Iron Oxide Nanoparticle Image Analysis Using Machine Learning Algorithms

Bannigidad, Parashuram; Potraj, Namita; Gurubasavaraj, Prabhuodeyara M.; Anigol, Lakkappa B

doi:10.1007/978-981-19-5482-5_20

Cited by 1 publication

(1 citation statement)

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“…(10) . Parashuram Bannigidad et.al (11) made an effort to automate a tool that can determine the size of TEM images of Iron Oxide Nanoparticles using the Gaussian Mixture Model -Expectancy Maximization (GMM-EM) segmentation technique to extract various geometrical features from the nanoparticles. Mekki-Berradaey et.…”

Section: Literature Surveymentioning

confidence: 99%

An Improved Machine Learning Algorithm for Silver Nanoparticle Images: A Study on Computational Nano-Materials

Bannigidad¹,

Potraj²,

Gurubasavaraj³

2023

IJST

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Objectives: To study the determination and classification of Scanning Electron Microscopy (SEM) images of silver nanoparticles using digital image processing techniques and classifying the images using various machine learning classifiers namely; SVM, K-NN, and PNN classifiers. Methods: Segmentation techniques namely; Fuzzy C-Means (FCM) and K-Means were applied to extract geometric features from SEM images of silver nanoparticles. The size of nanoparticles was determined and classified based on various nano applications. The categorization of silver nanoparticles was done based on the geometrical feature value, i.e. area, and was further classified into three categories such as; 0-100 nm, 101-200 nm, and 201-500 nm using both the segmentation techniques. Further, the proposed method extracts texture features such as; kurtosis, skewness, and entropy and used SVM, K-NN, and PNN classifiers to classify the silver nanoparticle SEM images. The proposed results are analyzed and interpreted by chemical experts to observe the effectiveness of the proposed method. Findings: To improve the classification accuracy of Silver nanoparticle images, textural features such as; kurtosis, skewness, and entropy were extracted and SVM, K-NN, and PNN classifiers are used to classify the silver nanoparticle images. The performance of the proposed algorithm is evaluated based on performance evaluation measures such as; specificity, sensitivity, true predictive value, and false positive value. Higher TPV, specificity, and sensitivity values show promising results. It is observed that the SVM classifier has 75.00%, K-NN has 62.50%, and PNN yielded an accuracy of 93.00%. Novelty: Traditional synthesis and characterization techniques are inefficient and time-consuming and require more space and infrastructure to analyze the size of the nanoparticle. The current study circumvents these limitations. The silver nanoparticle images used in the present study are synthesized using rudanti fruit seeds, and the process is named as green synthesis process or biological method. This method is widely used because it does not use anytoxic chemicals, is inexpensive, and https://www.indjst.org/ 1284

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Section: Literature Surveymentioning

confidence: 99%