Testing the applicability of artificial intelligence techniques to the subject of erythemal ultraviolet solar radiation. Part two: An intelligent system based on multi-classifier technique
The problem we address here describes the on-going research effort that takes place to shed light on the applicability of using artificial intelligence techniques to predict the local noon erythemal UV irradiance in the plain areas of Egypt. In light of this fact, we use the bootstrap aggregating (...
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| Published in | Journal of photochemistry and photobiology. B, Biology Vol. 90; no. 3; pp. 198 - 206 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Switzerland
Elsevier B.V
28.03.2008
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1011-1344 1873-2682 |
| DOI | 10.1016/j.jphotobiol.2007.12.001 |
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| Abstract | The problem we address here describes the on-going research effort that takes place to shed light on the applicability of using artificial intelligence techniques to predict the local noon erythemal UV irradiance in the plain areas of Egypt. In light of this fact, we use the bootstrap aggregating (
bagging) algorithm to improve the prediction accuracy reported by a multi-layer perceptron (MLP) network. The results showed that, the overall prediction accuracy for the MLP network was only 80.9%. When
bagging algorithm is used, the accuracy reached 94.8%; an improvement of about 13.9% was achieved. These improvements demonstrate the efficiency of the
bagging procedure, and may be used as a promising tool at least for the plain areas of Egypt. |
|---|---|
| AbstractList | The problem we address here describes the on-going research effort that takes place to shed light on the applicability of using artificial intelligence techniques to predict the local noon erythemal UV irradiance in the plain areas of Egypt. In light of this fact, we use the bootstrap aggregating (bagging) algorithm to improve the prediction accuracy reported by a multi-layer perceptron (MLP) network. The results showed that, the overall prediction accuracy for the MLP network was only 80.9%. When bagging algorithm is used, the accuracy reached 94.8%; an improvement of about 13.9% was achieved. These improvements demonstrate the efficiency of the bagging procedure, and may be used as a promising tool at least for the plain areas of Egypt.The problem we address here describes the on-going research effort that takes place to shed light on the applicability of using artificial intelligence techniques to predict the local noon erythemal UV irradiance in the plain areas of Egypt. In light of this fact, we use the bootstrap aggregating (bagging) algorithm to improve the prediction accuracy reported by a multi-layer perceptron (MLP) network. The results showed that, the overall prediction accuracy for the MLP network was only 80.9%. When bagging algorithm is used, the accuracy reached 94.8%; an improvement of about 13.9% was achieved. These improvements demonstrate the efficiency of the bagging procedure, and may be used as a promising tool at least for the plain areas of Egypt. The problem we address here describes the on-going research effort that takes place to shed light on the applicability of using artificial intelligence techniques to predict the local noon erythemal UV irradiance in the plain areas of Egypt. In light of this fact, we use the bootstrap aggregating (bagging) algorithm to improve the prediction accuracy reported by a multi-layer perceptron (MLP) network. The results showed that, the overall prediction accuracy for the MLP network was only 80.9%. When bagging algorithm is used, the accuracy reached 94.8%; an improvement of about 13.9% was achieved. These improvements demonstrate the efficiency of the bagging procedure, and may be used as a promising tool at least for the plain areas of Egypt. The problem we address here describes the on-going research effort that takes place to shed light on the applicability of using artificial intelligence techniques to predict the local noon erythemal UV irradiance in the plain areas of Egypt. In light of this fact, we use the bootstrap aggregating ( bagging) algorithm to improve the prediction accuracy reported by a multi-layer perceptron (MLP) network. The results showed that, the overall prediction accuracy for the MLP network was only 80.9%. When bagging algorithm is used, the accuracy reached 94.8%; an improvement of about 13.9% was achieved. These improvements demonstrate the efficiency of the bagging procedure, and may be used as a promising tool at least for the plain areas of Egypt. |
| Author | Riad, A.M. Elminir, Hamdy K. Own, Hala S. Azzam, Yosry A. |
| Author_xml | – sequence: 1 givenname: Hamdy K. surname: Elminir fullname: Elminir, Hamdy K. email: hamdy_elminir@hotmail.com – sequence: 2 givenname: Hala S. surname: Own fullname: Own, Hala S. – sequence: 3 givenname: Yosry A. surname: Azzam fullname: Azzam, Yosry A. – sequence: 4 givenname: A.M. surname: Riad fullname: Riad, A.M. |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/18280747$$D View this record in MEDLINE/PubMed |
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| CitedBy_id | crossref_primary_10_1016_j_ast_2014_12_013 crossref_primary_10_1016_j_ast_2015_03_011 crossref_primary_10_1016_j_envres_2017_01_035 crossref_primary_10_1016_j_ast_2017_06_017 crossref_primary_10_1038_s41598_021_84396_2 crossref_primary_10_1016_j_compag_2018_10_014 |
| Cites_doi | 10.1016/j.csda.2004.06.019 10.1029/96JD01242 10.1007/BF00696813 10.1029/2000JD000136 10.1029/2002JD003325 10.1117/12.688289 10.1111/j.1751-1097.2004.tb00095.x 10.1029/96GL01958 10.1029/1999JD900907 10.1016/j.trc.2006.08.003 10.1016/S0960-1481(02)00039-3 10.1016/j.solener.2004.01.008 10.1029/2002JD002134 10.1029/2001JD001350 10.1029/1999JD901131 10.1562/0031-8655(2002)076<0281:PODSGU>2.0.CO;2 10.1111/j.1751-1097.1990.tb01968.x 10.5194/angeo-24-2105-2006 10.1016/0038-092X(90)90028-B 10.1029/1998GL900140 10.1109/IMSCCS.2006.155 10.1016/j.patrec.2005.03.017 10.1029/2000JD900258 10.1016/S0196-8904(99)00012-6 10.1016/j.jastp.2005.05.003 10.1023/A:1018054314350 10.1029/2001GL013034 10.1111/j.1751-1097.1982.tb03830.x 10.1016/j.agrformet.2003.08.017 |
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| Keywords | Erythemal UV irradiance Artificial neural network Multi-classifier technique Total ozone |
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| SubjectTerms | Algorithms Artificial Intelligence Artificial neural network Egypt Erythema - etiology Erythemal UV irradiance Models, Theoretical Multi-classifier technique Neural Networks (Computer) Reproducibility of Results Time Factors Total ozone Ultraviolet Rays - adverse effects |
| Title | Testing the applicability of artificial intelligence techniques to the subject of erythemal ultraviolet solar radiation. Part two: An intelligent system based on multi-classifier technique |
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