Symbolic DNN-Tuner

This study explores the potential of the GPBoost approach for groundwater quality assessment in comparison to three other gradient boosting-based algorithms. Three methods, random search, grid search, and Bayesian optimization were used to find the optimal values of various hyperparameters with all four-gradient boosting-based algorithms. One hundred and two samples of Entropy weighted water quality index with 14 input parameters are used for assessing groundwater quality. The calculated EWQI values for drinking range between 80.4 and 394.96 in pre-monsoon and 39.6 to 338.79 during the post-monsoon period. Moreover, spatial distribution maps displayed that the central portions of the study area fall under medium water quality. The performances of models were compared based on multiple statistical criteria, including Correlation Coefficient (CC), root mean square error (RMSE), and mean absolute error (MAE). The results reveal that the CC value by all modeling approaches is more than 0.93, suggesting a comparable performance by all methods. Results in terms of RMSE values in predicting the EWQI values suggest GPBoost (random search) model performed better than the other three models, thus suggesting a competitive performance by GPBoost in comparison to other gradient boosting-based approaches. Relative importance analysis provided by random and grid search methods highlights the significance of NO3 −, Mg2+, TDS, EC, and TH as important input parameters for predicting EWQI.

User reviews on Google Play Store reflect satisfaction and expectations regarding digital services, including E-Government applications. This study aims to optimize IndoBERT performance in sentiment classification through fine-tuning and hyperparameter exploration using three methods: Grid Search, Random Search, and Bayesian Optimization. Experiments were conducted on Sinaga Mobile app reviews, evaluated using accuracy, precision, recall, F1-score, learning curve, and confusion matrix. The results show that Grid Search with a learning rate of 5e-5 and a batch size of 16 provides the best results, with an accuracy of 90.55%, precision of 91.16%, recall of 90.55%, and F1-score of 89.75%. The learning curve indicates stable training without overfitting. This study provides practical contributions as a guide for improving IndoBERT in Indonesian sentiment analysis and as a foundation for developing NLP-based review monitoring systems to enhance public digital services.

<bold>Background</bold>Neural networks, with their powerful fitting capabilities, can learn the relationships between input and output variables based on large amounts of data, often serve as proxy models for physical programs in the field of engineering calculations, including nuclear engineering calculations. Neutron transport calculations, as one of the core links in neutronics simulations, are often suffer from lengthy computational times. However, this issue can also be addressed by utilizing neural network models. Nevertheless, neural network models have a series of hyperparameters that need to be set, but manually adjusting these hyperparameters is laborious, repetitive, and reliant only on experience. Moreover, these hyperparameters are not reusable when solving different problems.<bold>Purpose</bold>This study aims to propose a novel algorithm to improve computational efficiency of core neutronic parameter prediction, and provide reference for the application of artificial intelligence in core physics calculation.<bold>Methods</bold>The dataset was derived from the VITAS program's calculations of the TAKEDA1 and TAKEDA2 benchmark problems, consisting of 10 000 and 20 000 sets respectively. The core layout sequence was taken as input whilst the output included the effective multiplication factor and the regional integral flux. Thereafter, the core layout sequence was mapped into a one-dimensional vector, and the dataset was divided into training and validation sets in a 6:4 ratio. Then, the Bayesian optimization algorithm was employed to adjust fully connected neural network (FCNN) hyperparameters, and the adaptive learning rate decay and loss function optimization methods were combined to automatically search for the optimal combination of hyperparameters for datasets with different problems. Finally, critical core parameters, i.e., the effective multiplication factor and the regional integrated flux, were fitted to valid the generalization and accuracy of abovementioned algorithm, and a comparative experiment was conducted using manually set hyperparameters and hyperparameters output by Bayesian optimization as neural network training parameters.<bold>Results</bold>The results demonstrate that Bayesian optimization effectively enhances the neural network's accuracy. Specifically, the average error of the effective multiplication factor is within 1.50×10^-3, while that of the regional integral flux on the TAKEDA1 dataset is 1.72%, with a maximum error rate of 7.56%.<bold>Conclusions</bold>This approach can automatically search for the optimal combination of hyperparameters for different datasets to achieve the best performance, demonstrating high flexibility, efficiency, and strong generalization.

