Load forecasting of microgrid based on an adaptive cuckoo search optimization improved neural network by Liping Fan, Pengju Yang

“…Finally, the weights and biases of the forecasting model were optimized by the improved cuckoo search algorithm. The results showed that the BP network optimized by the improved cuckoo search optimization enhanced the global search ability, avoided the local optima, quickened the convergence speed, and presented excellent performance in load forecasting. …”
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Camouflage Target Detection Method with Mutual Compensation of Local-Global Features by HE Wenhao, GE Haibo

“…Experiments on 3 public datasets show that this algorithm is better than the other 8 latest models. …”
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Machine-Learning-Based Optimal Feed Rate Determination in Machining: Integrating GA-Calibrated Cutting Force Modeling and Vibration Analysis by Yu-Peng Yeh, Han-Hao Tsai, Jen-Yuan Chang

“…This study proposes a machine learning-based approach to optimize feed rate in machining operations by integrating a genetic algorithm (GA)-calibrated cutting force model with vibration analysis. …”
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Application of support vector machine system introducing multiple submodels in data mining by Weinan Tang

“…Finally, combined with the designed parallel support vector machine algorithm, video facial and expression recognition is carried out. …”
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“Bias Correction Method” for Regional Correction Experiment of Warm Season Rainstorm in Zhejiang by Chengyan Mao, Xin Pan, Haowen Li, Weibiao Li, Haoya Liu

“…This study employs the K-means clustering algorithm to partition warm-season precipitation in Zhejiang Province into distinct regions. …”
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Co-Optimization of Vibration Suppression and Data Efficiency in Robotic Manipulator Dynamic Modeling by Xiaowei Han, Kunru Wu, Nanmu Hui

“…Then, a linear regression formulation of the manipulator’s structural dynamics is established, and the BLS network is employed to model the unstructured residuals—primarily arising from nonlinear friction—with high precision. Finally, the PSO algorithm is applied to optimize the hyperparameters of the BLS network, achieving global model optimality. …”
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Utilising AI technique to identify depression risk among doctoral students by Changhong Teng, Chunmei Yang, Qiushi Liu

“…Based on the data from the 2019 Nature Global Doctoral Student Survey, we first screened 13 highly relevant features from a total of 37 features potentially related to the risk of depression among doctoral students by Random Forest algorithm. Subsequently, we trained the optimal prediction model to predict the doctoral students with depression risk using a Multilayer Perceptron (MLP), achieving an accuracy of 89.09% on the test set. …”
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STUDY ON ENERGY ABSORPTION CHARACTERISTIC OF SINUSOIDAL BELLOWS FILLED EMULSION ABSORBER by ZHANG JianZhuo, ZHANG WanJiu, PAN YiShan, GUO Hao, WANG ShuWen

“…The pre⁃folded energy absorber is the only energy absorber structure used in rock burst mine at present.It has excellent energy absorption characteristics,but there are some problems in the buckling deformation process such as large load fluctuation and spark due to friction.In view of the above situation,a sinusoidal bellows(reduced diameter round pipe)filled with emulsion was designed to form a solid⁃liquid coupling body energy absorption component(composite component).The effects of three factors,namely wall thickness of the bellows,amplitude of the busbar and diameter of the liquid outlet,on the energy absorption characteristics of the energy absorption component were studied.The prediction model of average load,load fluctuation coefficient and specific energy absorption was established.The smoothed partiole hydrodynamics(SPH)particle algorithm of Abaqus finite element software was used for fluid⁃structure coupling analysis of the model.The structural parameters of energy absorption components were optimized by response surface method and the optimization results were obtained.The results show that when the wall thickness of corrugated pipe is 3.959 mm,the amplitude of busbar is 3.721 mm,and the diameter of outlet is 22.161 mm,the energy absorption characteristics of the member are the best.The average load is 438.684 kN,the load fluctuation coefficient is 1.178,and the specific energy absorption is 12.123 kJ/kg.Through comparison and verification,the results have high reliability,which provides a superior energy absorption component for the energy absorption link of anti⁃impact support equipment.…”
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Bathymetry Inversion Using a Deep‐Learning‐Based Surrogate for Shallow Water Equations Solvers by Xiaofeng Liu, Yalan Song, Chaopeng Shen

“…The inversion loss due to flow prediction error and the two regularizations play dominant roles in the initial and final stages, respectively. …”
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Research on Sugar Concentration Sensing Based on Real-Time Polarization and Interaction Effects by Qiong Gong, Tongxiao Lyu, Song Ye, Xinqiang Wang

