{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T15:54:58Z","timestamp":1759334098767,"version":"build-2065373602"},"reference-count":71,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2025,9,30]],"date-time":"2025-09-30T00:00:00Z","timestamp":1759190400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002848","name":"Agencia Nacional de Investigaci\u00f3n y Desarrollo","doi-asserted-by":"publisher","award":["FONDEF VIU240001"],"award-info":[{"award-number":["FONDEF VIU240001"]}],"id":[{"id":"10.13039\/501100002848","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.mdpi.com"],"crossmark-restriction":true},"short-container-title":["MAKE"],"abstract":"<jats:p>This paper proposes a transfer learning-based approach to enhance video-driven safety risk detection in industrial environments, addressing the critical challenge of limited generalization across diverse operational scenarios. Conventional deep learning models trained on specific operational contexts often fail when applied to new environments with different lighting, camera angles, or machinery configurations, exhibiting a significant drop in performance (e.g., F1-score declining below 0.85). To overcome this issue, an incremental feature transfer learning strategy is introduced, enabling efficient adaptation of risk detection models using only small amounts of data from new scenarios. This approach leverages prior knowledge from pre-trained models to reduce the reliance on large-labeled datasets, particularly valuable in industrial settings where rare but critical safety risk events are difficult to capture. Additionally, training efficiency is improved compared with a classic approach, supporting deployment on resource-constrained edge devices. The strategy involves incremental retraining using video segments with average durations ranging from 2.5 to 25 min (corresponding to 5\u201350% of new scenario data), approximately, enabling scalable generalization across multiple forklift-related risk activities. Interpretability is enhanced through SHAP-based analysis, which reveals a redistribution of feature relevance toward critical components, thereby improving model transparency and reducing annotation demands. Experimental results confirm that the transfer learning strategy significantly improves detection accuracy, robustness, and adaptability, making it a practical and scalable solution for safety monitoring in dynamic industrial environments.<\/jats:p>","DOI":"10.3390\/make7040111","type":"journal-article","created":{"date-parts":[[2025,9,30]],"date-time":"2025-09-30T08:21:51Z","timestamp":1759220511000},"page":"111","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Transfer Learning for Generalized Safety Risk Detection in Industrial Video Operations"],"prefix":"10.3390","volume":"7","author":[{"given":"Luciano","family":"Radrigan","sequence":"first","affiliation":[{"name":"Electrical Engineering Department, Universidad de Concepcion, Concepcion 4070409, Chile"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8692-5749","authenticated-orcid":false,"given":"Sebasti\u00e1n E.","family":"Godoy","sequence":"additional","affiliation":[{"name":"Electrical Engineering Department, Universidad de Concepcion, Concepcion 4070409, Chile"}]},{"given":"Anibal S.","family":"Morales","sequence":"additional","affiliation":[{"name":"Centro de Transici\u00f3n Energ\u00e9tica (CTE), Facultad de Ingenier\u00eda, Universidad San Sebasti\u00e1n, Concepci\u00f3n 4081339, Chile"}]}],"member":"1968","published-online":{"date-parts":[[2025,9,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Ludwika, A.S., and Rifai, A.P. (2024). Deep Learning for Detection of Proper Utilization and Adequacy of Personal Protective Equipment in Manufacturing Teaching Laboratories. Safety, 10.","DOI":"10.3390\/safety10010026"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"218","DOI":"10.3390\/vibration6010014","article-title":"Deep Transfer Learning Models for Industrial Fault Diagnosis Using Vibration and Acoustic Sensors Data: A Review","volume":"6","author":"Bhuiyan","year":"2023","journal-title":"Vibration"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1049\/cit2.12088","article-title":"Mallikarjuna Multi-gradient-direction based deep learning model for arecanut disease identification","volume":"7","author":"Mallikarjuna","year":"2022","journal-title":"CAAI Trans. Intell. Technol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"759","DOI":"10.1007\/s10845-019-01476-x","article-title":"Tabernik Segmentation-based deep-learning approach for surface-defect detection","volume":"31","author":"Tabernik","year":"2020","journal-title":"J. Intell. Manuf."