{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T06:31:42Z","timestamp":1782887502809,"version":"3.54.5"},"reference-count":59,"publisher":"Springer Science and Business Media LLC","issue":"20","license":[{"start":{"date-parts":[[2023,4,7]],"date-time":"2023-04-07T00:00:00Z","timestamp":1680825600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,4,7]],"date-time":"2023-04-07T00:00:00Z","timestamp":1680825600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2023,7]]},"DOI":"10.1007\/s00521-023-08511-2","type":"journal-article","created":{"date-parts":[[2023,4,7]],"date-time":"2023-04-07T17:02:36Z","timestamp":1680886956000},"page":"15201-15220","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["Intelligent IDS in wireless sensor networks using deep fuzzy convolutional neural network"],"prefix":"10.1007","volume":"35","author":[{"given":"Shalini","family":"Subramani","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1990-7978","authenticated-orcid":false,"given":"M.","family":"Selvi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,4,7]]},"reference":[{"key":"8511_CR1","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1007\/s11277-020-07705-4","volume":"116","author":"M Selvi","year":"2021","unstructured":"Selvi M, Santhosh Kumar SVN, Ganapathy S, Ayyanar A, Khanna Nehemiah H, Kannan A (2021) An energy efficient clustered gravitational and fuzzy based routing algorithm in WSNs. Wirel Pers Commun 116:61\u201390","journal-title":"Wirel Pers Commun"},{"issue":"5","key":"8511_CR2","doi-asserted-by":"crossref","first-page":"888","DOI":"10.1049\/iet-com.2019.0172","volume":"14","author":"P Nancy","year":"2020","unstructured":"Nancy P, Muthurajkumar S, Ganapathy S, Santhosh Kumar SVN, Selvi M, Arputharaj K (2020) Intrusion detection using dynamic feature selection and fuzzy temporal decision tree classification for wireless sensor networks. IET Commun 14(5):888\u2013895","journal-title":"IET Commun"},{"issue":"20","key":"8511_CR3","doi-asserted-by":"crossref","first-page":"4383","DOI":"10.3390\/s19204383","volume":"19","author":"M Alqahtani","year":"2019","unstructured":"Alqahtani M, Gumaei A, Mathkour H, Ben Ismail MM (2019) A genetic-based extreme gradient boosting model for detecting intrusions in wireless sensor networks. Sensors 19(20):4383","journal-title":"Sensors"},{"issue":"12","key":"8511_CR4","doi-asserted-by":"crossref","first-page":"7859","DOI":"10.1007\/s00521-019-04187-9","volume":"32","author":"S Kaur","year":"2020","unstructured":"Kaur S, Singh M (2020) Hybrid intrusion detection and signature generation using Deep Recurrent Neural Networks. Neural Comput Appl 32(12):7859\u20137877","journal-title":"Neural Comput Appl"},{"key":"8511_CR5","doi-asserted-by":"crossref","first-page":"107247","DOI":"10.1016\/j.comnet.2020.107247","volume":"174","author":"Y Zhou","year":"2020","unstructured":"Zhou Y, Cheng G, Jiang S, Dai M (2020) Building an efficient intrusion detection system based on feature selection and ensemble classifier. Comput Netw 174:107247","journal-title":"Comput Netw"},{"key":"8511_CR6","first-page":"387","volume":"20","author":"SN Mighan","year":"2021","unstructured":"Mighan SN, Kahani M (2021) A novel scalable intrusion detection system based on deep learning. Int J InfSecur 20:387\u2013403","journal-title":"Int J InfSecur"},{"issue":"22","key":"8511_CR7","doi-asserted-by":"crossref","first-page":"17265","DOI":"10.1007\/s00500-020-05017-0","volume":"24","author":"B Riyaz","year":"2020","unstructured":"Riyaz B, Ganapathy S (2020) A deep learning approach for effective intrusion detection in wireless networks using CNN. Soft Comput 24(22):17265\u201317278","journal-title":"Soft Comput"},{"key":"8511_CR8","doi-asserted-by":"publisher","unstructured":"Zheng S (2021) network intrusion detection model based on convolutional neural network. In: proceedings of the IEEE conference on advanced information technology, electronic and automation control conference (IAEAC), pp 