{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T21:03:47Z","timestamp":1790197427482,"version":"4.1.0"},"reference-count":70,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2025,9,4]],"date-time":"2025-09-04T00:00:00Z","timestamp":1756944000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"European Commission via the Marie Sklodowska Curie program","award":["955681"],"award-info":[{"award-number":["955681"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Modern inspection mobile robots can carry multiple sensors that can provide opportunities to take advantage of the fusion of information obtained from different sensors. In real-world condition monitoring, harsh environmental conditions can significantly affect the sensor\u2019s accuracy. To address this issue in this paper, we introduced a fusion approach around information gaps to handle the portion of false information that can be captured by the employed sensors. To test our idea, we looked at various types of data, such as sounds, color images, and infrared images taken by a mobile robot inspecting a mining site to check the condition of the belt conveyor idlers. The RGB images are used to classify the rotating idlers as stuck ones (late-stage faults); on the other hand, the acoustic signals are employed to identify early-stage faults. In this work, the cyclostationary analysis approach is employed to process the captured acoustic data to visualize the bearing fault signature in the form of Cyclic Spectral Coherence. Since convolutional neural networks (CNNs) and their transfer learning (TL) forms are popular approaches for performing classification tasks, a comparison study of eight CNN-TL models was conducted to find the best models to classify different fault signatures in captured RGB images and acquired Cyclic Spectral Coherence. Finally, to combine the collected information, we suggest a method called dynamic weighted majority voting, where each model\u2019s importance is regularly adjusted for each sample based on the surface temperature of the idler taken from IR images. We demonstrate that our method of combining information from multiple classifiers can work better than using just one sensor for monitoring conditions in real-world situations.<\/jats:p>","DOI":"10.3390\/s25175512","type":"journal-article","created":{"date-parts":[[2025,9,4]],"date-time":"2025-09-04T15:31:21Z","timestamp":1756999881000},"page":"5512","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Heterogeneous Information Fusion for Robot-Based Automated Monitoring of Bearings in Harsh Environments via Ensemble of Classifiers with Dynamic Weighted Voting"],"prefix":"10.3390","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2067-374X","authenticated-orcid":false,"given":"Mohammad","family":"Siami","sequence":"first","affiliation":[{"name":"Faculty of Geoengineering, Mining and Geology, Wroc\u0142aw University of Science and Technology, Na Grobli 15, 50-421 Wroc\u0142aw, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4026-708X","authenticated-orcid":false,"given":"Przemys\u0142aw","family":"D\u0105bek","sequence":"additional","affiliation":[{"name":"Faculty of Geoengineering, Mining and Geology, Wroc\u0142aw University of Science and Technology, Na Grobli 15, 50-421 Wroc\u0142aw, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1878-4718","authenticated-orcid":false,"given":"Hamid","family":"Shiri","sequence":"additional","affiliation":[{"name":"School of Electronics and Computer Science (ECS), University of Southampton, Southampton SO17 1BJ, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1297-3590","authenticated-orcid":false,"given":"Anna","family":"Michalak","sequence":"additional","affiliation":[{"name":"Faculty of Geoengineering, Mining and Geology, Wroc\u0142aw University of Science and Technology, Na Grobli 15, 50-421 Wroc\u0142aw, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3163-8678","authenticated-orcid":false,"given":"Jacek","family":"Wodecki","sequence":"additional","affiliation":[{"name":"Faculty of Geoengineering, Mining and Geology, Wroc\u0142aw University of Science and Technology, Na Grobli 15, 50-421 Wroc\u0142aw, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1656-4930","authenticated-orcid":false,"given":"Tomasz","family":"Barszcz","sequence":"additional","affiliation":[{"name":"Faculty of Mechanical Engineering and Robotics, AGH University of Krak\u00f3w, Al. Mickiewicza 30, 30-059 Krak\u00f3w, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4781-9972","authenticated-orcid":false,"given":"Rados\u0142aw","family":"Zimroz","sequence":"additional","affiliation":[{"name":"Faculty