Machine Learning Optimization Research Articles

Fast and Accurate Evacuation Planning Algorithm with Bayesian Optimization

In this work, we propose a method for generating an evacuation plan at a high speed to realize safe and swift evacuation in the event of a large-scale disaster such as an earthquake and its accompanying tsunami. Existing conventional methods have several problems. Simulation-based methods that use agents and methods that use existing time expansion networks have high computational costs, which makes it difficult for evacuation routes to be immediately changed according to the effects of disasters such as collapsed buildings and roads. Although heuristics with reduced calculation costs are also being researched, they may result in very long evacuation completion times and cannot generate optimal evacuation plans. We guarantee the optimal solution by reducing the number of maximum flow problem calculations, which constitute the bottleneck for methods using the existing time expansion network, through the use of the Bayesian optimization machine learning method. This reduces the calculation cost of the entire algorithm. The performance of our method is evaluated from the two viewpoints of the evacuation completion time, which indicates the quality of the evacuation plan, and the time required for the generation by the solution of the algorithm in computer experiments under multiple scenarios. In addition, the impact of the number of evacuees and the locations of the sinks are analyzed. We show that our method can quickly generate an optimal evacuation plan.

High‐Gain Triple‐Band T‐Shaped Dielectric Resonator Based Hybrid Two‐Element MIMO Antenna for 5G New Radio, Wi‐Fi 6, V2X, and C‐Band Applications With a Machine Learning Approach

ABSTRACTIn this article, a rectangular dielectric resonator (DR) based multiple input multiple output (MIMO) hybrid antenna with a machine learning (ML) optimization approach for 5G New Radio (NR), WiMAX, WLAN, Wi‐Fi 6, Vehicle to Everything (V2X), and C‐band uplink applications are presented. The proposed antenna provides a triple band. It covers n1, n2, and n39 5G NR at a lower frequency (1.59–2.26 GHz), n48 5G NR at the middle frequency (3.1–3.87 GHz), and n46, n47, n96, WiMAX, WLAN, and C‐band uplink at higher frequency (5.25–6.91 GHz). It achieved more than 20% impedance bandwidth and 20 dB isolation in all frequency bands. The maximum gain of the proposed antenna is 15 dBi. The calculated MIMO parameters are within the accepted range. It is optimized through various ML algorithms, including random forest (RF), K‐nearest neighbor (KNN), artificial neural network (ANN), extreme gradient boosting (XGB) and decision tree (DT). The RF ML algorithm gives 98.09% accuracy compared to other ML algorithms for S parameter prediction.

Scale Production of a Stretchable Fiber Triboelectric Nanogenerator in Customizable Textile for Human Motion Recognition.

Although the fiber-based triboelectric nanogenerator (F-TENG) has been recognized as one of the most promising flexible sensor systems, it is facing a challenge of balancing the performance and the processing scalability. Herein, we develop a hierarchical coaxial F-TENG possessing PU layer, Ag layer, and PA layer from the core to the outer part by an efficient and straightforward two-step braiding method. Owning a small diameter of 1 mm, the F-TENG presents a high linear sensing response, a wide working range of 5 to 150 kPa, and a quick reaction speed of around 200 ms. In addition, it shows high flexibility, cyclic washability, and superior mechanical stability. Furthermore, customizable textiles (e.g., wrist support and socks) that conform perfectly to the human body have been knitted from the F-TENGs as warps or wefts, which are able to monitor human motion signals. Together with an optimized machine learning algorithm, five human motions (stand, slow walk, normal walk, run, and jump) can be analyzed with a precision of up to 99%. In short, this work presents a scalable approach to develop customizable self-powered sensing textiles, offering an excellent wearable digital platform/system for potential motion capture/monitoring, identification, and smart-sports-related applications.

