{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T17:03:55Z","timestamp":1790787835741,"version":"4.1.0"},"reference-count":97,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2022,4,28]],"date-time":"2022-04-28T00:00:00Z","timestamp":1651104000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Council for Scientific and Industrial Research, Pretoria, South Africa"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJERPH"],"abstract":"<jats:p>Wireless Sensor Networks (WSNs) are increasingly deployed in Internet of Things (IoT) systems for applications such as smart transportation, telemedicine, smart health monitoring and fall detection systems for the elderly people. Given that huge amount of data, vital and critical information can be exchanged between the different parts of a WSN, good management and protection schemes are needed to ensure an efficient and secure operation of the WSN. To ensure an efficient management of WSNs, the Software-Defined Wireless Sensor Network (SDWSN) paradigm has been recently introduced in the literature. In the same vein, Intrusion Detection Systems, have been used in the literature to safeguard the security of SDWSN-based IoTs. In this paper, three popular Artificial Intelligence techniques (Decision Tree, Na\u00efve Bayes, and Deep Artificial Neural Network) are trained to be deployed as anomaly detectors in IDSs. It is shown that an IDS using the Decision Tree-based anomaly detector yields the best performances metrics both in the binary classification and in the multinomial classification. Additionally, it was found that an IDS using the Na\u00efve Bayes-based anomaly detector was only adapted for binary classification of intrusions in low memory capacity SDWSN-based IoT (e.g., wearable fitness tracker). Moreover, new state-of-the-art accuracy (binary classification) and F-scores (multinomial classification) were achieved by introducing an end-to-end feature engineering scheme aimed at obtaining 118 features from the 41 features of the Network Security Laboratory-Knowledge Discovery in Databases (NSL-KDD) dataset. The state-of-the-art accuracy was pushed to 0.999777 using the Decision Tree-based anomaly detector. Finally, it was found that the Deep Artificial Neural Network should be expected to become the next default anomaly detector in the light of its current performance metrics and the increasing abundance of training data.<\/jats:p>","DOI":"10.3390\/ijerph19095367","type":"journal-article","created":{"date-parts":[[2022,4,28]],"date-time":"2022-04-28T12:06:01Z","timestamp":1651147561000},"page":"5367","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["Artificial Intelligence-Driven Intrusion Detection in Software-Defined Wireless Sensor Networks: Towards Secure IoT-Enabled Healthcare Systems"],"prefix":"10.3390","volume":"19","author":[{"given":"Shimbi","family":"Masengo Wa Umba","sequence":"first","affiliation":[{"name":"Department of Electrical, Electronic and Computer Engineering, University of Pretoria, Pretoria 0002, South Africa"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6413-3924","authenticated-orcid":false,"given":"Adnan M.","family":"Abu-Mahfouz","sequence":"additional","affiliation":[{"name":"Department of Electrical, Electronic and Computer Engineering, University of Pretoria, Pretoria 0002, South Africa"},{"name":"Council for Scientific and Industrial Research (CSIR), Pretoria 0184, South Africa"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3672-5022","authenticated-orcid":false,"given":"Daniel","family":"Ramotsoela","sequence":"additional","affiliation":[{"name":"Department of Electrical, Electronic and Computer Engineering, University of Pretoria, Pretoria 0002, South Africa"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,4,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1645","DOI":"10.1016\/j.future.2013.01.010","article-title":"Internet of Things (IoT): A vision, architectural elements, and future directions","volume":"29","author":"Gubbi","year":"2013","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_2","unstructured":"Graf, B., King, R.S., Schiller, C., and Roessner, A. (2016, January 21\u201322). Development of an intelligent care cart and new supplies concepts for care homes and hospitals. Proceedings of the ISR 2016: 47th International Symposium on Robotics, Munich, Germany."