{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T01:45:56Z","timestamp":1774316756627,"version":"3.50.1"},"reference-count":28,"publisher":"IEEE","license":[{"start":{"date-parts":[[2020,10,1]],"date-time":"2020-10-01T00:00:00Z","timestamp":1601510400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2020,10,1]],"date-time":"2020-10-01T00:00:00Z","timestamp":1601510400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2020,10,1]],"date-time":"2020-10-01T00:00:00Z","timestamp":1601510400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,10]]},"DOI":"10.1109\/rivf48685.2020.9140786","type":"proceedings-article","created":{"date-parts":[[2020,7,15]],"date-time":"2020-07-15T21:32:06Z","timestamp":1594848726000},"page":"1-5","source":"Crossref","is-referenced-by-count":32,"title":["Comparative Analysis of ANN-ICA and ANN-GWO for Crop Yield Prediction"],"prefix":"10.1109","author":[{"given":"Saeed","family":"Nosratabadi","sequence":"first","affiliation":[]},{"given":"Karoly","family":"Szell","sequence":"additional","affiliation":[]},{"given":"Bertalan","family":"Beszedes","sequence":"additional","affiliation":[]},{"given":"Felde","family":"Imre","sequence":"additional","affiliation":[]},{"given":"Sina","family":"Ardabili","sequence":"additional","affiliation":[]},{"given":"Amir","family":"Mosavi","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/TENCON.2015.7372938"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1016\/j.petrol.2016.06.017"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/CEC.2007.4425083"},{"key":"ref13","first-page":"25","author":"bruzzone","year":"2016","journal-title":"Simulation Based Design of Innovative Quick Response Processes in Cloud Supply Chain Management for “Slow Food” Distribution in Theory Methodology Tools and Applications for Modeling and Simulation of Complex Systems"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-014-1690-1"},{"key":"ref15","first-page":"1","article-title":"A predictive model based on an optimized ANN combined with ICA for predicting the stability of slopes","author":"gao","year":"2019","journal-title":"Engineering With Computers"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.3390\/app9132630"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1016\/j.advengsoft.2013.12.007"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1016\/j.molliq.2018.11.017"},{"key":"ref19","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1007\/978-3-030-36841-8_21","article-title":"Advances in Machine Learning Modeling Reviewing Hybrid and Ensemble Methods","volume":"101","author":"ardabili","year":"2020","journal-title":"Lecture Notes in Networks and Systems"},{"key":"ref28","doi-asserted-by":"crossref","first-page":"202","DOI":"10.1007\/978-3-030-36841-8_20","article-title":"List of Deep Learning Models","volume":"101","author":"mosavi","year":"2020","journal-title":"Lecture Notes in Networks and Systems"},{"key":"ref4","first-page":"1","article-title":"Application of artificial neural network in predicting crop yield: A review","volume":"4","author":"khairunniza-bejo","year":"2014","journal-title":"Journal of Food Science and Engineering"},{"key":"ref27","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1007\/978-3-030-36841-8_19","article-title":"Building Energy Information: Demand and Consumption Prediction with Machine Learning Models for Sustainable and Smart","volume":"101","author":"ardabili","year":"2020","journal-title":"Lecture Notes in Networks and Systems"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1016\/j.jag.2005.06.002"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.13031\/2013.22264"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.3386\/w13799"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.13031\/2013.12541"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2009.08.003"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1111\/j.1752-1688.2011.00570.x"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1038\/s41567-019-0554-0"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1016\/j.envsci.2009.04.007"},{"key":"ref20","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1007\/978-3-030-36841-8_2","article-title":"Systematic Review of Deep Learning and Machine Learning Models in Biofuels Research","volume":"101","author":"ardabili","year":"2020","journal-title":"Lecture Notes in Networks and Systems"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.3390\/ijerph17030731"},{"key":"ref21","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1007\/978-3-030-36841-8_34","article-title":"Prediction of Combine Harvester Performance Using Hybrid Machine Learning Modeling and Response Surface Methodology","volume":"101","author":"gundoshmian","year":"2020","journal-title":"Lecture Notes in Networks and Systems"},{"key":"ref24","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1007\/978-3-030-36841-8_5","article-title":"Deep Learning and Machine Learning in Hydrological Processes Climate Change and Earth Systems a Systematic Review","volume":"101","author":"ardabili","year":"2020","journal-title":"Lecture Notes in Networks and Systems"},{"key":"ref23","doi-asserted-by":"crossref","first-page":"228","DOI":"10.1007\/978-3-030-36841-8_22","article-title":"State of the Art Survey of Deep Learning and Machine Learning Models for Smart Cities and Urban Sustainability","volume":"101","author":"nosratabadi","year":"2020","journal-title":"Lecture Notes in Networks and Systems"},{"key":"ref26","doi-asserted-by":"crossref","first-page":"1536","DOI":"10.3390\/w10111536","article-title":"Flood prediction using machine learning models: Literature review","volume":"10","author":"mosavi","year":"2018","journal-title":"WATER"},{"key":"ref25","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1007\/978-3-030-36841-8_3","article-title":"Modelling Temperature Variation of Mushroom Growing Hall Using Artificial Neural Networks","volume":"101","author":"ardabili","year":"2020","journal-title":"Lecture Notes in Networks and Systems"}],"event":{"name":"2020 RIVF International Conference on Computing and Communication Technologies (RIVF)","location":"Ho Chi Minh, Vietnam","start":{"date-parts":[[2020,10,14]]},"end":{"date-parts":[[2020,10,15]]}},"container-title":["2020 RIVF International Conference on Computing and Communication Technologies (RIVF)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9138608\/9140725\/09140786.pdf?arnumber=9140786","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,6,28]],"date-time":"2022-06-28T21:56:10Z","timestamp":1656453370000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9140786\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,10]]},"references-count":28,"URL":"https:\/\/doi.org\/10.1109\/rivf48685.2020.9140786","relation":{},"subject":[],"published":{"date-parts":[[2020,10]]}}}