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Meta-heuristic endured deep learning model for big data classification: image analytics

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A Correction to this article was published on 08 August 2023

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Abstract

Image processing is currently developing as a unique and the inventive field in computer research and applications in the modern area. Most image processing algorithms produce a large quantity of data as an outcome, which is termed as Big-data. These algorithms process and store bulky information either as structured or unstructured data. The use of big data analytics to mine the data produced by image processing technology has huge potential in areas like education, governments, medical establishments, production units, finance and banking, and retail business centers. This paper well defined the innovations made in Big Data analytics and image processing. In this study, a novel data classification approach especially for image analytics is proposed. To improve image quality, pre-processing is applied to huge data that has been gathered. Then, most relevant features like spatial information, texture GLCM, and color and shape features are extracted from the pre-processed image. Since the dimensions of the features are huge in size, an adaptive map-reduce framework with Improved Shannon Entropy has been introduced to lessen the dimensionality of the extracted features. Then, in the big data classification phase, an optimized deep learning classifier deep convolutional neural network (DCNN) is introduced to classify the images accurately. The weight function of the DCNN is fine-tuned using the newly proposed dragonfly updated mothsearch (DAUMS) Algorithm to enhance the classification accuracy and to solve the optimization problems of the research work. The moth search algorithm and dragonfly algorithm are both concepts in this hybrid algorithm DAUMS.

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References

  1. Cavallaro G, Riedel M, Richerzhagen M, Benediktsson JA, Plaza A (2015) On understanding big data impacts in remotely sensed image classification using support vector machine methods. IEEE J Sel Top Appl Earth Obs Remote Sens 8(10):4634–4646

    Article  Google Scholar 

  2. Ulfarsson MO, Palsson F, Sigurdsson J, Sveinsson JR (2016) Classification of big data with application to imaging genetics. Proc IEEE 104(11):2137–2154

    Article  Google Scholar 

  3. Hravnak M, Chen L, Dubrawski A, Bose E, Clermont G, Pinsky MR (2016) Real alerts and artifact classification in archived multi-signal vital sign monitoring data: implications for mining big data. J Clin Monit Comput 30(6):875–888

    Article  Google Scholar 

  4. Zhai J, Zhang S, Zhang M, Liu X (2018) Fuzzy integral-based ELM ensemble for imbalanced big data classification. Soft Comput 22:1–13

    Article  Google Scholar 

  5. Fernández A, del Río S, Bawakid A, Herrera F (2017) Fuzzy rule based classification systems for big data with MapReduce: granularity analysis. Adv Data Anal Classif 11(4):711–730

    Article  MathSciNet  MATH  Google Scholar 

  6. Varatharajan R, Manogaran G, Priyan MK (2017) A big data classification approach using LDA with an enhanced SVM method for ECG signals in cloud computing. Multim Tools Appl 77:1–21

    Google Scholar 

  7. Zhang XM, He GJ, Zhang ZM, Peng Y, Long TF (2017) Spectral-spatial multi-feature classification of remote sensing big data based on a random forest classifier for land cover mapping. Clust Comput 20(3):2311–2321

    Article  Google Scholar 

  8. Lin K-C, Zhang K-Y, Huang Y-H, Hung JC, Yen N (2016) Feature selection based on an improved cat swarm optimization algorithm for big data classification. J Supercomput 72(8):3210–3221

    Article  Google Scholar 

  9. Ramírez-Gallego S, Krawczyk B, García S, Woźniak M, Benítez JM, Herrera F (2017) Nearest neighbor classification for high-speed big data streams using spark. IEEE Trans Syst Man Cybern Syst 47(10):2727–2739

    Article  Google Scholar 

  10. Zhan J, Fan X, Cai L, Gao Y, Zhuang J (2018) TPTVer: a trusted third party based trusted verifier for multi-layered outsourced big data system in cloud environment. China Commun 15(2):122–137

    Article  Google Scholar 

  11. Qi Q, Tao F (2018) Digital twin and big data towards smart manufacturing and industry 4.0: 360 degree comparison. IEEE Access 6:3585–3593

    Article  Google Scholar 

  12. Sheng G, Hou H, Jiang X, Chen Y (2018) A novel association rule mining method of big data for power transformers state parameters based on probabilistic graph model. IEEE Trans Smart Grid 9(2):695–702

    Article  Google Scholar 

  13. García-Magariño I, Lacuesta R, Lloret J (2018) Agent-based simulation of smart beds with Internet-of-Things for exploring big data analytics. IEEE Access 6:366–379

    Article  Google Scholar 

  14. He X, Wang K, Huang H, Liu B (2018) QoE-driven big data architecture for smart city. IEEE Commun Mag 56(2):88–93

    Article  Google Scholar 

  15. Chaudhary R, Aujla GS, Kumar N, Rodrigues JJPC (2018) Optimized big data management across multi-cloud data centers: software-defined-network-based analysis. IEEE Commun Mag 56(2):118–126

    Article  Google Scholar 

  16. Rehman MHU, Ahmed E, Yaqoob I, Hashem IAT, Imran M, Ahmad S (2018) Big data analytics in industrial IoT using a concentric computing model. IEEE Commun Mag 56(2):37–43

    Article  Google Scholar 

  17. Gohar M, Ahmed SH, Khan M, Guizani N, Ahmed A, Rahman AU (2018) A Big data analytics architecture for the internet of small things. IEEE Commun Mag 56(2):128–133

    Article  Google Scholar 

  18. Ahmed E et al (2018) Recent advances and challenges in mobile big data. IEEE Commun Mag 56(2):102–108

    Article  Google Scholar 

  19. Sezer OB, Dogdu E, Ozbayoglu AM (2018) Context-Aware computing, learning, and big data in internet of things: a survey. IEEE Internet Things J 5(1):1–27

