{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,20]],"date-time":"2026-06-20T15:51:59Z","timestamp":1781970719571,"version":"3.54.5"},"reference-count":63,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/100018693","name":"Horizon Europe","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100018693","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Expert Systems with Applications"],"published-print":{"date-parts":[[2026,9]]},"DOI":"10.1016\/j.eswa.2026.132872","type":"journal-article","created":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T11:25:21Z","timestamp":1778757921000},"page":"132872","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Fusion of image and tabular data for predicting substance concentration in flotation froth"],"prefix":"10.1016","volume":"327","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-7099-6574","authenticated-orcid":false,"given":"Dominik","family":"Borys","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1379-0200","authenticated-orcid":false,"given":"Dariusz","family":"Foszcz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8328-5512","authenticated-orcid":false,"given":"Jacek","family":"Galas","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-5438-7717","authenticated-orcid":false,"given":"Ewelina","family":"Kasi\u0144ska-Pilut","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5734-9575","authenticated-orcid":false,"given":"Dariusz","family":"Litwin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9999-6239","authenticated-orcid":false,"given":"Daniel","family":"Saramak","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5715-6239","authenticated-orcid":false,"given":"\u0141ukasz","family":"Wr\u00f3bel","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3573-7638","authenticated-orcid":false,"given":"Micha\u0142","family":"Kozielski","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.eswa.2026.132872_bib0001","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TIM.2020.3026456","article-title":"Two-stream deep feature-based froth flotation monitoring using visual attention clues","volume":"70","author":"Ai","year":"2021","journal-title":"IEEE Transactions on Instrumentation and Measurement"},{"key":"10.1016\/j.eswa.2026.132872_sbref0002","doi-asserted-by":"crossref","DOI":"10.1016\/j.mineng.2022.107823","article-title":"Recent advances in flotation froth image analysis","volume":"188","author":"Aldrich","year":"2022","journal-title":"Minerals Engineering"},{"issue":"12","key":"10.1016\/j.eswa.2026.132872_sbref0003","doi-asserted-by":"crossref","first-page":"3493","DOI":"10.1016\/j.apt.2018.09.032","article-title":"An evaluation of machine learning and artificial intelligence models for predicting the flotation behavior of fine high-ash coal","volume":"29","author":"Ali","year":"2018","journal-title":"Advanced Powder Technology"},{"issue":"3","key":"10.1016\/j.eswa.2026.132872_bib0004","doi-asserted-by":"crossref","first-page":"23","DOI":"10.4236\/ijnm.2016.53004","article-title":"Estimation of copper and molybdenum grades and recoveries in the industrial flotation plant using the artificial neural network","volume":"5","author":"Allahkarami","year":"2016","journal-title":"International Journal of Nonferrous Metallurgy"},{"key":"10.1016\/j.eswa.2026.132872_sbref0005","doi-asserted-by":"crossref","DOI":"10.1016\/j.compchemeng.2023.108476","article-title":"Artificial intelligence for enhanced flotation monitoring in the mining industry: A convLSTM-based approach","volume":"180","author":"Bendaouia","year":"2024","journal-title":"Computers & Chemical Engineering"},{"key":"10.1016\/j.eswa.2026.132872_sbref0006","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2023.107680","article-title":"Hybrid features extraction for the online mineral grades determination in the flotation froth using deep learning","volume":"129","author":"Bendaouia","year":"2024","journal-title":"Engineering Applications of Artificial