Optimized Twin Support Vector Clustering in Transmission Electron Microscope of Cobalt Nanoparticles

  • Atrab A. Abd El-AzizEmail author
  • Heba Al ShaterEmail author
  • A. Dakhlaoui
  • Aboul Ella HassanienEmail author
  • Deepak Gupta
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1087)


Twin support vector clustering (TWSVC) algorithm has attracted a growing focus in recent years. In this paper, an enhanced TWSVC algorithm is proposed by using particle swarm optimization (PSO) for obtaining k-cluster planes and assigning each data sample to a correct cluster. This enhanced algorithm basically uses TWSVC to refine the clusters formed by PSO. The hybrid PSO-TWSVC algorithm is evaluated on a collected cobalt dataset in the nanotechnology field and then tested on four public datasets. The experiments of hybrid PSO-TWSVC with the classical K-means and TWSVC algorithms have proved that the hybrid PSO-TWSVC clustering algorithm has obvious advantages on accuracy.


Particle swarm optimization Twin SVC Nanotechnology Cobalt TEM 


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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.FCIKafrelSheikh UniversityKafr El-SheikhEgypt
  2. 2.Forensic and Clinical Toxicology DepartmentEl Menoufia University HospitalAl MinufyaEgypt
  3. 3.Département de ChimieFaculté Des Sciences de BizerteJarzounaTunisia
  4. 4.Faculty of Computers and InformationCairo UniversityGizaEgypt
  5. 5.Maharaja Agrasen Institute of TechnologyDelhiIndia

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