Machine Learning Techniques for Predicting Metamaterial Microwave Absorption Performance: A Comparison

Jain, Prince; Chhabra, Himanshu; Chauhan, Urvashi; Prakash, Krishna; Samant, Piyush; Singh, Dhiraj Kumar; Soliman, Mohamed S.; Islam, Mohammad Tariqul

doi:10.1109/access.2023.3332731

Cited by 21 publications

(4 citation statements)

References 44 publications

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“…The approach using several machine learning regression methods as powerful tools that can significantly reduce the time and resources needed to simulate complex systems is established in many scientific fields, as shown by Jain et al [29], who used an array of ML regressors to forecast the performance of microwave absorption. In contrast, the amount of research on aroma partitioning using machine learning regression methods is limited.…”

Section: Modeling Aroma Partitioningmentioning

confidence: 99%

Using a Machine Learning Regression Approach to Predict the Aroma Partitioning in Dairy Matrices

Anker,

Borsum,

Zhang

et al. 2024

Processes

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Aroma partitioning in food is a challenging area of research due to the contribution of several physical and chemical factors that affect the binding and release of aroma in food matrices. The partition coefficient measured by the Kmg value refers to the partition coefficient that describes how aroma compounds distribute themselves between matrices and a gas phase, such as between different components of a food matrix and air. This study introduces a regression approach to predict the Kmg value of aroma compounds of a wide range of physicochemical properties in dairy matrices representing products of different compositions and/or processing. The approach consists of data cleaning, grouping based on the temperature of Kmg analysis, pre-processing (log transformation and normalization), and, finally, the development and evaluation of prediction models with regression methods. We compared regression analysis with linear regression (LR) to five machine-learning-based regression algorithms: Random Forest Regressor (RFR), Gradient Boosting Regression (GBR), Extreme Gradient Boosting (XGBoost, XGB), Support Vector Regression (SVR), and Artificial Neural Network Regression (NNR). Explainable AI (XAI) was used to calculate feature importance and therefore identify the features that mainly contribute to the prediction. The top three features that were identified are log P, specific gravity, and molecular weight. For the prediction of the Kmg in dairy matrices, R2 scores of up to 0.99 were reached. For 37.0 °C, which resembles the temperature of the mouth, RFR delivered the best results, and, at lower temperatures of 7.0 °C, typical for a household fridge, XGB performed best. The results from the models work as a proof of concept and show the applicability of a data-driven approach with machine learning to predict the Kmg value of aroma compounds in different dairy matrices.

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Section: Modeling Aroma Partitioningmentioning

confidence: 99%

Using a Machine Learning Regression Approach to Predict the Aroma Partitioning in Dairy Matrices

Anker,

Borsum,

Zhang

et al. 2024

Processes

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“…Further, machine learning, deep learning and domain adaptation algorithms were tested directly to know the capabilities of the proposed methodology. Standard Machine learning algorithms [74,75] such as (Logistic regression (LR), KNNs, classification and regression tree (CART), support vector machine (SVM), random forest (RF), and artificial neural networks (ANN)), and traditional deep learning joint distribution adaptation (JDA), balanced distribution adaptation (BDA), easy transfer learning (EasyTL) were compared against ADDA. Machine learning and traditional domain adaptation algorithms were trained and tested using hand crafted features as shown in table 2.…”

Section: Target Testingmentioning

confidence: 99%

Genetically optimised SMOTE-based adversarial discriminative domain adaptation for rotor fault diagnosis at variable operating conditions

Rajagopalan,

Purohit,

Singh

2024

Meas. Sci. Technol.

