Modeling and multi-class classification of vibroarthographic signals via time domain curvilinear divergence random forest

Abstract:: Millions of deaths all over the world are caused by breast cancer every year. It has become the most common type of cancer in women. Early detection will help in better prognosis and increases the chance of survival. Automating the classification using Computer-Aided Diagnosis (CAD) systems can make the diagnosis less prone to errors. Multi class classification and Binary classification of breast cancer is a challenging problem. Convolutional neural network architectures extract specific feature descriptors from images, which cannot represent different types of breast cancer. This leads to false positives in classification, which is undesirable in disease diagnosis. The current paper presents an ensemble Convolutional neural network for multi class classification and Binary classification of breast cancer. The feature descriptors from each network are combined to produce the final classification. In this paper, histopathological images are taken from publicly available BreakHis dataset and classified between 8 classes. The proposed ensemble model can perform better when compared to the methods proposed in the literature. The results showed that the proposed model could be a viable approach for breast cancer classification.

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Classification of Germination Images of Pear Pollen Using Random Forest and Convolution Neural Network Models

IEEE Access ◽

10.1109/access.2021.3067677 ◽

2021 ◽

Vol 9 ◽

pp. 45993-45999

Author(s):

Ung Yang ◽

Seungwon Oh ◽

Seung Gon Wi ◽

Bok-Rye Lee ◽

Sang-Hyun Lee ◽

...

Keyword(s):

Neural Network ◽

Random Forest ◽

Network Models ◽

Convolution Neural Network ◽

Neural Network Models

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Image Classification of Rice Leaf Diseases Using Random Forest Algorithm

2021 Joint International Conference on Digital Arts, Media and Technology with ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunication Engineering ◽

10.1109/ectidamtncon51128.2021.9425696 ◽

2021 ◽

Author(s):

Panuwat Mekha ◽

Nutnicha Teeyasuksaet

Keyword(s):

Random Forest ◽

Image Classification ◽

Random Forest Algorithm ◽

Rice Leaf

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Multivariate Analysis for the Classification of Chocolate According to its Percentage of Cocoa by Using Terahertz Time-Domain Spectroscopy (THz-TDS)

Proceedings ◽

10.3390/foods_2020-08029 ◽

2020 ◽

Vol 70 (1) ◽

pp. 109

Author(s):

Jimy Oblitas ◽

Jorge Ruiz

Keyword(s):

Machine Learning ◽

Time Domain ◽

Electromagnetic Pulse ◽

Machine Learning Algorithms ◽

Classification Models ◽

Terahertz Time Domain Spectroscopy ◽

Time Domain Spectroscopy ◽

Svm Algorithm ◽

Classification Of Images

Terahertz time-domain spectroscopy is a useful technique for determining some physical characteristics of materials, and is based on selective frequency absorption of a broad-spectrum electromagnetic pulse. In order to investigate the potential of this technology to classify cocoa percentages in chocolates, the terahertz spectra (0.5–10 THz) of five chocolate samples (50%, 60%, 70%, 80% and 90% of cocoa) were examined. The acquired data matrices were analyzed with the MATLAB 2019b application, from which the dielectric function was obtained along with the absorbance curves, and were classified by using 24 mathematical classification models, achieving differentiations of around 93% obtained by the Gaussian SVM algorithm model with a kernel scale of 0.35 and a one-against-one multiclass method. It was concluded that the combined processing and classification of images obtained from the terahertz time-domain spectroscopy and the use of machine learning algorithms can be used to successfully classify chocolates with different percentages of cocoa.

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