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Basaklar, T., Tuncel, Y., An, S., and Ogras, U. (2021, January 26\u201328). Wearable Devices and Low-Power Design for Smart Health Applications: Challenges and Opportunities. Proceedings of the 2021 IEEE\/ACM International Symposium on Low Power Electronics and Design (ISLPED), Boston, MA, USA.","DOI":"10.1109\/ISLPED52811.2021.9502491"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1038\/s41528-021-00107-x","article-title":"Advances in healthcare wearable devices","volume":"5","author":"Iqbal","year":"2021","journal-title":"NPJ Flex. Electron."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1038\/s41591-018-0300-7","article-title":"High-performance medicine: The convergence of human and artificial intelligence","volume":"25","author":"Topol","year":"2019","journal-title":"Nat. Med."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"9970","DOI":"10.1109\/JIOT.2021.3050445","article-title":"Edge-Based Hybrid System Implementation for Long-Range Safety and Healthcare IoT Applications","volume":"8","author":"Wu","year":"2021","journal-title":"IEEE Internet Things J."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1873","DOI":"10.1109\/JBHI.2015.2461555","article-title":"An Emerging Era in the Management of Parkinson\u2019s Disease: Wearable Technologies and the Internet of Things","volume":"19","author":"Pasluosta","year":"2015","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Kleinsasser, M., Tharpe, B., Akula, N., Tirumani, H., Sunderraman, R., and Bourgeois, A.G. (2022, January 4\u20138). Detecting and Predicting Sleep Activity using Biometric Sensor Data. Proceedings of the 2022 14th International Conference on COMmunication Systems & NETworkS (COMSNETS), Bangalore, India.","DOI":"10.1109\/COMSNETS53615.2022.9668347"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Kasinathan, P., Pastrone, C., Spirito, M.A., and Vinkovits, M. (2013, January 25). Denial-of-Service detection in 6LoWPAN based Internet of Things. Proceedings of the 2013 IEEE 9th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob), Lyon, France.","DOI":"10.1109\/WiMOB.2013.6673419"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Kreutz, D., Ramos, F.M.V., and Verissimo, P. (2013, January 16). Towards secure and dependable software-defined networks. Proceedings of the Second ACM SIGCOMM Workshop on Hot Topics in Software Defined Networking, Hong Kong, China.","DOI":"10.1145\/2491185.2491199"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Ndiaye, M., Hancke, P.G., and Abu-Mahfouz, M.A. (2017). Software Defined Networking for Improved Wireless Sensor Network Management: A Survey. Sensors, 17.","DOI":"10.3390\/s17051031"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1617","DOI":"10.1109\/SURV.2014.012214.00180","article-title":"A Survey of Software-Defined Networking: Past, Present, and Future of Programmable Networks","volume":"16","author":"Nunes","year":"2014","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1872","DOI":"10.1109\/ACCESS.2017.2666200","article-title":"A Survey on Software-Defined Wireless Sensor Networks: Challenges and Design Requirements","volume":"5","author":"Kobo","year":"2017","journal-title":"IEEE Access"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1109\/MCOM.2002.1024422","article-title":"A survey on sensor networks","volume":"40","author":"Akyildiz","year":"2002","journal-title":"IEEE Commun. Mag."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Liu, A., and Ning, P. (2008, January 2). TinyECC: A configurable library for elliptic curve cryptography in wireless sensor networks. Proceedings of the 7th International Conference on Information Processing in Sensor Networks, St. Louis, MO, USA.","DOI":"10.1109\/IPSN.2008.47"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"e3588","DOI":"10.1002\/ett.3588","article-title":"Efficient controller placement and reelection mechanism in distributed control system for software defined wireless sensor networks","volume":"30","author":"Kobo","year":"2019","journal-title":"Trans. Emerg. Telecommun. Technol."