Bus frequency optimization in a large-scale multi-modal transportation system: integrating 3D-MFD and dynamic traffic assignment by Kai Yuan, Dandan Cui, Jiancheng Long

“…A variational inequality model for the user equilibrium in mode choices is presented and solved using a double projection algorithm. A surrogate model-based algorithm is used to solve the bi-level programming problem.…”
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A quasi affine transformation evolution algorithm with evolution matrix selection operation for parameter estimation of proton exchange membrane fuel cells by Mohammad Aljaidi, Pradeep Jangir, Sunilkumar P. Agrawal, Sundaram B. Pandya, Anil Parmar, Samar Hussni Anbarkhan, Laith Abualigah

“…The results of these findings confirm the effectiveness and practicability of the QUATRE-EMS algorithm for improving the accuracy of BCS500W, NedStackPS6, SR12, H12, HORIZON, and Standard 250W PEMFC stack references.…”
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A realistic 2D multi-offset, multi-frequency synthetic GPR data set as a benchmark for testing new algorithms by G. Roncoroni, P. Koyan, E. Forte, J. Tronicke, M. Pipan

“…The data set provides a robust framework for validating advanced GPR algorithms and techniques such as pre-stack depth migration, amplitude versus offset analysis and full waveform inversion. …”
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Optimal Adaptive Modeling of Hydrogen Polymer Electrolyte Membrane Fuel Cells Based on Meta-Heuristic Algorithms Considering the Membrane Aging Factor by Mohamed Ahmed Ali, Mohey Eldin Mandour, Mohammed Elsayed Lotfy

“…In this work, a number of state-of-the-art algorithms have been adapted to optimize the complex electrochemical PEMFC model. …”
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Adaptive Threshold Algorithm for Outlier Elimination in 3D Topography Data of Metal Additive Manufactured Surfaces Obtained from Focus Variation Microscopy by Xin Xu, Tobias Pahl, Sebastian Hagemeier, Peter Lehmann

“…In order to eliminate this problem, we introduce a new algorithm based on dual convolving a vertical Sobel operator with cross sections of an image stack parallel to the scanning direction of the so-called depth scan. …”
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GenePixKolor (GPK) Fusion: A Novel Evolutionary Algorithm-Based Optimized NFT Card Generation and Rarity Ranking Method for Gaming Tokenomics by J. Guruprakash, P. Pradeep, L. B. Krithika

“…GPK Fusion leverages genetic algorithms, image processing and machine learning to create a comprehensive, four-stage system that optimizes both the trait generation and visual appeal of NFTs, while providing an advanced rarity ranking mechanism. …”
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Assessing the effect of ensemble learning algorithms and validation approach on estimating forest aboveground biomass: a case study of natural secondary forest in Northeast China by Hungil Jin, Yinghui Zhao, Unil Pak, Zhen Zhen, Kumryong So

“…The leave-one-out cross-validation produced much higher accuracy than the 10-fold cross-validation using the same prediction model and tends to generate over-optimistic AGB estimates compared to 10-fold cross-validation, especially for the averaging and stacking ensemble learning algorithms (i.e. SA, WA, SG). …”
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Amismart an advanced metering infrastructure for power consumption monitoring and forecasting in smart buildings by Sarah Hadri, Mehdi Najib, Mohamed Bakhouya, Youssef Fakhri, Mohamed El aroussi, Zaradatcht Taifour, Jaafar Gaber

“…A single Machine learning algorithm using Long Short-Term Memory (LSTM) and hybrid Machine learning algorithms (CNN-LSTM), and ensembles machine learning approaches including eXtreme Gradient Boosting Machine (XGBoost) and Random Forest (RF). …”
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Estimation of annual harvested wood products based on remote sensing and TPO survey data by Weishu Gong, Chengquan Huang, Feng Zhao, Jiaming Lu

“…First, a forest disturbance product was derived based on Vegetation Change Tracker (VCT) algorithm using LTSS from 1985 to 2016. Then, by linking the predictor variables derived from the disturbance data and the TPO survey data, two regression algorithms were tested and compared, and Random Forest was selected to create TPO estimation models for different wood types. …”
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Quality-related and Quality-irrelevant Fault Detection and Diagnosis in Batch Fermentation Process Based on NSSAE by Zhong LIU, Zheng ZHANG, Xuyang LOU, Jinlin ZHU

“…To address potential unnecessary shutdowns caused by quality-unrelated faults during batch fermentation processes, the paper proposed a noise semi-supervised stacked auto-encoder (NSSAE) to differentiate the quality-relevant and the quality-irrelevant faults. …”
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Predicting Running Vertical Ground Reaction Forces Using Neural Network Models Based on an IMU Sensor by Shangxiao Li, Jiahui Pan, Dongmei Wang, Shufang Yuan, Jin Yang, Weiya Hao