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"4483","DOI":"10.1007\/s00521-020-05275-x","article-title":"Domain-adaptive intelligence for fault diagnosis based on deep transfer learning from scientific test rigs to industrial applications","volume":"33","author":"Cao","year":"2020","journal-title":"Neural Comput. Appl."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"69759","DOI":"10.1007\/s11042-024-18352-3","article-title":"El-Latif Siraj Khan Heterogeneous transfer learning: Recent developments, applications, and challenges","volume":"83","author":"Khan","year":"2024","journal-title":"Multimed. Tools Appl."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"4667","DOI":"10.1007\/s10462-022-10293-3","article-title":"Recent advances in the application of deep learning for fault diagnosis of rotating machinery using vibration signals","volume":"56","author":"Tama","year":"2022","journal-title":"Artif. Intell. Rev."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1109\/MIE.2020.3034884","article-title":"Deep Transfer Learning for Industrial Automation: A Review and Discussion of New Techniques for Data-Driven Machine Learning","volume":"15","author":"Maschler","year":"2021","journal-title":"IEEE Ind. Electron. Mag."},{"key":"ref_9","unstructured":"Sharma, P. (2024, July 07). Understanding Transfer Learning for Deep Learning. Anal. Vidhya 2021. Available online: https:\/\/www.analyticsvidhya.com\/blog\/2021\/10\/understanding-transfer-learning-for-deep-learning\/."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"40","DOI":"10.3390\/technologies11020040","article-title":"Mohammadreza Iman A Review of Deep Transfer Learning and Recent Advancements","volume":"11","author":"HIman","year":"2023","journal-title":"Technologies"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"012029","DOI":"10.1088\/1742-6596\/2273\/1\/012029","article-title":"Deep Learning (CNN) and Transfer Learning: A Review","volume":"2273","author":"Gupta","year":"2022","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_12","first-page":"101214","article-title":"Daniel Fernando Santos-Bustos Towards automated eye cancer classification via VGG and ResNet networks using transfer learning","volume":"35","author":"Nguyen","year":"2022","journal-title":"Eng. Sci. Technol. Int. J."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"6111","DOI":"10.1007\/s00521-019-04097-w","article-title":"A transfer convolutional neural network for fault diagnosis based on ResNet-50","volume":"32","author":"Wen","year":"2020","journal-title":"Neural Comput. Appl."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Reddy, A.S.B., and Juliet, D.S. (2019, January 4\u20136). Transfer Learning with ResNet-50 for Malaria Cell-Image Classification. Proceedings of the 2019 International Conference on Communication and Signal Processing (ICCSP), Chennai, India.","DOI":"10.1109\/ICCSP.2019.8697909"},{"key":"ref_15","first-page":"2169","article-title":"Transfer Learning With Adaptive Fine-Tuning","volume":"8","author":"Podgorelec","year":"2020","journal-title":"IEEE Access"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1089\/big.2021.0218","article-title":"Subramanian Hyperparameter Optimization for Transfer Learning of VGG16 for Disease Identification in Corn Leaves Using Bayesian Optimization","volume":"10","author":"Subramanian","year":"2022","journal-title":"Big Data"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"26587","DOI":"10.1007\/s11042-020-09268-9","article-title":"Static facial expression recognition using convolutional neural networks based on transfer learning and hyperparameter optimization","volume":"79","author":"Ozcan","year":"2020","journal-title":"Multimed. Tools Appl."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Bernico, M., Li, Y., and Zhang, D. (2018, January 15\u201316). Investigating the Impact of Data Volume and Domain Similarity on Transfer Learning Applications. Proceedings of the Future Technologies Conference (FTC), Vancouver, BC, Canada.","DOI":"10.1007\/978-3-030-02683-7_5"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Cengiz, A.B., and McGough, A.S. (2023, January 15\u201318). How much data do I need? A case study on medical data. Proceedings of the 2023 IEEE International Conference on Big Data (BigData), Sorrento, Italy.","DOI":"10.1109\/BigData59044.2023.10386440"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1016\/j.neucom.2020.07.088","article-title":"A comprehensive review on convolutional neural network in machine fault diagnosis","volume":"111","author":"Jiao","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_21","first-page":"736","article-title":"End-to-end CNN + LSTM deep learning approach for bearing fault diagnosis","volume":"51","author":"MKhorram","year":"2020","journal-title":"Appl. Soft Comput."