Intelligence"},{"key":"10.1016\/j.eswa.2026.132872_sbref0007","doi-asserted-by":"crossref","first-page":"910","DOI":"10.1016\/j.procs.2024.09.510","article-title":"Classification of flotation froth images using neural networks with explanations","volume":"246","author":"Borys","year":"2024","journal-title":"Procedia Computer Science"},{"key":"10.1016\/j.eswa.2026.132872_sbref0008","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2025.110283","article-title":"Recent advances in flotation froth image analysis via deep learning","volume":"147","author":"Chen","year":"2025","journal-title":"Engineering Applications of Artificial Intelligence"},{"issue":"1","key":"10.1016\/j.eswa.2026.132872_bib0009","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1080\/15567036.2025.2451073","article-title":"Froth-tailings fusion image-based concentrate ash content prediction in coal flotation using stacking model","volume":"47","author":"Chunlong Zhang","year":"2025","journal-title":"Energy Sources, Part A: Recovery, Utilization, and Environmental Effects"},{"issue":"6","key":"10.1016\/j.eswa.2026.132872_sbref0010","doi-asserted-by":"crossref","DOI":"10.1002\/eng2.12167","article-title":"Prediction of flotation efficiency of metal sulfides using an original hybrid machine learning model","volume":"2","author":"Cook","year":"2020","journal-title":"Engineering Reports"},{"key":"10.1016\/j.eswa.2026.132872_sbref0011","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1016\/j.mineng.2017.10.005","article-title":"Froth image analysis by use of transfer learning and convolutional neural networks","volume":"115","author":"Fu","year":"2018","journal-title":"Minerals Engineering"},{"issue":"21","key":"10.1016\/j.eswa.2026.132872_sbref0012","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1016\/j.ifacol.2018.09.408","article-title":"Using convolutional neural networks to develop state-of-the-art flotation froth image sensors","volume":"51","author":"Fu","year":"2018","journal-title":"IFAC-PapersOnLine"},{"key":"10.1016\/j.eswa.2026.132872_sbref0013","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1016\/j.mineng.2018.12.011","article-title":"Flotation froth image recognition with convolutional neural networks","volume":"132","author":"Fu","year":"2019","journal-title":"Minerals Engineering"},{"issue":"8","key":"10.1016\/j.eswa.2026.132872_sbref0014","doi-asserted-by":"crossref","DOI":"10.3390\/min12081052","article-title":"Machine learning technique for recognition of flotation froth images in a nonstable flotation process","volume":"12","author":"Galas","year":"2022","journal-title":"Minerals"},{"key":"10.1016\/j.eswa.2026.132872_sbref0015","doi-asserted-by":"crossref","DOI":"10.1016\/j.mineng.2021.107059","article-title":"A layered working condition perception integrating handcrafted with deep features for froth flotation","volume":"170","author":"Gao","year":"2021","journal-title":"Minerals Engineering"},{"key":"10.1016\/j.eswa.2026.132872_sbref0016","doi-asserted-by":"crossref","DOI":"10.1016\/j.mineng.2022.107627","article-title":"Prediction of grade and recovery in flotation from physicochemical and operational aspects using machine learning models","volume":"183","author":"Gomez-Flores","year":"2022","journal-title":"Minerals Engineering"},{"key":"10.1016\/j.eswa.2026.132872_sbref0017","doi-asserted-by":"crossref","DOI":"10.1016\/j.jii.2024.100697","article-title":"Flotation separation of lithium\u2013ion battery electrodes predicted by a long short-term memory network using data from physicochemical kinetic simulations and experiments","volume":"42","author":"Gomez-Flores","year":"2024","journal-title":"Journal of Industrial Information Integration"},{"key":"10.1016\/j.eswa.2026.132872_sbref0018","series-title":"Advances in neural information processing systems","first-page":"507","article-title":"Why do tree-based models still outperform deep learning on typical tabular data?","volume":"vol. 