On the limits of the intervention on complex systems guided by functional networks by Massimiliano Zanin

“…We show how this approach becomes less optimal the more complex the topology is; up to becoming marginally better than choosing nodes at random in the real case of the European air transport network.…”
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Analysing the effectiveness of unsignalized crossing infrastructure in improving pedestrian safety using multiple data-driven approaches by Shengqi Liu, Harry Evdorides

“…This study investigates the effectiveness of unsignalized crossings to enhance pedestrian safety through a robust data-driven approach utilizing multiple machine learning models, including the statistical classifier Logistic Regression, Decision Tree, Random Forest, and Neural Network Multi-Layer Perceptron (MLP). …”
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Reliability Modeling of Wind Turbine Gearbox System Considering Failure Correlation Under Shock–Degradation by Xiaojun Liu, Ziwen Wu, Yiping Yuan, Wenlei Sun, Jianxiong Gao

“…Based on stress–strength interference theory, random shocks within damage thresholds are integrated to form a coupled reliability model. …”
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Adaptive Noise-Powered Diffusion Model for Efficient and Accurate Object Detection by Xingyu Zou, Kaixu Han, Xinle Zhang, Wenhao Wang, Ning Wu

“…However, its reliance on numerous random noise-based object candidates limits its efficiency. …”
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An improved multiclass classification of acute lymphocytic leukemia using enhanced glowworm swarm optimization by Saranya N, Kalamani M

“…Popular classifiers -Decision tree, Random Forest, Multi-Layer Perceptron, Naive Bayes and Linear, Polynomial, Radial basis function, sigmoid kernels of Support Vector Machine were used for multiclass classification. …”
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Improved Block Element Method for Simulating Rock Failure by Yan Han, Qingwen Ren, Lei Shen, Yajuan Yin

“…As a discontinuous deformation method, the block element method (BEM) characterizes a material’s elastoplastic behavior through the constitutive relation of thin-layer elements between adjacent blocks. To realistically simulate rock damage paths, this work improves the traditional BEM by using random Voronoi polygonal grids for discrete modeling. …”
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RAPO: An Automated Performance Optimization Tool for Redis Clusters in Distributed Storage Metadata Management by Yunkai Zhu, Tian Xia, Te Zhu, Zhoujie Zhao, Kexin Li, Xingbo Hu

“…By optimizing with the greedy and random iterative search algorithms, the cluster’s LBI was reduced by an average of 29.36% and 25.20% respectively during random read operations and by 24.98% and 24.03% respectively during imbalanced write operations. …”
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A sandwich-like nanofibrous scaffold with macrophage phenotype transformation and myogenic differentiation for skeletal muscle regeneration by Shue Jin, Yongrui Cai, Yaxing Li, Jing Wen, Xiaoxue Fu, Ping Song, Pengyu Lu, Anjing Chen, Zeyu Luo, Weinan Zeng, Jidong Li, Zongke Zhou

“…Specifically, the outer layer of the sandwich-like scaffold consists of highly aligned fibers, while the middle layer is a core-shell structured random fibers containing hyaluronic acid, and the fiber matrix is composed of optimized proportions of polycaprolactone and gelatin. …”
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Feature-Driven Density Prediction of Maraging Steel Additively Manufactured Samples Using Pyrometer Sensor and Supervised Machine Learning by Rajesh Kumar Balaraman, Shaista Hussain, John Kgee Ong, Qing Yang Tan, Nagarajan Raghavan

“…With these three sets of input features, the study investigates the effectiveness of four supervised machine learning (ML) regression models: Random Forest (RF), K-Nearest Neighbor (KNN), Support Vector Regression (SVR), and Multi-Layer Perceptron (MLP), for predicting the density of printed samples. …”
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Position Normalization of Propellant Grain Point Clouds by Junchao Wang, Fengnian Tian, Renfu Li, Zhihui Li, Bin Zhang, Xuelong Si

“…In the coarse normalization stage, a layer-by-layer feature points detection scheme based on k-dimensional trees (KD-tree) and k-means clustering (k-means) is designed to extract feature points from the propellant grain point cloud. …”
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Unveiling the Efficacy of AI-based Algorithms in Phishing Attack Detection by Tajamul Shahzad, Kashif Aman

