Scopus İndeksli Yayınlar Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395

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Subject: search.filters.subject.Support Vector MachineWoS Q: search.filters.wosQuartile.Q3

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  • Article
    Citation - WoS: 1
    Citation - Scopus: 1
    PSO Supported Ensemble Algorithm for Bad Data Detection Against Intelligent Hacking Algorithm
    (Frontiers Media S.A., 2021-07-23) Yavuz, Levent; Soran, Ahmet; Onen, Ahmet; Muyeen, S. M.
    Power system cybersecurity has recently become important due to cyber-attacks. Due to advanced computer science and machine learning (ML) applications being used by malicious attackers, cybersecurity is becoming crucial to creating sustainable, reliable, efficient, and well-protected cyber-systems. Power system operators are needed to develop sophisticated detection mechanisms. In this study, a novel machine-learning-based detection algorithm that combines the five most popular ML algorithms with Particle Swarm Optimizer (PSO) is developed and tested by using an intelligent hacking algorithm that is specially developed to measure the effectiveness of this study. The hacking algorithm provides three different types of injections: random, continuous random, and slow injections by adaptive manner. This would make detection harder. Results shows that recall values with the proposed algorithm for each different type of attack have been increased.
  • Article
    Citation - WoS: 1
    Citation - Scopus: 1
    3-State Protein Secondary Structure Prediction Based on Scope Classes
    (Inst Tecnologia Parana, 2021) Atasever, Sema; Azginoglu, Nuh; Erbay, Hasan; Aydin, Zafer
    Improving the accuracy of protein secondary structure prediction has been an important task in bioinformatics since it is not only the starting point in obtaining tertiary structure in hierarchical modeling but also enhances sequence analysis and sequence-structure threading to help determine structure and function. Herein we present a model based on DSPRED classifier, a hybrid method composed of dynamic Bayesian networks and a support vector machine to predict 3-state secondary structure information of proteins. We used the SCOPe (Structural Classification of Proteins-extended) database to train and test the model. The results show that DSPRED reached a Q(3) accuracy rate of 82.36% when trained and tested using proteins from all SCOPe classes. We compared our method with the popular PSI PRED on the SCOPe test datasets and found that our method outperformed PSI PRED.