Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Chandar S, Kumar Title A name given to the resource Artificial Intelligence Driven Air Quality Prediction for Sustainable Goa Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Proceedings of 3rd International Conference on Augmented Intelligence and Sustainable Systems, ICAISS 2025;pp.1181-1192 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICAISS61471.2025.11041723" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICAISS61471.2025.11041723</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105010755728?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105010755728?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Chandar S K., Christ University, School of Business and Management, India Description An account of the resource Clean Air is essential for the health and survival of both humans and wildlife. Air pollution has been linked to various serious diseases, including cancer. Rapid industrial growth and increasing population have contributed to rising pollution from transportation, industries, and agriculture. As a result, air pollution has become a major issue, particularly in developing countries like India. To ensure good air quality, accurate and reliable monitoring and prediction are required. Machine Learning (ML) models have shown promise in predicting Air Quality Index (AQI) over traditional methods. This research aims to propose a AQI prediction model using Attention based Bi-directional Long Short-Term Memory (ABiLSTM) to predict AQI in various cities across Goa, India. Data processing methods are used to manage date before providing it into the ABiLSTM model. Daily AQI series from 2022 to 2024 for six cities in Goa- Panaji, Pond, Assanora, Codli, Tilamol, and Tuem are collected and utilized to verify the proposed model. Two models are tested, including BiLSTM and ABiLSTM. Experimental results showed that the ABiLSTM model outperformed BiLSTM model in all cities, reporting lower error values and higher R2 scores. A comprehensive analysis with a set of evaluation indices confirmed that the proposed ABiLSTM model effectively captures the characteristics of the original AQI series and achieves a higher accuracy in AQI prediction. 2025 IEEE. Subject The topic of the resource Air Quality Index Prediction; Attention Based bidirectional Short-Term Memory; Deep Learning; Machine Learning Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833150724-4; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online