Skip to main content
SpringerLink
Log in

Analyze Performance of Embedded Systems with Machine Learning Algorithms

  • Conference paper
  • First Online:
Trends in Data Engineering Methods for Intelligent Systems (ICAIAME 2020)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 76))

  • 694 Accesses

Abstract

This article aims to analyze the performance of embedded systems using various machine learning algorithms on embedded systems. For this, data set were analyzed using Python programming language and related libraries. Using different machine learning algorithms, it has been useful in terms of giving us an idea about how these cards are sufficient for solving deep learning and artificial intelligence problems or how close they will be to the desired performances. Raspberry pi and jatson nano embedded system boards were used for performance measurement and they were compared with notebook performance.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 219.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 279.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Şansal, U., Temurtaş, F.: Gömülü Sistem Tabanlı Ev Otomasyon ve Güvenlik Sistemi Tasarımı. Bozok Üniversitesi Fen Bilimleri Enstitüsü, Mekatronik Müh. Anabilim Dalı Yüksek Lisans Tezi, Yozgat

    Google Scholar 

  2. İlhan, H.O.: Gömülü Sistemlerde Kablosuz Haberleşme Protokolü ile Görüntü ve Video Aktarımı. Yalova Üniversitesi, Fen Bilimleri Enstitüsü, Bilgisayar Müh. Anabilim Dalı Yüksek Lisans Tezi, Yalova

    Google Scholar 

  3. https://medium.com/@k.ulgen90/python-ile-k%C3%BCmeleme-algoritmalar%C4%B1-makine-%C3%B6%C4%9Frenimi-b%C3%B6l%C3%BCm-8-8204ffa702f2. Accessed 12 Jan 2020

  4. https://makineogrenimi.wordpress.com/2017/05/25/makine-ogrenmesi-teknikleri/. Accessed 12 Jan 2020

  5. Zidek, K., Piteľ, J., Hošovský, A.: Machine learning algorithms ımplementation into embedded systems with web application user ınterface. In: INES 2017 21st International Conference on Intelligent Engineering Systems, Larnaca, Cyprus, 20–23 October 2017 (2017)

    Google Scholar 

  6. Dünder, E., Cengiz, M.A., Koç, H., Savaş, N.: Bayesci ağlarda risk analizi: Bankacılık sektörü üzerine bir uygulama. Erzincan Üniversitesi Sosyal Bilimler Enstitüsü Dergisi 6(1), 1–14 (2013)

    Google Scholar 

  7. Bayer, H., Çoban, T.: “Web İstatistiklerinde Makine Öğrenmesi Algoritmaları ile Kritik Parametre Tespiti” (Electronic Journal of Vocational Colleges- Special Issue: The Latest Trends in Engineering), 28 Aralık 2015

    Google Scholar 

  8. https://erdincuzun.com/makine_ogrenmesi/decision-tree-karar-agaci-id3-algoritmasi-classification-siniflama/. Accessed 11 Jan 2020

Download references

Author information

Authors and Affiliations

  1. Suleyman Demirel University, Isparta, Turkey

    Mevlüt Ersoy

  2. Iskenderun Technical University, Hatay, Turkey

    Uğur Şansal

Authors
  1. Mevlüt Ersoy
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Uğur Şansal
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Uğur Şansal .

Editor information

Editors and Affiliations

  1. Department of Electronics and Communication Engineering, Karunya University, Coimbatore, Tamil Nadu, India

    Jude Hemanth

  2. Department of Computer Engineering, Faculty of Engineering, Süleyman Demirel University, Isparta, Turkey

    Tuncay Yigit

  3. Department of Computer Science, Alexandru Ioan Cuza University, Iasi, Romania

    Bogdan Patrut

  4. Department of Computer Science and Engineering, University of Westminster, London, UK

    Anastassia Angelopoulou

Rights and permissions

Reprints and permissions

Copyright information

© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Ersoy, M., Şansal, U. (2021). Analyze Performance of Embedded Systems with Machine Learning Algorithms. In: Hemanth, J., Yigit, T., Patrut, B., Angelopoulou, A. (eds) Trends in Data Engineering Methods for Intelligent Systems. ICAIAME 2020. Lecture Notes on Data Engineering and Communications Technologies, vol 76. Springer, Cham. https://doi.org/10.1007/978-3-030-79357-9_23

Download citation

Publish with us

Policies and ethics

Search

Navigation