Prediction of CO<sub>2</sub> uptake in bio-waste based porous carbons using model agnostic explainable artificial intelligence
<p dir="ltr">This study introduces comprehensive research on the prediction of the carbon dioxide (CO<sub>2</sub>) uptake from the biomass-waste derived-porous carbons (BWDPCs), by using scientometrics and model agnostic multi-layered explainable artificial intelligence (...
محفوظ في:
| المؤلف الرئيسي: | |
|---|---|
| مؤلفون آخرون: | , , , , |
| منشور في: |
2025
|
| الموضوعات: | |
| الوسوم: |
إضافة وسم
لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!
|
| _version_ | 1864513555992150016 |
|---|---|
| author | Mohd Azfar Shaida (19756971) |
| author2 | Saad Shamim Ansari (19756974) Raeesh Muhammad (4867672) Syed Muhammad Ibrahim (19756977) Izharul Haq Farooqi (19756980) Abdulkarem Amhamed (14778130) |
| author2_role | author author author author author |
| author_facet | Mohd Azfar Shaida (19756971) Saad Shamim Ansari (19756974) Raeesh Muhammad (4867672) Syed Muhammad Ibrahim (19756977) Izharul Haq Farooqi (19756980) Abdulkarem Amhamed (14778130) |
| author_role | author |
| dc.creator.none.fl_str_mv | Mohd Azfar Shaida (19756971) Saad Shamim Ansari (19756974) Raeesh Muhammad (4867672) Syed Muhammad Ibrahim (19756977) Izharul Haq Farooqi (19756980) Abdulkarem Amhamed (14778130) |
| dc.date.none.fl_str_mv | 2025-01-15T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1016/j.fuel.2024.133183 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Prediction_of_CO_sub_2_sub_uptake_in_bio-waste_based_porous_carbons_using_model_agnostic_explainable_artificial_intelligence/27130065 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Information and computing sciences Artificial intelligence Machine learning CO2 uptake Biomass waste Scientometrics Machine learning Explainable artificial intelligence |
| dc.title.none.fl_str_mv | Prediction of CO<sub>2</sub> uptake in bio-waste based porous carbons using model agnostic explainable artificial intelligence |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">This study introduces comprehensive research on the prediction of the carbon dioxide (CO<sub>2</sub>) uptake from the biomass-waste derived-porous carbons (BWDPCs), by using scientometrics and model agnostic multi-layered explainable artificial intelligence (XAI) techniques. It aims to identify the main characteristics, and trends that are specific to this domain, and to establish, compare and analyse the four different black box machine learning (ML) models for CO<sub>2</sub> uptake prediction. For this study, through model evaluation parameters, and scatter plots, statistical analysis supports the fact that the Extreme Gradient Boosting (XGBoost) model is found to be the best performing model for CO<sub>2</sub> uptake prediction with low errors and high coefficient of correlation for both training (<i>MSE</i>: 0.157, <i>RMSE</i>: 0.397, <i>MAE</i>: 0.294, <i>MAPE</i>: 0.112, <i>R</i><sup><em>2</em></sup>: 0.931) and testing phases (<i>MSE</i>: 0.345, <i>RMSE</i>: 0.588, <i>MAE</i>: 0.461, <i>MAPE</i>: 0.121, <i>R</i><sup><em>2</em></sup>: 0.860). Now, with the best performing black box ML model as XGBoost model, it serves as the basis for the multi-layered XAI analysis. Using multi-layered XAI techniques to interpret the black box ML model and covert it to a white box model, it makes clearer insights into the significant key features that affect the CO<sub>2</sub> uptake both at the global and local level. The study demonstrates that using multi-layered XAI analysis helps in improving the trust of the predictive model and provides a way forward for the application of white box models in CO<sub>2</sub> uptake.</p><h2>Other Information</h2><p dir="ltr">Published in: Fuel<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.fuel.2024.133183" target="_blank">https://dx.doi.org/10.1016/j.fuel.2024.133183</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_744bf404d4d45211875e95ce0ff9598e |