Model‐Driven Manufacturing of High‐Energy‐Density Batteries: A Review

AbstractThe rapid advancement in energy storage technologies, particularly high‐energy density batteries, is pivotal for diverse applications ranging from portable electronics to electric vehicles and grid storage. This review paper provides a comprehensive analysis of the recent progress in model‐driven manufacturing approaches for high‐energy‐density batteries, highlighting the integration of computational models and simulations with experimental manufacturing processes to optimize performance, reliability, safety, and cost‐effectiveness. We systematically examine various modeling techniques, including electrochemical, thermal, and mechanical models, and their roles in elucidating the complex interplay of materials, design, and manufacturing parameters. The review also discusses the challenges and opportunities in scaling up these model‐driven approaches, addressing key issues such as model validation, parameter sensitivity, and the integration of machine learning and artificial intelligence for predictive modeling, process optimization, and quality assurance. By synthesizing current research findings and industry practices, this paper aims to outline a roadmap for future developments in model‐driven manufacturing of high‐energy density batteries, emphasizing the need for interdisciplinary collaboration and innovation to meet the increasing demands for energy storage solutions.

Benchmarking Digital Quantum Simulations Above Hundreds of Qubits Using Quantum Critical Dynamics

The real-time simulation of large many-body quantum systems is a formidable task, that may only be achievable with a genuine quantum computational platform. Currently, quantum hardware with a number of qubits sufficient to make classical emulation challenging is available. This condition is necessary for the pursuit of a so-called quantum advantage, but it also makes verifying the results very difficult. In this paper, we flip the perspective and utilize known theoretical results about many-body quantum critical dynamics to qualitatively benchmark digital quantum hardware and various error mitigation techniques. In particular, we benchmark against known universal scaling laws in the Hamiltonian simulation of a time-dependent transverse-field Ising Hamiltonian of up to 133 qubits. Incorporating only basic error mitigation and suppression methods, our study shows reliable control up to a two-qubit gate depth of 28, featuring a maximum of 1396 two-qubit gates, before noise becomes prevalent. These results are transferable to applications such as Hamiltonian simulation, variational algorithms, optimization, or quantum machine learning. We demonstrate this on the example of digitized quantum annealing for optimization and identify an optimal working point in terms of both circuit depth and time step on a 133-site optimization problem. Published by the American Physical Society 2024

Multi-Objective Optimization for Controlling Conflicts in Roadway Surrounding Rock Induced by Floor Stress-Relief Groove

This paper studies the effectiveness of the stress-relief groove on the floor of deep coal roadway and determines the influence of the stress-relief groove parameters on the surrounding rock through qualitative analysis. Based on the displacement conflict problem, evaluation indicators were established, and the optimal solution set was obtained. The innovations of this research include: 1. For geotechnical numerical simulations, novel stress monitoring and plastic zone monitoring techniques have been introduced to accurately reflect the condition of the surrounding rock; 2. The effects of floor relief grooves in deep roadway on surrounding rock have been analyzed, and the advantages and utilities of central and corner relief grooves have been determined; 3. The usability of small datasets has been enhanced by applying SEGA to optimize machine learning models with data augmentation techniques; 4. Multi-objective optimization algorithms have been applied to geotechnical engineering, providing valuable references for decision making. The results demonstrate that multi-objective optimization can significantly enhance the effectiveness of surrounding rock control, resolve conflicts, and achieve more reasonable construction plans. This research provides new theoretical foundations and practical guidance for deep mine roadway-surrounding rock control.

Optimized Machine Learning Algorithms for Predicting the Punching Shear Resistance of Flat Slabs with Transverse Reinforcement

In the calculation of reinforced concrete (RC) flat slabs with transverse reinforcement, punching shear resistance is one of the most critical factors. It is true that design provisions may be implemented, but they often result in significant biases and deviations from expectations. This study aims to present an optimized machine learning (ML) algorithm for estimating the punching shear resistance. Four machine learning (ML) algorithms (SVR, DT, RF, and XGBoost) with Bayesian optimization (BO) are presented in this paper to provide accurate predictions for flat slabs. The adoptability and optimization of the models are achieved through the analysis of a database of 337 test specimens with nine design parameters. Machine learning (ML) techniques are used to estimate punching shear resistance, which is compared with design provisions and equations relating to critical shear crack theory (CSCT). According to this study, Bayesian optimization is still capable of improving the performance of conventional machine learning algorithms, while the XGBoost-based model offers advanced capabilities. Predictions based on BO-XGBoost are in good agreement with actual values (MAE, RMSE, and R2 are 0.09 MN, 0.14 MN, and 0.92, respectively) in test set. Following a detailed explanation using Shapley additive explanation (SHAP), a high-performance ML approach is used to investigate the predictive results. With the proposed optimized algorithms, it is possible to determine the punching shear resistance of flat slabs with transverse reinforcement during the preliminary stages of the construction.