634\u2013637. https:\/\/doi.org\/10.1109\/IAEAC50856.2021.9390930","DOI":"10.1109\/IAEAC50856.2021.9390930"},{"key":"8511_CR9","doi-asserted-by":"crossref","DOI":"10.1016\/j.ijleo.2022.170419","volume":"273","author":"S Subramani","year":"2023","unstructured":"Subramani S, Selvi M (2023) Multi-objective PSO based feature selection for intrusion detection in IoT based wireless sensor networks. Optik 273:170419","journal-title":"Optik"},{"key":"8511_CR10","doi-asserted-by":"publisher","first-page":"102317","DOI":"10.1016\/j.adhoc.2020.102317","volume":"110","author":"P Maheshwari","year":"2021","unstructured":"Maheshwari P, Sharma AK, Verma K (2021) Energy efficient cluster based routing protocol for WSN using butterfly optimization algorithm and ant colony optimization. Ad Hoc Netw 110:102317. https:\/\/doi.org\/10.1016\/j.adhoc.2020.102317","journal-title":"Ad Hoc Netw"},{"issue":"1","key":"8511_CR11","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1007\/s11276-015-0951-y","volume":"22","author":"PK Batra","year":"2016","unstructured":"Batra PK, Kant K (2016) LEACH-MAC: a new cluster head selection algorithm for wireless sensor networks. Wirel Netw 22(1):49\u201360. https:\/\/doi.org\/10.1007\/s11276-015-0951-y","journal-title":"Wirel Netw"},{"issue":"2","key":"8511_CR12","doi-asserted-by":"publisher","first-page":"391","DOI":"10.1007\/s40747-020-00137-4","volume":"6","author":"R Pal","year":"2020","unstructured":"Pal R, Yadav S, Aarti KR (2020) EEWC: energy-efficient weighted clustering method based on genetic algorithm for HWSNs. Complex Intell Syst 6(2):391\u2013400. https:\/\/doi.org\/10.1007\/s40747-020-00137-4","journal-title":"Complex Intell Syst"},{"key":"8511_CR13","doi-asserted-by":"publisher","unstructured":"Bhushan S, Pal R, Antoshchuk SG (2018) Energy efficient clustering protocol for heterogeneous wireless sensor network: a hybrid approach using GA and K-means. In: proceedings of the IEEE conference on data stream min process DSMP, pp 381\u2013385. https:\/\/doi.org\/10.1109\/DSMP.2018.8478538","DOI":"10.1109\/DSMP.2018.8478538"},{"key":"8511_CR14","doi-asserted-by":"crossref","DOI":"10.1016\/j.cosrev.2020.100342","volume":"39","author":"A Singh","year":"2021","unstructured":"Singh A, Sharma S, Singh J (2021) Nature-inspired algorithms for wireless sensor networks: a comprehensive survey. Comput Sci Rev 39:100342","journal-title":"Comput Sci Rev"},{"key":"8511_CR15","doi-asserted-by":"publisher","unstructured":"Cao Y, Zhao Y, Dai, F (2019) Node localization in wireless sensor networks based on quantum annealing algorithm and edge computing. In: proceedings of the IEEE conference on Internet of Things (iThings) and IEEE green computing and communications (greencom) and ieee cyber, physical and social computing (cpscom) and ieee smart data (SmartData), pp 564\u2013568). https:\/\/doi.org\/10.1109\/iThings\/GreenCom\/CPSCom\/SmartData.2019.00112","DOI":"10.1109\/iThings\/GreenCom\/CPSCom\/SmartData.2019.00112"},{"key":"8511_CR16","doi-asserted-by":"crossref","first-page":"1475","DOI":"10.1007\/s11277-019-06155-x","volume":"105","author":"M Selvi","year":"2019","unstructured":"Selvi M, Thangaramya K, Sannasi G, Kulothungan K, Khannah Nehemiah H, Kannan A (2019) An energy aware trust based secure routing algorithm for effective communication in wireless sensor networks. Wirel Pers Commun 105:1475\u20131490","journal-title":"Wirel Pers Commun"},{"key":"8511_CR17","doi-asserted-by":"publisher","unstructured":"Ashwini B A, Manivannan S S (2020) Supervised machine learning classification algorithmic approach for finding anomaly type of intrusion detection in wireless sensor network. Opt. Mem. Neural Netw Inf Opt 29(3), 244\u2013256. https:\/\/doi.org\/10.3103\/S1060992X20030029","DOI":"10.3103\/S1060992X20030029"},{"key":"8511_CR18","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1007\/978-981-16-8512-5_5","volume":"355","author":"B Bhushan","year":"2022","unstructured":"Bhushan B (2022) Intrusion detection system (IDS) for security enhancement in wireless