Skip to main content
SpringerLink
Log in

Detection of weather images by using spiking neural networks of deep learning models

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

The transmission of weather information of a location at certain time intervals affects the living conditions of the people there directly or indirectly. According to weather information, people shape their behavior in daily life. Besides, agricultural activities are carried out according to the weather conditions. Considering the importance of this subject, it is possible to make weather predictions based on the weather images in today’s technology exploiting the computer systems. However, the recent mention of the name of artificial intelligence technology in every field has made it compulsory for computer systems to benefit from this technology. The dataset used in the study has four classes: cloudy, rain, shine, and sunrise. In the study, GoogLeNet and VGG-16 models and the spiking neural network (SNN) were used together. The features extracted from GoogLeNet and VGG-16 models were combined and given to the SNNs as the input. As a result, the SNNs contributed to the success of classification with the proposed approach. The classification accuracy rates of cloudy, rain, shine, and sunrise classes were 98.48%, 97.58%, 97%, and 98.48%, respectively, together with SNN. Also, the use of SNNs in combination with deep learning models to obtain a successful result is proved in this study.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  1. Elhoseiny M, Huang S, Elgammal A (2015) Weather classification with deep convolutional neural networks. In: International conference on ımage processing

  2. Renda A (2019) Artificial ıntelligence ethics, governance and policy challenges. Report of a CEPS Task Force

  3. Fu X (2019) Application of artificial ıntelligence technology in medical cell biology. In: 2019 International conference on robots and ıntelligent system (ICRIS), pp 401–404

  4. Zhao B, Li X, Lu X, Wang Z (2018) A CNN–RNN architecture for multi-label weather recognition. Neurocomputing 322:47–57. https://doi.org/10.1016/j.neucom.2018.09.048

    Article  Google Scholar 

  5. Lu C, Lin D, Jia J, Tang C (2017) Two-class weather classification. IEEE Trans Pattern Anal Mach Intell 39:2510–2524. https://doi.org/10.1109/tpami.2016.2640295

    Article  Google Scholar 

  6. An J, Chen Y, Shin H (2018) Weather classification using convolutional neural networks. In: BT—International SoC design conference, ISOCC 2018, Daegu, South Korea, November 12–15, pp 245–246

  7. Villarreal Guerra JC, Khanam Z, Ehsan S et al (2018) Weather classification: a new multi-class dataset, data augmentation approach and comprehensive evaluations of convolutional neural networks. NASA/ESA Conf Adapt Hardw Syst AHS 2018:305–310. https://doi.org/10.1109/ahs.2018.8541482

    Article  Google Scholar 

  8. Ajayi G (2018) Mendeley data—multi class weather dataset for image classification. https://data.mendeley.com/datasets/4drtyfjtfy/1. Accessed 28 Dec 2019

  9. Toğaçar M, Ergen B, Cömert Z (2020) Waste classification using AutoEncoder network with integrated feature selection method in convolutional neural network models. Measurement 153:107459. https://doi.org/10.1016/j.measurement.2019.107459

  10. Cömert Z (2020) Fusing fine-tuned deep features for recognizing different tympanic membranes. Biocybern Biomed Eng 40:40–51. https://doi.org/10.1016/j.bbe.2019.11.001

    Article  Google Scholar 

  11. Bochinski E, Senst T, Sikora T (2018) Hyper-parameter optimization for convolutional neural network committees based on evolutionary algorithms. In: Proceedings of ınternational conference on ımage processing, ICIP 2017-September, pp 3924–3928. https://doi.org/10.1109/icip.2017.8297018

  12. Toğaçar M, Özkurt KB, Ergen B, Cömert Z (2020) BreastNet: a novel convolutional neural network model through histopathological images for the diagnosis of breast cancer. Phys A Stat Mech Appl. https://doi.org/10.1016/j.physa.2019.123592

  13. Shima Y (2018) Image Augmentation for object ımage classification based on combination of pre-trained CNN and SVM. J Phys: Conf Ser 1004:1–8. https://doi.org/10.1088/1742-6596/1004/1/012001

    Article  Google Scholar 

  14. Yadav SS, Jadhav SM (2019) Deep convolutional neural network based medical image classification for disease diagnosis. J Big Data 6:113. https://doi.org/10.1186/s40537-019-0276-2

    Article  Google Scholar 

  15. Sertkaya ME, Ergen B, Togacar M (2019) Diagnosis of eye retinal diseases based on convolutional neural networks using optical coherence ımages. In: 2019 23rd International conference electronics, pp 1–5

  16. Mungofa P, Schumann A, Waldo L (2018) Chemical crystal identification with deep learning machine vision. BMC Res Notes 11:703. https://doi.org/10.1186/s13104-018-3813-8

