{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,11]],"date-time":"2026-10-11T12:18:00Z","timestamp":1791721080229,"version":"4.3.4"},"reference-count":62,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2021,12,26]],"date-time":"2021-12-26T00:00:00Z","timestamp":1640476800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Developing cyber security is very necessary and has attracted considerable attention from academy and industry organizations worldwide. It is also very necessary to provide sustainable computing for the the Internet of Things (IoT). Machine learning techniques play a vital role in the cybersecurity of the IoT for intrusion detection and malicious identification. Thus, in this study, we develop new feature extraction and selection methods and for the IDS system using the advantages of the swarm intelligence (SI) algorithms. We design a feature extraction mechanism depending on the conventional neural networks (CNN). After that, we present an alternative feature selection (FS) approach using the recently developed SI algorithm, Aquila optimizer (AQU). Moreover, to assess the quality of the developed IDS approach, four well-known public datasets, CIC2017, NSL-KDD, BoT-IoT, and KDD99, were used. We also considered extensive comparisons to other optimization methods to verify the competitive performance of the developed method. The results show the high performance of the developed approach using different evaluation indicators.<\/jats:p>","DOI":"10.3390\/s22010140","type":"journal-article","created":{"date-parts":[[2021,12,27]],"date-time":"2021-12-27T01:06:54Z","timestamp":1640567214000},"page":"140","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":162,"title":["Advanced Feature Extraction and Selection Approach Using Deep Learning and Aquila Optimizer for IoT Intrusion Detection System"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9097-0945","authenticated-orcid":false,"given":"Abdulaziz","family":"Fatani","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China"},{"name":"Computer Science Department, Umm Al-Qura University, Makkah 24381, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abdelghani","family":"Dahou","sequence":"additional","affiliation":[{"name":"LDDI Laboratory, Faculty of Science and Technology, University of Ahmed DRAIA, Adrar 01000, Algeria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6956-7641","authenticated-orcid":false,"given":"Mohammed A. A.","family":"Al-qaness","sequence":"additional","affiliation":[{"name":"Faculty of Engineering, Sana\u2019a University, Sana\u2019a 12544, Yemen"},{"name":"State Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4489-2488","authenticated-orcid":false,"given":"Songfeng","family":"Lu","sequence":"additional","affiliation":[{"name":"School of Cyber Science & Engineering, Huazhong University of Science and Technology, Wuhan 430074, China"},{"name":"Shenzhen Huazhong University of Science and Technology Research Institute, Shenzhen 518057, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7682-6269","authenticated-orcid":false,"given":"Mohamed Abd","family":"Abd Elaziz","sequence":"additional","affiliation":[{"name":"Department of Mathematics, Faculty of Science, Zagazig University, Zagazig 44519, Egypt"},{"name":"Artificial Intelligence Research Center (AIRC), Ajman University, Ajman 346, United Arab Emirates"},{"name":"Faculty of Computer Science & Engineering, Galala University, Suze 435611, Egypt"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,12,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"107247","DOI":"10.1016\/j.comnet.2020.107247","article-title":"Building an efficient intrusion detection system based on feature selection and ensemble classifier","volume":"174","author":"Zhou","year":"2020","journal-title":"Comput. Netw."