Comparison and general law research of multiple machine-learning models for proton exchange membrane electrolytic cell parameters prediction by Yukun Wang, Hai-Wen Li, Wenhan An, Yudong Mao, Kaimin Yang, Jiying Liu

“…By establishing a three-dimensional PEMEC physical model, the study analyzes the effects of voltage, inlet water flow rate and temperature, membrane thickness, anode gas diffusion layer porosity and thickness, and anode catalyst layer conductivity on the three performance evaluation indicators of current density, hydrogen mole fraction, and temperature, and a dataset of 2061 parameter-value pairs is generated. …”
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Explainable Artificial Intelligence for Crowd Forecasting Using Global Ensemble Echo State Networks by Chamod Samarajeewa, Daswin De Silva, Milos Manic, Nishan Mills, Prabod Rathnayaka, Andrew Jennings

“…This approach replaces the random input mapping layer with a clustering layer, allowing the network to learn input projections on cluster centroids. …”
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Pyrolysis Kinetics and Gas Evolution of Flame-Retardant PVC and PE: A TG-FTIR-GC/MS Study by Wen-Wei Su, Yang Li, Peng-Rui Man, Ya-Wen Sheng, Jian Wang

“…In contrast, flame-retardant PE demonstrates a more stable pyrolysis process dominated by random chain scission and the formation of a dense char layer, significantly enhancing its flame-retardant performance. …”
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Emergent properties of magnetic ions and nanoparticles in micellar solutions of surfactants: Use in fine technologies by Yu. A. Mirgorod, N. A. Borsch, A. M. Storozhenko, L. S. Ageeva

“…In particular, Gd3+ ions in an aqueous micellar solution of sodium dodecyl sulfate exhibit paramagnetic properties, possibly indicating their random arrangement in solution contrary to the classical theory of micellization with an ordered adsorption layer on micelles. …”
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Optimized ANN Model for Predicting Buckling Strength of Metallic Aerospace Panels Under Compressive Loading by Shahrukh Khan, Saiaf Bin Rayhan, Md Mazedur Rahman, Jakiya Sultana, Gyula Varga

“…The ANN model was systematically fine-tuned by testing various batch sizes, learning rates, optimizers, dense layer configurations, and activation functions. The optimized model featured an eight-layer architecture (200/100/50/25/12/6/3/1 neurons), used a selu–relu–linear activation sequence, and was trained using the Nadam optimizer (learning rate = 0.0025, batch size = 8). …”
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The Potential of Hybrid Pixel Detectors in the Search for the Neutrinoless Double-Beta Decay of 116Cd by Thilo Michel, Thomas Gleixner, Jürgen Durst, Mykhaylo Filipenko, Stefan Geißelsöder

“…We investigated the potential of the energy resolving hybrid pixel detector Timepix contacted to a CdTe sensor layer for the search for the neutrinoless double-beta decay of Cd. …”
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Non-Fourier heat transfer analysis of sandwich conical shells with GPLs reinforced face sheets and porous core under moving heat flux by Yasin Heydarpour, Parviz Malekzadeh, Hanxing Zhu, Morteza Mohammadzaheri

“…In both core layer and face sheets, GPLs have uniform distribution and random orientation. …”
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The clinical efficacy of monolayer suture combined with hem-o-lok clip in partial nephrectomy among patient with renal cell carcinoma: a quasi-experimental study by Guochang Zheng, Jinming Li, Qiang Zhao, Hongfeng Nie, Liyan Zhao, Jing Zhang

“…They were assigned into a control group and an intervention group, each with 40 cases using random number table. The control group received double-layer sutures on the wound, while the intervention group had single-layer sutures with a hem-o-lok clip for hemostasis. …”
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Keep the bees off the trees: the vulnerability of species in the periphery of mutualistic networks to shock perturbations by Lukas Halekotte, Anna Vanselow, Ulrike Feudel

“…Finally, we extend the mutualistic system to a multilayer network, where a species’ position in the mutualistic network layer determines its position in a competitive network layer. …”
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A Shooting Distance Adaptive Crop Yield Estimation Method Based on Multi-Modal Fusion by Dan Xu, Ba Li, Guanyun Xi, Shusheng Wang, Lei Xu, Juncheng Ma

“…Second, the height information was expanded into a data matrix matching the RGB image dimensions, and multi-modal fusion models were investigated through input-layer and output-layer fusion strategies. Finally, two additional approaches were explored: direct fusion of RGB and depth images, and extraction of average shooting height from depth images for estimation. …”
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A predictive analytics approach with Bayesian-optimized gentle boosting ensemble models for diabetes diagnosis by Behnaz Motamedi, Balázs Villányi