“…The sliding time window synchronization (STWS) algorithm was developed to sync IMU data with vGRF data. …”
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Ensemble genetic and CNN model-based image classification by enhancing hyperparameter tuning by Wajahat Hussain, Muhammad Faheem Mushtaq, Mobeen Shahroz, Urooj Akram, Ehab Seif Ghith, Mehdi Tlija, Tai-hoon Kim, Imran Ashraf

“…The convolutional neural network (CNN) model is combined with a genetic algorithm GA) using stacking based on the Modified National Institute of Standards and Technology (MNIST) dataset to enhance efficiency and prediction rate on image classification. …”
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A Computational Intelligence Framework Integrating Data Augmentation and Meta-Heuristic Optimization Algorithms for Enhanced Hybrid Nanofluid Density Prediction Through Machine and... by Priya Mathur, Hammad Shaikh, Farhan Sheth, Dheeraj Kumar, Amit Kumar Gupta

“…The findings underscore the synergy of advanced data augmentation, meta-heuristic optimization, and modern predictive algorithms in modelling hybrid nanofluid density with unprecedented precision. …”
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A Global Revolutionary Class Will Ride the Tiger of Alienation by Hardy Hanappi

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Adaptive Freeform Optics Design and Multi-Objective Genetic Optimization for Energy-Efficient Automotive LED Headlights by Shaohui Xu, Xing Peng, Ci Song

“…To optimize the parameter design, a genetic algorithm is employed to fine-tune the design parameter <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>θ</mi></mrow><mrow><mi>m</mi><mi>a</mi><mi>x</mi></mrow></msub></mrow></semantics></math></inline-formula>, thereby attaining the optimal <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>θ</mi></mrow><mrow><mi>m</mi><mi>a</mi><mi>x</mi></mrow></msub></mrow></semantics></math></inline-formula> that strikes a balance between volume and luminous efficiency. …”
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A Comparative Analysis of Buckling Pressure Prediction in Composite Cylindrical Shells Under External Loads Using Machine Learning by Hyung Gi Lee, Jung Min Sohn

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Evaluating reservoir permeability from core data: Leveraging boosting techniques and ANN for heterogeneous reservoirs by Amad Hussen, Tanveer Alam Munshi, Minhaz Chowdhury, Labiba Nusrat Jahan, Abu Bakker Siddique, Mahamudul Hashan

“…All proposed algorithms are systematically ranked based on performance criteria. …”
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Designing a new optimal controller for a PEMFC by an improved design of the Coot Optimizer by Zheng Wang, Mehrdad Rezaie, Gholamreza Fathi

“…The core of this strategy is an Improved Coot Optimizer algorithm (ICOA), designed to optimize a PID controller for precise voltage regulation of the PEMFC stack. …”
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Encrypted traffic identification method based on deep residual capsule network with attention mechanism by Guozhen SHI, Kunyang LI, Yao LIU, Yongjian YANG

“…With the improvement of users’ security awareness and the development of encryption technology, encrypted traffic has become an important part of network traffic, and identifying encrypted traffic has become an important part of network traffic supervision.The encrypted traffic identification method based on the traditional deep learning model has problems such as poor effect and long model training time.To address these problems, the encrypted traffic identification method based on a deep residual capsule network (DRCN) was proposed.However, the original capsule network was stacked in the form of full connection, which lead to a small model coupling coefficient and it was impossible to build a deep network model.The DRCN model adopted the dynamic routing algorithm based on the three-dimensional convolutional algorithm (3DCNN) instead of the fully-connected dynamic routing algorithm, to reduce the parameters passed between each capsule layer, decrease the complexity of operations, and then build the deep capsule network to improve the accuracy and efficiency of recognition.The channel attention mechanism was introduced to assign different weights to different features, and then the influence of useless features on the recognition results was reduced.The introduction of the residual network into the capsule network layer and the construction of the residual capsule network module alleviated the gradient disappearance problem of the deep capsule network.In terms of data pre-processing, the first 784byte of the intercepted packets was converted into images as input of the DRCN model, to avoid manual feature extraction and reduce the labor cost of encrypted traffic recognition.The experimental results on the ISCXVPN2016 dataset show that the accuracy of the DRCN model is improved by 5.54% and the training time of the model is reduced by 232s compared with the BLSTM model with the best performance.In addition, the accuracy of the DRCN model reaches 94.3% on the small dataset.The above experimental results prove that the proposed recognition scheme has high recognition rate, good performance and applicability.…”
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Development of a research environment for the operational and computational architecture of central bank digital currency software by A. S. Albychev, S. A. Kudzh

“…Digital technologies required for forming an CBDC implementation stack are under development in many countries of the world. …”
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