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Dolev, S., and David, S.T. (2014, January 21\u201323). SDN-based private interconnection. Proceedings of the 2014 IEEE 13th International Symposium on Network Computing and Applications, Cambridge, MA, USA.","DOI":"10.1109\/NCA.2014.26"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"10081","DOI":"10.3390\/s140610081","article-title":"Security enhanced user authentication protocol for wireless sensor networks using elliptic curves cryptography","volume":"14","author":"Choi","year":"2014","journal-title":"Sensors"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Lounis, A., Hadjidj, A., Bouabdallah, A., and Challal, Y. (2012, January 31). Secure and scalable cloud-based architecture for e-health wireless sensor networks. Proceedings of the 2012 21st International Conference on Computer Communications and Networks (ICCCN), Munich, Germany.","DOI":"10.1109\/ICCCN.2012.6289252"},{"key":"ref_20","unstructured":"Forouzan, B.A. (2007). Cryptography & Network Security, McGraw-Hill, Inc."},{"key":"ref_21","unstructured":"Hoffstein, J., Pipher, J., Silverman, J.H., and Silverman, J.H. (2008). An Introduction to Mathematical Cryptography, Springer."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1109\/MWC.2008.4599219","article-title":"Anomaly detection in wireless sensor networks","volume":"15","author":"Rajasegarar","year":"2008","journal-title":"IEEE Wirel. Commun."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Gogoi, P., Bhuyan, M.H., Bhattacharyya, D., and Kalita, J.K. (2012, January 6\u20138). Packet and flow based network intrusion dataset. Proceedings of the International Conference on Contemporary Computing, Noida, India.","DOI":"10.1007\/978-3-642-32129-0_34"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Hodo, E., Bellekens, X., Hamilton, A., Dubouilh, P.-L., Iorkyase, E., Tachtatzis, C., and Atkinson, R. (2016, January 17). 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_25","doi-asserted-by":"crossref","first-page":"1368","DOI":"10.1109\/JSEN.2015.2502401","article-title":"BSN-Care: A Secure IoT-Based Modern Healthcare System Using Body Sensor Network","volume":"16","author":"Gope","year":"2016","journal-title":"IEEE Sens. J."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"4307","DOI":"10.1109\/JIOT.2018.2875926","article-title":"Learning-Based Privacy-Aware Offloading for Healthcare IoT With Energy Harvesting","volume":"6","author":"Min","year":"2019","journal-title":"IEEE Internet Things J."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Tang, T.A., Mhamdi, L., McLernon, D., Zaidi, S.A.R., and Ghogho, M. (2016, January 26\u201329). Deep learning approach for Network Intrusion Detection in Software Defined Networking. Proceedings of the 2016 International Conference on Wireless Networks and Mobile Communications (WINCOM), Fez, Morocco.","DOI":"10.1109\/WINCOM.2016.7777224"},{"key":"ref_28","unstructured":"Umba, S.M.W., Abu-Mahfouz, A.M., Ramotsoela, T.D., and Hancke, G.P. (2019, January 12\u201314). A Review of Artificial Intelligence Based Intrusion Detection for Software-Defined Wireless Sensor Networks. Proceedings of the 2019 IEEE 28th International Symposium on Industrial Electronics (ISIE), Vancouver, BC, Canada."},{"key":"ref_29","unstructured":"Umba, S.M.W., Abu-Mahfouz, A.M., Ramotsoela, T.D., and Hancke, G.P. (2019, January 12\u201314). Comparative Study of Artificial Intelligence Based Intrusion Detection for Software-Defined Wireless Sensor Networks. Proceedings of the 2019 IEEE 28th International Symposium on Industrial Electronics (ISIE), Vancouver, BC, Canada."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"343","DOI":"10.1109\/SURV.2010.032210.00054","article-title":"An overview of IP flow-based intrusion detection","volume":"12","author":"Sperotto","year":"2010","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1016\/j.bjp.2013.10.014","article-title":"Combining OpenFlow and sFlow for an effective and scalable anomaly detection and mitigation mechanism on SDN environments","volume":"62","author":"Giotis","year":"2014","journal-title":"Comput. Netw."