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For safety, reliability, and uninterrupted output of gas turbines, aviation engines, power-generating equipment, pumps, gears, compressors etc., rotor mass imbalance must be detected and diagnosed to avoid catastrophic failure. Industry 4.0 relies on predictive digital maintenance and deep learning-based convolutional neural network (CNN), which predicts defects but fails if the operating conditions change. Research studies in various fields indicate that the domain shift issue occurs due to source and target samples being from different domains, which reduces prediction capability. Moreover, research studies are scarce in examining prediction capability under varying operating speeds for rotor mass imbalance. Hence, this research proposes the adversarial discriminative domain adaptation (ADDA) technique which predicts machine failures under various operational conditions. The efficacy of ADDA has been explored by introducing 1D-CNN as a source and a target encoder inside ADDA's architecture to take advantage of CNN's feature extraction capability. Further, this research effectively tackles CNN's inherent issues of overfitting and hyperparameters value selection. Furthermore, The real-world scenario has more healthy samples than fault condition samples, causing a multiclass imbalance in sample data, which affects the classification decision boundary and causes biased prediction. Hence, the proposed methodology first addresses the class imbalance through synthetic minority oversampling (SMOTE), then genetic algorithm optimizes 1D-CNN's hyperparameters, and the effective dropout layer positioning solves the overfitting. Finally, the deep learning-based SMOTE_ADDA_GO-1D-CNN decreases domain discrepancy with adversarial discriminative domain adaptation. The proposed methodology's efficacy has been explored through F1-Score, which is used as multiclass evaluation metrics, and it has been benchmarked against standard machine learning and deep learning algorithms. The test results of the proposed methodology surpassed all of them with maximum prediction accuracy. Thus, this study contributes to rotor mass imbalance detection and diagnosis for multiclass imbalanced data under varying operational conditions by successfully overcoming potential challenges during fault prediction.

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“…The paper also addresses challenges and limitations of the approach while hinting at future directions. In another research article, a team led by Jain, Chhabra [21] introduces machine learning techniques to predict metamaterial microwave absorption performance. They propose a compact, ultra-thin metamaterial absorber exhibiting four distinct absorption peaks in X-and Ku-band applications.…”

Section: Introductionmentioning

confidence: 99%

Advancements in Understanding Interface Friction: A Combined Experimental and Machine Learning Approach Using Multiple Linear and Random Forest Regressions

Daghistani,

Abuel-Naga

2024

Geotechnics

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The interface friction between granular materials and continuum surfaces is fundamental in civil engineering, especially in geotechnical projects where sand of varying sizes and shapes contacts surfaces with different roughness and hardness. The aim of this research is to investigate the parameters that influence the peak interface friction, taking into consideration the properties of both sand and continuum surfaces. This will be accomplished by employing a combination of experimental and machine learning techniques. In the experiment, a series of interface shear tests were conducted using a direct shear apparatus under differing levels of normal stress and density. Utilising machine learning techniques, the study considered eleven input features: mean particle size, void ratio, specific gravity, particle regularity, coefficient of uniformity, coefficient of curvature, granular rubber content, carpet fibre content, normal stress, surface roughness, and surface hardness. The output measured was the peak interface friction. The machine learning techniques enable us to explore the complex relationships between the input features and the peak interface friction, and to develop an empirical equation that can accurately predict the interface friction. The experiment findings reveal that density, inclusion of recycled material, and normalised roughness impact peak interface friction. The machine learning findings validate the efficacy of both multiple linear regression and random forest regression models in predicting the peak interface friction, with the latter outperforming the former in terms of accuracy when compared to the experiment results. Furthermore, the most important features from both models were identified.

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Machine Learning Techniques for Predicting Metamaterial Microwave Absorption Performance: A Comparison

Abstract: This work presents a metamaterial absorber (MMA) for X-and Ku-bands with a metallic resonating patch on top and a ground plane separated by substrate FR-4 with a thickness of 0.053 λ at the lowest resonance frequency.

Cited by 21 publications

References 44 publications

Using a Machine Learning Regression Approach to Predict the Aroma Partitioning in Dairy Matrices

Using a Machine Learning Regression Approach to Predict the Aroma Partitioning in Dairy Matrices

Genetically optimised SMOTE-based adversarial discriminative domain adaptation for rotor fault diagnosis at variable operating conditions

Advancements in Understanding Interface Friction: A Combined Experimental and Machine Learning Approach Using Multiple Linear and Random Forest Regressions

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