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Soil Morphology Based on Deep Learning, Polynomial Learning and Gabor Teager-Kaiser Energy Operators

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Machine Learning and Big Data Analytics Paradigms: Analysis, Applications and Challenges

Part of the book series: Studies in Big Data ((SBD,volume 77))

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Abstract

Soil Morphology is considered the main observable characteristics of the different soil horizons. It helps farmers to determine what kind of soil they can use for their different plants. The observable characteristics include soil structure, color, distribution of roots and pores. The main concept of this chapter is to classify the different soils based on their morphology. Furthermore, the chapter contains a comparison between polynomial neural network and deep learning for soil classification. The chapter introduces a background about the different methods of feature extraction including the Gabor wavelet transform, Teager-Kaiser operator, deep learning, and polynomial neural networks. The chapter, also, includes two goals. The first goal is to improve the extraction of soil features based on Gabor wavelet transform but followed by the Teager-Kaiser Operator. The second goal is to classify the types of different morphological soil based on two methods: deep learning and polynomial neural network. We achieved accuracy limits of (95–100%) for the polynomial and deep learning classification achieved accuracy up to 95% but the deep learning is more accurate and very powerful. Finally, we compare our work results with the previous work and research. Results show an accuracy range of (98–100%) for our work compared with (95.1–98.8%) for the previous algorithms based on PNN. Furthermore, the accuracy of using DNN in this chapter comparing with pervious works achieved a good accuracy rather than the others.

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References

  1. Ayan, E., Ünver, H.M.: Data augmentation importance for classification of skin lesions via deep learning. In: 2018 Electric Electronics, Computer Science, Biomedical Engineerings’ Meeting (EBBT), pp. 1–4. IEEE (2018)

    Google Scholar 

  2. Bhattacharya, B., Solomatine, D.P.: Machine learning in soil classification. Neur. Netw. 19(2), 186–195 (2006)

    Article  Google Scholar 

  3. Boudraa, A.O., Salzenstein, F.: Teager–Kaiser energy methods for signal and image analysis: a review. Digit. Signal Process. 78, 338–375 (2018)

    Article  Google Scholar 

  4. Cheng, G., Han, J.: A survey on object detection in optical remote sensing images. ISPRS J. Photogramm. Remote Sens. 117, 11–28 (2016)

    Article  Google Scholar 

  5. Cramer, S., et al.: An extensive evaluation of seven machine learning methods for rainfall prediction in weather derivatives. Expert Syst. Appl. 85, 169–181 (2017)

    Article  Google Scholar 

  6. Dallali, A., Kachouri, A., Samet, M.: Fuzzy C-means clustering neural network, WT, and HRV for classification of cardiac arrhythmia. ARPN J Eng Appl Sci 6(10), 112–118 (2011)

    Google Scholar 

  7. Eslami, E., et al.: A real-time hourly ozone prediction system using deep convolutional neural network. arXiv preprint arXiv:1901.11079 (2019)

  8. Ford, W., Land, W.: A latent space support vector machine (LSSVM) model for cancer prognosis. Procedia Comput. Sci. 36, 470–475 (2014)

    Article  Google Scholar 

  9. Hu, M., et al.: modern machine learning techniques for univariate tunnel settlement forecasting: a comparative study. In: Mathematical Problems in Engineering. Hindawi (2019)

    Google Scholar 

  10. Jahmunah, V., et al.: Computer-aided diagnosis of congestive heart failure using ECG signals—a review. Physica Med. 62, 95–104 (2019)

    Article  Google Scholar 

  11. Kwasigroch, A., Mikołajczyk, A., Grochowski, M.: ‘Deep neural networks approach to skin lesions classification—a comparative analysis. In: 2017 22nd International Conference on Methods and Models in Automation and Robotics (MMAR), pp. 1069–1074. IEEE (2017)

    Google Scholar 

  12. Maia, L.B., et al.: Evaluation of melanoma diagnosis using deep features. In: 2018 25th International Conference on Systems, Signals and Image Processing (IWSSIP), pp. 1–4. IEEE (2018)

    Google Scholar 

  13. Manit, J., Schweikard, A., Ernst, F.: Deep convolutional neural network approach for forehead tissue thickness estimation. Curr. Direct. Biomed. Eng. 3(2), 103–107 (2017)

