Abstract
The social internet of things (SIoT) facilitates numerous networking services and novel applications for the IoT, making it more productive and powerful. The state-of-the-art machine learning architectures often face different challenges when processing big data such as increased memory volume, increased training time, and higher computational costs. To tackle these challenges, this paper presents a novel framework for big data classification. Initially, an adaptive filter removes unwanted data and noises. The dimensionality of the filter data is then reduced via the Hadoop map reducer. After that, the battle royale optimization (BRO) algorithm is applied for feature selection. The optimal feature selection process performed by the BRO algorithm improves the accuracy and performance of the proposed fuzzy optimal deep convolutional neural network (FO-DCNN). The FO-DCNN model is formed by integrating the fuzzy-based remora optimization (F-RO) algorithm and deep CNN architecture for robust SIoT data classification. When evaluated using the Twitter, Rotten tomato, skin disease, diabetes, and hepatitis datasets, the proposed model offers improvements in terms of classification accuracy, memory consumption, and computational time.

















Similar content being viewed by others
Data availability
Data sharing is not applicable to this article as no new data were created or analyzed in this study.
Code availability
Not applicable.
Change history
23 February 2023
The affiliation of K.L. Nisha has been updated for a typo.
References
Bodkhe U, Tanwar S (2020) Taxonomy of secure data dissemination techniques for IoT environment. IET Software 14(6):563–571
Sun X, Wang G, Xu L, Yuan H (2021) Data replication techniques in the internet of things: a systematic literature review. Library Hi Tech.
Wan S, Zhao Y, Wang T, Gu Z, Abbasi QH, Choo KKR (2019) Multi-dimensional data indexing and range query processing via Voronoi diagram for Internet of things. Futur Gener Comput Syst 91:382–391
Megalingam RK, Sathi SR, Pula BT, Chandrika D, Reddy NS (2019) Cloud computation based urban management system using Ros. In: 2019 3rd International conference on trends in electronics and informatics (ICOEI). IEEE, pp 764–769
Gaddipati MSS, Krishnaja S, Gopan A, Thayyil AG, Devan AS, Nair A (2020) Real-time human intrusion detection for home surveillance based on IOT. In: International conference on information and communication technology for intelligent systems. Springer, Singapore, pp 493–505
Asok A, Mohan P (2019) Implementation and comparison of different data hiding techniques in image steganography. In: 2019 3rd international conference on trends in electronics and informatics (ICOEI). IEEE, pp 1180–1183
Parvathy P, Ajai AR (2020) VLSI implementation of blowfish algorithm for secure image data transmission. In: 2020 international conference on communication and signal processing (ICCSP). IEEE, pp 0770–0774
Huang M, Liu A, Xiong NN, Wang T, Vasilakos AV (2020) An effective service-oriented networking management architecture for 5G-enabled Internet of things. Comput Netw 173:107208
Lv X (2019) The big data impact and application study on the like ecosystem construction of open Internet of things. Clust Comput 22(2):3563–3572
Al‐Turjman F, Baali I (2019) Machine learning for wearable IoT‐based applications: A survey. Trans Emerg Telecommun Technol, p e3635.
Suthaharan S (2014) Big data classification: problems and challenges in network intrusion prediction with machine learning. ACM SIGMETRICS Perform Eval Rev 41(4):70–73
Fernández A, del Río S, Chawla NV, Herrera F (2017) An insight into imbalanced big data classification: outcomes and challenges. Complex Intell Syst 3(2):105–120
Triguero I, Peralta D, Bacardit J, García S, Herrera F (2015) MRPR: a MapReduce solution for prototype reduction in big data classification. Neurocomputing 150:331–345
Babar M, Arif F, Jan MA, Tan Z, Khan F (2019) Urban data management system: towards big data analytics for internet of things based smart urban environment using customized Hadoop. Futur Gener Comput Syst 96:398–409
Iyapparaja M (2020) Effective feature selection using hybrid GA-EHO for classifying big data SIoT. Int J Web Portals (IJWP) 12(1):12–25
Luo F, Liu G, Guo W, Chen G, Xiong N (2021) ML-KELM: a kernel extreme learning machine scheme for multi-label classification of real time data stream in SIoT. IEEE Trans Netw Sci Eng.