The availability of data is an important thing in the decision-making process. With the trend of using Machine Learning and Data Mining, humans are facilitated in learning and finding hidden knowledge in the data. One technique used in the classification of data is the Deep Learning method. Deep Learning is widely used because of its ability to solve problems with satisfying results. In other words, Deep Learning has shown to be the one of the most powerful and widely used methods. However, the key of success depends on the setting the right parameters. With this reason, we need a way to optimize the use of hyperparameter in Deep Learning. The method commonly used in this case is Grid Search. However, this method requires a long time and high computation. While other methods such as Random Search and Bayesian Optimization, both are very reliable and efficient compared to Grid Search. The results of the experiments conducted in this study indicate that Bayesian Optimization is better than Random Search in performing hyperparameter optimization processes in Deep Learning.

Multichannel surface electromyography-based hand gesture recognition plays an important role in the development of intuitive prosthetic and human-machine interfaces. It requires high accuracy, minimal noise degradation, and electrode drift tolerance for real-world deployment. The goal is to find the best tuned classifier configurations that balance accuracy and real-world robustness, comparing grid search, random search, and Bayesian optimization for the four classical classifiers: K-Nearest Neighbors (KNN), Decision Tree (DT), Random Forest (RF), and Support Vector Machine (SVM). Their performance is evaluated by repeated stratified 5 × 5 cross-validation with 25 runs and leave-one-subject-out testing. Furthermore, we use a multi-objective Pareto front analysis considering three factors: (1) accuracy, (2) adaptation to simulated drift, and (3) relative accuracy degradation under noise. KNN reaches stable 98.4% accuracy on all optimizers, but Bayesian optimization performs better than other optimization techniques in terms of accuracy and other performance metrics when using SVM, DT, and RF classifiers. Additionally, Bayesian optimization gets KNN into the Pareto front with 98.44% accuracy, 98.9% adaptability, and a minimal 0.10% decrease in noise, which likewise clearly improves SVM and RF. These results provide guidance about how to choose effective, real-time electromyography classifiers not only for accuracy but also through trade-offs of accuracy, adaptability, and noise tolerance in prosthetic and rehabilitation systems.

&lt;span&gt;Automated recognition or categorization of fruits or crops through image processing technology is a highly sought-after technique in agriculture. &lt;br /&gt; This approach has been demonstrated to outperform manual monitoring in humans. It reduces the time and expenses incurred by farmers and ensures that end users receive high-quality products. This study details the process for identifying dry beans. We employed a support vector machine (SVM) model and three optimization techniques: Bayesian optimization, random search optimization, and grid search optimization. This study aimed to determine the optimal optimization technique for identifying dry beans using an SVM model. Various metrics, including the validation accuracy, test accuracy, total validation cost, total test cost, and minimum classification error, were computed and analyzed to compare the performance of each optimization method. In comparison, Bayesian optimization was determined to be the most effective optimization technique for identifying dry beans, with a validation accuracy of 93.06%, test accuracy of 92.29%, and minimum classification error of 0.069384.&lt;/span&gt;

Optimizing hyperparameters is crucial for enhancing the performance of deep learning (DL) models. The process of configuring optimal hyperparameters, known as hyperparameter tuning, can be performed using various methods. Traditional approaches like grid search and random search have significant limitations. In contrast, Bayesian optimization (BO) utilizes a surrogate model and an acquisition function to intelligently navigate the hyperparameter space, aiming to provide deeper insights into performance disparities between naïve and advanced methods. This study evaluates BO's efficacy compared to baseline methods such as random search, manual search, and grid search across multiple DL architectures, including multi-layer perceptron (MLP), convolutional neural network (CNN), and LeNet, applied to the Modified National Institute of Standards and Technology (MNIST) and CIFAR-10 datasets. The findings indicate that BO, employing the tree-structured parzen estimator (TPE) search method and expected improvement (EI) acquisition function, surpasses alternative methods in intricate DL architectures such as LeNet and CNN. However, grid search shows superior performance in smaller DL architectures like MLP. This study also adopts a multi-objective (MO) perspective, balancing conflicting performance objectives such as accuracy, F1 score, and model size (parameter count). This MO assessment offers a comprehensive understanding of how these performance metrics interact and influence each other, leading to more informed hyperparameter tuning decisions.