“…Through a detail literature 14 AI algorithms which are repeatedly used for detection, and these are Random Forests, Convolutional Neural Network, Naïve Bayes, K-Nearest Neighbours algorithm, Decision Trees, long short-term memory, gated recurrent unit, Artificial Neural Network, AdaBoost, Logistic Regression, Gradient Boost, Multi-layer perceptron, Recurrent Neural Network, Extreme gradient boosting, and Support Vector Machine to detect phishing attacks. …”
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Hierarchical Charging Scheduling Strategy for Electric Vehicles Based on NSGA-II by Yikang Chen, Zhicheng Bao, Yihang Tan, Jiayang Wang, Yang Liu, Haixiang Sang, Xinmei Yuan

“…A hierarchical model was developed based on NSGA-II, where the upper layer generates Pareto-optimal power allocations and then the lower layer dispatches individual vehicles under these allocations. …”
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Benchmarking of feed-forward neural network models for genomic prediction of quantitative traits in pigs by Junjian Wang, Francesco Tiezzi, Yijian Huang, Christian Maltecca, Jicai Jiang

“…We evaluated the predictive performance of feed-forward neural network (FFNN) models implemented in TensorFlow with architectures ranging from single-layer (no hidden layers) to four-layer structures (three hidden layers). …”
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Mortality prediction of inpatients with NSTEMI in a premier hospital in China based on stacking model. by Li Wang, Yu Zhang, Feng Li, Caiyun Li, Hongzeng Xu

“…Seven classical artificial intelligence methods of Logistic Regression (LR), Decision Tree (DT), Support Vector Machine (SVM), Random Forest (RF), Adaptive Boosting (ADB), Extra Tree (ET), and Gradient Boosting Decision Tree (GBDT) were selected as candidate models for the base model of the first layer of the model, and extreme gradient enhancement (XGBOOST) was selected as the meta-model for the second layer.…”
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An intelligent recognition method of deep shale gas reservoir laminaset based on laminaset clustering and R-L-M algorithm by Yu Zeng, Fuqiang Lai, Haijie Zhang, Yi Jiang, Junwei Pu, Tongtong Luo, Xiaoxia Zhao

“…The development density of clay-siliceous (organic-lean) laminaset from the Longyi 1–4 small layer to the lower Wufeng Formation firstly decreased and then increased and the minimum value was found in Longyi 1-1 small layer. …”
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Multi-objective Cooperative Capacity Determination Method for Integrated System of Wind, Photovoltaic and Storage of Smelting Enterprises Requiring Energy Saving and Carbon Reducti... by Wenguang ZHU, Wei WANG, Bin OUYANG, Hua ZHANG, Xin WANG, Rui MA

“…Considering the uncertainty of new energy and the carbon emissions of users, a multi-objective capacity determination optimization method for energy storage of smelting enterprises considering economy and cleanliness is proposed. A two-layer energy storage optimization model is established, in which the upper layer model optimizes the energy storage configuration for large users in smelting industry who have installed or plan to install new energy power generation, and the lower layer model optimizes the timing output of energy storage. …”
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A Study on Small-Scale Snake Image Classification Based on Improved SimCLR by Lingyan Li, Ruiqing Kang, Wenjie Huang, Wenhui Feng

“…The training strategy incorporates random erasing and random grayscale data augmentation techniques to improve performance further. …”
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Torque Prediction In Deep Hole Drilling: Artificial Neural Networks Versus Nonlinear Regression Model by Ngoc Hung- Chu, Hoai Nam- Nguyen, Van Du- Nguyen, Dang Binh- Nguyen

“…It leads to a rapid increase in cutting forces and strong random fluctuations. The discontinuous chip evacuation process makes the cutting force signal strongly nonlinear and random, making it difficult to predict accurately. …”
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Enhanced wind power forecasting using machine learning, deep learning models and ensemble integration by T. A. Rajaperumal, C. Christopher Columbus

“…A wide range of ML models—Random Forest (RF), Decision Trees, Linear Regression, K-Nearest Neighbors (KNN), Extreme Gradient Boosting (XGBoost), Adaptive Boosting (AdaBoost), and Gradient Boosting—alongside DL models such as Multi-Layer Perceptron (MLP) and Long Short-Term Memory (LSTM) were evaluated. …”
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Prediction of Metal Additively Manufactured Bead Geometry Using Deep Neural Network by Min Seop So, Mohammad Mahruf Mahdi, Duck Bong Kim, Jong-Ho Shin

“…The DNN model architecture consists of multiple hidden layers with varying neuron counts, trained using backpropagation, and optimized using the Adam optimizer. …”
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