Association Factor for Identifying Linear and Nonlinear Correlations in Noisy Conditions

Kachouie, Nezamoddin N.; Deebani, Wejdan

doi:10.3390/e22040440

Cited by 5 publications

(2 citation statements)

References 21 publications

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“…Even though traditional data analysis techniques such as scatterplots and Pearson correlation heatmaps are essential to discover the synergies between 2 variables, there is difficulty in displaying the interaction of variables in more complex interactions (i.e., nonlinear, highly noisy) in higher dimensional space (i.e., multivariate interaction). Therefore, machine learning and feature importance analysis is utilized to discover the interactions of complex variables and simplifying the synergies for human understanding .…”

Section: Advancement Of 2d Materials Research With Data Science and M...mentioning

confidence: 99%

Liquid Phase Exfoliation of 2D Materials and Its Electrochemical Applications in the Data-Driven Future

Chavalekvirat,

Hirunpinyopas,

Deshsorn

et al. 2024

Precision Chemistry

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The electrochemical properties of 2D materials, particularly transition metal dichalcogenides (TMDs), hinge on their structural and chemical characteristics. To be practically viable, achieving large-scale, high-yield production is crucial, ensuring both quality and electrochemical suitability for applications in energy storage, electrocatalysis, and potentialbased ionic sieving membranes. A prerequisite for success is a deep understanding of the synthesis process, forming a critical link between materials synthesis and electrochemical performance. This review extensively examines the liquid-phase exfoliation technique, providing insights into potential advancements and strategies to optimize the TMDs nanosheet yield while preserving their electrochemical attributes. The primary goal is to compile techniques for enhancing TMDs nanosheet yield through direct liquid-phase exfoliation, considering parameters like solvents, surfactants, centrifugation, and sonication dynamics. Beyond addressing the exfoliation yield, the review emphasizes the potential impact of these parameters on the structural and chemical properties of TMD nanosheets, highlighting their pivotal role in electrochemical applications. Acknowledging evolving research methodologies, the review explores integrating machine learning and data science as tools for understanding relationships and key characteristics. Envisioned to advance 2D material research, including the optimization of graphene, MXenes, and TMDs synthesis for electrochemical applications, this compilation charts a course toward data-driven techniques. By bridging experimental and machine learning approaches, it promises to reshape the landscape of knowledge in electrochemistry, offering a transformative resource for the academic community.

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Section: Advancement Of 2d Materials Research With Data Science and M...mentioning

confidence: 99%

Liquid Phase Exfoliation of 2D Materials and Its Electrochemical Applications in the Data-Driven Future

Chavalekvirat,

Hirunpinyopas,

Deshsorn

et al. 2024

Precision Chemistry

View full text Add to dashboard Cite

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“…Other studies aim at expanding and utilizing the measures in a general manner. They proposed methods based on the measures for the following: statistical tests [44]- [46], robustness improvement [47]- [49], algorithmic strategies for multi dependence detection [50], [51], feature selection [52]- [56], and feature extraction [57], [58]. The other studies are to utilize the measures in specific domains, the applications in short, which are as given in Section I-A.…”

Section: Cc(x Y) =mentioning

confidence: 99%

NNR-GL: A Measure to Detect Co-Nonlinearity Based on Neural Network Regression Regularized by Group Lasso

et al. 2021

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For finding keys to understand and elucidate a phenomenon, it is essential to detect dependences among variables, and so measures for that have been proposed. Correlation coefficient and its variants are most common, but they only detect a linear dependence (co-linearity) between two variables. Some recent measures can detect a nonlinear dependence (co-nonlinearity) by means of kernelization or segmentation. They are supposed to handle two variables only and open to discussion with regard to performance in detection and difficulty in setup. There is room for a novel measure based on Neural Networks (NNs), since usual NNs aim at prediction but not at variable dependence detection. For the high-performance detection of co-nonlinearities among multi variables, we propose a measure called NNR-GL based on Neural Network Regression (NNR) regularized by Group Lasso (GL). NNR-GL embodies the detection through multi-input single-output regression by NNR and regularization on the input layer by GL. NNR-GL then calculates how strong the detected co-nonlinearities are by unifying the regression performance and the weights on input variables. We conducted experiments using artificial data to examine the behaviors and fundamental effectiveness of NNR-GL. The performance was estimated by a comprehensive detection performance criterion (CDP-AUC in short), which is the mean of area under curves representing true positive and true negative detections. NNR-GL achieved the values of CDP-AUC from 0.7472 to 0.9681, where 0 means complete failure and 1 means complete success in detection. These values were consistently higher than those from 0.5972 to 0.9259 of the conventional measures for all the different conditions of dependence, data size, and noise rate. Consequently, the effectiveness and robustness of NNR-GL were clearly confirmed.

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Data assimilation for urban stormwater and water quality simulations using deep reinforcement learning

Jeung

Jang

Yoon

et al. 2023

Journal of Hydrology

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Association Factor for Identifying Linear and Nonlinear Correlations in Noisy Conditions

Cited by 5 publications

References 21 publications

Liquid Phase Exfoliation of 2D Materials and Its Electrochemical Applications in the Data-Driven Future

Liquid Phase Exfoliation of 2D Materials and Its Electrochemical Applications in the Data-Driven Future

NNR-GL: A Measure to Detect Co-Nonlinearity Based on Neural Network Regression Regularized by Group Lasso

Data assimilation for urban stormwater and water quality simulations using deep reinforcement learning

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