Abstract
Diabetes is more severe in women, according to various medical reports and surveys. Sometimes diabetes is difficult to identify due to various common symptoms, such as headache, fatigue, slow healing of cuts and blurry vision. Thus, this paper introduces novel big data and classification techniques such as effective map reducing technologies are used to recognize the diabetes. Initially, the data were collected from a large dataset, and the map reducing concept is applied to compose the small chunk of data efficiently. Following this process, the noise present in the collected dataset is removed using the normalization process. After that, the statistical features are selected using the ant bee colony approach that uses the ant characteristics such as wandering. The selected features are trained with the help of the support vector machine with multilayer neural network. The trained or learned features are efficiently classified using the associated neural network, and the efficiency of the system is evaluated with the help of experimental results in terms of error rate, sensitivity, specificity and accuracy.
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30 September 2022
This article has been retracted. Please see the Retraction Notice for more detail: https://doi.org/10.1007/s11227-022-04858-w
References
Pugoy RA, Mariano V (2011) Automated rice leaf disease detection using shape image analysis. In: 3rd International Conference on Digital Image Processing (ICDIP 2011), Chengdu, China, 15–17 April 2011
Orillo JW et al (2014) Identification of diseases in rice plant (Oryza sativa) using back propagation artificial neural network. IEEE. https://doi.org/10.1109/HNICEM.2014.7016248
Revenaz A, Ruggeri M, Martelli M (2010) Wireless communication protocol for agricultural machines synchronization and fleet management. In: Proc. IEEE Intl Symp Industrial Electronics, Bari, Italy, 04–07 Jul. 2010, pp 3498–3504
Abdul Aziz ID et al (2009) Remote monitoring in agricultural greenhouse using wireless sensor and short message service (SMS). Intl J Eng Technol 9(9):35–43
Nambiar R, Bhardwaj R, Sethi A, Vargheese R (2013) A look at challenges and opportunities of big data analytics in healthcare, Big data. In: 2013 IEEE International Conference on 17–22
Makandar A, Patrot A (2015) Computation pre-processing techniques for image restoration. Int J Comput Appl 113(4):11–17
Shyni S, Shantha Mary Joshitta R, Arockiam L (2016) Applications of big data analytics for diagnosing diabetic mellitus: issues and challenges. Int J Recent Trends Eng Res (IJRTER) 02(06):454–461
Viceconti M, Hunter P, Hose R (2015) Big data, big knowledge: big data for personalized healthcare. IEEE J Biomed Health Inform 19(4):1209–1215
Yogamangalam R, Karthikeyan B (2013) Segmentation techniques comparison in image processing. Int J Eng Technol 5(1):307–313
https://www.plantvillage.org/en/topics/grape, cucumber, tomato, cotton. Accessed 12 Feb 2017
http://www.gsmarena.com/samsung_galaxy_s7-review-1408p8.php. Accessed 15 Mar 2016
Yogamangalam R (2013) Segmentation techniques comparison in image processing. Int J Eng Technol IJET 5(1):307–313
https://www.eppo.int/DATABASES/databases. Accessed 18 Mar 2017
https://www.gene.affrc.go.jp/databases-micro_pl_diseases_en. Accessed 02 Nov 2017
http://www.ipmimages.org. Accessed 11 Mar 2017
Francis J et al (2016) Identification of leaf diseases in pepper plants using soft computing techniques. In: Conference on emerging devices and smart systems in IEEE
http://www.uaex.edu/yard-garden/resource-library/diseases. Accessed 05 Dec 2017
Lin C, Liu K, Chen M (2005) Dual clustering: integrating data clustering over optimization and constraint domains. IEEE 17(5):628–637
Chen Y et al (2016) Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE Trans Geosci Remote Sens 54(10):6232–6251
Pushpavalli R, Sivarajde G (2013) Image enhancement using adaptive neuro-fuzzy inference system. Int J Sci Technol Res 2(6):256–262
William J, Dela Cruz J, Agapito L (2014) Identification of diseases in rice plant (Oryza Sativa) using back propagation artificial neural network. In: 7th IEEE International Conference Humanoid, Nanotechnology, Information Technology Communication and Control, Environment and Management (HNICEM), IEEE
Prasad S, Peddoju SK, Ghosh D (2014) Energy efficient mobile vision system for plant leaf disease identification, IEEE. https://doi.org/10.1109/WCNC.2014.6953083
Khirade SD, Patil AB (2015) Plant disease detection using image processing. In: 2015 International Conference on Computing Communication Control and Automation, IEEE
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This project was supported by King Saud University, Deanship of Scientific Research, Community College Research Unit.
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This article has been retracted. Please see the retraction notice for more detail: https://doi.org/10.1007/s11227-022-04858-w"
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AlZubi, A.A. RETRACTED ARTICLE: Big data analytic diabetics using map reduce and classification techniques. J Supercomput 76, 4328–4337 (2020). https://doi.org/10.1007/s11227-018-2362-1
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DOI: https://doi.org/10.1007/s11227-018-2362-1