Cellular automata modeling on uniform corrosion behavior of solid copper in gallium-based liquid metals by Yujie Ding, Yuntao Cui, Wei Rao, Jing Liu

“…Through comparing with short-term and long-term corrosion test data, the model shows high accuracy in describing diffusion of gallium and formation of multi-layered intermetallic compounds. Sensitivity analysis points out that growth rate of corrosion layers is mainly determined by gallium mass fraction in liquid metal and diffusion probability in Cu. …”
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An ecological network for large carnivores as a key tool for protecting landscape connectivity in the Carpathians by Kristýna Vlková, Vladimír Zýka, Cristian Remus Papp, Dušan Romportl

“…From the model, we derived a layer of patches of suitable habitat of required quality and resistance surface to express landscape permeability. …”
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Application of BiLSTM in Lithology Identification of Beach-Bar Sand Reservoir by CHEN Ganghua, ZHANG Yuxia, WANG Jun, ZHANG Huafeng, WANG Youwen

“…However, it is characterized by deep burial, thin single-layer thickness, ultra-low permeability, complex pore structure, and extremely low natural productivity of the single well and it is difficult to classify the reservoir and identify the lithology. …”
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Spoof speech classification using deep speaker embeddings and machine learning models by Mohammed Hamzah Alsalihi, Dávid Sztahó

“…These embeddings are used with five classifiers: Support Vector Machine, Random Forest, Multi-Layer Perceptron, Logistic regression, and XGBoost, to classify if a speech sample is a deepfake or not. …”
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RETRACTED ARTICLE: Detection of hate: speech tweets based convolutional neural network and machine learning algorithms by Hameda A. Sennary, Ghada Abozaid, Ashraf Hemeida, Alexey Mikhaylov

“…The classifiers involved are Logistic Regression (LR), Naive Bayes (NB), Multi-layer Perceptron (MLP), and Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbor (KNN), K-Means, Decision Tree (DT), Gradient Boosting classifier (GBC), and the Extra Trees (ET) in addition to the convolutional neural network (CNN). …”
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A Surrogate Model for the Rapid Prediction of Factor of Safety in Slopes with Spatial Variability by Xitailang Cao, Shan Lin, Miao Dong, Quanke Hu, Hong Zheng

“…While traditional numerical methods combined with Monte Carlo simulations and Gaussian random field theory provide accurate stability analysis, their high computational cost makes them impractical for large-scale engineering applications. …”
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A Multiple Kernel Learning Approach for Air Quality Prediction by Hong Zheng, Haibin Li, Xingjian Lu, Tong Ruan

“…To demonstrate the performance of the proposed MKL model, its performance is compared to that of classical autoregressive integrated moving average (ARIMA) model; widely used parametric models like random forest (RF) and support vector machine (SVM); popular neural network models like multiple layer perceptron (MLP); and long short-term memory neural network. …”
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Evaluation of a Deep Learning Model for Automatic Detection of Schizophrenia Using EEG Signals by Swetha Padmavathi Polisetty, Radhamani Ellapparaj, Karthikeyan M P

“…After data preprocessing to reduce noise and artifacts from EEGs, an 11-layer deep learning model consisting of convolution and LSTM layers with LeakyReLU activation function and different kernel sizes was implemented to automatically extract and classify features. …”
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SNUH methylation classifier for CNS tumors by Kwanghoon Lee, Jaemin Jeon, Jin Woo Park, Suwan Yu, Jae-Kyung Won, Kwangsoo Kim, Chul-Kee Park, Sung-Hye Park

“…Compared to two published CNS tumor methylation classification models (DKFZ-MC: Deutsches Krebsforschungszentrum Methylation Classifier v11b4: RandomForest, 767-MC: Multi-Layer Perceptron), our SNUH-MC showed improved performance in F1-score. …”
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On-chip non-contact mechanical cell stimulation - quantification of SKOV-3 alignment to suspended microstructures by Sevgi Onal, Maan M. Alkaisi, Volker Nock

“…Although the accumulation of random genetic mutations has been traditionally viewed as the main cause of cancer progression, altered mechanobiological profiles of the cells and microenvironment also play a major role as a mutation-independent element. …”
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Retinal thickness and vascular density changes in Keratoconus: A systematic review and meta-analysis by Hadi Vahedi, Mirsaeed Abdollahi, Reza Moshfeghinia, Shima Emami, Navid Sobhi, Rana Sorkhabi, Ali Jafarizadeh

