{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T00:08:38Z","timestamp":1779235718787,"version":"3.51.4"},"reference-count":49,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Applied Soft Computing"],"published-print":{"date-parts":[[2026,4]]},"DOI":"10.1016\/j.asoc.2026.114727","type":"journal-article","created":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T17:10:27Z","timestamp":1769533827000},"page":"114727","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Hybrid-conditional generative adversarial network framework for climate fault detection in vertical farming environments"],"prefix":"10.1016","volume":"191","author":[{"given":"P.K.S.","family":"Tejes","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6355-0304","authenticated-orcid":false,"given":"Abhishek","family":"Dasore","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Norhashila","family":"Hashim","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bukke Kiran","family":"Naik","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Challa Monisha","family":"Reddy","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"78","reference":[{"key":"10.1016\/j.asoc.2026.114727_bib1","doi-asserted-by":"crossref","DOI":"10.1016\/j.scs.2024.105357","article-title":"Food for future: exploring cutting-edge technology and practices in vertical farm","volume":"106","author":"Erekath","year":"2024","journal-title":"Sustain. Cities Soc."},{"key":"10.1016\/j.asoc.2026.114727_bib2","article-title":"Recent developments and inventive approaches in vertical farming","volume":"8","author":"Chennu","year":"2024","journal-title":"Front. Sustain. Food Syst."},{"key":"10.1016\/j.asoc.2026.114727_bib3","doi-asserted-by":"crossref","DOI":"10.1016\/j.jclepro.2022.131354","article-title":"Energy efficiency of heating, ventilation and air conditioning systems in production environments through model-predictive control schemes: the case of battery production","volume":"350","author":"Vogt","year":"2022","journal-title":"J. Clean. Prod."},{"key":"10.1016\/j.asoc.2026.114727_bib4","article-title":"Review on energy efficient artificial illumination in aquaponics","volume":"2","author":"Gillani","year":"2022","journal-title":"Clean. Circ. Bioeco."},{"key":"10.1016\/j.asoc.2026.114727_bib5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.buildenv.2013.11.021","article-title":"Fault detection and diagnosis for buildings and HVAC systems using combined neural networks and subtractive clustering analysis","volume":"73","author":"Du","year":"2014","journal-title":"Build. Environ."},{"key":"10.1016\/j.asoc.2026.114727_bib6","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1038\/s41597-020-0398-6","article-title":"Building fault detection data to aid diagnostic algorithm creation and performance testing","volume":"7","author":"Granderson","year":"2020","journal-title":"Sci. Data"},{"key":"10.1016\/j.asoc.2026.114727_bib7","doi-asserted-by":"crossref","DOI":"10.1016\/j.egyai.2023.100235","article-title":"Deep learning in fault detection and diagnosis of building HVAC systems: a systematic review with meta-analysis","volume":"12","author":"Zhang","year":"2023","journal-title":"Energy AI"},{"issue":"Part A","key":"10.1016\/j.asoc.2026.114727_bib8","article-title":"Machine learning-based fault detection and diagnosis of electrical conductivity and pH sensors in hydroponic systems","volume":"237","author":"Karimzadeh","year":"2025","journal-title":"Comput. Electron. Agric."},{"issue":"15","key":"10.1016\/j.asoc.2026.114727_bib9","doi-asserted-by":"crossref","DOI":"10.1016\/j.heliyon.2024.e34998","article-title":"Empowering vertical farming through IoT and AI-driven technologies: a comprehensive review","volume":"10","author":"Rathor","year":"2024","journal-title":"Heliyon"},{"key":"10.1016\/j.asoc.2026.114727_bib10","doi-asserted-by":"crossref","DOI":"10.1016\/j.jclepro.2023.138923","article-title":"A critical review on efficient thermal environment controls in indoor vertical farming","volume":"425","author":"Ahamed","year":"2023","journal-title":"J. Clean. Prod."