Development of physical property prediction models for polypropylene composites with optimizing random forest hyperparameters

Joo, Chonghyo; Park, Hyundo; Lim, Jonghun; Cho, Hyungtae; Kim, Junghwan

doi:10.1002/int.22700

Cited by 27 publications

(17 citation statements)

References 53 publications

(91 reference statements)

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“…RF 40,41 is an ensemble algorithm, which is composed of univariate decision trees. For each decision tree, some samples are randomly selected from the training set, and subset features are randomly selected from the total feature set to construct the decision tree.…”

Section: Preliminaries On Machine Learning Methodsmentioning

confidence: 99%

Towards blind detection of steganography in low‐bit‐rate speech streams

Sun

Tian

Mazurczyk

et al. 2022

Int J of Intelligent Sys

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To prevent the abuse of low‐rate speech‐based steganography from threatening cyberspace security, the corresponding steganalysis approaches have been developed and received significant attention from research community. However, most existing steganalysis methods assume that steganography methods are known in advance, which in practice is impractical. That is why, in this paper, we present three blind detection schemes suitable for steganography in low‐bit‐rate speech streams. The first is based on mixed sample data augmentation. It randomly selects a certain proportion of steganographic samples from the sample set of each steganographic method to form a training set together with the original carrier samples for training to enhance the robustness of the model. The second relies on decision fusion where first step is to train a dedicated classification model for each steganography method and then use a majority voting mechanism in the detection stage to fuse the outputs of each model to give the final detection result. Compared to the other two steganalysis schemes, the third one design the detection model based on self‐paced ensemble according to the distribution characteristics of speech samples. Its main idea is to fully train multiple base classifiers through multiple iterations as well as under‐sampling processes, and organically fuse them to form a powerful ensemble classifier. In each iteration, differing from the traditional ensemble classifier solution, we put more attention to the steganographic samples at the decision boundary for the under‐sampling process of the steganography set composed of multiple steganography methods, rather than randomly selecting steganographic samples. The steganographic samples at the decision boundary are searched using the classification hardness given by the ensemble classifier trained in the last iteration, which is more informative and more conducive to improve the performance of base classifiers. The experimental results show that the proposed three schemes can achieve efficient blind detection for low‐bit‐rate speech‐based steganography, and the steganalysis scheme based on the self‐paced ensemble has the best performance. Specifically, when the embedding rate is at 30%, the accuracy of the steganalysis scheme based on self‐paced ensemble is more than 85%, while the accuracy of the other two steganalysis method is less than 80%. Additionally, the steganalysis scheme based on the self‐paced ensemble learning even outperforms dedicated detectors for specific steganographic methods in terms of recall for steganographic sample detection.

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Section: Preliminaries On Machine Learning Methodsmentioning

confidence: 99%

Towards blind detection of steganography in low‐bit‐rate speech streams

Sun

Tian

Mazurczyk

et al. 2022

Int J of Intelligent Sys

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show abstract

“…R 2 is the coefficient of determination, which means a measure of how well the estimated linear model fits the given data. The closer the R 2 value is to 1, the higher the correlation between the actual value and the predicted value, and the higher the performance of the forecasting model 26–28 …”

Section: Preliminariesmentioning

confidence: 99%

“…The closer the R 2 value is to 1, the higher the correlation between the actual value and the predicted value, and the higher the performance of the forecasting model. [26][27][28] R 2 expressed as:…”

Section: Rmse and Rmentioning

confidence: 99%

A framework for energy optimization of distillation process using machine learning‐based predictive model

Park

Kwon

Cho

et al. 2022

Energy Science & Engineering

Self Cite

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The distillation process is one of the most common and energy‐intensive processes in the chemical industry. Most chemical processes are nonlinear and complex, because of which, it is difficult to find optimal operating conditions. To solve this problem, we developed a framework for energy optimization of the distillation process based on a machine learning (ML) model. The framework enables the efficient operation of the process by using the optimal operating conditions recommended by the ML‐based predictive model. The predictive model, which is a key component, is developed in three steps: learning, validation, and improvement. In the learning step, we select an algorithm suitable for the purpose of the process and learn process data. In the validation step, the model is validated using hold‐out cross‐validation. Finally, in the improvement step, the model performance is improved through hyper‐parameter optimization. We applied the framework to a commercial mixed butane distillation columns of 45,000 metric tons per annum capacity. The predictive model was based on commercial process data, and it can be used to predict the temperature at the product stage. The model recommended the steam flow rate required to maintain the target temperature of the product stage as per the operating conditions. The recommended steam flow rate will be guideline for the on‐site operator. The software is developed that the predictive model can be easily applied to the commercial processes, and it identifies the state of the process and recommends optimal operating conditions.

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“…We additionally train a Deep Neural Network (DNN) which learns a set of hierarchical nonlinear transformations [74]. To control the learning process, we have to tune hyperparameters because they cannot be inferred while training classification models [75]. Grid search with cross-validation has been frequently used to obtain optimal hyperparameter combinations.…”

Section: B Training Classification Models To Predict New Technology C...mentioning

confidence: 99%

A Supervised Learning-Based Approach to Anticipating Potential Technology Convergence

2022

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Development of physical property prediction models for polypropylene composites with optimizing random forest hyperparameters

Cited by 27 publications

References 53 publications

Towards blind detection of steganography in low‐bit‐rate speech streams

Towards blind detection of steganography in low‐bit‐rate speech streams

A framework for energy optimization of distillation process using machine learning‐based predictive model

A Supervised Learning-Based Approach to Anticipating Potential Technology Convergence

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