35","author":"Grinsztajn","year":"2022"},{"key":"10.1016\/j.eswa.2026.132872_bib0019","series-title":"Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR)","first-page":"770","article-title":"Deep residual learning for image recognition","author":"He","year":"2016"},{"issue":"2","key":"10.1016\/j.eswa.2026.132872_sbref0020","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.ifacol.2017.12.003","article-title":"Performance of convolutional neural networks for feature extraction in froth flotation sensing","volume":"50","author":"Horn","year":"2017","journal-title":"IFAC-PapersOnLine"},{"issue":"7","key":"10.1016\/j.eswa.2026.132872_bib0021","doi-asserted-by":"crossref","first-page":"911","DOI":"10.1080\/00986445.2014.886201","article-title":"Modeling the relationship between froth bubble size and flotation performance using image analysis and neural networks","volume":"202","author":"Hosseini","year":"2015","journal-title":"Chemical Engineering Communications"},{"issue":"1","key":"10.1016\/j.eswa.2026.132872_bib0022","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1038\/s41746-020-00341-z","article-title":"Fusion of medical imaging and electronic health records using deep learning: A systematic review and implementation guidelines","volume":"3","author":"Huang","year":"2020","journal-title":"NPJ Digital Medicine"},{"key":"10.1016\/j.eswa.2026.132872_sbref0023","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2025.127174","article-title":"Rti-net: A decision support system for fish stress classification using multimodal learning network","volume":"276","author":"Huang","year":"2025","journal-title":"Expert Systems with Applications"},{"key":"10.1016\/j.eswa.2026.132872_sbref0024","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1016\/j.mineng.2014.08.003","article-title":"Prediction of the metallurgical performances of a batch flotation system by image analysis and neural networks","volume":"69","author":"Jahedsaravani","year":"2014","journal-title":"Minerals Engineering"},{"issue":"3","key":"10.1016\/j.eswa.2026.132872_bib0025","doi-asserted-by":"crossref","first-page":"923","DOI":"10.1007\/s42461-023-00768-4","article-title":"Prediction of froth flotation performance using convolutional neural networks","volume":"40","author":"Jahedsaravani","year":"2023","journal-title":"Mining, Metallurgy & Exploration"},{"key":"10.1016\/j.eswa.2026.132872_sbref0026","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2024.124603","article-title":"Advancing multimodal diagnostics: Integrating industrial textual data and domain knowledge with large language models","volume":"255","author":"Jose","year":"2024","journal-title":"Expert Systems with Applications"},{"issue":"2","key":"10.1016\/j.eswa.2026.132872_bib0027","doi-asserted-by":"crossref","first-page":"538","DOI":"10.1109\/JBHI.2018.2824327","article-title":"Seven-point checklist and skin lesion classification using multitask multimodal neural nets","volume":"23","author":"Kawahara","year":"2019","journal-title":"IEEE Journal of Biomedical and Health Informatics"},{"key":"10.1016\/j.eswa.2026.132872_sbref0028","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2023.120153","article-title":"A hybrid convolutional neural network with fusion of handcrafted and deep features for FHSS signals classification","volume":"225","author":"Khan","year":"2023","journal-title":"Expert Systems with Applications"},{"issue":"2","key":"10.1016\/j.eswa.2026.132872_sbref0029","doi-asserted-by":"crossref","first-page":"256","DOI":"10.1111\/srt.12422","article-title":"A feature fusion system for basal cell carcinoma detection through data-driven feature learning and patient profile","volume":"24","author":"Kharazmi","year":"2018","journal-title":"Skin Research and Technology"},{"issue":"9","key":"10.1016\/j.eswa.2026.132872_sbref0030","doi-asserted-by":"crossref","DOI":"10.3390\/min14090894","article-title":"Research on prediction of ash content in flotation-recovered clean coal based on