of Geoengineering, Mining and Geology, Wroc\u0142aw University of Science and Technology, Na Grobli 15, 50-421 Wroc\u0142aw, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,9,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Shiri, H., Wodecki, J., Zi\u0229tek, B., and Zimroz, R. (2021). Inspection robotic UGV platform and the procedure for an acoustic signal-based fault detection in belt conveyor idler. Energies, 14.","DOI":"10.3390\/en14227646"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Dabek, P., Szrek, J., Zimroz, R., and Wodecki, J. (2022). An Automatic Procedure for Overheated Idler Detection in Belt Conveyors Using Fusion of Infrared and RGB Images Acquired during UGV Robot Inspection. Energies, 15.","DOI":"10.3390\/en15020601"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Siami, M., Barszcz, T., Wodecki, J., and Zimroz, R. (2024). Semantic segmentation of thermal defects in belt conveyor idlers using thermal image augmentation and U-Net-based convolutional neural networks. Sci. Rep., 14.","DOI":"10.1038\/s41598-024-55864-2"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Boloz, L., and Bialy, W. (2020). Automation and Robotization of Underground Mining in Poland. Appl. Sci., 10.","DOI":"10.3390\/app10207221"},{"key":"ref_5","first-page":"41","article-title":"A high resolution system for automatic diagnosing the condition of the core of conveyor belts with steel cords","volume":"15","year":"2014","journal-title":"Diagnostyka"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"111869","DOI":"10.1016\/j.measurement.2022.111869","article-title":"Measurement of idlers rotation speed in belt conveyors based on image data analysis for diagnostic purposes","volume":"202","author":"Krot","year":"2022","journal-title":"Measurement"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Bortnowski, P., Kr\u00f3l, R., Nowak-Szpak, A., and Ozdoba, M. (2022). A Preliminary Studies of the Impact of a Conveyor Belt on the Noise Emission. Sustainability, 14.","DOI":"10.3390\/su14052785"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Bortnowski, P., Kr\u00f3l, R., and Ozdoba, M. (2023). Modelling of transverse vibration of conveyor belt in aspect of the trough angle. Sci. Rep., 13.","DOI":"10.1038\/s41598-023-46534-w"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"112884","DOI":"10.1016\/j.measurement.2023.112884","article-title":"Forecasting blockades of conveyor transfer points based on vibrodiagnostics","volume":"216","author":"Bortnowski","year":"2023","journal-title":"Measurement"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Bortnowski, P., Gondek, H., Kr\u00f3l, R., Marasova, D., and Ozdoba, M. (2023). Detection of Blockages of the Belt Conveyor Transfer Point Using an RGB Camera and CNN Autoencoder. Energies, 16.","DOI":"10.3390\/en16041666"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Siami, M., Barszcz, T., and Zimroz, R. (2024). Advanced Image Analytics for Mobile Robot-Based Condition Monitoring in Hazardous Environments: A Comprehensive Thermal Defect Processing Framework. Sensors, 24.","DOI":"10.2139\/ssrn.4776801"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Bogacz, P., Cie\u015blik, \u0141., Osowski, D., and Kochaj, P. (2022). Analysis of the Scope for Reducing the Level of Energy Consumption of Crew Transport in an Underground Mining Plant Using a Conveyor Belt System Mining Plant. Energies, 15.","DOI":"10.3390\/en15207691"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Topolsky, D., Topolskaya, I., Plaksina, I., Shaburov, P., Yumagulov, N., Fedorov, D., and Zvereva, E. (2022). Development of a Mobile Robot for Mine Exploration. Processes, 10.","DOI":"10.3390\/pr10050865"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Ball, A.D., Ouyang, H., Sinha, J.K., and Wang, Z. (2024). Deep Learning-Based Semantic Segmentation of Thermal Defects Using AResU-Net and REAL-ESRGAN for the Infrared Image Resolution Enhancement. Proceedings of the UNIfied Conference of DAMAS, IncoME and TEPEN Conferences (UNIfied 2023), Springer.","DOI":"10.1007\/978-3-031-49413-0"},{"key":"ref_15","unstructured":"Siami, M., Barszcz, T., Wodecki, J., and Zimroz, R. (2025, August 10). Automated ir Image Segmentation for Identification of Overheated Idlers in Belt Conveyor Systems Based on Outlier-Detection Method. Available online: https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=4054247."