Case Modelling Odour Profiles and Temperature Intensity of Water: A Comparative Analysis using Case-Based Reasoning and K-Nearest Neighbours
Water, a vital resource for human life and global economic activities, prompts ongoing water quality studies in Malaysia due to excessive usage. This study investigates Malaysia's water resources condition, focusing on identifying water through its odor profile relative to temperature intensity...
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| Language: | en |
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Penerbit UMP
2024
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| Online Access: | http://umpir.ump.edu.my/id/eprint/43005/1/Case%20Modelling%20Odour%20Profiles%20and%20Temperature%20Intensity%20of%20Water.pdf http://umpir.ump.edu.my/id/eprint/43005/ https://doi.org/10.15282/mekatronika.v6i2.10729 |
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| author | Muhammad Naqiuddin, Ali Ibrahim Muhammad Sharfi, Najib Suhaimi, Mohd Daud |
| author_facet | Muhammad Naqiuddin, Ali Ibrahim Muhammad Sharfi, Najib Suhaimi, Mohd Daud |
| author_sort | Muhammad Naqiuddin, Ali Ibrahim |
| building | UMPSA Library |
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| content_provider | Universiti Malaysia Pahang Al-Sultan Abdullah |
| content_source | UMPSA Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | Water, a vital resource for human life and global economic activities, prompts ongoing water quality studies in Malaysia due to excessive usage. This study investigates Malaysia's water resources condition, focusing on identifying water through its odor profile relative to temperature intensity. Clean and pure drinking water is crucial for global human health, necessitating knowledge of water source content to mitigate health risks from water quality degradation caused by inorganic contaminants, heavy metals, and microbial pollutants. Recent interest in water quality stems from the high demand for clean water and population growth. This research employs E-Anfun, mimicking the human nose, to establish a case library profile for tap and lake water samples based on odor attributes. Using Case-Based Reasoning (CBR) and K-Nearest Neighbour (KNN), the study classifies water odor profiles and evaluates performance. The E-nose simplifies the process with gas sensors of varying odor sensitivity. Samples collected based on temperature intensity are managed using Microsoft Excel and MATLAB for normalization. CBR, utilizing four cycles, intelligently classifies by solving new problems based on prior successful solutions. KNN enhances CBR by classifying data samples based on proximity to learning data. Evaluation using a recognized confusion matrix indicates 100% accuracy, sensitivity, and specificity for CBR. For KNN, the accuracy increases with the ratio, starting at 97.056% for k=3 with a 10:90 ratio, accompanied by 84.833% sensitivity and 98.369% specificity. Both CBR and KNN successfully classify tap and lake water odour profiles. |
| format | Article |
| id | my.ump.umpir.43005 |
| institution | Universiti Malaysia Pahang |
| language | en |
| publishDate | 2024 |
| publisher | Penerbit UMP |
| record_format | eprints |
| spelling | my.ump.umpir.430052024-12-03T03:45:38Z http://umpir.ump.edu.my/id/eprint/43005/ Case Modelling Odour Profiles and Temperature Intensity of Water: A Comparative Analysis using Case-Based Reasoning and K-Nearest Neighbours Muhammad Naqiuddin, Ali Ibrahim Muhammad Sharfi, Najib Suhaimi, Mohd Daud TK Electrical engineering. Electronics Nuclear engineering Water, a vital resource for human life and global economic activities, prompts ongoing water quality studies in Malaysia due to excessive usage. This study investigates Malaysia's water resources condition, focusing on identifying water through its odor profile relative to temperature intensity. Clean and pure drinking water is crucial for global human health, necessitating knowledge of water source content to mitigate health risks from water quality degradation caused by inorganic contaminants, heavy metals, and microbial pollutants. Recent interest in water quality stems from the high demand for clean water and population growth. This research employs E-Anfun, mimicking the human nose, to establish a case library profile for tap and lake water samples based on odor attributes. Using Case-Based Reasoning (CBR) and K-Nearest Neighbour (KNN), the study classifies water odor profiles and evaluates performance. The E-nose simplifies the process with gas sensors of varying odor sensitivity. Samples collected based on temperature intensity are managed using Microsoft Excel and MATLAB for normalization. CBR, utilizing four cycles, intelligently classifies by solving new problems based on prior successful solutions. KNN enhances CBR by classifying data samples based on proximity to learning data. Evaluation using a recognized confusion matrix indicates 100% accuracy, sensitivity, and specificity for CBR. For KNN, the accuracy increases with the ratio, starting at 97.056% for k=3 with a 10:90 ratio, accompanied by 84.833% sensitivity and 98.369% specificity. Both CBR and KNN successfully classify tap and lake water odour profiles. Penerbit UMP 2024-10-05 Article PeerReviewed pdf en cc_by_nc_4 http://umpir.ump.edu.my/id/eprint/43005/1/Case%20Modelling%20Odour%20Profiles%20and%20Temperature%20Intensity%20of%20Water.pdf Muhammad Naqiuddin, Ali Ibrahim and Muhammad Sharfi, Najib and Suhaimi, Mohd Daud (2024) Case Modelling Odour Profiles and Temperature Intensity of Water: A Comparative Analysis using Case-Based Reasoning and K-Nearest Neighbours. Mekatronika - Journal of Intelligent Manufacturing & Mechatronics, 6 (2). pp. 66-75. ISSN 2637-0883. (Published) https://doi.org/10.15282/mekatronika.v6i2.10729 https://doi.org/10.15282/mekatronika.v6i2.10729 |
| spellingShingle | TK Electrical engineering. Electronics Nuclear engineering Muhammad Naqiuddin, Ali Ibrahim Muhammad Sharfi, Najib Suhaimi, Mohd Daud Case Modelling Odour Profiles and Temperature Intensity of Water: A Comparative Analysis using Case-Based Reasoning and K-Nearest Neighbours |
| title | Case Modelling Odour Profiles and Temperature Intensity of Water: A Comparative Analysis using Case-Based Reasoning and K-Nearest Neighbours |
| title_full | Case Modelling Odour Profiles and Temperature Intensity of Water: A Comparative Analysis using Case-Based Reasoning and K-Nearest Neighbours |
| title_fullStr | Case Modelling Odour Profiles and Temperature Intensity of Water: A Comparative Analysis using Case-Based Reasoning and K-Nearest Neighbours |
| title_full_unstemmed | Case Modelling Odour Profiles and Temperature Intensity of Water: A Comparative Analysis using Case-Based Reasoning and K-Nearest Neighbours |
| title_short | Case Modelling Odour Profiles and Temperature Intensity of Water: A Comparative Analysis using Case-Based Reasoning and K-Nearest Neighbours |
| title_sort | case modelling odour profiles and temperature intensity of water: a comparative analysis using case-based reasoning and k-nearest neighbours |
| topic | TK Electrical engineering. Electronics Nuclear engineering |
| url | http://umpir.ump.edu.my/id/eprint/43005/1/Case%20Modelling%20Odour%20Profiles%20and%20Temperature%20Intensity%20of%20Water.pdf http://umpir.ump.edu.my/id/eprint/43005/ https://doi.org/10.15282/mekatronika.v6i2.10729 https://doi.org/10.15282/mekatronika.v6i2.10729 |
| url_provider | http://umpir.ump.edu.my/ |