Enhancing pricing strategies in the aftermarket sector with machine learning

PurposeThis research explores the application of machine learning to optimize pricing strategies in the aftermarket sector, particularly focusing on parts with no assigned values and the detection of outliers. The study emphasizes the need to incorporate technical features to improve pricing accuracy and decision-making.Design/methodology/approachThe methodology involves data collection from web scraping and backend sources, followed by data preprocessing, feature engineering and model selection to capture the technical attributes of parts. A Random Forest Regressor model is chosen and trained to predict prices, achieving a 76.14% accuracy rate.FindingsThe model demonstrates accurate price prediction for parts with no assigned values while remaining within an acceptable price range. Additionally, outliers representing extreme pricing scenarios are successfully identified and predicted within the acceptable range.Originality/valueThis research bridges the gap between industry practice and academic research by demonstrating the effectiveness of machine learning for aftermarket pricing optimization. It offers an approach to address the challenges of pricing parts without assigned values and identifying outliers, potentially leading to increased revenue, sharper pricing tactics and a competitive advantage for aftermarket companies.

Terahertz metasurface biosensor for high-sensitivity salinity detection and data encoding with machine learning optimization based on random forest regression

Terahertz metasurface biosensor for high-sensitivity salinity detection and data encoding with machine learning optimization based on random forest regression

Stacking Ensemble Technique Using Optimized Machine Learning Models with Boruta–XGBoost Feature Selection for Landslide Susceptibility Mapping: A Case of Kermanshah Province, Iran

Landslides cause significant human and financial losses in different regions of the world. A high-accuracy landslide susceptibility map (LSM) is required to reduce the adverse effects of landslides. Machine learning (ML) is a robust tool for LSM creation. ML models require large amounts of data to predict landslides accurately. This study has developed a stacking ensemble technique based on ML and optimization to enhance the accuracy of an LSM while considering small datasets. The Boruta–XGBoost feature selection was used to determine the optimal combination of features. Then, an intelligent and accurate analysis was performed to prepare the LSM using a dynamic and hybrid approach based on the Adaptive Fuzzy Inference System (ANFIS), Extreme Learning Machine (ELM), Support Vector Regression (SVR), and new optimization algorithms (Ladybug Beetle Optimization [LBO] and Electric Eel Foraging Optimization [EEFO]). After model optimization, a stacking ensemble learning technique was used to weight the models and combine the model outputs to increase the accuracy and reliability of the LSM. The weight combinations of the models were optimized using LBO and EEFO. The Root Mean Square Error (RMSE) and Area Under the Receiver Operating Characteristic Curve (AUC-ROC) parameters were used to assess the performance of these models. A landslide dataset from Kermanshah province, Iran, and 17 influencing factors were used to evaluate the proposed approach. Landslide inventory was 116 points, and the combined Voronoi and entropy method was applied for non-landslide point sampling. The results showed higher accuracy from the stacking ensemble technique with EEFO and LBO algorithms with AUC-ROC values of 94.81% and 94.84% and RMSE values of 0.3146 and 0.3142, respectively. The proposed approach can help managers and planners prepare accurate and reliable LSMs and, as a result, reduce the human and financial losses associated with landslide events.

A novel Taguchi-based approach for optimizing neural network architectures: application to elastic short fiber composites

This study presents an innovative application of the Taguchi design of experiment method to optimize the structure of an Artificial Neural Network (ANN) model for the prediction of elastic properties of short fiber reinforced composites. The main goal is to minimize the computational effort required for hyperparameter optimization while enhancing the prediction accuracy. By utilizing a robust experimental design framework, the structure of an ANN model is optimized. This approach involves identifying a combination of hyperparameters that provides optimal predictive accuracy with the fewest algorithmic runs, thereby significantly reducing the required computational effort. The results suggested that the Taguchi-based developed ANN model with three hidden layers, 20 neurons in each hidden layer, elu activation function, Adam optimizer, and a learning rate of 0.001 can predict the anisotropic elastic properties of short fiber reinforced composites with a prediction accuracy of 97.71%. Then, external validation of the proposed ANN model was done using experimental data, and differences of less than 10% were obtained, indicating an appropriate predictive performance of the proposed ANN algorithm. Our findings demonstrate that the Taguchi method not only streamlines the hyperparameter tuning process but also substantially improves the algorithm's performance. These results highlight the potential of the Taguchi method as a powerful tool for optimizing machine learning algorithms, especially in scenarios where computational resources are limited. The implications of this study are far-reaching, offering insights for future research in the optimization of different algorithms for improved accuracies and computational efficiencies.