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Zhao, X., and Zhang, W. (2016, January 21\u201323). An anomaly intrusion detection method based on improved k-means of cloud computing. Proceedings of the 2016 Sixth International Conference on Instrumentation & Measurement, Computer, Communication and Control (IMCCC), Harbin, China.","DOI":"10.1109\/IMCCC.2016.108"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Kumar, G.R., Mangathayaru, N., and Narasimha, G. (2015, January 24\u201326). An improved k-Means Clustering algorithm for Intrusion Detection using Gaussian function. Proceedings of the The International Conference on Engineering & MIS 2015, Istanbul, Turkey.","DOI":"10.1145\/2832987.2833082"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Modi, C., Patel, D., Borisanya, B., Patel, A., and Rajarajan, M. (2012, January 25\u201327). A novel framework for intrusion detection in cloud. Proceedings of the fifth International Conference on Security of Information and Networks, Jaipur, India.","DOI":"10.1145\/2388576.2388585"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"4680867","DOI":"10.1155\/2018\/4680867","article-title":"Intrusion detection system based on decision tree over big data in fog environment","volume":"2018","author":"Peng","year":"2018","journal-title":"Wirel. Commun. Mob. Comput."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Ghosh, P., Mandal, A.K., and Kumar, R. (2015). An efficient cloud network intrusion detection system. Information Systems Design and Intelligent Applications, Springer.","DOI":"10.1007\/978-81-322-2250-7_10"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"567","DOI":"10.1007\/s13198-014-0277-7","article-title":"HIDS: A host based intrusion detection system for cloud computing environment","volume":"9","author":"Deshpande","year":"2018","journal-title":"Int. J. Syst. Assur. Eng. Manag."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"e5922","DOI":"10.1002\/cpe.5922","article-title":"An intrusion detection algorithm based on bag representation with ensemble support vector machine in cloud computing","volume":"32","author":"Wei","year":"2020","journal-title":"Concurr. Comput. Pract. Exp."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Schueller, Q., Basu, K., Younas, M., Patel, M., and Ball, F. (2018, January 21\u201323). A hierarchical intrusion detection system using support vector machine for SDN network in cloud data center. Proceedings of the 2018 28th International Telecommunication Networks and Applications Conference (ITNAC), Sydney, NSW, Australia.","DOI":"10.1109\/ATNAC.2018.8615255"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Hodo, E., Bellekens, X., Hamilton, A., Dubouilh, P.L., Iorkyase, E., Tachtatzis, C., and Atkinson, R. (2016, January 11\u201313). Threat analysis of IoT networks using artificial neural network intrusion detection system. Proceedings of the 2016 International Symposium on Networks, Computers and Communications (ISNCC), Yasmine Hammamet, Tunisia.","DOI":"10.1109\/ISNCC.2016.7746067"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"50850","DOI":"10.1109\/ACCESS.2018.2868993","article-title":"A novel intrusion detection model for a massive network using convolutional neural networks","volume":"6","author":"Wu","year":"2018","journal-title":"IEEE Access"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"102031","DOI":"10.1016\/j.simpat.2019.102031","article-title":"Deep recurrent neural network for IoT intrusion detection system","volume":"101","author":"Almiani","year":"2020","journal-title":"Simul. Model. Pract. Theory"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1080\/10095020.2019.1612600","article-title":"Device-free human micro-activity recognition method using WiFi signals","volume":"22","year":"2019","journal-title":"Geo-Spat. Inf. Sci."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Seth, J.K., and Chandra, S. (2018, January 20\u201321). MIDS: Metaheuristic based intrusion detection system for cloud using k-NN and MGWO. Proceedings of the International Conference on Advances in Computing and Data Sciences, Dehradun, India.","DOI":"10.1007\/978-981-13-1810-8_41"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1016\/j.comcom.2020.05.048","article-title":"An effective feature engineering for DNN using hybrid PCA-GWO for intrusion detection in IoMT architecture","volume":"160","author":"RM","year":"2020","journal-title":"Comput. Commun."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"106997","DOI":"10.1016\/j.asoc.2020.106997","article-title":"An efficient metaheuristic algorithm based feature selection and recurrent neural network for DoS attack detection in cloud computing environment","volume":"100","author":"SaiSindhuTheja","year":"2021","journal-title":"Appl. Soft Comput."