    Article  Google Scholar 

  17. Nwankpa C, Ijomah W, Gachagan A, Marshall S (2018) Activation functions: comparison of trends in practice and research for deep learning, pp 1–20

  18. Toğaçar M, Ergen B, Sertkaya ME (2019) Subclass separation of white blood cell ımages using convolutional neural network models. Elektron Elektrotechn 25:63–68. https://doi.org/10.5755/j01.eie.25.5.24358

    Article  Google Scholar 

  19. Ahmadi M, Vakili S, Langlois JMP, Gross W (2018) Power reduction in CNN pooling layers with a preliminary partial computation strategy. In: 2018 16th IEEE ınternational new circuits and systems conference (NEWCAS), pp 125–129

  20. Ghosh A, Singh S, Sheet D (2017) Simultaneous localization and classification of acute lymphoblastic leukemic cells in peripheral blood smears using a deep convolutional network with average pooling layer. In: 2017 IEEE International conference on ındustrial and ınformation systems (ICIIS), pp 1–6

  21. Qu Y, Ke Y, Yu W (2018) A solution for input limit in CNN due to fully-connected layer. In: 2018 IEEE 9th ınternational conference on software engineering and service science (ICSESS), pp 611–616

  22. Krizhevsky A, Sutskever I, Hinton GE (2012) ImageNet classification with deep convolutional neural networks. In: Pereira F, Burges CJC, Bottou L, Weinberger KQ (eds) Proceedings of the 25th ınternational conference on neural ınformation processing systems—vol. 1. Curran Associates, USA, pp 1097–1105

  23. Russakovsky O, Deng J, Su H et al (2015) ImageNet large scale visual recognition challenge. Int J Comput Vis 115:211–252. https://doi.org/10.1007/s11263-015-0816-y

    Article  MathSciNet  Google Scholar 

  24. Szegedy C, Liu W, Jia Y, et al (2015) Going deeper with convolutions. In: 2015 IEEE conference on computer vision and pattern recognition (CVPR). IEEE, pp 1–9

  25. Simonyan K, Zisserman A (2015) Very deep convolutional networks for large-scale image recognition. In: International conference on learning representations

  26. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, Las Vegas, NV, USA, pp 770–778

  27. Lim S, Bae J-H, Eum J-H et al (2019) Adaptive learning rule for hardware-based deep neural networks using electronic synapse devices. Neural Comput Appl 31:8101–8116. https://doi.org/10.1007/s00521-018-3659-y

    Article  Google Scholar 

  28. Başaran E, Cömert Z, Şengür A et al (2019) Chronic tympanic membrane diagnosis based on deep convolutional neural network. In: 2019 4th ınternational conference on computer science and engineering (UBMK), pp 1–4

  29. Awad M, Khanna R (2015) Support vector machines for classification BT—efficient learning machines: theories, concepts, and applications for engineers and system designers. In: Awad M, Khanna R (eds) Apress, Berkeley, CA, pp 39–66

  30. Doǧan Ü, Glasmachers T, Igel C (2016) A unified view on multi-class support vector classification. J Mach Learn Res 17:1–32

    MathSciNet  MATH  Google Scholar 

  31. Zou F, Shen L, Jie Z, et al (2018) A sufficient condition for convergences of Adam and RMSProp. 11127–11135

  32. Konecny J, Richtarik P (2017) Semi-stochastic gradient descent methods. Front Appl Math Stat 3:9. https://doi.org/10.3389/fams.2017.00009

    Article  MATH  Google Scholar 

  33. Huang S, Cai N, Pacheco PP, et al (2017) Applications of support vector machine (SVM) learning in cancer genomics. Cancer Genomics Proteomics 15:41–51. https://doi.org/10.21873/cgp.20063

  34. Battineni G, Chintalapudi N, Amenta F (2019) Machine learning in medicine: Performance calculation of dementia prediction by support vector machines (SVM). Inform Med Unlocked 16:100200. https://doi.org/10.1016/j.imu.2019.100200

  35. Wu H, Wang L, Zhao Z et al (2018) Support vector machine based differential pulse-width pair brillouin optical time domain analyzer. IEEE Photonics J 10:1–11. https://doi.org/10.1109/jphot.2018.2858235

    Article  Google Scholar 

  36. Sharif I, Chaudhuri D (2019) A multiseed-based SVM classification technique for training sample reduction. Turk J Electr Eng Comput Sci 27:595–604. https://doi.org/10.3906/elk-1801-157

    Article  Google Scholar 

  37. Govada A, Gauri B, Sahay SK (2015) Centroid based binary tree structured SVM for multi classification. In: 2015 International conference on advances in computing, communications and ınformatics, pp 258–262

