Diagnosis of Sleep Apnea Using Neural Cryptography Techniques |
| title_sort | intelligent co2 monitoring for diagnosis of sleep apnea using neural cryptography techniques |
| url | http://dx.doi.org/10.1155/2022/6349335 |
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