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"326","DOI":"10.1016\/j.measurement.2018.10.089","article-title":"Multi-sensors based condition monitoring of rotary machines: An approach of multidimensional time-series analysis","volume":"134","author":"Wang","year":"2019","journal-title":"Measurement"},{"key":"ref_17","unstructured":"Siami, M., Shiri, H., Barszcz, T., Wodecki, J., and Zimroz, R. (2023, January 10\u201313). Information Fusion of Infrared Images and Acoustic Signals for Bearing Fault Diagnosis of Rotating Machinery. Proceedings of the Surveillance, Vibrations, Shock and Noise, Institut Sup\u00e9rieur de l\u2032A\u00e9ronautique et de l\u2032Espace [ISAE-SUPAERO], Toulouse, France."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Polikar, R. (2012). Ensemble learning. Ensemble Machine Learning: Methods and Applications, Springer.","DOI":"10.1007\/978-1-4419-9326-7_1"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"e1249","DOI":"10.1002\/widm.1249","article-title":"Ensemble learning: A survey","volume":"8","author":"Sagi","year":"2018","journal-title":"Wiley Interdiscip. Rev. Data Min. Knowl. Discov."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Rokach, L. (2019). Ensemble Learning: Pattern Classification Using Ensemble Methods, World Scientific.","DOI":"10.1142\/11325"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1601","DOI":"10.1177\/14759217221112451","article-title":"An information fusion approach for increased reliability of condition monitoring with homogeneous and heterogeneous sensor systems","volume":"22","author":"Kannan","year":"2023","journal-title":"Struct. Health Monit."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Siciliano, B., and Khatib, O. (2016). Multisensor Data Fusion. Springer Handbook of Robotics, Springer International Publishing.","DOI":"10.1007\/978-3-319-32552-1"},{"key":"ref_23","unstructured":"Joyce, J. (2025, August 10). Bayes\u2019 Theorem. Available online: https:\/\/plato.stanford.edu\/entries\/bayes-theorem\/."},{"key":"ref_24","unstructured":"Matthies, L., and Elfes, A. (1988, January 19\u201324). Integration of sonar and stereo range data using a grid-based representation. Proceedings of the 1988 IEEE International Conference on Robotics and Automation, Philadelphia, PA, USA."},{"key":"ref_25","unstructured":"Bell, K., Corwin, T., Stone, L., and Streit, R. (2013). Bayesian Multiple Target Tracking, Artech. [2nd ed.]."},{"key":"ref_26","unstructured":"Babu, A.C., Karri, R.K., and Nisha, M.S. (2018, January 25\u201328). Sensor data fusion using Kalman filter. Proceedings of the 2018 International Conference on Design Innovations for 3Cs Compute Communicate Control (ICDI3C), Bangalore, India."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Abdulhafiz, W.A., and Khamis, A. (2013, January 10\u201312). Bayesian approach to multisensor data fusion with Pre-and Post-Filtering. Proceedings of the 2013 10th IEEE International Conference on Networking, Sensing and Control (ICNSC), Evry, France.","DOI":"10.1109\/ICNSC.2013.6548766"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1145\/504729.504754","article-title":"Probabilistic robotics","volume":"45","author":"Thrun","year":"2002","journal-title":"Commun. ACM"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/1362542.1362546","article-title":"Reputation-based framework for high integrity sensor networks","volume":"4","author":"Ganeriwal","year":"2008","journal-title":"Acm Trans. Sens. Netw. (TOSN)"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Casavola, A., Franz\u00e8, G., and Tedesco, F. (2021, January 7\u201310). Sensors Selection via a Distributed Reputation Mechanism: An Information Fusion Approach. Proceedings of the 2021 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), Vasteras, Sweden.","DOI":"10.1109\/ETFA45728.2021.9613378"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1274","DOI":"10.1109\/OJCOMS.2023.3280359","article-title":"Reputation-aware multi-agent DRL for secure hierarchical federated learning in IoT","volume":"4","author":"Abdallah","year":"2023","journal-title":"IEEE Open J. Commun. Soc."},{"key":"ref_32","first-page":"357","article-title":"A reputation-based model for mobile agent migration for information search and retrieval","volume":"37","author":"Zuo","year":"2017","journal-title":"Int. J. Inf. Manag."