“…The addition of the interaction effect model significantly improves the prediction accuracy, with an RMSE of 0.36934 and a relative error within ±5%. …”
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Surrogate model for calculating spatial magnetic track forces in permanent magnet levitation transportation by LIU Haodong, FENG Yang, ZHAO Chunfa, LUO Shihui, SONG Xiaolin

“…Furthermore, the predicted overall resultant and corresponding torque for the 600 mm long vehicle-mounted PML module also demonstrate high accuracy. …”
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Evaluation of inflammatory bowel disease-related sleep disorders based on an interpretable machine learning approach: a multicenter study in China by Jiayi Sun, Junhai Zhen, Chuan Liu, Changqing Jiang, Jie Shi, Kaichun Wu, Weiguo Dong

“…Finally, the Local Interpretable Model–Agnostic Explanations algorithm (Lime) was utilized to interpret the results of the best ML model. …”
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Migrative armadillo optimization enabled a one-dimensional quantum convolutional neural network for supply chain demand forecasting. by Mohamed Irhuma, Ahmad Alzubi, Tolga Öz, Kolawole Iyiola

“…The Migrative Armadillo Optimization (MAO) algorithm effectively optimizes the hyperparameters of the model. …”
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Optimization of a Heavy-Duty Hydrogen-Fueled Internal Combustion Engine Injector for Optimum Performance and Emission Level by Murat Ozkara, Mehmet Zafer Gul

“…This study presents a high-fidelity optimization framework that couples a validated computational fluid dynamics (CFD) combustion model with a surrogate-assisted multi-objective genetic algorithm (MOGA). The CFD model was validated using particle image velocimetry (PIV) data from non-reacting flow experiments conducted in an optically accessible research engine developed by Sandia National Laboratories, ensuring accurate prediction of in-cylinder flow structures. …”
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A 37 years [1984–2021] Landsat/Sentinel-2 derived snow cover time-series for Switzerland by Charlotte Poussin, Pascal Peduzzi, Bruno Chatenoux, Gregory Giuliani

“…Hereafter, we present a 37-year monthly time-series of snow cover derived from Landsat and Sentinel-2 data using the Snow Observations from Space algorithm and processed in the Swiss Data Cube that facilitates the analysis, production and reuse of this Essential Climate Variable, enhancing environmental monitoring efforts at national scale.…”
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3D conformation and crystal interaction insights into drug development challenges for HCV drug analogues via molecular simulations by Richard S. Hong, Alessandra Mattei, Mark E. Tuckerman, Ahmad Y. Sheikh

“…In this article, we demonstrate how a suite of molecular simulation approaches, including crystal structure prediction augmented with a new hydrate CSP algorithm, free-energy perturbation, molecular dynamics (MD) based solubility predictions, and topological assessment to evaluate surface re-crystallization tendencies, provide key atomistic-level insights into the differentiated performance of the two analogs. …”
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Construction of Industrial Economic Management Decision Model Based on Three Parameter Interval Grey Numbers by Pao-Ching Lin, Tzu-Jung Wu, Jui-Chan Huang

“…This model characterizes the uncertainty of decision information by introducing three parameter interval grey numbers, and innovatively adopts grey relational clustering decision-making method, combined with genetic algorithm to dynamically optimize key parameters. The results demonstrated that the model exhibited excellent performance on multiple datasets, with an accuracy stable between 95 and 96%. …”
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Developing an alternative data-driven model to resemble geomorphologic rainfall-runoff models by Pin-Chun Huang, Kwan Tun Lee

“…The topological distribution inherent in the input data space is analyzed to improve predictive accuracy. The proposed artificial intelligence (AI) model, which incorporates a classification algorithm for preprocessing input features prior to training a model based on the recurrent neural network, exhibits outstanding performance in runoff discharge prediction. …”
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Intelligent anti-jamming communication technology with electromagnetic spectrum feature cognition. by Hui Zhao, Guobin Zhao, Xichun Wang, Zhonghui Zhang, Xianchao Xun

“…This study proposes an intelligent anti-interference algorithm that combines deep neural networks and game theory, and constructs a model based on near-end strategy optimization. …”
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Incorporating Semi-Automated Approach for Effective Software Requirements Prioritization: A Framework Design by Fang-Fang Chua, Tek-Yong Lim, Bushra Tajuddin, Amarilis Putri Yanuarifiani

“…SARiP concentrates on the areas related to prediction of requirements priority group and ranks requirements using classification tree and ranking algorithm. …”
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