Akademik Veri Yönetim Sistemi
5th International Eurasian Conference on Science, Engineering and Technology (EurasianSciEnTech 2024), Ankara, Türkiye, 26 - 28 Haziran 2024, ss.87-88
Nanomaterials are an important research topic of great interest in the fields of materials science and
nanotechnology. These materials can have unique physical, chemical and mechanical properties, especially when
their size is on the nanometre scale. These properties make nanomaterials potentially valuable for many industrial
applications, for example, in areas such as biomedical, electronics, energy storage and catalysis. In nanomaterial
research, the characterisation of the structural and physical properties of the material is of vital importance. In this
characterisation process, imaging techniques offer powerful tools for visual analysis and classification of
nanomaterial properties. However, the complex and multidimensional nature of nanomaterials can make it difficult
to apply conventional image processing and analysis methods. In this context, machine learning techniques offer
a novel and effective approach for image classification of nanomaterial features. Machine learning is known for
its ability to analyse large amounts of data and recognise patterns and can facilitate the analysis of these data by
extracting features from nanomaterial images and building classification models. Bag of Features methods have
been used in many computer vision fields such as image classification. Bag of Features method for image
classification of nanomaterial features is a technique used in the field of machine learning. This method includes
feature extraction and classification from images. The aim of this study is to use Bag of Features method and
machine learning techniques for image classification of nanomaterial features. The results of this study will
contribute to faster and more effective analysis of nanomaterial properties by showing how image processing and
machine learning techniques can be used in nanomaterial research.