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"112411","DOI":"10.1016\/j.measurement.2022.112411","article-title":"Hybrid metric-feature mapping based on camera and Lidar sensor fusion","volume":"207","author":"Liu","year":"2023","journal-title":"Measurement"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"3460","DOI":"10.1109\/TGRS.2016.2518930","article-title":"Improving sensor fusion: A parametric method for the geometric coalignment of airborne hyperspectral and LiDAR data","volume":"54","author":"Brell","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Yang, G.Z., Andreu-Perez, J., Hu, X., and Thiemjarus, S. (2014). Multi-sensor fusion. Body Sensor Networks, Springer.","DOI":"10.1007\/978-1-4471-6374-9_8"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"134","DOI":"10.62762\/CJIF.2024.999646","article-title":"Complex evidence theory for multisource data fusion","volume":"1","author":"Xiao","year":"2024","journal-title":"Chin. J. Inf. Fusion"},{"key":"ref_37","unstructured":"Shitsukane, A., Cheruiyot, W., Otieno, C., and Mvurya, M. (2018, January 9\u201311). Fuzzy logic sensor fusion for obstacle avoidance mobile robot. Proceedings of the 2018 IST-Africa Week Conference (IST-Africa), Gaborone, Botswana."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"510","DOI":"10.1109\/41.679010","article-title":"Accurate differential global positioning system via fuzzy logic Kalman filter sensor fusion technique","volume":"45","author":"Kobayashi","year":"2002","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"3809","DOI":"10.1109\/TITS.2023.3234595","article-title":"Learning type-2 fuzzy logic for factor graph based-robust pose estimation with multi-sensor fusion","volume":"24","year":"2023","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Wang, Z., Xu, X., Song, D., Zheng, Z., and Li, W. (2025). A Novel Bearing Fault Diagnosis Method Based on Improved Convolutional Neural Network and Multi-Sensor Fusion. Machines, 13.","DOI":"10.3390\/machines13030216"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1457","DOI":"10.1109\/TGRS.2008.916089","article-title":"Classifying multilevel imagery from SAR and optical sensors by decision fusion","volume":"46","author":"Waske","year":"2008","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1016\/S0951-8320(00)00089-2","article-title":"Reliability optimization for weighted voting system","volume":"71","author":"Levitin","year":"2001","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"833","DOI":"10.4304\/jcp.6.5.833-840","article-title":"A Novel Weighted Voting for K-Nearest Neighbor Rule","volume":"6","author":"Gou","year":"2011","journal-title":"J. Comput."},{"key":"ref_44","first-page":"661","article-title":"A sensor fusion based approach for bearing fault diagnosis of rotating machine","volume":"236","author":"Mian","year":"2022","journal-title":"Proc. Inst. Mech. Eng. Part O J. Risk Reliab."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"4803","DOI":"10.1007\/s00521-021-06668-2","article-title":"A novel deep learning approach for intelligent fault diagnosis applications based on time-frequency images","volume":"34","year":"2022","journal-title":"Neural Comput. Appl."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"3279","DOI":"10.1007\/s00170-021-08282-x","article-title":"Image-based tool condition monitoring based on convolution neural network in turning process","volume":"119","author":"Kou","year":"2022","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_47","unstructured":"Wang, Z., and Oates, T. (2015). Imaging time-series to improve classification and imputation. arXiv."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"544","DOI":"10.17531\/ein.2020.3.18","article-title":"Worm gear condition monitoring and fault detection from thermal images via deep learning method","volume":"22","author":"Karabacak","year":"2020","journal-title":"Eksploat. I Niezawodn."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Siami, M., Barszcz, T., Wodecki, J., and Zimroz, R. (2022). Automated Identification of Overheated Belt Conveyor Idlers in Thermal Images with Complex Backgrounds Using Binary Classification with CNN. Sensors, 22.","DOI":"10.3390\/s222410004"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"497","DOI":"10.1016\/j.jsv.2007.02.029","article-title":"Cyclic spectral analysis of rolling-element bearing signals: Facts and fictions","volume":"304","author":"Antoni","year":"2007","journal-title":"J. Sound Vib."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1186\/s40537-018-0151-6","article-title":"A survey on addressing high-class imbalance in big data","volume":"5","author":"Leevy","year":"2018","journal-title":"J. Big Data"},{"key":"ref_52","unstructured":"Masko, D., and Hensman, P. (2025, August 10). The Impact of Imbalanced Training Data for Convolutional Neural Networks. Available online: https:\/\/www.semanticscholar.org\/paper\/The-Impact-of-Imbalanced-Training-Data-for-Neural-Masko-Hensman\/62e81797fff75603a3d7c7759e6efac4fd2b6b31."