sensing applications. Proc Innov Electron Commun Eng (ICIECE) 355:39\u201349","journal-title":"Proc Innov Electron Commun Eng (ICIECE)"},{"issue":"2","key":"8511_CR19","doi-asserted-by":"crossref","first-page":"1967","DOI":"10.1007\/s11277-021-08976-1","volume":"122","author":"S Madhu","year":"2022","unstructured":"Madhu S, Prasad RK, Ramotra P, Edla DR, Lipare A (2022) A location-less energy efficient algorithm for load balanced clustering in wireless sensor networks. Wirel Pers Commun 122(2):1967\u20131985","journal-title":"Wirel Pers Commun"},{"key":"8511_CR20","doi-asserted-by":"crossref","DOI":"10.1016\/j.adhoc.2022.102872","volume":"132","author":"C Zhao","year":"2022","unstructured":"Zhao C, Wu Q, Lin D, Zhang Z, Zhang Y, Kong L, Guan YL (2022) An energy-balanced unequal clustering approach for circular wireless sensor networks. Ad Hoc Netw 132:102872","journal-title":"Ad Hoc Netw"},{"key":"8511_CR21","doi-asserted-by":"crossref","unstructured":"Muthurajkumar S, Ganapathy S, Vijayalakshmi M, Kannan A (2015) Secured temporal log management techniques for cloud. In: proceedings of the IEEE conference on Procedia Comput Sci 46, 589\u2013595.","DOI":"10.1016\/j.procs.2015.02.098"},{"issue":"7","key":"8511_CR22","doi-asserted-by":"crossref","first-page":"4513","DOI":"10.1007\/s11276-021-02748-2","volume":"27","author":"S Rajasoundaran","year":"2021","unstructured":"Rajasoundaran S, Santhosh Kumar SVN, Selvi M, Ganapathy S, Rakesh R, Kannan A (2021) Machine learning based volatile block chain construction for secure routing in decentralized military sensor networks. Wirel Netw 27(7):4513\u20134534","journal-title":"Wirel Netw"},{"issue":"4","key":"8511_CR23","first-page":"746","volume":"16","author":"B Senthilnayaki","year":"2019","unstructured":"Senthilnayaki B, Venkatalakshmi K, Kannan A (2019) Intrusion detection system using fuzzy rough set feature selection and modified KNN classifier. Int Arab J Inf Technol 16(4):746\u2013753","journal-title":"Int Arab J Inf Technol"},{"key":"8511_CR24","doi-asserted-by":"crossref","first-page":"21954","DOI":"10.1109\/ACCESS.2017.2762418","volume":"5","author":"C Yin","year":"2017","unstructured":"Yin C, Zhu Y, Fei J, He X (2017) A deep learning approach for intrusion detection using recurrent neural networks. IEEE Access 5:21954\u201321961","journal-title":"IEEE Access"},{"issue":"6","key":"8511_CR25","doi-asserted-by":"crossref","first-page":"4149","DOI":"10.1007\/s11276-020-02321-3","volume":"26","author":"S Hosseini","year":"2020","unstructured":"Hosseini S (2020) A new machine learning method consisting of GA-LR and ANN for attack detection. Wirel Networks 26(6):4149\u20134162","journal-title":"Wirel Networks"},{"key":"8511_CR26","doi-asserted-by":"crossref","DOI":"10.1016\/j.swevo.2020.100772","volume":"60","author":"BM Sahoo","year":"2021","unstructured":"Sahoo BM, Pandey HM, Amgoth T (2021) GAPSO-H: a hybrid approach towards optimizing the cluster based routing in wireless sensor network. Swarm Evol Comput 60:100772","journal-title":"Swarm Evol Comput"},{"key":"8511_CR27","first-page":"1","volume":"2023","author":"SVN Santhosh Kumar","year":"2023","unstructured":"Santhosh Kumar SVN, Selvi M, Kannan A (2023) A comprehensive survey on machine learning-based intrusion detection systems for secure communication in internet of things. Comput Intell Neurosci 2023:1\u201324","journal-title":"Comput Intell Neurosci"},{"issue":"1","key":"8511_CR28","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-021-99269-x","volume":"12","author":"A Singh","year":"2022","unstructured":"Singh A, Amutha J, Nagar J, Sharma S, Lee CC (2022) AutoML-ID: automated machine learning model for intrusion detection using wireless sensor network. Sci Rep 12(1):1\u201314","journal-title":"Sci Rep"},{"issue":"2","key":"8511_CR29","doi-asserted-by":"crossref","first-page":"264","DOI":"10.23919\/JCN.2022.000002","volume":"24","author":"S Subbiah","year":"2022","unstructured":"Subbiah S, Anbananthen KSM, Thangaraj S, Kannan