    Article  Google Scholar 

  14. Mukherjee, S., Adhikari, A., Roy, M.: Malignant melanoma classification using cross-platform dataset with deep learning CNN architecture BT. In: Recent Trends in Signal and Image Processing, pp. 31–41. Springer Singapore, Singapore

    Google Scholar 

  15. Nabizadeh, N., Kubat, M.: Brain tumors detection and segmentation in MR images: Gabor wavelet vs. statistical features. Comput. Electr. Eng. 45, 286–301 (2015)

    Article  Google Scholar 

  16. Odgers, N.P., McBratney, A.B.: Soil material classes. In: Pedometrics, pp. 223–264. Springer (2018)

    Google Scholar 

  17. Perez, D., et al.: Deep learning for effective detection of excavated soil related to illegal tunnel activities. In: 2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON), pp. 626–632. IEEE (2017)

    Google Scholar 

  18. Pham, B.T., et al.: Hybrid integration of Multilayer Perceptron Neural Networks and machine learning ensembles for landslide susceptibility assessment at Himalayan area (India) using GIS. CATENA 149, 52–63 (2017)

    Article  Google Scholar 

  19. Potter, C., Weigand, J.: Imaging analysis of biological soil crusts to understand surface heating properties in the Mojave Desert of California. CATENA 170, 1–9 (2018)

    Article  Google Scholar 

  20. Rahouma, K.H., et al.: Analysis of electrocardiogram for heart performance diagnosis based on wavelet transform and prediction of future complications. Egypt. Comput. Sci. J., 41 (2017)

    Google Scholar 

  21. Rançon, F., et al.: Comparison of SIFT encoded and deep learning features for the classification and detection of Esca disease in bordeaux vineyards. Remote Sens. 11(1), 1 (2019)

    Article  Google Scholar 

  22. Singh, B., et al.: Estimation of permeability of soil using easy measured soil parameters: assessing the artificial intelligence-based models. ISH J. Hydraul. Eng, 1–11 (2019)

    Google Scholar 

  23. Sweilam, N.H., Tharwat, A.A., Moniem, N.K.A.: Support vector machine for diagnosis cancer disease: a comparative study. Egypt. Inf. J. 11(2), 81–92 (2010)

    Google Scholar 

  24. Tekin, E., Akbas, S.O.: Predicting groutability of granular soils using adaptive neuro-fuzzy inference system. In: Neural Computing and Applications, pp. 1–11. Springer (2017)

    Google Scholar 

  25. Torabi, M., et al.: A hybrid machine learning approach for daily prediction of solar radiation. In: International Conference on Global Research and Education, pp. 266–274. Springer (2018)

    Google Scholar 

  26. Vargas, M.R., De Lima, B.S.L.P., Evsukoff, A.G.: Deep learning for stock market prediction from financial news articles. In: 2017 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA), pp. 60–65. IEEE (2017)

    Google Scholar 

  27. Wang, L., et al.: Spectral–spatial multi-feature-based deep learning for hyperspectral remote sensing image classification. Soft Comput. 21(1), 213–221 (2017)

    Article  Google Scholar 

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Authors and Affiliations

  1. Electrical Engineering Department, Faculty of Engineering, Minia University, Minia, Egypt

    Kamel H. Rahouma

  2. The Higher Institute for Management Technology and Information, Minia, Egypt

    Rabab Hamed M. Aly

Authors
  1. Kamel H. Rahouma
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  2. Rabab Hamed M. Aly
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Correspondence to Kamel H. Rahouma .

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Editors and Affiliations

  1. Faculty of Computer and Artificial Intelligence, Cairo University, Giza, Egypt

    Aboul Ella Hassanien

  2. Faculty of Science, Helwan University, Helwan, Egypt

    Ashraf Darwish

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Rahouma, K.H., Aly, R.H.M. (2021). Soil Morphology Based on Deep Learning, Polynomial Learning and Gabor Teager-Kaiser Energy Operators. In: Hassanien, A.E., Darwish, A. (eds) Machine Learning and Big Data Analytics Paradigms: Analysis, Applications and Challenges. Studies in Big Data, vol 77. Springer, Cham. https://doi.org/10.1007/978-3-030-59338-4_17

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  • DOI: https://doi.org/10.1007/978-3-030-59338-4_17

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-59337-7

  • Online ISBN: 978-3-030-59338-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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