Letter to editor: Optimized Machine Learning Model for Predicting Unplanned Reoperation After Rectal Cancer Anterior Resection.

Letter to editor: Optimized Machine Learning Model for Predicting Unplanned Reoperation After Rectal Cancer Anterior Resection.

Daily Runoff Forecasting Using Novel Optimized Machine Learning Methods

Accurate runoff forecasting is crucial for effective water resource management, yet existing models often face challenges due to the complexity of hydrological systems. This study addresses these challenges by introducing a novel bio-inspired metaheuristic algorithm, Artificial Rabbits Optimization (ARO), integrated with various machine learning (ML) models for runoff forecasting in the Carson and Chehalis River basins. The ARO-ML models are rigorously evaluated, demonstrating their superior ability to enhance forecasting accuracy compared to widely used ML techniques, including gradient boosting (GB), multi-layer perceptron (MLP), and K-nearest neighbor regression (KNN). In the Carson River, the GB model achieves the highest forecasting accuracy, which is significantly improved by ARO, resulting in a 24.8% reduction in root mean square error (RMSE). The MLP model also benefits notably from ARO, with RMSE improvements of 4.8% and a substantial 48.9% reduction in mean absolute error (MAE). The ARO-optimized KNN model shows exceptional performance, surpassing the baseline KNN during testing. In the Chehalis River Basin, ARO integration leads to substantial improvements in both GB and MLP models across various performance metrics during training and testing. Crucially, the results indicate that in both river basins, rainfall in preceding days has the most significant influence on streamflow forecasts, followed by air temperature. This study advances hydrological forecasting by validating the efficacy of ARO-ML models, offering a robust framework for enhanced predictive accuracy and providing deeper insights into the key drivers of streamflow in diverse hydrological contexts.

Comment on “Optimized machine learning model for predicting unplanned reoperation after rectal cancer anterior resection”.

Comment on “Optimized machine learning model for predicting unplanned reoperation after rectal cancer anterior resection”.

Measurement of four main catechins content in green tea based on visible and near-infrared spectroscopy using optimized machine learning algorithm

Measurement of four main catechins content in green tea based on visible and near-infrared spectroscopy using optimized machine learning algorithm

Time series-based machine learning for forecasting multivariate water quality in full-scale drinking water treatment with various reagent dosages

Accurately predicting drinking water quality is critical for intelligent water supply management and for maintaining the stability and efficiency of water treatment processes. This study presents an optimized time series machine learning approach for accurately predicting multivariate drinking water quality, explicitly considering the time-dependent effects of reagent dosing. By leveraging data from a full-scale treatment plant, we constructed feature-engineered time series datasets incorporating influent water quality parameters, reagent dosages and effluent water characteristics. Seven predictive models, including both traditional machine learning (ML) and deep learning (DL) models were developed and rigorously evaluated against a naive mean baseline model. Our results demonstrate that traditional ML models, enhanced with time feature engineering, rivaled the performance of both widely used DL models and the naive mean baseline model. Specifically, an XGBoost model achieved superior prediction accuracy in dynamically forecasting four water quality characteristics at a 12-hour time lag step, outperforming the naive baseline model by 3-4% in terms of Mean Absolute Percentage Error (MAPE). This finding underscores the importance of incorporating a 12-hour interval to effectively capture the delayed impact of reagent dosing on water quality prediction. Furthermore, SHAP model interpretability analysis provided valuable insights into the XGBoost model's decision-making process, revealing its strong data-driven foundation aligned with established water treatment principles. This research highlights the significant potential of optimized machine learning techniques for enhancing water purification processes and enabling more informed, data-driven decision-making in the water supply industry.