NRBO-CNN-LSTM","volume":"14","author":"Li","year":"2024","journal-title":"Minerals"},{"issue":"5","key":"10.1016\/j.eswa.2026.132872_bib0031","doi-asserted-by":"crossref","first-page":"7428","DOI":"10.1109\/TII.2024.3359416","article-title":"Operating condition recognition of industrial flotation processes using visual and acoustic bimodal autoencoder with manifold learning","volume":"20","author":"Liu","year":"2024","journal-title":"IEEE Transactions on Industrial Informatics"},{"issue":"2","key":"10.1016\/j.eswa.2026.132872_sbref0032","doi-asserted-by":"crossref","first-page":"2329","DOI":"10.1016\/j.ifacol.2023.10.1202","article-title":"Flotation froth image recognition using vision transformers","volume":"56","author":"Liu","year":"2023","journal-title":"IFAC-PapersOnLine"},{"issue":"12","key":"10.1016\/j.eswa.2026.132872_bib0033","doi-asserted-by":"crossref","first-page":"2080","DOI":"10.1080\/19392699.2024.2308549","article-title":"Predicting the ash content in coal flotation concentrate based on convolutional neural network","volume":"44","author":"Liu","year":"2024","journal-title":"International Journal of Coal Preparation and Utilization"},{"key":"10.1016\/j.eswa.2026.132872_bib0034","series-title":"Proceedings of the IEEE\/CVF international conference on computer vision (ICCV)","first-page":"10012","article-title":"Swin transformer: Hierarchical vision transformer using shifted windows","author":"Liu","year":"2021"},{"key":"10.1016\/j.eswa.2026.132872_sbref0035","doi-asserted-by":"crossref","DOI":"10.1016\/j.aei.2024.102934","article-title":"A novel semi-supervised prediction modeling method based on deep learning for flotation process with large drift of working conditions","volume":"62","author":"Lu","year":"2024","journal-title":"Advanced Engineering Informatics"},{"issue":"12","key":"10.1016\/j.eswa.2026.132872_sbref0036","doi-asserted-by":"crossref","DOI":"10.3390\/pr11123425","article-title":"Prediction of clean coal ash content in coal flotation through a convergent model unifying deep learning and likelihood function, incorporating froth velocity and reagent dosage parameters","volume":"11","author":"Lu","year":"2023","journal-title":"Processes"},{"key":"10.1016\/j.eswa.2026.132872_sbref0037","doi-asserted-by":"crossref","first-page":"330","DOI":"10.1016\/j.powtec.2018.11.056","article-title":"Machine vision based monitoring and analysis of a coal column flotation circuit","volume":"343","author":"Massinaei","year":"2019","journal-title":"Powder Technology"},{"issue":"6","key":"10.1016\/j.eswa.2026.132872_sbref0038","doi-asserted-by":"crossref","first-page":"983","DOI":"10.1016\/j.ijmst.2015.09.016","article-title":"Application and comparison of RNN, RBFNN and MNLR approaches on prediction of flotation column performance","volume":"25","author":"Nakhaei","year":"2015","journal-title":"International Journal of Mining Science and Technology"},{"issue":"3","key":"10.1016\/j.eswa.2026.132872_bib0039","doi-asserted-by":"crossref","first-page":"7812","DOI":"10.1080\/15567036.2019.1677807","article-title":"A comprehensive review of froth surface monitoring as an aid for grade and recovery prediction of flotation process. Part b: Texture and dynamic features","volume":"45","author":"Nakhaei","year":"2023","journal-title":"Energy Sources, Part A: Recovery, Utilization, and Environmental Effects"},{"issue":"1","key":"10.1016\/j.eswa.2026.132872_bib0040","doi-asserted-by":"crossref","first-page":"1103","DOI":"10.1038\/s41598-018-37387-9","article-title":"Multi-channel 3d deep feature learning for survival time prediction of brain tumor patients using multi-modal neuroimages","volume":"9","author":"Nie","year":"2019","journal-title":"Scientific Reports"},{"key":"10.1016\/j.eswa.2026.132872_bib0041","unstructured":"Oliveira, E. M. (2017). Quality prediction in a mining process. Kaggle dataset, https:\/\/www.kaggle.com\/datasets\/edumagalhaes\/quality-prediction-in-a-mining-process. Accessed: 2025-03-26."