Mujeeb SM, Praveen Sam R, Madhavi K (2021) Adaptive EHTARA: an energy-efficient and trust aware secure routing algorithm for big data classification in IoT network. Wireless Personal Commun, 1–26
Hernández G, Zamora E, Sossa H, Téllez G, Furlán F (2020) Hybrid neural networks for big data classification. Neurocomputing 390:327–340
El-Hasnony IM, Mostafa RR, Elhoseny M, Barakat SI (2021) Leveraging mist and fog for big data analytics in IoT environment. Trans Emerg Telecommun Technol 32(7):e4057
Sd M, Prakash SS, Krinkin K (2022) Service oriented R-ANN knowledge model for social internet of things. Big Data Cogn Comput 6(1):32
Latif R (2022) ConTrust: a novel context-dependent trust management model in social internet of things. IEEE Access 10:46526–46537
Zhang H, Li H, Chen N, Chen S, Liu J (2022) Novel fuzzy clustering algorithm with variable multi-pixel fitting spatial information for image segmentation. Pattern Recogn 121:108201
Shaji B, Lal Raja Singh R, Nisha KL (2022) A novel deep neural network based marine predator model for effective classification of big data from social internet of things. Concurrency Comput Practice Exper, p e7244
Barroso NF, Ushirobira R, Efimov D, Sow M, Massabuau JC (2020) Model-based adaptive filtering of harmonic perturbations applied to high-frequency noninvasive valvometry. IFAC-PapersOnLine 53(2):16715–16720
Palmer JE, Searle SJ (2012, May) Evaluation of adaptive filter algorithms for clutter cancellation in passive bistatic radar. In: 2012 IEEE Radar conference, pp 0493–0498. IEEE
Merceedi KJ, Sabry NA. A comprehensive survey for hadoop distributed file system
Elteir M, Lin H, Feng WC (2010) Enhancing mapreduce via asynchronous data processing. In: 2010 IEEE 16th international conference on parallel and distributed systems, pp 397–405. IEEE.
RahkarFarshi T (2021) Battle royale optimization algorithm. Neural Comput Appl 33:1139–1157
Agahian S, Akan T (2021) Battle royale optimizer for training multi-layer perceptron Evolv Syst, 1–13
Kalchbrenner N, Grefenstette E, Blunsom P (2014) A convolutional neural network for modelling sentences. arXiv preprint arXiv:1404.2188.
Acharya UR, Oh SL, Hagiwara Y, Tan JH, Adam M, Gertych A, San Tan R (2017) A deep convolutional neural network model to classify heartbeats. Comput Biol Med 89:389–396
Jia H, Peng X, Lang C (2021) Remora optimization algorithm. Expert Syst Appl 185:115665
Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67
Hasuike T, Mehlawat MK (2018) Investor-friendly and robust portfolio selection model integrating forecasts for financial tendency and risk-averse. Ann Oper Res 269(1):205–221
Ghorui N, Ghosh A, Mondal SP, Bajuri MY, Ahmadian A, Salahshour S, Ferrara M (2021) Identification of dominant risk factor involved in spread of COVID-19 using hesitant fuzzy MCDM methodology. Results Phys 21:103811
Chakraborty A, Maity S, Jain S, Mondal SP, Alam S (2021) Hexagonal fuzzy number and its distinctive representation, ranking, defuzzification technique and application in production inventory management problem. Granular Comput 6(3):507–521
Maity S, De SK, Prasad Mondal S (2019) A study of an EOQ model under lock fuzzy environment. Mathematics 7(1):75
Mahata A, Roy B, Mondal SP, Alam S (2017) Application of ordinary differential equation in glucose-insulin regulatory system modeling in fuzzy environment. Ecol Genetics Genom 3:60–66
Mortazavi A, Moloodpoor M (2021) Enhanced butterfly optimization algorithm with a new fuzzy regulator strategy and virtual butterfly concept. Knowl-Based Syst 228:107291
Cabada RZ, Rangel HR, Estrada MLB, Lopez HMC (2020) Hyperparameter optimization in CNN for learning-centered emotion recognition for intelligent tutoring systems. Soft Comput 24(10):7593–7602
Mostafa SS, Mendonça F, Ravelo-Garcia AG, Juliá-Serdá GG, Morgado-Dias F (2020) Multi-objective hyperparameter optimization of convolutional neural network for obstructive sleep apnea detection. IEEE Access 8:129586–129599
Lakshmanaprabu SK, Shankar K, Khanna A, Gupta D, Rodrigues JJ, Pinheiro PR, De Albuquerque VHC (2018) Effective features to classify big data using social Internet of things. IEEE Access 6:24196–24204
Tiwari P, Mishra BK, Kumar S, Kumar V (2020) Implementation of n-gram methodology for rotten tomatoes review dataset sentiment analysis. In: Cognitive analytics: concepts, methodologies, tools, and applications, pp 689–701. IGI Global.
Tschandl P, Rosendahl C, Kittler H (2019) The ham10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions. 2018.
Srivastava A, Rai M, Application of data mining methods in the diagnosis of UCI based pima indians diabetes database.
Mureşan H, Oltean M (2017) Fruit recognition from images using deep learning. arXiv preprint arXiv:1712.00580.
Suthaharan S (2016) Machine learning models and algorithms for big data classification. Integr Ser Inf Syst 36:1–12
Ayma VA, Ferreira RS, Happ P, Oliveira D, Feitosa R, Costa G, Plaza A, Gamba P (2015) Classification algorithms for big data analysis, a map reduce approach. Int Archives Photogram Remote Sens Spatial Inf Sci 40(3):17
Funding
Not applicable.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Human and animal rights
This article does not contain any studies with human or animal subjects performed by any of the authors.
Informed consent
Informed consent was obtained from all individual participants included in the study.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Shaji, B., Singh, R.L.R. & Nisha, K.L. High-performance fuzzy optimized deep convolutional neural network model for big data classification based on the social internet of things. J Supercomput 79, 9509–9537 (2023). https://doi.org/10.1007/s11227-022-04974-7
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11227-022-04974-7