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"418","DOI":"10.1016\/j.future.2020.07.042","article-title":"Genetic convolutional neural network for intrusion detection systems","volume":"113","author":"Nguyen","year":"2020","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.knosys.2017.07.005","article-title":"An efficient intrusion detection system based on hypergraph-Genetic algorithm for parameter optimization and feature selection in support vector machine","volume":"134","author":"Raman","year":"2017","journal-title":"Knowl.-Based Syst."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Malhotra, S., Bali, V., and Paliwal, K. (2017, January 12\u201313). Genetic programming and K-nearest neighbour classifier based intrusion detection model. Proceedings of the 2017 7th International Conference on Cloud Computing, Data Science & Engineering-Confluence, Noida, India.","DOI":"10.1109\/CONFLUENCE.2017.7943121"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"9545","DOI":"10.1007\/s00500-021-05889-w","article-title":"Modified whale optimization algorithm for solving unrelated parallel machine scheduling problems","volume":"25","author":"Ewees","year":"2021","journal-title":"Soft Comput."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"3609","DOI":"10.1007\/s12652-019-01611-9","article-title":"Best features based intrusion detection system by RBM model for detecting DDoS in cloud environment","volume":"12","author":"Mayuranathan","year":"2019","journal-title":"J. Ambient. Intell. Humaniz. Comput."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Ghosh, P., Karmakar, A., Sharma, J., and Phadikar, S. (2019). CS-PSO based intrusion detection system in cloud environment. Emerging Technologies in Data Mining and Information Security, Springer.","DOI":"10.1007\/978-981-13-1951-8_24"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"107250","DOI":"10.1016\/j.cie.2021.107250","article-title":"Aquila Optimizer: A novel meta-heuristic optimization Algorithm","volume":"157","author":"Abualigah","year":"2021","journal-title":"Comput. Ind. Eng."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Wang, S., Jia, H., Abualigah, L., Liu, Q., and Zheng, R. (2021). An Improved Hybrid Aquila Optimizer and Harris Hawks Algorithm for Solving Industrial Engineering Optimization Problems. Processes, 9.","DOI":"10.3390\/pr9091551"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Abd Elaziz, M., Dahou, A., Alsaleh, N.A., Elsheikh, A.H., Saba, A.I., and Ahmadein, M. (2021). Boosting COVID-19 Image Classification Using MobileNetV3 and Aquila Optimizer Algorithm. Entropy, 23.","DOI":"10.3390\/e23111383"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"AlRassas, A.M., Al-qaness, M.A., Ewees, A.A., Ren, S., Abd Elaziz, M., Dama\u0161evi\u010dius, R., and Krilavi\u010dius, T. (2021). Optimized ANFIS model using Aquila Optimizer for oil production forecasting. Processes, 9.","DOI":"10.3390\/pr9071194"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"101863","DOI":"10.1016\/j.cose.2020.101863","article-title":"IoT malicious traffic identification using wrapper-based feature selection mechanisms","volume":"94","author":"Shafiq","year":"2020","journal-title":"Comput. Secur."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"4540","DOI":"10.1109\/JIOT.2020.3026660","article-title":"A Multikernel and Metaheuristic Feature Selection Approach for IoT Malware Threat Hunting in the Edge Layer","volume":"8","author":"Haddadpajouh","year":"2020","journal-title":"IEEE Internet Things J."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"3242","DOI":"10.1109\/JIOT.2020.3002255","article-title":"CorrAUC: A malicious bot-IoT traffic detection method in IoT network using machine-learning techniques","volume":"8","author":"Shafiq","year":"2020","journal-title":"IEEE Internet Things J."},{"key":"ref_30","first-page":"63","article-title":"A lightweight Anomaly detection model using SVM for WSNs in IoT through a hybrid feature selection algorithm based on GA and GWO","volume":"7","author":"Davahli","year":"2020","journal-title":"J. Comput. Secur."