“…BOGBEnsemble is evaluated in comparison to leading models, such as Random Forest (RF), Adaptive Boosting (AdaBoost), Logistic Boosting (LogitBoost), Random Undersampling Boosting (RUSBoost), conventional GentleBoost, and Multi-Layer Perceptron (MLP) architectures. …”
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Experimental demonstration of 8190-km long-haul semiconductor-laser chaos synchronization induced by digital optical communication signal by Anbang Wang, Junli Wang, Lin Jiang, Longsheng Wang, Yuncai Wang, Lianshan Yan, Yuwen Qin

“…Abstract Common-signal-induced synchronization of semiconductor lasers have promising applications in physical-layer secure transmission with high speed and compatibility with the current fiber communication. …”
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Comparative Analysis of Machine Learning Techniques for Fault Diagnosis of Rolling Element Bearing with Wear Defects by Devendra Sahu, Ritesh Kumar Dewangan, Surendra Pal Singh Matharu

“…The study investigates and optimizes fault diagnosis of rolling element bearings using various machine learning techniques, including Random Forest (RF), Support Vector Machine (SVM), Logistic Regression (LR), K-Nearest Neighbors (KNN), and Multi-Layer Perceptron (MLP). …”
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Physics Informed Neural Networks for Modeling Large-Scale Wind Driven Ocean Circulation by Boohyun An, Mohammad Z. Shanti, Chan Yeob Yeun, Ernesto Damiani, Sungmun Lee, Tae-Yeon Kim

“…The effects of different training point distributions, such as uniform, uniform-refined, random, and random-refined, were also examined. The results show that refining the distribution of training points near the western boundary layer can achieve similar accuracy and training performance even with fewer points. …”
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Improving air quality prediction using hybrid BPSO with BWAO for feature selection and hyperparameters optimization by Mohamed S. Sawah, Hela Elmannai, Alaa A. El-Bary, Kh. Lotfy, Osama E. Sheta

“…Machine learning models, including Random Forest (RF), Gradient Boosting (GB), K-Nearest Neighbors (KNN), Multi-Layer Perceptron (MLP), Support Vector Machine (SVM), and Linear Regression (LR), were evaluated before and after feature selection. …”
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Machine learning-driven insights into phase prediction for high entropy alloys by Reliance Jain, Sandeep Jain, Sheetal Kumar Dewangan, Lokesh Kumar Boriwal, Sumanta Samal

“…The ML models such as multi layer precreptron MLP, Decision Tree (DT), Random Forest (RF), Gradient Boosting (GB), KNN, XGB nad SVM Classifier algorithm were used for the identifying the phase of HEAs. …”
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A Comparison of Machine Learning Algorithms for Predicting Alzheimer’s Disease Using Neuropsychological Data by Zakaria Mokadem, Mohamed Djerioui, Bilal Attallah, Youcef Brik

“…This study investigates the predictive performance of nine supervised machine learning algorithms—Logistic Regression, Decision Tree, Random Forest, K-Nearest Neighbors, Support Vector Machine, Gaussian Naïve Bayes, Multi-Layer Perceptron, eXtreme Gradient Boost, and Gradient Boosting—using neuropsychological assessment data. …”
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An interpretable machine learning model for predicting depression in middle-aged and elderly cancer patients in China: a study based on the CHARLS cohort by Yue Xiao, Zejin Zhao, Chen-guang Su, Jian Li, Jinlong Liu

“…After screening the predictive features by LASSO regression, in order to determine the best model for prediction, six machine learning models—Support Vector Machine, K-Nearest Neighbors, Multi-layer Perceptron, Decision Tree, XGBoost and Random Forest were trained. …”
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Texture Analysis of Inconel 718 with Different Modes During Single-Track Laser Surface Re-Melting by Liuqing Yang, Tongjun Niu, Joe Stilgenbauer, Brandon Lane, Nan Li, Jordan Weaver, Youxing Chen

“…This implies that laser surface re-melting provides the potential to modify the surface structure from a random grain orientation to a crystallographically layered structure.…”
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Native language identification from text using a fine-tuned GPT-2 model by Yuzhe Nie

“…Our approach leverages deep learning techniques, including tokenization, embedding extraction, and multi-layer transformer-based classification. Experimental results show that our fine-tuned GPT-2 model significantly outperforms traditional machine learning methods (e.g., SVM, Random Forest) and other pre-trained language models (e.g., BERT, RoBERTa, BioBERT), achieving a weighted F1 score of 0.9419 and an accuracy of 94.65%. …”
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