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Lee, H., Park, M., and Kim, J. (2016, January 25\u201328). Plankton classification on imbalanced large scale database via convolutional neural networks with transfer learning. Proceedings of the 2016 IEEE International Conference on Image Processing (ICIP), Phoenix, AZ, USA.","DOI":"10.1109\/ICIP.2016.7533053"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Jumaboev, S., Jurakuziev, D., and Lee, M. (2022). Photovoltaics Plant Fault Detection Using Deep Learning Techniques. Remote Sens., 14.","DOI":"10.3390\/rs14153728"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"2017","DOI":"10.1162\/jocn_a_01544","article-title":"Convolutional neural networks as a model of the visual system: Past, present, and future","volume":"33","author":"Lindsay","year":"2021","journal-title":"J. Cogn. Neurosci."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"769","DOI":"10.1061\/(ASCE)CP.1943-5487.0000179","article-title":"Automated visual recognition of dump trucks in construction videos","volume":"26","author":"McCabe","year":"2012","journal-title":"J. Comput. Civ. Eng."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., and Fei-Fei, L. (2009, January 20\u201325). Imagenet: A large-scale hierarchical image database. Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref_59","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015, January 7\u201312). Going deeper with convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Chollet, F. (2017, January 21\u201326). Xception: Deep learning with depthwise separable convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.195"},{"key":"ref_63","unstructured":"Ho, T.K. (1995, January 14\u201316). Random Decision Forest. Proceedings of the 3rd International Conference on Document Analysis and Recognition, Montreal, QC, Canada."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"107252","DOI":"10.1016\/j.compeleceng.2021.107252","article-title":"Deep pre-trained networks as a feature extractor with XGBoost to detect tuberculosis from chest X-ray","volume":"93","author":"Rahman","year":"2021","journal-title":"Comput. Electr. Eng."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Tang, J., Su, Q., Su, B., Fong, S., Cao, W., and Gong, X. (2020). Parallel ensemble learning of convolutional neural networks and local binary patterns for face recognition. Comput. Methods Programs Biomed., 197.","DOI":"10.1016\/j.cmpb.2020.105622"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"309","DOI":"10.2174\/1574893614666191202153824","article-title":"A sequential ensemble model for communicable disease forecasting","volume":"15","author":"Sultana","year":"2020","journal-title":"Curr. Bioinform."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"757","DOI":"10.1016\/j.jksuci.2023.01.014","article-title":"A comprehensive review on ensemble deep learning: Opportunities and challenges","volume":"35","author":"Mohammed","year":"2023","journal-title":"J. King Saud-Univ.-Comput. Inf. Sci."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1007\/s11063-010-9157-6","article-title":"Parallel approach for ensemble learning with locally coupled neural networks","volume":"32","author":"Valle","year":"2010","journal-title":"Neural Process. Lett."},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Valdovinos, R.M., S\u00e1nchez, J.S., and Barandela, R. (2005). Dynamic and static weighting in classifier fusion. Proceedings of the Pattern Recognition and Image Analysis: Second Iberian Conference, IbPRIA 2005, Estoril, Portugal, 7\u20139 June 2005, Springer.","DOI":"10.1007\/11492542_8"},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Siami, M., Barszcz, T., Wodecki, J., and Zimroz, R. (2022). Design of an Infrared Image Processing Pipeline for Robotic Inspection of Conveyor Systems in Opencast Mining Sites. Energies, 15.","DOI":"10.3390\/en15186771"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/25\/17\/5512\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:39:34Z","timestamp":1760035174000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/25\/17\/5512"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,4]]},"references-count":70,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2025,9]]}},"alternative-id":["s25175512"],"URL":"https:\/\/doi.org\/10.3390\/s25175512","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,4]]}}}