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.future.2020.05.020","article-title":"Augmented whale feature selection for IoT attacks: Structure, analysis and applications","volume":"112","author":"Mafarja","year":"2020","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s42452-021-04579-4","article-title":"A novel GPU based intrusion detection system using deep autoencoder with Fruitfly optimization","volume":"3","author":"Sekhar","year":"2021","journal-title":"SN Appl. Sci."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1881","DOI":"10.1007\/s10586-020-03229-5","article-title":"Building an efficient intrusion detection system using grasshopper optimization algorithm for anomaly detection","volume":"24","author":"Dwivedi","year":"2021","journal-title":"Clust. Comput."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1016\/j.ins.2021.03.060","article-title":"A novel IoT network intrusion detection approach based on Adaptive Particle Swarm Optimization Convolutional Neural Network","volume":"568","author":"Kan","year":"2021","journal-title":"Inf. Sci."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Alimi, O.A., Ouahada, K., Abu-Mahfouz, A.M., Rimer, S., and Alimi, K.O.A. (2021, January 13\u201315). Intrusion Detection for Water Distribution Systems based on an Hybrid Particle Swarm Optimization with Back Propagation Neural Network. Proceedings of the 2021 IEEE AFRICON, Arusha, Tanzania.","DOI":"10.1109\/AFRICON51333.2021.9570951"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"HajKacem, M.A.B., Moslah, M., and Essoussi, N. (2021). Spark Based Intrusion Detection System Using Practical Swarm Optimization Clustering. Artificial Intelligence and Blockchain for Future Cybersecurity Applications, Springer.","DOI":"10.1007\/978-3-030-74575-2_11"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Nandy, S., Adhikari, M., Khan, M.A., Menon, V.G., and Verma, S. (2021). An intrusion detection mechanism for secured IoMT framework based on swarm-neural network. IEEE J. Biomed. Health Inform.","DOI":"10.1109\/JBHI.2021.3101686"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"012021","DOI":"10.1088\/1742-6596\/1752\/1\/012021","article-title":"Na\u00efve Bayes Classifier and Particle Swarm Optimization Feature Selection Method for Classifying Intrusion Detection System Dataset","volume":"1752","author":"Talita","year":"2021","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Angel, J., Aroyehun, S.T., Tamayo, A., and Gelbukh, A. (2020, January 12\u201313). NLP-CIC at SemEval-2020 Task 9: Analysing sentiment in code-switching language using a simple deep-learning classifier. Proceedings of the Fourteenth Workshop on Semantic Evaluation, Barcelona, Spain.","DOI":"10.18653\/v1\/2020.semeval-1.123"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Fan, H., Du, W., Dahou, A., Ewees, A.A., Yousri, D., Elaziz, M.A., Elsheikh, A.H., Abualigah, L., and Al-qaness, M.A. (2021). Social Media Toxicity Classification Using Deep Learning: Real-World Application UK Brexit. Electronics, 10.","DOI":"10.3390\/electronics10111332"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1080\/10095020.2020.1812445","article-title":"Coarse-to-fine waterlogging probability assessment based on remote sensing image and social media data","volume":"24","author":"Xu","year":"2021","journal-title":"Geo-Spat. Inf. Sci."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"AL-Alimi, D., Shao, Y., Feng, R., Al-qaness, M.A., Elaziz, M.A., and Kim, S. (2019). Multi-scale geospatial object detection based on shallow-deep feature extraction. Remote Sens., 11.","DOI":"10.3390\/rs11212525"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"15364","DOI":"10.1038\/s41598-020-71294-2","article-title":"COVID-19 image classification using deep features and fractional-order marine predators algorithm","volume":"10","author":"Sahlol","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Okewu, E., Misra, S., Maskeli\u016bnas, R., Dama\u0161evi\u010dius, R., and Fernandez-Sanz, L. (2017). Optimizing green computing awareness for environmental sustainability and economic security as a stochastic optimization problem. Sustainability, 9.","DOI":"10.3390\/su9101857"},{"key":"ref_45","first-page":"143","article-title":"Deep neural networks for curbing climate change-induced farmers-herdsmen clashes in a sustainable social inclusion initiative","volume":"14","author":"Okewu","year":"2019","journal-title":"Probl. Ekorozwoju"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1080\/10095020.2020.1718003","article-title":"Deep learning for geometric and semantic tasks in photogrammetry and remote sensing","volume":"23","author":"Heipke","year":"2020","journal-title":"Geo-Spat. Inf. Sci."