{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,27]],"date-time":"2026-04-27T19:43:24Z","timestamp":1777319004405,"version":"3.51.4"},"reference-count":55,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2020,11,5]],"date-time":"2020-11-05T00:00:00Z","timestamp":1604534400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>Time-series prediction is an important area that inspires numerous research disciplines for various applications, including air quality databases. Developing a robust and accurate model for time-series data becomes a challenging task, because it involves training different models and optimization. In this paper, we proposed and tested three machine learning techniques\u2014recurrent neural networks (RNN), heuristic algorithm and ensemble learning\u2014to develop a predictive model for estimating atmospheric particle number concentrations in the form of a time-series database. Here, the RNN included three variants\u2014Long-Short Term Memory, Gated Recurrent Network, and Bi-directional Recurrent Neural Network\u2014with various configurations. A Genetic Algorithm (GA) was then used to find the optimal time-lag in order to enhance the model\u2019s performance. The optimized models were used to construct a stacked ensemble model as well as to perform the final prediction. The results demonstrated that the time-lag value can be optimized by using the heuristic algorithm; consequently, this improved the model prediction accuracy. Further improvement can be achieved by using ensemble learning that combines several models for better performance and more accurate predictions.<\/jats:p>","DOI":"10.3390\/computers9040089","type":"journal-article","created":{"date-parts":[[2020,11,5]],"date-time":"2020-11-05T09:04:34Z","timestamp":1604567074000},"page":"89","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["An Optimal Stacked Ensemble Deep Learning Model for Predicting Time-Series Data Using a Genetic Algorithm\u2014An Application for Aerosol Particle Number Concentrations"],"prefix":"10.3390","volume":"9","author":[{"given":"Ola M.","family":"Surakhi","sequence":"first","affiliation":[{"name":"Department of Computer Science, The University of Jordan, Amman 11942, Jordan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6348-1230","authenticated-orcid":false,"given":"Martha Arbayani","family":"Zaidan","sequence":"additional","affiliation":[{"name":"Institute for Atmospheric and Earth System Research (INAR\/Physics), University of Helsinki, FI-00014 Helsinki, Finland"},{"name":"Joint International Research Laboratory of Atmospheric and Earth System Sciences, School of Atmospheric Sciences, Nanjing University, Nanjing 210023, China"}]},{"given":"Sami","family":"Serhan","sequence":"additional","affiliation":[{"name":"Department of Computer Science, The University of Jordan, Amman 11942, Jordan"}]},{"given":"Imad","family":"Salah","sequence":"additional","affiliation":[{"name":"Department of Computer Science, The University of Jordan, Amman 11942, Jordan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0241-6435","authenticated-orcid":false,"given":"Tareq","family":"Hussein","sequence":"additional","affiliation":[{"name":"Institute for Atmospheric and Earth System Research (INAR\/Physics), University of Helsinki, FI-00014 Helsinki, Finland"},{"name":"Department Material Analysis and Indoor Chemistry, Fraunhofer WKI, D-38108 Braunschweig, Germany"},{"name":"Department of Physics, The University of Jordan, Amman 11942, Jordan"}]}],"member":"1968","published-online":{"date-parts":[[2020,11,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Chung, H., and Shin, K.-S. 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