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Boero, L., Marchese, M., and Zappatore, S. (2017, January 4\u20138). Support Vector Machine Meets Software Defined Networking in IDS Domain. Proceedings of the 2017 29th International Teletraffic Congress (ITC 29), Genoa, Italy.","DOI":"10.23919\/ITC.2017.8065806"},{"key":"ref_33","first-page":"258","article-title":"Network intrusion detection using naive bayes","volume":"7","author":"Panda","year":"2007","journal-title":"Int. J. Comput. Sci. Netw. Secur."},{"key":"ref_34","unstructured":"MeeraGandhi, G., Appavoo, K., and Srivasta, S. (2010). Effective network intrusion detection using classifiers decision trees and decision rules. Int. J. Adv. Netw. Appl., 686\u2013692."},{"key":"ref_35","unstructured":"Ioannis, K., Dimitriou, T., and Freiling, F.C. (2007, January 1\u20134). Towards intrusion detection in wireless sensor networks. Proceedings of the 13th European Wireless Conference, Paris, France."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"2986","DOI":"10.1109\/TC.2016.2519914","article-title":"Building an Intrusion Detection System Using a Filter-Based Feature Selection Algorithm","volume":"65","author":"Ambusaidi","year":"2016","journal-title":"IEEE Trans. Comput."},{"key":"ref_37","unstructured":"Ryan, J., Lin, M.-J., and Miikkulainen, R. (1997, January 1\u20136). Intrusion detection with neural networks. In Proceeding of the 1997 Conference on Advances in Neural Information Processing Systems, Denver, CO, USA."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"2089","DOI":"10.1007\/s00366-019-00930-x","article-title":"A novel approach to predict shear strength of tilted angle connectors using artificial intelligence techniques","volume":"37","author":"Shariati","year":"2021","journal-title":"Eng. Comput."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Daniels, J., Herrero, P., and Georgiou, P. (2022). A Deep Learning Framework for Automatic Meal Detection and Estimation in Artificial Pancreas Systems. Sensors, 22.","DOI":"10.3390\/s22020466"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1016\/S2213-8587(21)00267-9","article-title":"Open-source automated insulin delivery: International consensus statement and practical guidance for health-care professionals","volume":"10","author":"Braune","year":"2022","journal-title":"Lancet Diabetes Endocrinol."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"2181","DOI":"10.1109\/COMST.2014.2326417","article-title":"A survey on software-defined network and openflow: From concept to implementation","volume":"16","author":"Hu","year":"2014","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Ramotsoela, D., Abu-Mahfouz, A., and Hancke, G.P. (2018). A Survey of Anomaly Detection in Industrial Wireless Sensor Networks with Critical Water System Infrastructure as a Case Study, MDPI.","DOI":"10.3390\/s18082491"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"\u00c5kerberg, J., Gidlund, M., and Bj\u00f6rkman, M. (2011, January 6). Future research challenges in wireless sensor and actuator networks targeting industrial automation. Proceedings of the 2011 9th IEEE International Conference on Industrial Informatics, Lisbon, Portugal.","DOI":"10.1109\/INDIN.2011.6034912"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1","DOI":"10.2514\/1.48403","article-title":"Cooperative Control of Multiple Uninhabited Aerial Vehicles for Monitoring and Fighting Wildfires","volume":"8","author":"Kumar","year":"2011","journal-title":"J. Aerosp. Comput. Inf. Commun."},{"key":"ref_45","unstructured":"(2019, October 15). KDD Dataset 1999. Available online: http:\/\/kdd.ics.uci.edu\/databases\/kddcup99\/."