},{"issue":"17","key":"10.1016\/j.eswa.2026.132872_bib0042","doi-asserted-by":"crossref","first-page":"13639","DOI":"10.1007\/s00521-020-04773-2","article-title":"Purities prediction in a manufacturing froth flotation plant: The deep learning techniques","volume":"32","author":"Pu","year":"2020","journal-title":"Neural Computing and Applications"},{"key":"10.1016\/j.eswa.2026.132872_sbref0043","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1016\/j.powtec.2020.07.102","article-title":"Flotationnet: A hierarchical deep learning network for froth flotation recovery prediction","volume":"375","author":"Pu","year":"2020","journal-title":"Powder Technology"},{"issue":"5","key":"10.1016\/j.eswa.2026.132872_bib0044","article-title":"Developing a data-driven soft sensor to predict silicate impurity in iron ore flotation concentrate","volume":"59","author":"Pural","year":"2023","journal-title":"Physicochemical Problems of Mineral Processing"},{"issue":"7","key":"10.1016\/j.eswa.2026.132872_bib0045","doi-asserted-by":"crossref","first-page":"4573","DOI":"10.1007\/s11042-019-07927-0","article-title":"Detection of microcytic hypochromia using cbc and blood film features extracted from convolution neural network by different classifiers","volume":"79","author":"Purwar","year":"2020","journal-title":"Multimedia Tools and Applications"},{"key":"10.1016\/j.eswa.2026.132872_sbref0046","first-page":"737","article-title":"Fusion of deep learning models of MRI scans, mini\u2013mental state examination, and logical memory test enhances diagnosis of mild cognitive impairment","volume":"10","author":"Qiu","year":"2018","journal-title":"Alzheimer\u2019s & Dementia: Diagnosis, Assessment & Disease Monitoring"},{"issue":"7","key":"10.1016\/j.eswa.2026.132872_bib0047","doi-asserted-by":"crossref","first-page":"4139","DOI":"10.1007\/s00500-022-06802-9","article-title":"Employing multimodal co-learning to evaluate the robustness of sensor fusion for industry 5.0 tasks","volume":"27","author":"Rahate","year":"2023","journal-title":"Soft Computing"},{"issue":"6","key":"10.1016\/j.eswa.2026.132872_bib0048","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1109\/MSP.2017.2738401","article-title":"Deep multimodal learning: A survey on recent advances and trends","volume":"34","author":"Ramachandram","year":"2017","journal-title":"IEEE Signal Processing Magazine"},{"key":"10.1016\/j.eswa.2026.132872_bib0049","series-title":"2018 40Th annual international conference of the IEEE engineering in medicine and biology society (EMBC)","first-page":"1271","article-title":"A multi-modal convolutional neural network framework for the prediction of alzheimer\u2019s disease","author":"Spasov","year":"2018"},{"issue":"4","key":"10.1016\/j.eswa.2026.132872_sbref0050","doi-asserted-by":"crossref","DOI":"10.3390\/min14040331","article-title":"Advancements in machine learning for optimal performance in flotation processes: A review","volume":"14","author":"Szmigiel","year":"2024","journal-title":"Minerals"},{"key":"10.1016\/j.eswa.2026.132872_bib0051","series-title":"International conference on machine learning","first-page":"6105","article-title":"Efficientnet: Rethinking model scaling for convolutional neural networks","author":"Tan","year":"2019"},{"issue":"10","key":"10.1016\/j.eswa.2026.132872_bib0052","doi-asserted-by":"crossref","first-page":"12407","DOI":"10.1109\/TII.2024.3424214","article-title":"Long sequence multivariate time-series forecasting for industrial processes using SASGNN","volume":"20","author":"Wang","year":"2024","journal-title":"IEEE Transactions on Industrial Informatics"},{"key":"10.1016\/j.eswa.2026.132872_sbref0053","doi-asserted-by":"crossref","DOI":"10.1016\/j.mineng.2021.107251","article-title":"Deep learning-based ash content prediction of coal flotation concentrate using convolutional neural network","volume":"174","author":"Wen","year":"2021","journal-title":"Minerals Engineering"},{"key":"10.1016\/j.eswa.2026.132872_sbref0054","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2024.123972","article-title":"Advancing cross-subject olfactory EEG recognition: A novel framework for