Interpretable causal machine learning optimization tool for improving efficiency of internal carbon source-biological denitrification

Interpretable causal machine learning (ICML) was used to predict the performance of denitrification and clarify the relationships between influencing factors and denitrification. Multiple models were examined, and XG-Boost model provided the best prediction (R2 = 0.8743). Based on the ICML framework, hydraulic retention time (HRT), mixture chemical oxygen demand/total nitrogen (COD/TN = C/N), mixture COD concentration, and pretreatment technology were identified as important features affecting the denitrification performance. Further, tapping point and partial dependence analyses provided the range of key factors that precisely regulate denitrification. In the application analysis, HRT (6–10.5 h), mixture C/N (6–12), and mixture COD concentration (300–600 mg L−1) were the appropriate operating ranges, achieving TN removal of approximately 73 %–77 %. The effluent TN and COD concentrations met the discharge standards for wastewater in China (class 1A) and EU. These findings provide support for regulating excess sludge as internal carbon source to promote denitrification.

A gentle introduction to gradient-based optimization and variational inequalities for machine learning

Abstract The rapid progress in machine learning in recent years has been based on a highly productive connection to gradient-based optimization. Further progress hinges in part on a shift in focus from pattern recognition to decision-making and multi-agent problems. In these broader settings, new mathematical challenges emerge that involve equilibria and game theory instead of optima. Gradient-based methods remain essential—given the high dimensionality and large scale of machine-learning problems—but simple gradient descent is no longer the point of departure for algorithm design. We provide a gentle introduction to a broader framework for gradient-based algorithms in machine learning, beginning with saddle points and monotone games, and proceeding to general variational inequalities. While we provide convergence proofs for several of the algorithms that we present, our main focus is that of providing motivation and intuition.

Comparative analysis of hybrid cognitive radar techniques for enhanced target detection and tracking: A performance evaluation perspective

This paper introduces an innovative hybrid algorithmic approach for cognitive radar systems, by integrating unique machine learning optimization techniques such as, YOLO (You Only Look Once), Mask R-CNN, and Recurrent Neural Networks (RNNs). Through extensive simulations, the integrated approach demonstrates notable enhancements in target detection, instance segmentation, and target tracking within radar systems. Leveraging deep learning models, the framework facilitates adaptive and intelligent processing of radar data, augmenting system performance in dynamic environments. By seamlessly integrating state-of-the-art techniques, the proposed framework showcases a comprehensive solution to the challenges faced by traditional radar systems. This research represents a significant stride forward in radar technology, promising transformative impacts across various domains, including surveillance, remote sensing, and autonomous navigation. As radar systems continue to evolve, the adoption of advanced deep learning techniques offers unprecedented opportunities for enhancing situational awareness and decision-making capabilities in complex operational scenarios.

Maximizing waste cooking oil biodiesel production employing novel Brotia costula derived catalyst through statistical and machine learning optimization techniques

The limitations of homogeneous catalysts associated with biodiesel production such as non-reusability, emulsification, and saponification have led to the increased cost of biodiesel synthesis. To overcome these limitations, the current work aims to investigate the applicability and effectiveness of green catalyst made from Brotia costula shells (BCS) for waste cooking oil biodiesel production. The catalyst was synthesized by calcinating BCS at 800℃−1000℃ for 4 h. Catalyst characterization showed that BC900 (BCS calcined at 900℃) contained 98.15 CaO wt% and exhibited the smallest grain size of 39.23 nm with the highest specific surface area of 14.28 m2/g and highest catalytic activity. Optimization of biodiesel synthesis was carried out using response surface methodology - Box-Behnken design (RSM-BBD), artificial neural network (ANN) coupled with genetic algorithm (GA), and adaptive neuro-fuzzy inference system (ANFIS) coupled with GA. Maximum experimental biodiesel yield of 97.78±0.59 % at optimum conditions of methanol: WCO molar ratio 9.73: 1, reaction time 2.12 h, catalyst dose wt% 7.59 and reaction temperature 69.3°C was obtained with ANFIS-GA optimization model. The prediction accuracy of the ANFIS model (R2 = 0.988) was better than those of the RSM-BBD (R2 = 0.982) and ANN (R2 = 0.955) models. The synthesized catalyst exhibited high catalytic activity up to four cycles of reusability (yield > 88 %). It is environment-friendly, inexpensive, renewable, and performs on par with catalysts synthesized from other mollusk shells. Using this catalyst for biodiesel production will contribute to the biorefinery's ability to explore sustainable raw materials and may lower overall biodiesel cost.