Adaptive wavelet diagnostic neuro-fuzzy network for biomedical tasks

Bodyanskiy, Yevgeniy; Perova, Iryna; Vynokurova, Olena; Izonin, Ivan

doi:10.1109/tcset.2018.8336299

Cited by 18 publications

(7 citation statements)

References 12 publications

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“…-recall [35]; -precision [36]; -average precision [37]; -precision at the level of 5 content articles [38]; -precision at the level of 10 content articles [39]; -R-precision [40]; -11-point matrix (TREC) [41]; -modified 11-point matrix (RIRES) [42]. To categorize information-retrieval systems, most often used are recall, precision, accuracy, error, and F-measure [43].…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

Design of the architecture of an intelligent system for distributing commercial content in the internet space based on SEO-technologies, neural networks, and Machine Learning

Lytvyn

Vysotska

Демчук³

et al. 2019

EEJET

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Section: Literature Review and Problem Statementmentioning

confidence: 99%

Design of the architecture of an intelligent system for distributing commercial content in the internet space based on SEO-technologies, neural networks, and Machine Learning

Lytvyn

Vysotska

Демчук³

et al. 2019

EEJET

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“…K-NN was the worst in all aspects, even obtaining a negative value for MCC. Although a simple and traditional method has been presented, it is worth experimenting with newer methods such as [46,47] that have shown to perform better.…”

Section: Resultsmentioning

confidence: 99%

A Clinical Decision Support Tool to Detect Invasive Ductal Carcinoma in Histopathological Images Using Support Vector Machines, Naïve-Bayes, and K-Nearest Neighbor Classifiers

Lopez

Magboo

2020

Machine Learning and Artificial Intelligence

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This study aims to describe a model that will apply image processing and traditional machine learning techniques specifically Support Vector Machines, Naïve-Bayes, and k-Nearest Neighbors to identify whether or not a given breast histopathological image has Invasive Ductal Carcinoma (IDC). The dataset consisted of 54,811 breast cancer image patches of size 50px x 50px, consisting of 39,148 IDC negative and 15,663 IDC positive. Feature extraction was accomplished using Oriented FAST and Rotated BRIEF (ORB) descriptors. Feature scaling was performed using Min-Max Normalization while K-Means Clustering on the ORB descriptors was used to generate the visual codebook. Automatic hyperparameter tuning using Grid Search Cross Validation was implemented although it can also accept user supplied hyperparameter values for SVM, Naïve Bayes, and K-NN models should the user want to do experimentation. Aside from computing for accuracy, the AUPRC and MCC metrics were used to address the dataset imbalance. The results showed that SVM has the best overall performance, obtaining accuracy = 0.7490, AUPRC = 0.5536, and MCC = 0.2924.

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“…When registering a new purse, there is no need to enter any data, including passport [34]. This is exactly how the maximum level of security for a payment is achieved, as well as anonymity of payments [35]. None of the currently existing virtual coins can be fake, because to do this it would be necessary to recalculate the entire blockchain, by replacing the required data on all computers [36].…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

Development of the system to integrate and generate content considering the cryptocurrent needs of users

Lytvyn

Vysotska

Kuchkovskiy³

et al. 2019

EEJET

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Adaptive wavelet diagnostic neuro-fuzzy network for biomedical tasks

Cited by 18 publications

References 12 publications

Design of the architecture of an intelligent system for distributing commercial content in the internet space based on SEO-technologies, neural networks, and Machine Learning

Design of the architecture of an intelligent system for distributing commercial content in the internet space based on SEO-technologies, neural networks, and Machine Learning

A Clinical Decision Support Tool to Detect Invasive Ductal Carcinoma in Histopathological Images Using Support Vector Machines, Naïve-Bayes, and K-Nearest Neighbor Classifiers

Development of the system to integrate and generate content considering the cryptocurrent needs of users

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