Aim/Purpose: Traditional hyperparameter tuning methods’ inefficiency and low accuracy in diabetes prediction. Application and evaluation of Bayesian optimization aim to increase the accuracy of diabetes prediction models. Background: Bayesian optimization (BO) has emerged as a powerful technique for hyperparameter tuning in machine learning models with its methodical approach for optimizing complex functions with expensive evaluations. Selecting the optimal hyperparameters for models can significantly increase prediction accuracy in the healthcare sector, particularly for diabetes prediction, hence improving patient outcomes and resource management. Methodology: Among the well-known diabetes databases used in the study is the Pima Indian Diabetes Database. Among the machine learning models developed and whose hyperparameters are modified via Bayesian optimization are Support Vector Machines (SVM), Random Forest, and Gradient Boosting Machines (GBM). The optimization process is compared with traditional tuning methods to assess improvements in model performance. Contribution: Problems include the increasing prevalence of diabetes worldwide and the role vital function prediction models play in early diagnosis and therapy. Especially when dealing with enormous datasets common in healthcare, grid search, and random search are sometimes computationally taxing and inefficient. However, Bayesian optimization promises a more practical and economical approach by selecting hyperparameters iteratively based on earlier evaluations. Findings: Bayesian optimization not only yields faster calculation times but also outperforms traditional methods. More precisely, models adapted by Bayesian optimization show greater sensitivity and specificity, which are crucial for accurate and prompt diabetes diagnosis. Recommendation for Researchers: By highlighting the specific elements and network characteristics that are behind the anomalies it discovers, on a real-time or large scale dataset. Future Research: This work can be improvised using several recent artificial intelligence algorithms with the integration of IoT on real-time datasets.

Recently, the wafers used in solar cells have been increasing in size, leading to larger module sizes and weights. The increased weight can cause deflection of photovoltaic (PV) module, which may lead to decreased cell efficiency. In this study, we developed a deep neural network (DNN)-based finite element (FE) surrogate model to obtain the optimal frame design factors that can improve deflection in large-scale bifacial PV module. Initially, an FE model was constructed for large-scale bifacial PV module. Based on this, the FE surrogate model was trained using 243 FEA datasets generated within the proposed range of factors. Furthermore, it was improved through Bayesian optimization and k-fold validation. As a result, the final loss value was 3.743×10-4\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$3.743 \ imes 10^{-4}$$\\end{document}, and the average mean absolute percentage error (MAPE) and coefficient of determination (R2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$R^2$$\\end{document}) values for deflection and weight were 0.0017, 0.9972 for the training set, and 0.0020, 0.9962 for the test set, respectively. This indicates that the trained FE surrogate model possesses significant accuracy. After generating 1 million datasets within the range of frame design factors, the trained model was used to obtain predictions. Based on this data, the frame design factors that minimize both deflection and weight were identified as about a = 1.5, b = 13.7, c = 1.5, d = 3.0, e = 4.3. At this point, the deflection was 11.1 mm, and the weight was 3.6 kg. After altering the frame shape with the derived factors, FEA was conducted. The results matched for both deflection and weight, with almost no error. At this point, the weight increased by approximately 12.8% compared to the existing, while the deflection decreased by about 9.6%. Additionally, we analyzed the relationship between deflection and weight for each factor and secured the basis for the derived results. Consequently, our FE surrogate model accurately predicted the FEA results and quickly identified the optimal factors that minimize deflection and weight.

Abstarct Engineering design problems typically require optimizing a quality measure by finding the right combination of controllable input parameters. In Additive Manufacturing (AM), the output characteristics of the process can often be non-stationary functions of the process parameters. Bayesian Optimization (BO) is a methodology to optimize such “black-box” functions, i.e., the input–output relationship is unknown and expensive to compute. Optimization tasks involving “black-box” functions widely use BO with Gaussian Process (GP) regression surrogate model. Using GPs with standard kernels is insufficient for modeling non-stationary functions, while GPs with non-stationary kernels are typically over-parameterized. On the other hand, a Deep Gaussian Process (DGP) can overcome GPs’ shortcomings by considering a composition of multiple GPs. Inference in a DGP is challenging due to its structure resulting in a non-Gaussian posterior, and using DGP as a surrogate model for BO is not straightforward. Stochastic Imputation (SI)-based inference is promising in speed and accuracy for BO. This work proposes a bootstrap aggregation-based procedure to effectively utilize the SI-based inference for BO with a DGP surrogate model. The proposed BO algorithm DGP-SI-BO is faster and empirically better than the state-of-the-art BO method in optimizing non-stationary functions. Several analytical test functions and a case study in metal AM simulation demonstrate the applicability of the proposed method.