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"122827","DOI":"10.1016\/j.eswa.2023.122827","article-title":"Graph neural network-based bearing fault diagnosis using Granger causality test","volume":"242","author":"Zhang","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Mohammadi, S., Rahmanian, V., Sattarpanah Karganroudi, S., and Adda, M. (2025). Smart Defect Detection in Aero-Engines: Evaluating Transfer Learning with VGG19 and Data-Efficient Image Transformer Models. Machines, 13.","DOI":"10.3390\/machines13010049"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"e70003","DOI":"10.1049\/cvi2.70003","article-title":"Human activity recognition: A review of deep learning-based methods","volume":"19","author":"Dutta","year":"2025","journal-title":"IET Comput. Vis."},{"key":"ref_25","unstructured":"Singh, M.T., Prasad, R.K., Michael, G.R., Singh, N.H., and Kaphungkui, N.K. (2024). Tiken Singh Spatial-Temporal Bearing Fault Detection Using Graph Attention Networks and LSTM. arXiv."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Reyes, R.C., Sevilla, R.V., Zapanta, G.S., Merin, J.V., Maaliw, R.R., and Santiago, A.F. (2022, January 8\u201310). Safety Gear Compliance Detection Using Data Augmentation-Assisted Transfer Learning in Construction Work Environment. Proceedings of the 2022 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT), Bangalore, India.","DOI":"10.1109\/CONECCT55679.2022.9865757"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"e41","DOI":"10.1017\/dce.2024.54","article-title":"The transfer learning of uncertainty quantification for industrial plant fault diagnosis system design","volume":"5","author":"Blair","year":"2024","journal-title":"Data-Centric Eng."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Yan, P., Abdulkadir, A., Luley, P.P., Rosenthal, M., Schatte, G.A., Grewe, B.F., and Stadelmann, T. (2023). A Comprehensive Survey of Deep Transfer Learning for Anomaly Detection in Industrial Time Series: Methods, Applications, and Directions. Mach. Learn., 11.","DOI":"10.1109\/ACCESS.2023.3349132"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Abdulazeez Jebur, S., Hussein, K.A., Kadhim Hoomod, H., Alzubaidi, L., Saihood, A.A., and Gu, Y. (2024). A Scalable and Generalized Deep Learning Framework for Anomaly Detection in Surveillance Videos. arXiv.","DOI":"10.1155\/int\/1947582"},{"key":"ref_30","first-page":"27868","article-title":"Transfer learning model for anomalous event recognition in big video data","volume":"14","author":"Taha","year":"2024","journal-title":"Nature"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Aboulola, O.I. (2024). Improving traffic accident severity prediction using MobileNet transfer learning model and SHAP XAI technique. PLoS ONE, 19.","DOI":"10.1371\/journal.pone.0300640"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Sajjadi, P., Dinmohammadi, F., and Shafiee, M. (2025). Fault Detection of Cyber-Physical Systems Using a Transfer Learning Method Based on Pre-Trained Transformers. Sensors, 25.","DOI":"10.3390\/s25134164"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Sadaiyandi, J., Arumugam, P., Sangaiah, A.K., and Zhang, C. (2023). Sadaiyandi Stratified Sampling-Based Deep Learning Approach to Increase Prediction Accuracy of Unbalanced Dataset. Electronics, 12.","DOI":"10.3390\/electronics12214423"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"3337","DOI":"10.1038\/s41467-025-58606-8","article-title":"Data splitting to avoid information leakage with DataSAIL","volume":"16","author":"Joeres","year":"2024","journal-title":"Nat. Commun."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Fogliato, R., Patil, P., Monfort, M., and Perona, P. (2024). A Framework for Efficient Model Evaluation Through Stratification, Sampling, and Estimation. European Conference on Computer Vision, Springer.","DOI":"10.1007\/978-3-031-73223-2_9"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Khan, M., and Hossni, Y. (2025). A comparative analysis of LSTM models aided with attention and squeeze and excitation blocks for activity recognition. Sci. Rep., 15.","DOI":"10.1038\/s41598-025-88378-6"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1803","DOI":"10.1016\/j.ymssp.2010.11.018","article-title":"Prognostic modelling options for remaining useful life estimation by industry","volume":"25","author":"Sikorska","year":"2011","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"666","DOI":"10.1016\/j.promfg.2020.02.131","article-title":"Avoiding Environmental Consequences of Equipment Failure via an LSTM-Based Model for Predictive Maintenance","volume":"43","author":"Wu","year":"2020","journal-title":"Procedia Manuf."