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Tavallaee, M., Bagheri, E., Lu, W., and Ghorbani, A.A. (2009, January 8\u201310). A detailed analysis of the KDD CUP 99 data set. Proceedings of the Symposium on Computational Intelligence for Security and Defense Applications, Ottawa, ON, Canada.","DOI":"10.1109\/CISDA.2009.5356528"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Shalev-Shwartz, S., and Ben-David, S. (2014). Understanding Machine Learning: From Theory to Algorithms, Cambridge University Press.","DOI":"10.1017\/CBO9781107298019"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Goutte, C., and Gaussier, E. (2005, January 21\u201323). A probabilistic interpretation of precision, recall and F-score, with implication for evaluation. Proceedings of the European Conference on Information Retrieval, Santiago de Compostela, Spain.","DOI":"10.1007\/978-3-540-31865-1_25"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1109\/MPRV.2008.6","article-title":"Denial-of-service in wireless sensor networks: Attacks and defenses","volume":"7","author":"Raymond","year":"2008","journal-title":"IEEE Pervasive Comput."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"4554","DOI":"10.1109\/TVT.2009.2019663","article-title":"Multi-user broadcast authentication in wireless sensor networks","volume":"58","author":"Ren","year":"2009","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Pongle, P., and Chavan, G. (2015, January 8\u201310). A survey: Attacks on RPL and 6LoWPAN in IoT. Proceedings of the 2015 International conference on pervasive computing (ICPC), Pune, India.","DOI":"10.1109\/PERVASIVE.2015.7087034"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"356","DOI":"10.1109\/TNSE.2018.2881152","article-title":"Design and analysis of probing route to defense sink-hole attacks for Internet of Things security","volume":"7","author":"Liu","year":"2018","journal-title":"IEEE Trans. Netw. Sci. Eng."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"266","DOI":"10.1109\/SURV.2013.050113.00191","article-title":"A survey of intrusion detection systems in wireless sensor networks","volume":"16","author":"Butun","year":"2013","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1016\/j.ipm.2004.08.006","article-title":"Information gain and divergence-based feature selection for machine learning-based text categorization","volume":"42","author":"Lee","year":"2006","journal-title":"Inf. Process. Manag."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"1024","DOI":"10.1016\/j.knosys.2011.04.014","article-title":"A two-stage feature selection method for text categorization by using information gain, principal component analysis and genetic algorithm","volume":"24","year":"2011","journal-title":"Knowl.-Based Syst."},{"key":"ref_56","unstructured":"Zhang, C., Vinyals, O., Munos, R., and Bengio, S. (2018). A study on overfitting in deep reinforcement learning. arXiv."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"326","DOI":"10.1145\/212094.212114","article-title":"Overfitting and undercomputing in machine learning","volume":"27","author":"Dietterich","year":"1995","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1023\/A:1022604100933","article-title":"An empirical comparison of pruning methods for decision tree induction","volume":"4","author":"Mingers","year":"1989","journal-title":"Mach. Learn."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Bradford, J.P., Kunz, C., Kohavi, R., Brunk, C., and Brodley, C.E. (1998, January 21\u201323). Pruning decision trees with misclassification costs. Proceedings of the European Conference on Machine Learning, Chemnitz, Germany.","DOI":"10.1007\/BFb0026682"},{"key":"ref_60","unstructured":"Goodfellow, I., Bengio, Y., Courville, A., and Bengio, Y. (2016). Deep Learning, MIT Press Cambridge."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Gnedenko, B.V. (2018). Theory of Probability, Routledge.","DOI":"10.1201\/9780203718964"},{"key":"ref_62","unstructured":"DeGroot, M.H., and Schervish, M.J. (2012). Probability and Statistics, Pearson Education, Inc."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Deisenroth, M.P., Faisal, A.A., and Ong, C.S. (2020). Mathematics for Machine Learning, Cambridge University Press.","DOI":"10.1017\/9781108679930"},{"key":"ref_64","first-page":"29","article-title":"Kernel functions for machine learning applications","volume":"3","author":"Souza","year":"2010","journal-title":"Creat. Commons Attrib.