S, Chelliah D (2022) Intrusion detection technique in wireless sensor network using grid search random forest with Boruta feature selection algorithm. J Commun Netw 24(2):264\u2013273","journal-title":"J Commun Netw"},{"key":"8511_CR30","first-page":"1","volume":"2019","author":"J Ren","year":"2019","unstructured":"Ren J, Guo J, Qian W, Yuan H, Hao X, Jingjing H (2019) Building an effective intrusion detection system by using hybrid data optimization based on machine learning algorithms. Secur Commun Networks 2019:1\u201311","journal-title":"Secur Commun Networks"},{"issue":"3","key":"8511_CR31","doi-asserted-by":"crossref","first-page":"1281","DOI":"10.1007\/s12652-019-01382-3","volume":"11","author":"J Bhola","year":"2020","unstructured":"Bhola J, Soni S, Cheema GK (2020) Genetic algorithm based optimized leach protocol for energy efficient wireless sensor networks. J Ambient Intell Humaniz Comput 11(3):1281\u20131288","journal-title":"J Ambient Intell Humaniz Comput"},{"key":"8511_CR32","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.knosys.2017.07.005","volume":"134","author":"MR Gauthama Raman","year":"2017","unstructured":"Gauthama Raman MR, Somu N, Kirthivasan K, Liscano R, Shankar Sriram VS (2017) An efficient intrusion detection system based on hypergraph\u2014genetic algorithm for parameter optimization and feature selection in support vector machine. Knowledge-Based Syst 134:1\u201312","journal-title":"Knowledge-Based Syst"},{"issue":"7","key":"8511_CR33","doi-asserted-by":"crossref","first-page":"989","DOI":"10.3390\/electronics11070989","volume":"11","author":"L Dash","year":"2022","unstructured":"Dash L, Pattanayak BK, Mishra SK, Sahoo KS, Jhanjhi NZ, Baz M, Masud M (2022) A Data aggregation approach exploiting spatial and temporal correlation among sensor data in wireless sensor networks. Electronics 11(7):989","journal-title":"Electronics"},{"key":"8511_CR34","doi-asserted-by":"crossref","DOI":"10.1016\/j.comnet.2020.107168","volume":"173","author":"S Hosseini","year":"2020","unstructured":"Hosseini S, Zade BMH (2020) New hybrid method for attack detection using combination of evolutionary algorithms, SVM, and ANN. Comput Netw 173:107168","journal-title":"Comput Netw"},{"issue":"3","key":"8511_CR35","first-page":"427","volume":"22","author":"X Zhao","year":"2020","unstructured":"Zhao X, Huang G, Mousoli R (2020) A multi-threading solution to multimedia traffic in NIDS based on hybrid genetic algorithm. Int J Netw Secur 22(3):427\u2013436","journal-title":"Int J Netw Secur"},{"key":"8511_CR36","first-page":"1137","volume":"20","author":"CM Hsu","year":"2020","unstructured":"Hsu CM, Azhari MZ, Hsieh HY, Prakosa SW, Leu JS (2020) Robust network intrusion detection scheme using long-short term memory based convolutional neural networks. Mob Networks Appl 20:1137\u20131144","journal-title":"Mob Networks Appl"},{"issue":"2","key":"8511_CR37","doi-asserted-by":"crossref","first-page":"545","DOI":"10.1007\/s11277-020-07882-2","volume":"117","author":"S Prithi","year":"2021","unstructured":"Prithi S (2021) Sumathi S (2021) Automata based hybrid PSO\u2013GWO algorithm for secured energy efficient optimal routing in wireless sensor network. Wirel Pers Commun 117(2):545\u2013559","journal-title":"Wirel Pers Commun"},{"issue":"21","key":"8511_CR38","doi-asserted-by":"crossref","first-page":"16483","DOI":"10.1007\/s00500-020-04955-z","volume":"24","author":"K Thangaramya","year":"2020","unstructured":"Thangaramya K, Kulothungan K, Indira Gandhi S, Selvi M, Santhosh Kumar SVN, K. Arputharaj K, (2020) Intelligent fuzzy rule-based approach with outlier detection for secured routing in WSN. Soft Comput 24(21):16483\u201316497","journal-title":"Soft Comput"},{"issue":"s5","key":"8511_CR39","doi-asserted-by":"crossref","first-page":"10839","DOI":"10.1007\/s10586-017-1191-y","volume":"22","author":"M Selvi","year":"2019","unstructured":"Selvi M, Velvizhy P, Ganapathy S, Nehemiah HK, Kannan A (2019) A rule based delay constrained energy efficient