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Bampoula, X., Siaterlis, G., Nikolakis, N., and Alexopoulos, K. (2021). A Deep Learning Model for Predictive Maintenance in Cyber-Physical Production Systems Using LSTM Autoencoders. Sensors, 21.","DOI":"10.3390\/s21030972"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1016\/j.aej.2025.03.075","article-title":"WT-DSE-LSTM: A hybrid model for the multivariate prediction of dissolved oxygen","volume":"124","author":"Xu","year":"2025","journal-title":"Alex. Eng. J."},{"key":"ref_41","first-page":"134","article-title":"Deep Learning-Based Video Anomaly Detection Using Optimised Attention-Enhanced Autoencoders","volume":"24","author":"Anjali","year":"2025","journal-title":"Electron. Lett. Comput. Vis. Image Anal."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Li, X., Hao, T., Li, F., Zhao, L., and Wang, Z. (2023). Faster R-CNN-LSTM Construction Site Unsafe Behavior Recognition Model. Appl. Sci., 13.","DOI":"10.3390\/app131910700"},{"key":"ref_43","unstructured":"(2025, February 23). AWS. Available online: https:\/\/docs.aws.amazon.com\/rekognition\/latest\/customlabels-dg\/what-is.html."},{"key":"ref_44","unstructured":"(2025, February 22). NVIDIA. Available online: https:\/\/docs.nvidia.com\/metropolis\/deepstream\/dev-guide\/."},{"key":"ref_45","unstructured":"(2023, March 02). NVIDIA. Available online: https:\/\/developer.nvidia.com\/deepstream-sdk."},{"key":"ref_46","unstructured":"(2025, August 31). NVIDIA Corporation. Available online: https:\/\/developer.nvidia.com\/deepstream-sdk."},{"key":"ref_47","first-page":"15","article-title":"Hsiao Identify Subtle Fall Hazards Using Transfer Learning","volume":"91","author":"Hsiao","year":"2025","journal-title":"Engineering Proceedings"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Tanveer, M.H., Fatima, Z., Zardari, S., and Guerra-Zubiaga, D. (2023). Tanveer An In-Depth Analysis of Domain Adaptation in Computer and Robotic Vision. Appl. Sci., 23.","DOI":"10.3390\/app132312823"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"5362","DOI":"10.1109\/TPAMI.2024.3367329","article-title":"A Comprehensive Survey of Continual Learning: Theory, Method and Application","volume":"46","author":"Wang","year":"2024","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1016\/j.patrec.2022.01.024","article-title":"Incremental few-shot object detection via knowledge transfer","volume":"156","author":"Feng","year":"2022","journal-title":"Pattern Recognit. Lett."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"L\u00f3pez-Lozada, E., Sossa, H., Rubio-Espino, E., and Montiel-P\u00e9rez, J.Y. (2024). Action Recognition in Videos through a Transfer-Learning-Based Technique. Mathematics, 12.","DOI":"10.20944\/preprints202406.1670.v1"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"112375","DOI":"10.1016\/j.asoc.2024.112375","article-title":"A few-shot learning methodology for improving safety in industrial scenarios through universal self-supervised visual features and dense optical flow","volume":"167","author":"Ruiz","year":"2024","journal-title":"Appl. Soft Comput."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1","DOI":"10.37256\/aie.6120255255","article-title":"Intelligent Construction Risk Management Through Transfer Learning: Trends, Challenges and Future Strategies","volume":"6","author":"Junjia","year":"2024","journal-title":"Artif. Intell. Evol."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1186\/s40537-022-00652-w","article-title":"Asmaul Hosna Transfer learning: A friendly introductio","volume":"9","author":"Hosna","year":"2022","journal-title":"J. Big Data"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1109\/JPROC.2020.3004555","article-title":"A Comprehensive Survey on Transfer Learning","volume":"109","author":"Zhuang","year":"2021","journal-title":"Proc. IEEE"},{"key":"ref_56","unstructured":"(2025, February 03). Tzutalin. Available online: https:\/\/github.com\/heartexlabs\/labelImg."