},{"key":"10.1016\/j.asoc.2026.114727_bib11","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2022.107096","article-title":"Viable smart sensors and their application in data-driven agriculture","volume":"198","author":"Paul","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.asoc.2026.114727_bib12","doi-asserted-by":"crossref","DOI":"10.1016\/j.rineng.2025.103991","article-title":"Leveraging generative adversarial networks for data augmentation to improve fault detection in wind turbines with imbalanced data","volume":"25","author":"Chatterjee","year":"2025","journal-title":"Results Eng."},{"key":"10.1016\/j.asoc.2026.114727_bib13","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.compind.2019.01.001","article-title":"Generative adversarial networks for data augmentation in machine fault diagnosis","volume":"106","author":"Shao","year":"2019","journal-title":"Comput. Ind."},{"key":"10.1016\/j.asoc.2026.114727_bib14","doi-asserted-by":"crossref","DOI":"10.1016\/j.enbuild.2025.115531","article-title":"Hybrid-CGAN: a hybrid approach combining simulation and generative adversarial networks for fault detection and diagnosis in buildings","volume":"335","author":"Han","year":"2025","journal-title":"Energy Build."},{"key":"10.1016\/j.asoc.2026.114727_bib15","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1613\/jair.953","article-title":"SMOTE: Synthetic minority over-sampling technique","volume":"16","author":"Chawla","year":"2002","journal-title":"J. Artif. Intell. Res."},{"key":"10.1016\/j.asoc.2026.114727_bib16","article-title":"Imbalanced-learn: a Python toolbox to tackle the curse of imbalanced datasets in machine learning","author":"Lema\u00eetre","year":"2016","journal-title":"arXiv"},{"key":"10.1016\/j.asoc.2026.114727_bib17","series-title":"Proceedings of the 2011 SIAM International Conference on Data Mining (SDM)","first-page":"699","article-title":"A complexity-invariant distance measure for time series","author":"Batista","year":"2011"},{"key":"10.1016\/j.asoc.2026.114727_bib18","article-title":"Conditional generative adversarial nets","author":"Mirza","year":"2014","journal-title":"arXiv"},{"key":"10.1016\/j.asoc.2026.114727_bib19","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2024.110745","article-title":"A generalized fault diagnosis framework for rotating machinery based on phase entropy","volume":"256","author":"Wang","year":"2025","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"10.1016\/j.asoc.2026.114727_bib20","doi-asserted-by":"crossref","DOI":"10.1016\/j.aei.2024.102972","article-title":"Few-shot fault diagnosis for machinery using multi-scale perception multi-level feature fusion image quadrant entropy","volume":"63","author":"Wang","year":"2025","journal-title":"Adv. Eng. Inform."},{"key":"10.1016\/j.asoc.2026.114727_bib21","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2025.127715","article-title":"Multi-modal multi-scale multi-level fusion quadrant entropy for mechanical fault diagnosis","volume":"281","author":"Wang","year":"2025","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.asoc.2026.114727_bib22","first-page":"19487","article-title":"Rethinking conditional GAN training: an approach using geometrically structured latent manifolds","volume":"34","author":"Ramasinghe","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.asoc.2026.114727_bib23","author":"Boulahbal","year":"2022","journal-title":"Are conditional GANs explicitly conditional? arXiv preprint"},{"issue":"14","key":"10.1016\/j.asoc.2026.114727_bib24","doi-asserted-by":"crossref","first-page":"5413","DOI":"10.3390\/s22145413","article-title":"Synthesizing rolling bearing fault samples in new conditions: a framework based on a modified CGAN","volume":"22","author":"Ahang","year":"2022","journal-title":"Sensors"},{"key":"10.1016\/j.asoc.2026.114727_bib25","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.biosystemseng.2022.05.009","article-title":"Analysis of climate uniformity in indoor plant factory system with computational fluid dynamics (CFD)","volume":"220","author":"Zhang","year":"2022","journal-title":"Biosyst. Eng."