-Noncommer.-Share Alike"},{"key":"ref_65","unstructured":"Ramachandran, P., Zoph, B., and Le, Q.V. (2017). Searching for activation functions. arXiv."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Han, J., and Moraga, C. (1995, January 7\u20139). The influence of the sigmoid function parameters on the speed of backpropagation learning. Proceedings of the International Workshop on Artificial Neural Networks, Malaga-Torremolinos, Spain.","DOI":"10.1007\/3-540-59497-3_175"},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Duan, K., Keerthi, S.S., Chu, W., Shevade, S.K., and Poo, A.N. (2003, January 11\u201313). Multi-category classification by soft-max combination of binary classifiers. Proceedings of the International Workshop on Multiple Classifier Systems, Guildford, UK.","DOI":"10.1007\/3-540-44938-8_13"},{"key":"ref_68","unstructured":"Collobert, R., Bengio, S., and Mari\u00e9thoz, J. (2002). Torch: A Modular Machine Learning Software Library, Idiap."},{"key":"ref_69","unstructured":"Xu, B., Wang, N., Chen, T., and Li, M. (2015). Empirical evaluation of rectified activations in convolutional network. arXiv."},{"key":"ref_70","unstructured":"Clevert, D.-A., Unterthiner, T., and Hochreiter, S. (2015). Fast and accurate deep network learning by exponential linear units (elus). arXiv."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1109\/72.279181","article-title":"Learning long-term dependencies with gradient descent is difficult","volume":"5","author":"Bengio","year":"1994","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_72","unstructured":"Ng, A. (2017). Machine Learning Yearning, 2016, Stanford University Press."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.neunet.2014.09.003","article-title":"Deep learning in neural networks: An overview","volume":"61","author":"Schmidhuber","year":"2015","journal-title":"Neural Netw."},{"key":"ref_74","unstructured":"Ruder, S. (2016). An overview of gradient descent optimization algorithms. arXiv."},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Bottou, L. (2012). Stochastic gradient descent tricks. Neural Networks: Tricks of the Trade, Springer.","DOI":"10.1007\/978-3-642-35289-8_25"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1038\/323533a0","article-title":"Learning representations by back-propagating errors","volume":"323","author":"Rumelhart","year":"1986","journal-title":"Nature"},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Hertz, J.A. (2018). Introduction to the Theory of Neural Computation, CRC Press.","DOI":"10.1201\/9780429499661"},{"key":"ref_78","first-page":"1289","article-title":"An extensive empirical study of feature selection metrics for text classification","volume":"3","author":"Forman","year":"2003","journal-title":"J. Mach. Learn. Res."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1016\/j.protcy.2012.02.068","article-title":"A comparative study on the effect of feature selection on classification accuracy","volume":"1","author":"Karabulut","year":"2012","journal-title":"Procedia Technol."},{"key":"ref_80","unstructured":"(2019, November 21). Kdd-Cup-99-Analysis-Machine-Learning-Python. Available online: https:\/\/github.com\/chadlimedamine\/kdd-cup-99-Analysis-machine-learning-python."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"e28210","DOI":"10.1371\/journal.pone.0028210","article-title":"The influence of feature selection methods on accuracy, stability and interpretability of molecular signatures","volume":"6","author":"Haury","year":"2011","journal-title":"PLoS ONE"},{"key":"ref_82","doi-asserted-by":"crossref","unstructured":"Wang, W., Zhang, X., Gombault, S., and Knapskog, S.J. (2009, January 14\u201316). Attribute normalization in network intrusion detection. Proceedings of the 2009 10th International Symposium on Pervasive Systems, Algorithms, and Networks, Kaoshiung, Taiwan.","DOI":"10.1109\/I-SPAN.2009.49"},{"key":"ref_83","doi-asserted-by":"crossref","unstructured":"Chan, T.F., Golub, G.H., and LeVeque, R.J. (1982, January 1). Updating formulae and a pairwise algorithm for computing sample variances. Proceedings of the COMPSTAT 1982 5th Symposium, Toulouse, France.","DOI":"10.1007\/978-3-642-51461-6_3"},{"key":"ref_84","unstructured":"(2019, November 23). Sklearn. Available online: https:\/\/scikit-learn.org\/stable\/."