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"831","DOI":"10.1007\/s11633-022-1411-7","article-title":"A Survey of Synthetic Data Augmentation Methods in Machine Vision","volume":"21","author":"Mumuni","year":"2024","journal-title":"Mach. Intell. Res."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"De La Fuente, N., Maj\u00f3, M., Luzko, I., C\u00f3rdova, H., Fern\u00e1ndez-Esparrach, G., and Bernal, J. (2024). Enhancing Image Classification in Small and Unbalanced Datasets Through Synthetic Data Augmentation. Workshop on Clinical Image-Based Procedures, Springer.","DOI":"10.1007\/978-3-031-73083-2_2"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"e16807","DOI":"10.1016\/j.heliyon.2023.e16807","article-title":"Dynamic learning for imbalanced data in learning chest X-ray and CT images","volume":"9","author":"Iqbal","year":"2023","journal-title":"Heliyon"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"187536","DOI":"10.1109\/ACCESS.2024.3470122","article-title":"Image Data Augmentation Approaches: A Comprehensive Survey and Future Directions","volume":"12","author":"Kumar","year":"2024","journal-title":"IEEE Access"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"109853","DOI":"10.1016\/j.asoc.2022.109853","article-title":"The effects of data balancing approaches: A case study","volume":"132","author":"Mooijman","year":"2023","journal-title":"Appl. Soft Comput."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Rui, X., Li, Z., Cao, Y., Li, Z., and Song, W. (2023). DILRS: Domain-Incremental Learning for Semantic Segmentation in Multi-Source Remote Sensing Data. Remote Sens., 15.","DOI":"10.3390\/rs15102541"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Saarela, M., and Podgorelec, V. (2024). Recent Applications of Explainable AI (XAI): A Systematic Literature Review. Appl. Sci., 14.","DOI":"10.3390\/app14198884"},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Gebreyesus, Y., Dalton, D., De Chiara, D., Chinnici, M., and Chinnici, A. (2024). AI for Automating Data Center Operations: Model Explainability in the Data Centre Context Using Shapley Additive Explanations (SHAP). Electronics, 13.","DOI":"10.3390\/electronics13091628"},{"key":"ref_65","first-page":"10081","article-title":"Optimising TinyML with quantization and distillation of transformer and mamba models for indoor localisation on edge devices","volume":"15","author":"Suwannaphong","year":"2025","journal-title":"Nature"},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Das, S., Cheng, J., Rakshit, A., Boubin, J., and Ramnath, R. (2025, January 20\u201324). EPIC: Efficient Pruning for Inference on Constrained Devices. Proceedings of the PEARC\u201925: Practice and Experience in Advanced Research Computing, Columbus, OH, USA.","DOI":"10.1145\/3708035.3736107"},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Rey, L., Bernardos, A.M., Dobrzycki, A.D., Carrami\u00f1ana, D., Bergesio, L., Besada, J.A., and Casar, J.R. (2025). A Performance Analysis of You Only Look Once Models for Deployment on Constrained Computational Edge Devices in Drone Applications. Sensors, 14.","DOI":"10.3390\/electronics14030638"},{"key":"ref_68","first-page":"22835","article-title":"Sun Multimodal and multiscale feature fusion for weakly supervised video anomaly detection","volume":"14","author":"Sun","year":"2024","journal-title":"Nature"},{"key":"ref_69","first-page":"16291","article-title":"Multimodal anomaly detection in complex environments using video and audio fusion","volume":"15","author":"Wang","year":"2025","journal-title":"Nature"},{"key":"ref_70","first-page":"29497","article-title":"Automated violence monitoring system for real-time fistfight detection using deep learning-based temporal action localization","volume":"15","author":"Qi","year":"2025","journal-title":"Nature"},{"key":"ref_71","first-page":"25805","article-title":"Contextual information based anomaly detection for multi-scene aerial videos","volume":"15","author":"Verma","year":"2025","journal-title":"Nature"}],"container-title":["Machine Learning and Knowledge Extraction"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2504-4990\/7\/4\/111\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,30]],"date-time":"2025-09-30T09:07:40Z","timestamp":1759223260000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2504-4990\/7\/4\/111"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,30]]},"references-count":71,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["make7040111"],"URL":"https:\/\/doi.org\/10.3390\/make7040111","relation":{},"ISSN":["2504-4990"],"issn-type":[{"type":"electronic","value":"2504-4990"}],"subject":[],"published":{"date-parts":[[2025,9,30]]}}}