routing technique for wireless sensor networks. Cluster Comput 22(s5):10839\u201310848","journal-title":"Cluster Comput"},{"issue":"12","key":"8511_CR40","doi-asserted-by":"crossref","first-page":"5414","DOI":"10.35940\/ijitee.L3790081219","volume":"8","author":"SVN Santhosh Kumar","year":"2019","unstructured":"Santhosh Kumar SVN, Selvi M, Gayathri A, Ruby D, Kannan A (2019) Energy efficient rule based intelligent routing using fitness functions in wireless sensor networks. Int J Innov Technol Explor Eng 8(12):5414\u20135420","journal-title":"Int J Innov Technol Explor Eng"},{"key":"8511_CR41","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1016\/j.comnet.2019.01.024","volume":"151","author":"K Thangaramya","year":"2019","unstructured":"Thangaramya K, Kulothungan K, Logambigai R, Selvi M, Ganapathy S, Kannan A (2019) Energy aware cluster and neuro-fuzzy based routing algorithm for wireless sensor networks in IoT. Comput Netw 151:211\u2013223","journal-title":"Comput Netw"},{"key":"8511_CR42","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2021.107132","volume":"226","author":"P Rajesh Kanna","year":"2021","unstructured":"Rajesh Kanna P, Santhi P (2021) Unified deep learning approach for efficient intrusion detection system using integrated spatial-temporal features. Knowledge-Based Syst 226:107132","journal-title":"Knowledge-Based Syst"},{"issue":"4","key":"8511_CR43","doi-asserted-by":"crossref","first-page":"503","DOI":"10.1007\/s10776-020-00495-3","volume":"27","author":"X Wang","year":"2020","unstructured":"Wang X, Yin S, Li H, Wang J, Teng L (2020) A network intrusion detection method based on deep multi-scale convolutional neural network. Int J Wirel Inf 27(4):503\u2013517","journal-title":"Int J Wirel Inf"},{"key":"8511_CR44","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1016\/j.jnca.2017.03.018","volume":"87","author":"W Haider","year":"2017","unstructured":"Haider W, Hu J, Slay J, Turnbull BP, Xie Y (2017) Generating realistic intrusion detection system dataset based on fuzzy qualitative modeling. J Netw Comput Appl 87:185\u2013192","journal-title":"J Netw Comput Appl"},{"issue":"4","key":"8511_CR45","first-page":"457","volume":"34","author":"S Moraboena","year":"2020","unstructured":"Moraboena S, Ketepalli G, Ragam P (2020) A deep learning approach to network intrusion detection using deep autoencoder. Rev d\u2019Intelligence Artif 34(4):457\u2013463","journal-title":"Rev d\u2019Intelligence Artif"},{"key":"8511_CR46","doi-asserted-by":"publisher","unstructured":"Tavallaee M, Bagheri E, Lu W, Ghorbani AA (2009) A detailed analysis of the KDD CUP 99 data set. In: proceedings of the IEEE symposium on computational intelligence for security and defense applications, pp 1\u20136. https:\/\/doi.org\/10.1109\/CISDA.2009.5356528","DOI":"10.1109\/CISDA.2009.5356528"},{"key":"8511_CR47","doi-asserted-by":"crossref","first-page":"38367","DOI":"10.1109\/ACCESS.2018.2854599","volume":"6","author":"Z Wang","year":"2018","unstructured":"Wang Z (2018) Deep learning-based intrusion detection with adversaries. IEEE Access 6:38367\u201338384","journal-title":"IEEE Access"},{"key":"8511_CR48","doi-asserted-by":"publisher","unstructured":"Behera S, Pradhan A, Dash R (2018) Deep neural network architecture for anomaly based intrusion detection system. In: proceedings of the IEEE international conference on signal processing and integrated networks (SPIN), pp 270\u2013274. https:\/\/doi.org\/10.1109\/SPIN.2018.8474162","DOI":"10.1109\/SPIN.2018.8474162"},{"issue":"1","key":"8511_CR49","doi-asserted-by":"crossref","first-page":"686","DOI":"10.1109\/COMST.2018.2847722","volume":"21","author":"P Mishra","year":"2019","unstructured":"Mishra P, Varadharajan V, Tupakula U, Pilli ES (2019) A detailed investigation and analysis of using machine learning techniques for intrusion detection. IEEE Commun Surv Tutorials 21(1):686\u2013728","journal-title":"IEEE Commun Surv Tutorials"},{"key":"8511_CR50","unstructured":"Yin M, Li X, Zhang