},{"key":"ref_85","unstructured":"Chollet, F. (2017). Deep Learning with Python, Manning Publications Co."},{"key":"ref_86","doi-asserted-by":"crossref","unstructured":"Kolosnjaji, B., Zarras, A., Webster, G., and Eckert, C. (2016, January 5\u20138). Deep learning for classification of malware system call sequences. Proceedings of the Australasian Joint Conference on Artificial Intelligence, Hobart, Australia.","DOI":"10.1007\/978-3-319-50127-7_11"},{"key":"ref_87","unstructured":"Zhang, C., Bengio, S., Hardt, M., Recht, B., and Vinyals, O. (2016). Understanding deep learning requires rethinking generalization. arXiv."},{"key":"ref_88","doi-asserted-by":"crossref","unstructured":"Ugwuanyi, S., Paul, G., and Irvine, J. (2021). Survey of IoT for Developing Countries: Performance Analysis of LoRaWAN and Cellular NB-IoT Networks. Electronics, 10.","DOI":"10.3390\/electronics10182224"},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1109\/MIM.2009.4811133","article-title":"Energy management in wireless sensor networks with energy-hungry sensors","volume":"12","author":"Alippi","year":"2009","journal-title":"IEEE Instrum. Meas. Mag."},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"3207","DOI":"10.1109\/TWC.2014.042114.130347","article-title":"Low-latency SINR-based data gathering in wireless sensor networks","volume":"13","author":"Gong","year":"2014","journal-title":"IEEE Trans. Wirel. Commun."},{"key":"ref_91","doi-asserted-by":"crossref","unstructured":"Kraft, D., Srinivasan, K., and Bieber, G. (2020). Deep Learning Based Fall Detection Algorithms for Embedded Systems, Smartwatches, and IoT Devices Using Accelerometers. Technologies, 8.","DOI":"10.3390\/technologies8040072"},{"key":"ref_92","doi-asserted-by":"crossref","unstructured":"Wei, J., Wang, Z., and Xing, X. (2021). A Wireless High-Sensitivity Fetal Heart Sound Monitoring System. Sensors, 21.","DOI":"10.3390\/s21010193"},{"key":"ref_93","unstructured":"Kubat, M., and Matwin, S. (1997, January 8\u201312). Addressing the curse of imbalanced training sets: One-sided selection. Proceedings of the 14th International Conference on Machine Learning, San Francisco, CA, USA."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1016\/j.ins.2013.04.016","article-title":"Adjusted F-measure and kernel scaling for imbalanced data learning","volume":"257","author":"Maratea","year":"2014","journal-title":"Inf. Sci."},{"key":"ref_95","doi-asserted-by":"crossref","unstructured":"Taylor, L., and Nitschke, G. (2017). Improving deep learning using generic data augmentation. arXiv.","DOI":"10.1109\/SSCI.2018.8628742"},{"key":"ref_96","doi-asserted-by":"crossref","unstructured":"Cubuk, E.D., Zoph, B., Mane, D., Vasudevan, V., and Le, Q.V. (2019, January 15\u201320). Autoaugment: Learning augmentation strategies from data. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00020"},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"1415","DOI":"10.1109\/TPDS.2011.20","article-title":"Traffic-aware relay node deployment: Maximizing lifetime for data collection wireless sensor networks","volume":"22","author":"Wang","year":"2011","journal-title":"IEEE Trans. Parallel Distrib. Syst."}],"container-title":["International Journal of Environmental Research and Public Health"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1660-4601\/19\/9\/5367\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:02:49Z","timestamp":1760137369000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1660-4601\/19\/9\/5367"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4,28]]},"references-count":97,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2022,5]]}},"alternative-id":["ijerph19095367"],"URL":"https:\/\/doi.org\/10.3390\/ijerph19095367","relation":{},"ISSN":["1660-4601"],"issn-type":[{"value":"1660-4601","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,4,28]]}}}