Generative artificial intelligence (AI) models have made significant progress in automating the creation of 3D shapes, which has the potential to transform car design. In engineering design and optimization, evaluating engineering metrics is crucial. To make generative models performance-aware and enable them to create high-performing designs, surrogate modeling of these metrics is necessary. However, the currently used representations of 3D shapes either require extensive computational resources to learn or suffer from significant information loss, which impairs their effectiveness in surrogate modeling. To address this issue, we propose a new 2D representation of 3D shapes. We develop a surrogate drag model based on this representation to verify its effectiveness in predicting 3D car drag. We construct a diverse dataset of 4535 high-quality 3D car meshes labeled by drag coefficients computed from computational fluid dynamics simulations to train our model. Our experiments demonstrate that our model can accurately and efficiently evaluate drag coefficients with an R2 value above 0.84 for various car categories. Our model is implemented using deep neural networks, making it compatible with recent AI image generation tools (such as stable diffusion) and a significant step toward the automatic generation of drag-optimized car designs. Moreover, we demonstrate a case study using the proposed surrogate model to guide a diffusion-based deep generative model for drag-optimized car body synthesis.

Emergency Medical Services (EMS) require accurate and timely triage to ensure efficient patient prioritization and optimal resource allocation. Traditional triage methods, like ESI, NEWS, and MEWS, often face issues of subjectivity, variability, and limited predictive accuracy, potentially delaying critical care. This study proposes an Artificial Intelligence (AI)-driven triage system designed for EMS in Kalasin Province, Thailand, leveraging deep learning for risk assessment and patient evaluation prioritization. A dataset of 1,683 EMS cases was utilized, incorporating patient demographics, vital signs, chronic conditions, mobility, self-care ability, pain/discomfort, anxiety/depression, and a self-reported health scale (from 0 to 100). A Deep Neural Network (DNN) was trained using the Adam optimizer and categorical cross-entropy loss, with hyperparameter tuning applied via grid search and Bayesian optimization. Model performance was evaluated using AUC-ROC, sensitivity, specificity, F1-score, and calibration analysis. The results show that the AI model achieved an AUC-ROC of 0.91, with 88.5% sensitivity and 87.3% specificity, outperforming conventional triage tools. This AI-powered system enables real-time risk assessment and provides hospital selection recommendations, enhancing EMS decision-making. Despite its effectiveness, continuous updates are required to mitigate model drift, and further validation is necessary for broader EMS applications. Future research will focus on expanding datasets, integrating real-time patient monitoring, and enhancing model adaptability. This study highlights the transformative potential of AI in EMS triage, paving the way for faster, more accurate, and data-driven emergency healthcare systems.

Bayesian optimization (BO) has demonstrated potential for optimizing control performance in data-limited settings, especially for systems with unknown dynamics or unmodeled performance objectives. The BO algorithm efficiently trades-off exploration and exploitation by leveraging uncertainty estimates using surrogate models. These surrogates are usually learned using data collected from the target dynamical system to be optimized. Intuitively, the convergence rate of BO is better for surrogate models that can accurately predict the target system performance. In classical BO, initial surrogate models are constructed using very limited data points, and therefore rarely yield accurate predictions of system performance. In this paper, we propose the use of meta-learning to generate an initial surrogate model based on data collected from performance optimization tasks performed on a variety of systems that are different to the target system. To this end, we employ deep kernel networks (DKNs) which are simple to train and which comprise encoded Gaussian process models that integrate seamlessly with classical BO. The effectiveness of our proposed DKN-BO approach for speeding up control system performance optimization is demonstrated using a well-studied nonlinear system with unknown dynamics and an unmodeled performance function.

Estimation of internal states in a Li-ion battery using BiLSTM with Bayesian hyperparameter optimization

AdaBoost algorithm is a typical Boosting algorithm, which belongs to a successful representative in the Boosting family. This algorithm can upgrade a weak classifier with a better classification effect than random classification to a strong classifier with high classification accuracy, where n_estimators represents the number of iterations of the base classifier. If the value is too large, it will easily cause the model to overfit, if it is too small, it is easy. The model is under-fitting, and the parameter setting is not set randomly, but according to the current status of the data set. Aiming at the problem that the number of iterations in the AdaBoost algorithm is uncertain, this paper introduces a Bayesian optimization algorithm for hyperparameter tuning, which makes the value of hyper parameter in AdaBoost algorithm suitable for the current data set, and finally obtains a hyperparameter optimization AdaBoost algorithm. The experiment result shows the method that adopt Bayesian optimization algorithm for hyperparameter optimization and apply the optimized hyperparameter value to the AdaBoost algorithm does not only improves the classification accuracy of the AdaBoost algorithm, but also avoids overfitting and underfitting of the model.