Y, Wang S (2019) On the mathematical understanding of ResNet with Feynman path integral. arXiv preprint arXiv:1904.07568"},{"key":"8511_CR51","volume":"207","author":"B Zhang","year":"2022","unstructured":"Zhang B, Zou G, Qin D, Ni Q, Li MH, M, (2022) RCL-Learning: ResNet and convolutional long short-term memory-based spatiotemporal air pollutant concentration prediction model. Expert Syst Appl 207:118017","journal-title":"Expert Syst Appl"},{"issue":"6","key":"8511_CR52","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1119\/10.0000852","volume":"88","author":"K Ricardo","year":"2020","unstructured":"Ricardo K (2020) Schr\u00f6dinger\u2019s original struggles with a complex wave function. Am J Phys 88(6):433\u2013438","journal-title":"Am J Phys"},{"issue":"6","key":"8511_CR53","doi-asserted-by":"crossref","first-page":"1049","DOI":"10.1103\/PhysRev.28.1049","volume":"28","author":"E Schr\u00f6dinger","year":"1926","unstructured":"Schr\u00f6dinger E (1926) An undulatory theory of the mechanics of atoms and molecules. Phys Rev 28(6):1049","journal-title":"Phys Rev"},{"issue":"2","key":"8511_CR54","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1103\/RevModPhys.20.367","volume":"20","author":"R Feynman","year":"1948","unstructured":"Feynman R (1948) Space-time approach to non-relativistic quantum mechanics. Rev Mod Phys 20(2):367\u2013387","journal-title":"Rev Mod Phys"},{"key":"8511_CR55","unstructured":"Wang Y, Li Z (2021) Inverse problem of nonlinear Schrodinger equation as learning of convolutional neural network. CoRR abs\/2107.08593"},{"issue":"3\u20134","key":"8511_CR56","first-page":"439","volume":"20","author":"S Subramani","year":"2023","unstructured":"Subramani S, Selvi M (2023) Intrusion detection system using RBPSO and fuzzy neuro-genetic classification algorithms in wireless sensor networks. Int J Inf Secur 20(3\u20134):439\u2013461","journal-title":"Int J Inf Secur"},{"key":"8511_CR57","doi-asserted-by":"publisher","unstructured":"Selvi M, Logambigai R, Ganapathy S, Sai Ramesh L, Khanna Nehemiah H, Arputharaj K (2016) Fuzzy temporal approach for energy efficient routing in WSN. In: Proceedings of the international conference on informatics and analytics, ACM, pp 1\u20135. https:\/\/doi.org\/10.1145\/2980258.2982109","DOI":"10.1145\/2980258.2982109"},{"issue":"4","key":"8511_CR58","doi-asserted-by":"crossref","first-page":"660","DOI":"10.1109\/TWC.2002.804190","volume":"1","author":"WB Heinzelman","year":"2002","unstructured":"Heinzelman WB, Chandrakasan AP, Balakrishnan H (2002) An application-specific protocol architecture for wireless microsensor networks. IEEE Trans Wirel Commun 1(4):660\u2013670","journal-title":"IEEE Trans Wirel Commun"},{"key":"8511_CR59","doi-asserted-by":"crossref","first-page":"945","DOI":"10.1007\/s11276-015-1013-1","volume":"22","author":"R Logambigai","year":"2016","unstructured":"Logambigai R, Kannan A (2016) Fuzzy logic based unequal clustering for wireless sensor networks. Wirel Netw 22:945\u2013957","journal-title":"Wirel Netw"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-08511-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-023-08511-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-08511-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,6,2]],"date-time":"2023-06-02T10:10:58Z","timestamp":1685700658000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-023-08511-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4,7]]},"references-count":59,"journal-issue":{"issue":"20","published-print":{"date-parts":[[2023,7]]}},"alternative-id":["8511"],"URL":"https:\/\/doi.org\/10.1007\/s00521-023-08511-2","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,4,7]]},"assertion":[{"value":"23 July 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 March 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 April 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"There is no conflict of interest\u00a0for the authors.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}