Estimating the un-sampled ph value via neighbouring points using multi-layer neural network - genetic algorithm
This study shows a new method to estimate unsampled pH value by utilizing neighboring pH, which according to recent literature, has not been done yet. In investigating this method, three algorithms are used: Neural Network-Genetic Algorithm (MLNN-GA), MLNN with backpropagation (MLNN-BP), and averagi...
| Main Authors: | , , , , , |
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| Format: | Conference or Workshop Item |
| Language: | English English |
| Published: |
Institute of Electrical and Electronics Engineers Inc.
2023
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/38779/1/Estimating%20the%20Un-sampled%20pH%20Value%20via%20neighbouring%20points.pdf http://umpir.ump.edu.my/id/eprint/38779/2/Estimating%20the%20un-sampled%20ph%20value%20via%20neighbouring%20points%20using%20multi-layer%20neural%20network%20-%20genetic%20algorithm_ABS.pdf |
| _version_ | 1825815174875971584 |
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| author | Muhammad Aznil, Ab Aziz Mohammad Fadhil, Abas Muhamad Abdul Hasib, Ali Norhafidzah, Mohd Saad Mohd Hisyam, Ariff Mohamad Khairul Anwar, Abu Bashrin |
| author_facet | Muhammad Aznil, Ab Aziz Mohammad Fadhil, Abas Muhamad Abdul Hasib, Ali Norhafidzah, Mohd Saad Mohd Hisyam, Ariff Mohamad Khairul Anwar, Abu Bashrin |
| author_sort | Muhammad Aznil, Ab Aziz |
| collection | UMP |
| description | This study shows a new method to estimate unsampled pH value by utilizing neighboring pH, which according to recent literature, has not been done yet. In investigating this method, three algorithms are used: Neural Network-Genetic Algorithm (MLNN-GA), MLNN with backpropagation (MLNN-BP), and averaging method. MLNNGA and MLNN-BP are inputted with four pH values from distant adjacent locations on a similar basin. MLNN-GA and MLNN-BP utilize GA and backpropagation respectively to update the weight. GA optimizer is used in MLNN-GA where the result of each learning weight will be the initial weight of the next learning process. All three methods are compared based on RMSE, MSE and MAPE. MLNN-GA yielded the lowest average RMSE =0.026265, average MSE =0.000886 and average MAPE =0.003985 compared to MLNN-BP (average RMSE =0.042644, average MSE =0.002648, average MAPE =0.006862) and averaging method (average RMSE =0.136629, average MSE = 0.026128, average MAPE =0.150400). Noticeably, estimating unsampled pH value utilizing neighboring pH by using MLNNGA shows a better performance than MLNN-BP and averaging method. |
| first_indexed | 2024-03-06T13:09:30Z |
| format | Conference or Workshop Item |
| id | UMPir38779 |
| institution | Universiti Malaysia Pahang |
| language | English English |
| last_indexed | 2024-03-06T13:09:30Z |
| publishDate | 2023 |
| publisher | Institute of Electrical and Electronics Engineers Inc. |
| record_format | dspace |
| spelling | UMPir387792023-11-06T06:53:58Z http://umpir.ump.edu.my/id/eprint/38779/ Estimating the un-sampled ph value via neighbouring points using multi-layer neural network - genetic algorithm Muhammad Aznil, Ab Aziz Mohammad Fadhil, Abas Muhamad Abdul Hasib, Ali Norhafidzah, Mohd Saad Mohd Hisyam, Ariff Mohamad Khairul Anwar, Abu Bashrin T Technology (General) TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering This study shows a new method to estimate unsampled pH value by utilizing neighboring pH, which according to recent literature, has not been done yet. In investigating this method, three algorithms are used: Neural Network-Genetic Algorithm (MLNN-GA), MLNN with backpropagation (MLNN-BP), and averaging method. MLNNGA and MLNN-BP are inputted with four pH values from distant adjacent locations on a similar basin. MLNN-GA and MLNN-BP utilize GA and backpropagation respectively to update the weight. GA optimizer is used in MLNN-GA where the result of each learning weight will be the initial weight of the next learning process. All three methods are compared based on RMSE, MSE and MAPE. MLNN-GA yielded the lowest average RMSE =0.026265, average MSE =0.000886 and average MAPE =0.003985 compared to MLNN-BP (average RMSE =0.042644, average MSE =0.002648, average MAPE =0.006862) and averaging method (average RMSE =0.136629, average MSE = 0.026128, average MAPE =0.150400). Noticeably, estimating unsampled pH value utilizing neighboring pH by using MLNNGA shows a better performance than MLNN-BP and averaging method. Institute of Electrical and Electronics Engineers Inc. 2023 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/38779/1/Estimating%20the%20Un-sampled%20pH%20Value%20via%20neighbouring%20points.pdf pdf en http://umpir.ump.edu.my/id/eprint/38779/2/Estimating%20the%20un-sampled%20ph%20value%20via%20neighbouring%20points%20using%20multi-layer%20neural%20network%20-%20genetic%20algorithm_ABS.pdf Muhammad Aznil, Ab Aziz and Mohammad Fadhil, Abas and Muhamad Abdul Hasib, Ali and Norhafidzah, Mohd Saad and Mohd Hisyam, Ariff and Mohamad Khairul Anwar, Abu Bashrin (2023) Estimating the un-sampled ph value via neighbouring points using multi-layer neural network - genetic algorithm. In: 2023 19th IEEE International Colloquium on Signal Processing and Its Applications, CSPA 2023 - Conference Proceedings , 3-4 March 2023 , Kedah. pp. 207-212.. ISBN 978-166547692-8 (Published) https://doi.org/10.1109/CSPA57446.2023.10087388 |
| spellingShingle | T Technology (General) TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering Muhammad Aznil, Ab Aziz Mohammad Fadhil, Abas Muhamad Abdul Hasib, Ali Norhafidzah, Mohd Saad Mohd Hisyam, Ariff Mohamad Khairul Anwar, Abu Bashrin Estimating the un-sampled ph value via neighbouring points using multi-layer neural network - genetic algorithm |
| title | Estimating the un-sampled ph value via neighbouring points using multi-layer neural network - genetic algorithm |
| title_full | Estimating the un-sampled ph value via neighbouring points using multi-layer neural network - genetic algorithm |
| title_fullStr | Estimating the un-sampled ph value via neighbouring points using multi-layer neural network - genetic algorithm |
| title_full_unstemmed | Estimating the un-sampled ph value via neighbouring points using multi-layer neural network - genetic algorithm |
| title_short | Estimating the un-sampled ph value via neighbouring points using multi-layer neural network - genetic algorithm |
| title_sort | estimating the un sampled ph value via neighbouring points using multi layer neural network genetic algorithm |
| topic | T Technology (General) TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering |
| url | http://umpir.ump.edu.my/id/eprint/38779/1/Estimating%20the%20Un-sampled%20pH%20Value%20via%20neighbouring%20points.pdf http://umpir.ump.edu.my/id/eprint/38779/2/Estimating%20the%20un-sampled%20ph%20value%20via%20neighbouring%20points%20using%20multi-layer%20neural%20network%20-%20genetic%20algorithm_ABS.pdf |
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