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1080\/10095020.2020.1847002","article-title":"An investigation of the visual features of urban street vitality using a convolutional neural network","volume":"23","author":"Qi","year":"2020","journal-title":"Geo-Spat. Inf. Sci."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1007\/s00607-020-00869-8","article-title":"An improved YOLO-based road traffic monitoring system","volume":"103","author":"Abbasi","year":"2021","journal-title":"Computing"},{"key":"ref_49","unstructured":"Bochkovskiy, A., Wang, C.Y., and Liao, H.Y.M. (2020). YOLOv4: Optimal Speed and Accuracy of Object Detection. arXiv."},{"key":"ref_50","unstructured":"Nair, V., and Hinton, G.E. (2010, January 21\u201324). Rectified linear units improve restricted boltzmann machines. Proceedings of the 27th International Conference on Machine Learning (ICML-10), Haifa, Israel."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"2180","DOI":"10.1109\/TASLP.2018.2858559","article-title":"Adaptive pooling operators for weakly labeled sound event detection","volume":"26","author":"McFee","year":"2018","journal-title":"IEEE\/ACM Trans. Audio Speech Lang. Process."},{"key":"ref_52","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv."},{"key":"ref_53","first-page":"36","article-title":"Firefly algorithm: Recent advances and applications","volume":"1","author":"Yang","year":"2013","journal-title":"Int. J. Swarm Intell."},{"key":"ref_54","unstructured":"Kennedy, J., and Eberhart, R. (December, January 27). Particle swarm optimization. Proceedings of the ICNN\u201995-International Conference on Neural Networks, Perth, WA, Australia."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1016\/j.advengsoft.2016.01.008","article-title":"The whale optimization algorithm","volume":"95","author":"Mirjalili","year":"2016","journal-title":"Adv. Eng. Softw."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"228","DOI":"10.1016\/j.knosys.2015.07.006","article-title":"Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm","volume":"89","author":"Mirjalili","year":"2015","journal-title":"Knowl.-Based Syst."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"495","DOI":"10.1007\/s00521-015-1870-7","article-title":"Multi-verse optimizer: A nature-inspired algorithm for global optimization","volume":"27","author":"Mirjalili","year":"2016","journal-title":"Neural Comput. Appl."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Yang, X.S. (2010). A new metaheuristic bat-inspired algorithm. Nature Inspired Cooperative Strategies for Optimization (NICSO 2010), Springer.","DOI":"10.1007\/978-3-642-12538-6_6"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.advengsoft.2013.12.007","article-title":"Grey wolf optimizer","volume":"69","author":"Mirjalili","year":"2014","journal-title":"Adv. Eng. Softw."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"779","DOI":"10.1016\/j.future.2019.05.041","article-title":"Towards the development of realistic botnet dataset in the internet of things for network forensic analytics: Bot-iot dataset","volume":"100","author":"Koroniotis","year":"2019","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_61","first-page":"108","article-title":"Toward generating a new intrusion detection dataset and intrusion traffic characterization","volume":"1","author":"Sharafaldin","year":"2018","journal-title":"ICISSp"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1214\/aoms\/1177731944","article-title":"A comparison of alternative tests of significance for the problem of m rankings","volume":"11","author":"Friedman","year":"1940","journal-title":"Ann. Math. Stat."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/1\/140\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:53:42Z","timestamp":1760169222000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/1\/140"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,12,26]]},"references-count":62,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2022,1]]}},"alternative-id":["s22010140"],"URL":"https:\/\/doi.org\/10.3390\/s22010140","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,12,26]]}}}