Flow Regime detection using gamma-ray-based multiphase flowmeter: a machine learning approach

Zhu, Jian Hua; Munjal, Ritesh; Sivaram, Ani; Paul, Santhiyapillai Rajeevan; Tian, Jie; Jolivet, Guillaume

doi:10.2495/cmem-v10-n1-26-37

Cited by 4 publications

(1 citation statement)

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“…Deep learning methodologies are frequently employed for pattern recognition, classification, and feature extraction tasks due to their ability to resolve issues in machine learning systems using multilevel concepts [11]. In other words, these methods operate on multiple layers, with each layer performing distinct functions.…”

Section: Convolutional Neural Network (Cnn)mentioning

confidence: 99%

An Automated System for Osteoarthritis Severity Scoring Using Residual Neural Networks

Rachmad,

Sonata,

Hutagalung

et al. 2023

MMEP

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Osteoarthritis (OA) is a chronic disease, characterized by progressive deterioration of cartilage tissue and consequent thinning of the cartilage layer within joints. This degradation leads to an increased likelihood of bone collision during movement, typically manifesting in patients as joint pain, knee swelling, stiffness, and difficulties in executing daily activities. The diagnosis of OA often involves the analysis of physical examination results, patient anamnesis, and additional supportive examinations, which are predominantly conducted manually. Addressing these challenges, this study harnesses Convolutional Neural Network (CNN) algorithms, specifically the Residual Neural Network and Mobile Neural Network architectures, to develop an automated system for classifying OA severity. Utilizing a knee image dataset comprised of 8260 records procured from NDA OAI, the model is trained and tested with a data split of 80% and 20% respectively. The Residual Neural Network (ResNet-101) architecture is employed for model training, utilizing Adam optimization with a learning rate set at 0.0001 over 50 epochs. The resulting model yields a training accuracy of 67.65%, and a validation accuracy of 57.06%. This study demonstrates the potential of CNN methods for automated, accurate classification of OA severity using knee imagery, thus offering a promising avenue for enhancing diagnostic efficiency and precision.

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Section: Convolutional Neural Network (Cnn)mentioning

confidence: 99%