},{"key":"10.1016\/j.asoc.2026.114727_bib26","doi-asserted-by":"crossref","DOI":"10.1016\/j.egyai.2022.100216","article-title":"Conditional generative adversarial networks for modelling fuel sprays","volume":"12","author":"Ates","year":"2023","journal-title":"Energy AI"},{"key":"10.1016\/j.asoc.2026.114727_bib27","doi-asserted-by":"crossref","DOI":"10.1016\/j.ecoinf.2024.102631","article-title":"MuLA-GAN: multi-level attention GAN for enhanced underwater visibility","volume":"81","author":"Bakht","year":"2024","journal-title":"Ecol. Inform."},{"issue":"2","key":"10.1016\/j.asoc.2026.114727_bib28","doi-asserted-by":"crossref","first-page":"401","DOI":"10.1007\/s10618-020-00727-3","article-title":"The great multivariate time series classification bake off: a review and experimental evaluation of recent algorithmic advances","volume":"35","author":"Ruiz","year":"2021","journal-title":"Data Min. Knowl. Discov."},{"key":"10.1016\/j.asoc.2026.114727_bib29","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1017\/jfm.2019.238","article-title":"Super-resolution reconstruction of turbulent flows with machine learning","volume":"870","author":"Fukami","year":"2019","journal-title":"J. Fluid Mech."},{"key":"10.1016\/j.asoc.2026.114727_bib30","doi-asserted-by":"crossref","DOI":"10.1016\/j.compfluid.2023.106047","article-title":"Nonlinear reduced-order modeling for three-dimensional turbulent flow by large-scale machine learning","volume":"266","author":"Ando","year":"2023","journal-title":"Comput. Fluids"},{"key":"10.1016\/j.asoc.2026.114727_bib31","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1186\/s42774-023-00148-y","article-title":"Deep learning method for super-resolution reconstruction of the spatio-temporal flow field","volume":"5","author":"Bao","year":"2023","journal-title":"Adv. Aerodyn."},{"key":"10.1016\/j.asoc.2026.114727_bib32","first-page":"703","article-title":"MAD-GAN: multivariate anomaly detection for time series data with generative adversarial networks","volume":"11730","author":"Li","year":"2019"},{"issue":"8","key":"10.1016\/j.asoc.2026.114727_bib33","doi-asserted-by":"crossref","first-page":"3555","DOI":"10.1002\/mp.13626","article-title":"Ultra-low-dose PET reconstruction using generative adversarial network with feature matching and task-specific perceptual loss","volume":"46","author":"Ouyang","year":"2019","journal-title":"Med. Phys."},{"issue":"9","key":"10.1016\/j.asoc.2026.114727_bib34","doi-asserted-by":"crossref","first-page":"6977","DOI":"10.1016\/j.jksuci.2022.02.018","article-title":"The effect of loss function on conditional generative adversarial networks","volume":"34","author":"Abu-Srhan","year":"2022","journal-title":"J. King Saud. Univ. Comput. Inf. Sci."},{"issue":"4","key":"10.1016\/j.asoc.2026.114727_bib35","doi-asserted-by":"crossref","first-page":"3067","DOI":"10.1109\/TPAMI.2025.3528193","article-title":"Adaptive biased stochastic optimization","volume":"47","author":"Yang","year":"2025","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.asoc.2026.114727_bib36","series-title":"Proceedings of the 3rd International Conference on Learning Representations (ICLR)","article-title":"Adam: a method for stochastic optimization","author":"Kingma","year":"2015"},{"key":"10.1016\/j.asoc.2026.114727_bib37","series-title":"Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint","author":"Radford","year":"2015"},{"key":"10.1016\/j.asoc.2026.114727_bib38","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","first-page":"5967","article-title":"Image-to-image translation with conditional adversarial networks","author":"Isola","year":"2017"},{"key":"10.1016\/j.asoc.2026.114727_bib39","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1016\/j.patcog.2017.09.020","article-title":"shapeDTW: shape dynamic time warping","volume":"74","author":"Zhao","year":"2018","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.asoc.2026.114727_bib40","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1038\/s43586-024-00363-x","article-title":"Uniform manifold approximation and projection","volume":"4","author":"Healy","year":"2024","journal-title":"Nat. Rev. Methods Prim."