| identifier_str_mv | 10.1016/j.fuel.2024.133183 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/27130065 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Prediction of CO<sub>2</sub> uptake in bio-waste based porous carbons using model agnostic explainable artificial intelligenceMohd Azfar Shaida (19756971)Saad Shamim Ansari (19756974)Raeesh Muhammad (4867672)Syed Muhammad Ibrahim (19756977)Izharul Haq Farooqi (19756980)Abdulkarem Amhamed (14778130)Information and computing sciencesArtificial intelligenceMachine learningCO2 uptakeBiomass wasteScientometricsMachine learningExplainable artificial intelligence<p dir="ltr">This study introduces comprehensive research on the prediction of the carbon dioxide (CO<sub>2</sub>) uptake from the biomass-waste derived-porous carbons (BWDPCs), by using scientometrics and model agnostic multi-layered explainable artificial intelligence (XAI) techniques. It aims to identify the main characteristics, and trends that are specific to this domain, and to establish, compare and analyse the four different black box machine learning (ML) models for CO<sub>2</sub> uptake prediction. For this study, through model evaluation parameters, and scatter plots, statistical analysis supports the fact that the Extreme Gradient Boosting (XGBoost) model is found to be the best performing model for CO<sub>2</sub> uptake prediction with low errors and high coefficient of correlation for both training (<i>MSE</i>: 0.157, <i>RMSE</i>: 0.397, <i>MAE</i>: 0.294, <i>MAPE</i>: 0.112, <i>R</i><sup><em>2</em></sup>: 0.931) and testing phases (<i>MSE</i>: 0.345, <i>RMSE</i>: 0.588, <i>MAE</i>: 0.461, <i>MAPE</i>: 0.121, <i>R</i><sup><em>2</em></sup>: 0.860). Now, with the best performing black box ML model as XGBoost model, it serves as the basis for the multi-layered XAI analysis. Using multi-layered XAI techniques to interpret the black box ML model and covert it to a white box model, it makes clearer insights into the significant key features that affect the CO<sub>2</sub> uptake both at the global and local level. The study demonstrates that using multi-layered XAI analysis helps in improving the trust of the predictive model and provides a way forward for the application of white box models in CO<sub>2</sub> uptake.</p><h2>Other Information</h2><p dir="ltr">Published in: Fuel<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.fuel.2024.133183" target="_blank">https://dx.doi.org/10.1016/j.fuel.2024.133183</a></p>2025-01-15T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1016/j.fuel.2024.133183https://figshare.com/articles/journal_contribution/Prediction_of_CO_sub_2_sub_uptake_in_bio-waste_based_porous_carbons_using_model_agnostic_explainable_artificial_intelligence/27130065CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/271300652025-01-15T03:00:00Z |
| spellingShingle | Prediction of CO<sub>2</sub> uptake in bio-waste based porous carbons using model agnostic explainable artificial intelligence Mohd Azfar Shaida (19756971) Information and computing sciences Artificial intelligence Machine learning CO2 uptake Biomass waste Scientometrics Machine learning Explainable artificial intelligence |
| status_str | publishedVersion |
| title | Prediction of CO<sub>2</sub> uptake in bio-waste based porous carbons using model agnostic explainable artificial intelligence |
| title_full | Prediction of CO<sub>2</sub> uptake in bio-waste based porous carbons using model agnostic explainable artificial intelligence |
| title_fullStr | Prediction of CO<sub>2</sub> uptake in bio-waste based porous carbons using model agnostic explainable artificial intelligence |
| title_full_unstemmed | Prediction of CO<sub>2</sub> uptake in bio-waste based porous carbons using model agnostic explainable artificial intelligence |
| title_short | Prediction of CO<sub>2</sub> uptake in bio-waste based porous carbons using model agnostic explainable artificial intelligence |
| title_sort | Prediction of CO<sub>2</sub> uptake in bio-waste based porous carbons using model agnostic explainable artificial intelligence |
| topic | Information and computing sciences Artificial intelligence Machine learning CO2 uptake Biomass waste Scientometrics Machine learning Explainable artificial intelligence |