collaborative multimodal learning between human-machine","volume":"250","author":"Xia","year":"2024","journal-title":"Expert Systems with Applications"},{"issue":"1","key":"10.1016\/j.eswa.2026.132872_bib0055","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1148\/radiol.2019182716","article-title":"A deep learning mammography-based model for improved breast cancer risk prediction","volume":"292","author":"Yala","year":"2019","journal-title":"Radiology"},{"key":"10.1016\/j.eswa.2026.132872_sbref0056","doi-asserted-by":"crossref","DOI":"10.1016\/j.energy.2022.125027","article-title":"Ash determination of coal flotation concentrate by analyzing froth image using a novel hybrid model based on deep learning algorithms and attention mechanism","volume":"260","author":"Yang","year":"2022","journal-title":"Energy"},{"key":"10.1016\/j.eswa.2026.132872_sbref0057","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2024.125614","article-title":"Multi-scale neural network for accurate determination of the ash content of coal flotation concentrate using froth images","volume":"262","author":"Yang","year":"2025","journal-title":"Expert Systems with Applications"},{"issue":"11","key":"10.1016\/j.eswa.2026.132872_sbref0058","doi-asserted-by":"crossref","first-page":"1261","DOI":"10.1111\/exd.13777","article-title":"Multimodal skin lesion classification using deep learning","volume":"27","author":"Yap","year":"2018","journal-title":"Experimental Dermatology"},{"key":"10.1016\/j.eswa.2026.132872_sbref0059","doi-asserted-by":"crossref","DOI":"10.1016\/j.mineng.2020.106443","article-title":"Flotation froth image classification using convolutional neural networks","volume":"155","author":"Zarie","year":"2020","journal-title":"Minerals Engineering"},{"issue":"16","key":"10.1016\/j.eswa.2026.132872_bib0060","doi-asserted-by":"crossref","first-page":"4794","DOI":"10.1080\/00207543.2021.1894366","article-title":"Soft sensor of flotation froth grade classification based on hybrid deep neural network","volume":"59","author":"Zhang","year":"2021","journal-title":"International Journal of Production Research"},{"key":"10.1016\/j.eswa.2026.132872_sbref0061","doi-asserted-by":"crossref","DOI":"10.1016\/j.mineng.2020.106677","article-title":"Long short-term memory-based grade monitoring in froth flotation using a froth video sequence","volume":"160","author":"Zhang","year":"2021","journal-title":"Minerals Engineering"},{"key":"10.1016\/j.eswa.2026.132872_sbref0062","doi-asserted-by":"crossref","DOI":"10.1016\/j.mineng.2020.106332","article-title":"Convolutional memory network-based flotation performance monitoring","volume":"151","author":"Zhang","year":"2020","journal-title":"Minerals Engineering"},{"issue":"9","key":"10.1016\/j.eswa.2026.132872_sbref0063","doi-asserted-by":"crossref","DOI":"10.1145\/3649447","article-title":"Deep multimodal data fusion","volume":"56","author":"Zhao","year":"2024","journal-title":"ACM Computing Surveys"}],"container-title":["Expert Systems with Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0957417426017859?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0957417426017859?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,6,20]],"date-time":"2026-06-20T15:36:10Z","timestamp":1781969770000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0957417426017859"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9]]},"references-count":63,"alternative-id":["S0957417426017859"],"URL":"https:\/\/doi.org\/10.1016\/j.eswa.2026.132872","relation":{},"ISSN":["0957-4174"],"issn-type":[{"value":"0957-4174","type":"print"}],"subject":[],"published":{"date-parts":[[2026,9]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Fusion of image and tabular data for predicting substance concentration in flotation froth","name":"articletitle","label":"Article Title"},{"value":"Expert Systems with Applications","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.eswa.2026.132872","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"132872"}}