    Article  Google Scholar 

  20. Hong B, Kim HY, Kim M, Suh T, Xu L, Shi W (2018) FASTEN: an FPGA-based secure system for big data processing. IEEE Design Test 35(1):30–38

    Article  Google Scholar 

  21. Cong J, Fang Z, Huang M, Wang L, Wu D (2018) CPU-FPGA coscheduling for big data applications. IEEE Design Test 35(1):16–22

    Article  Google Scholar 

  22. Afzali GA, Mohammadi S (2018) Privacy preserving big data mining: association rule hiding using fuzzy logic approach. IET Inf Secur 12(1):15–24

    Article  Google Scholar 

  23. Li P, Chen Z, Yang LT, Zhang Q, Deen MJ (2018) Deep convolutional computation model for feature learning on big data in Internet of Things. IEEE Trans Ind Inf 14(2):790–798

    Article  Google Scholar 

  24. Aujla GS, Kumar N, Zomaya AY, Ranjan R (2018) Optimal decision making for big data processing at edge-cloud environment: an SDN perspective. IEEE Trans Ind Inform 14(2):778–789

    Article  Google Scholar 

  25. Mirjalili S (2016) Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems. Neural Comput Appl 27(4):1053–1073

    Article  Google Scholar 

  26. Wang G-G (2018) Moth search algorithm: a bio-inspired metaheuristic algorithm for global optimization problems. Memet Comput 10(1):151–154

    Article  Google Scholar 

  27. MarsalineBeno M, Valarmathi IR, Swamy SM, Rajakumar BR (2014) Threshold prediction for segmenting tumour from brain MRI scans. Int J Imaging Syst Technol 24(2):129–137. https://doi.org/10.1002/ima.22087

    Article  Google Scholar 

  28. Thomas R, Rangachar MJS (2018) Hybrid optimization based DBN for face recognition using low-resolution images. Multim Res 1(1):33–43

    Google Scholar 

  29. Srinivas V, Santhirani Ch (2020) Hybrid particle swarm optimization-deep neural network model for speaker recognition. Multim Res 3(1):1–10

    Google Scholar 

  30. Gopal A (2020) Hybrid classifier: Brain tumor classification and segmentation using genetic-based Grey Wolf optimization. Multim Res 3(2):1–10

    Article  Google Scholar 

  31. Wagh MB, Gomathi N (2019) Improved GWO-CS algorithm-based optimal routing strategy in VANET. J Netw Commun Syst 2(1):34–42

    Google Scholar 

  32. Bibin Prasad M, Suki Antely A (2018) MCGA modified compact genetic algorithm for PAPR reduction in MIMO-OFDM system. J Netw Commun Syst 1(1):10–18

    Google Scholar 

  33. Kelotra A, Pandey P (2019) Energy-aware cluster head selection in WSN using HPSOCS algorithm. J Netw Commun Syst 2(1):24–33

    Google Scholar 

  34. Sarkar A (2020) Optimization assisted convolutional neural network for facial emotion recognition. Multim Res 3(2):35–41

    Article  Google Scholar 

  35. Mapreduce. https://www.analyticsvidhya.com/blog/2014/05/introduction-mapreduce/. Accessed 31 Aug 2021

  36. Mohanaiah P, Sathyanarayana P, GuruKumar L (2013) Image texture feature extraction using GLCM approach. Int J Sci Res Publ 3(5):1–5

    Google Scholar 

  37. Kurtosis. https://en.wikipedia.org/wiki/Kurtosis

  38. Datset 1. https://github.com/cabani/MaskedFace-Net. Accessed 02 Sept 2021

  39. Abukhodair F, Alsaggaf W, Jamal AT, Abdel-Khalek S, Mansour RF (2021) An intelligent Metaheuristic binary pigeon optimization-based feature selection and big data classification in a MapReduce environment. Mathematics 9(20):2627

    Article  Google Scholar 

  40. Mansour RF et al (2021) Artificial intelligence with big data analytics-based brain intracranial hemorrhage e-diagnosis using CT images. Neural Comput Appl 1–13

  41. Mansour RF, Al-Otaibi S, Al-Rasheed A, Aljuaid H, Pustokhina I, Pustokhin DA (2021) An optimal big data analytics with concept drift detection on high-dimensional streaming data. CMC Comput Mater Contin 68(3):2843–2858

    Google Scholar 

  42. Mansour RF, Abdel-Khalek S, Hilali-Jaghdam I, Nebhen J, Cho W, Joshi GP (2021) An intelligent outlier detection with machine learning empowered big data analytics for mobile edge computing. Clust Comput 26:1–13

    Google Scholar 

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Acknowledgements

I would like to express my very great appreciation to the co-authors of this manuscript for their valuable and constructive suggestions during the planning and development of this research work.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, St. Joseph’s College of Engineering, Anna University, Chennai, Tamil Nadu, India

    P. Naveen & B. Diwan

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  1. P. Naveen
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  2. B. Diwan
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Contributions

PN conceived the presented idea and designed the analysis. Also, he carried out the experiment and wrote the manuscript with support from Dr. BD. All authors discussed the results and contributed to the final manuscript. All authors read and approved the final manuscript.

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Correspondence to P. Naveen.

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Naveen, P., Diwan, B. Meta-heuristic endured deep learning model for big data classification: image analytics. Knowl Inf Syst 65, 4655–4685 (2023). https://doi.org/10.1007/s10115-023-01888-5

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