Yazar "Fayez, Mustafa" seçeneğine göre listele

Listeleniyor 1 - 2 / 2
  • Küçük Resim
    Öğe
    Application area of classification techniques in medicine
    (Altınbaş Üniversitesi, 2018) Ata, Oğuz; Fayez, Mustafa
    The health care industry produces a huge amount of data that collects complex patient and medical information. Data mining is popular in various fields of research because of its applications and methodologies to extract information correctly. Data mining techniques have the capabilities to find out veiled forms or relationships among the objects in the medical data. In addition the most data mining algorithms that had used in medical industry until this time are neural network including deep learning, SVM, Bayesian and fizzy logic. The main reason of use these algorithms that because they are gave best results with high accuracy with different type of medicine datasets. Finally, data mining continues with medicine industry to help people with or solve different clinical problems.
  • [ X ]
    Öğe
    RETRACTED: Novel method for diagnosis diseases using advanced high-performance machine learning system
    (Springer Heidelberg, 2021) Fayez, Mustafa; Kurnaz, Sefer
    Machine learning (ML) is also seen as an advanced technique that is only usable by highly qualified specialists. This prohibits this instrument from being utilized by many doctors and biologists in their studies. This paper's purpose is to eradicate this obsolete perception. We claim that the recent creation of advanced high-performance ML techniques helps biomedical researchers to create competitive ML models rapidly without needing in-depth knowledge of the algorithms underlying them. This advanced system is implemented used best programming tool Python including two parts. Firstly, feature engineering and preprocessing with the Neighborhood Cleaning Rule (NCL) high-performance re-sampling procedure. Second, advanced models for high-performance machine learning, including AutoML, advanced XGBoost, and advanced ensemble bagging models. Finally, we believe that our developments would improve the way doctors interpret machine learning utilizing sophisticated and high-performance machine learning technologies and facilitate broad clinical use of Artificial Intelligence (AI) techniques.