A machinelearning technique to diagnose thyroid disease via proper analysis is a major classification problem. The thyroid organ is an important part of our body. It helps to control our metabolism. Less amount of th...
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ISBN:
(纸本)9781665414517
A machinelearning technique to diagnose thyroid disease via proper analysis is a major classification problem. The thyroid organ is an important part of our body. It helps to control our metabolism. Less amount of thyroid hormone causes hypothyroidism, and more amount of thyroid hormone causes hyperthyroidism. Therefore, the current work objective was to build a machinelearning-based classification model to classify samples with thyroid disease from a publically available dataset. The classes were labeled as healthy and thyroid disease with many explanatory variables. A class balancer, namely Synthetic Minority Oversampling Technique (SMOTE), was used to balance the minority class (thyroid disease) in the dataset. In this work, filterbased feature selection algorithms, specifically mutual information in conjunction with a two-class Neural Network (NN) classifier, was used with azuremachinelearning tools to construct a predictive model. Our proposed two-class NN model build using selected features in association with SMOTE performed better than other recent ML models with an F1Score (0.982), precision (0.968), recall (0.995), and accuracy (0.981), respectively.
The paper deals with the development of a system for automatic weld recognition using new information technologies based on cloud computing and single-board computer in the context of Industry 4.0. The proposed system...
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ISBN:
(纸本)9781538640111
The paper deals with the development of a system for automatic weld recognition using new information technologies based on cloud computing and single-board computer in the context of Industry 4.0. The proposed system is based on a visual system for weld recognition, and a neural network based on cloud computing for real-time weld evaluation, both implemented on a single-board low-cost computer. The proposed system was successfully verified on welding samples which correspond to a real welding process in the car production process. The system considerably contributes to the welds diagnostics in industrial processes of small-and medium-sized enterprises.
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