  38. Lobo JL, Del Ser J, Bifet A, Kasabov N (2020) Spiking Neural Networks and online learning: An overview and perspectives. Neural Netw 121:88–100. https://doi.org/10.1016/j.neunet.2019.09.004

  39. (2019) Spiking neural network. In: Wikipedia. https://en.wikipedia.org/wiki/Spiking_neural_network. Accessed 29 Dec 2019

  40. Soni D (2018) Spiking neural networks, the next generation of machine learning. In: Towar. Data Sci. https://towardsdatascience.com/spiking-neural-networks-the-next-generation-of-machine-learning-84e167f4eb2b. Accessed 29 Dec 2019

  41. Stimberg M, Brette R, Goodman DF (2019) Brian 2, an intuitive and efficient neural simulator. Elife 8:e47314. https://doi.org/10.7554/elife.47314

    Article  Google Scholar 

  42. Tavanaei A, Ghodrati M, Kheradpisheh SR, et al (2019) Deep learning in spiking neural networks. Neural Netw 111:47–63. https://doi.org/10.1016/j.neunet.2018.12.002

  43. Wang W, Pedretti G, Milo V et al (2019) Computing of temporal information in spiking neural networks with ReRAM synapses. Faraday Discuss 213:453–469. https://doi.org/10.1039/c8fd00097b

    Article  Google Scholar 

  44. Xie X, Qu H, Liu G et al (2016) An efficient supervised training algorithm for multilayer spiking neural networks. PLoS ONE 11:e0150329

    Article  Google Scholar 

  45. Jeyasothy A, Sundaram S, Sundararajan N (2019) SEFRON: a new spiking neuron model with time-varying synaptic efficacy function for pattern classification. IEEE Trans Neural Netw Learn Syst 30:1231–1240. https://doi.org/10.1109/tnnls.2018.2868874

    Article  Google Scholar 

  46. Wang X, Lin X, Dang X (2019) A delay learning algorithm based on spike train kernels for spiking neurons. Front Neurosci 13:252. https://doi.org/10.3389/fnins.2019.00252

    Article  Google Scholar 

  47. Cao Y, Chen Y, Khosla D (2015) Spiking deep convolutional neural networks for energy-efficient object recognition. Int J Comput Vis 113:54–66. https://doi.org/10.1007/s11263-014-0788-3

    Article  MathSciNet  Google Scholar 

  48. Xu Q, Qi Y, Yu H, et al (2018) CSNN: An augmented spiking based framework with perceptron-inception. IJCAI Int Jt Conf Artif Intell 1646–1652. https://doi.org/10.24963/ijcai.2018/228

  49. Olga R, Deng J, Su H, et al (2019) ImageNet Large Scale Visual Recognition Competition 2014 (ILSVRC2014). http://www.image-net.org/challenges/LSVRC/2014/. Accessed 30 Dec 2019

  50. Qureshi AS, Khan A, Shamim N, Durad MH (2020) Intrusion detection using deep sparse auto-encoder and self-taught learning. Neural Comput Appl 32:3135–3147. https://doi.org/10.1007/s00521-019-04152-6

    Article  Google Scholar 

  51. Toğaçar M, Ergen B, Cömert Z (2020) Classification of flower species by using features extracted from the intersection of feature selection methods in convolutional neural network models. Measurement 158:107703. https://doi.org/10.1016/j.measurement.2020.107703

  52. Akosa JS (2017) Predictive accuracy: a misleading performance measure for highly ımbalanced data. SAS Glob Forum 942:1–12

    Google Scholar 

  53. Ajayi GO, Wang Z (2019) Multi-class weather classification from still ımage using said ensemble method. In: Proceedings of 2019 South African Univ Power Eng Conf Mechatronics/Pattern Recognit Assoc South Africa, SAUPEC/RobMech/Prasa 2019, pp 135–140. https://doi.org/10.1109/RoboMech.2019.8704783

Download references

Author information

Authors and Affiliations

  1. Department of Computer Technology, Technical Sciences Vocational School, Fırat University, Elazig, Turkey

    Mesut Toğaçar

  2. Department of Computer Engineering, Faculty of Engineering, Fırat University, Elazig, Turkey

    Burhan Ergen

  3. Department of Software Engineering, Faculty of Engineering, Samsun University, Samsun, Turkey

    Zafer Cömert

Authors
  1. Mesut Toğaçar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Burhan Ergen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zafer Cömert
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mesut Toğaçar.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict to interest.

Ethical approval

This article does not contain any data, or other information from studies or experimentation, with the involvement of human or animal subjects.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Toğaçar, M., Ergen, B. & Cömert, Z. Detection of weather images by using spiking neural networks of deep learning models. Neural Comput & Applic 33, 6147–6159 (2021). https://doi.org/10.1007/s00521-020-05388-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-020-05388-3

Keywords

Search

Navigation