},{"key":"10.1016\/j.asoc.2026.114727_bib41","series-title":"In 2023 Advances in Science and Engineering Technology International Conferences (ASET)","first-page":"1","article-title":"Keep it simple: Random oversampling for imbalanced data","author":"Kamalov","year":"2023"},{"key":"10.1016\/j.asoc.2026.114727_bib42","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.ins.2019.07.070","article-title":"A comprehensive analysis of synthetic minority oversampling technique (SMOTE) for handling class imbalance","volume":"505","author":"Elreedy","year":"2019","journal-title":"Inf. Sci."},{"issue":"4","key":"10.1016\/j.asoc.2026.114727_bib43","doi-asserted-by":"crossref","first-page":"3313","DOI":"10.1109\/TKDE.2021.3130191","article-title":"A review on generative adversarial networks: algorithms, theory, and applications","volume":"35","author":"Gui","year":"2023","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"10.1016\/j.asoc.2026.114727_bib44","doi-asserted-by":"crossref","first-page":"895","DOI":"10.1016\/j.procs.2018.04.298","article-title":"Deep learning with gated recurrent unit networks for financial sequence predictions","volume":"131","author":"Shen","year":"2018","journal-title":"Procedia Comput. Sci."},{"issue":"5","key":"10.1016\/j.asoc.2026.114727_bib45","doi-asserted-by":"crossref","first-page":"1774","DOI":"10.1109\/TNNLS.2017.2673241","article-title":"Efficient kNN classification with different numbers of nearest neighbors","volume":"29","author":"Zhang","year":"2018","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"10.1016\/j.asoc.2026.114727_bib46","doi-asserted-by":"crossref","first-page":"592","DOI":"10.1016\/j.ins.2020.08.089","article-title":"Time works well: dynamic time warping based on time weighting for time series data mining","volume":"547","author":"Li","year":"2021","journal-title":"Inf. Sci."},{"key":"10.1016\/j.asoc.2026.114727_bib47","article-title":"Numerical evaluation and optimization of air distribution system in a small vertical farm with lateral air supply","volume":"17","author":"Kang","year":"2024","journal-title":"Dev. Built Environ."},{"key":"10.1016\/j.asoc.2026.114727_bib48","doi-asserted-by":"crossref","first-page":"121553","DOI":"10.1016\/j.applthermaleng.2023.121553","article-title":"Thermo-fluid dynamic analysis of the air flow inside an indoor vertical farming system","volume":"236","author":"Agati","year":"2024","journal-title":"Appl. Therm. Eng."},{"issue":"10","key":"10.1016\/j.asoc.2026.114727_bib49","doi-asserted-by":"crossref","DOI":"10.1145\/3559540","article-title":"Generative adversarial networks in time series: a systematic literature review","volume":"55","author":"Brophy","year":"2023","journal-title":"ACM Comput. Surv."}],"container-title":["Applied Soft Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1568494626001754?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1568494626001754?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T23:28:52Z","timestamp":1779233332000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1568494626001754"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4]]},"references-count":49,"alternative-id":["S1568494626001754"],"URL":"https:\/\/doi.org\/10.1016\/j.asoc.2026.114727","relation":{},"ISSN":["1568-4946"],"issn-type":[{"value":"1568-4946","type":"print"}],"subject":[],"published":{"date-parts":[[2026,4]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Hybrid-conditional generative adversarial network framework for climate fault detection in vertical farming environments","name":"articletitle","label":"Article Title"},{"value":"Applied Soft Computing","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.asoc.2026.114727","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"114727"}}