“…Purpose: This systematic review and meta-analysis aimed to assess the changes in retinal layer thickness and vascular density in patients diagnosed with keratoconus (KC). …”
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Combining UAV Remote Sensing with Ensemble Learning to Monitor Leaf Nitrogen Content in Custard Apple (<i>Annona squamosa</i> L.) by Xiangtai Jiang, Lutao Gao, Xingang Xu, Wenbiao Wu, Guijun Yang, Yang Meng, Haikuan Feng, Yafeng Li, Hanyu Xue, Tianen Chen

“…Random Forest (RF), Adaptive Boosting (ADA), Gradient Boosting Decision Trees (GBDT), Linear Regression (LR), and Extremely Randomized Trees (ERT) are among the basis estimators that are integrated in the first layer. …”
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A data-driven approach to predict fracture intensity using machine learning for presalt carbonate reservoirs: A feasibility study in the Mero Field, Santos Basin, Brazil by Eberton Rodrigues de Oliveira Neto, Fábio Júnior Damasceno Fernandes, Tuany Younis Abdul Fatah, Raquel Macedo Dias, Zoraida Roxana Tejada da Piedade, Antonio Fernando Menezes Freire, Wagner Moreira Lupinacci

“…Predicting fracture intensity is essential for optimising reservoir production and mitigating drilling risks in the Brazilian pre-salt layer. However, previous studies rely excessively on conceptual models and typically do not integrate multiple types of data to perform such task. …”
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Ultimate Compression: Joint Method of Quantization and Tensor Decomposition for Compact Models on the Edge by Mohammed Alnemari, Nader Bagherzadeh

“…A key contribution of this work is a novel layer sensitivity-based rank selection algorithm for tensor decomposition, which outperforms existing methods such as random selection and Variational Bayes Matrix Factorization (VBMF). …”
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Predicting agricultural drought in central Europe by using machine learning algorithms by Endre Harsányi

“…Thus, this research evaluates the patterns and magnitude of agriculture droughts using Standardized Precipitation Evapotranspiration Index (SPEI) from 1926 to 2020 in eastern Hungary, and assess the performance of six machine learning models (Random Forest (RF), Extra Trees (ET), Gradient Boosting (GB), Extreme Gradient Boost (XGB), Support Vector Machines (SVM), and Multi-Layer Perceptron (ANN-MLP)) in predicting agriculture droughts. …”
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The Geometry of Concepts: Sparse Autoencoder Feature Structure by Yuxiao Li, Eric J. Michaud, David D. Baek, Joshua Engels, Xiaoqing Sun, Max Tegmark

“…We quantify the spatial locality of these lobes with multiple metrics and find that clusters of co-occurring features, at coarse enough scale, also cluster together spatially far more than one would expect if feature geometry were random. (3) The “galaxy”-scale large-scale structure of the feature point cloud is not isotropic, but instead has a power law of eigenvalues with steepest slope in middle layers. …”
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A Mixed Chaotic Image Encryption Method Based on Parallel Rotation Scrambling in Rubik’s Cube Space by Lu Xu, Yun Chen, Yanlin Qin, Zhichao Yang

“…Then, the 3D cube is scrambled by dynamically selecting the rotating axis, layer number, and angle through the chaotic integer sequence. …”
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Enhancing disaster prediction with Bayesian deep learning: a robust approach for uncertainty estimation by Hao Peng, Sen Shen, Haichao Zhang, Fei Wang, Fawang Guo, Ruige Zhang

“…Comparative analyses demonstrate that the proposed approach markedly outperforms conventional models such as Random Forest, XGBoost, and Multi-layer Perceptron. …”
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An enhanced machine learning approach with stacking ensemble learner for accurate liver cancer diagnosis using feature selection and gene expression data by Amena Mahmoud, Eiko Takaoka

“…The selected features were then used to train a stacking ensemble model, which combined multiple base learners, including Multi-Layer Perceptron (MLP), Random Forest (RF) model, K-nearest neighbor (KNN) model, and Support vector machine (SVM), with a meta-learner Extreme Gradient Boosting (Xgboost) model to make final predictions. …”
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Link Between Stochastic Grid Perturbation and Location Uncertainty Framework by S. Clement, E. Blayo, L. Debreu, J.‐M. Brankart, P. Brasseur, L. Li, E. Mémin

“…The LU formulation, which introduces random velocity fluctuations, has shown efficacy in organizing large‐scale flow and replicating long‐term statistical characteristics. …”
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