Application of the metaheuristic algorithms to quantify the GSI based on the RMR classification by Pouya Koureh Davoodi, Farnusch Hajizadeh, Mohammad Rezaei

“…This study addresses this challenge by analyzing data from fourteen different rock types and employing three metaheuristic optimization algorithms, namely Particle Swarm Optimization (PSO), Simulated Annealing (SA), and Grey Wolf Optimization (GWO), to develop predictive models for quantifying GSI based on the RMR. …”
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Predictive reward-prediction errors of climbing fiber inputs integrate modular reinforcement learning with supervised learning. by Huu Hoang, Shinichiro Tsutsumi, Masanori Matsuzaki, Masanobu Kano, Keisuke Toyama, Kazuo Kitamura, Mitsuo Kawato

“…In this study, we investigated the cerebellum's role in executing reinforcement learning algorithms, with a particular emphasis on essential reward-prediction errors. …”
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Clinical Prediction Models Based on Traditional Methods and Machine Learning for Predicting First Stroke: Status and Prospects by ZHANG Zijiao, DING Shunjing, ZHAO Di, LIANG Jun, LEI Jianbo

“…In recent years, advancements in big data and artificial intelligence technologies have opened new avenues for stroke risk prediction. This article reviews the current research status of traditional methods and machine learning models in predicting first-ever stroke risk and outlines future development trends from three perspectives: First, emphasis should be placed on technological innovation by incorporating advanced algorithms such as deep learning and large models to further enhance the accuracy of predictive models. …”
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Prospects of Predicting the Polar Motion Based on the Results of the Second Earth Orientation Parameters Prediction Comparison Campaign by Tomasz Kur, Justyna Śliwińska‐Bronowicz, Malgorzata Wińska, Henryk Dobslaw, Jolanta Nastula, Aleksander Partyka, Santiago Belda, Christian Bizouard, Dale Boggs, Sara Bruni, Lue Chen, Mike Chin, Sujata Dhar, Robert Dill, PengShuo Duan, Jose M. Ferrandiz, Junyang Gou, Richard Gross, Sonia Guessoum, Songtao Han, Robert Heinkelmann, ChengLi Huang, Christopher Irrgang, Jacek Kudrys, Jia Li, Marcin Ligas, Lintao Liu, Weitao Lu, Volker Mayer, Wei Miao, Maciej Michalczak, Sadegh Modiri, Michiel Otten, Todd Ratcliff, Shrishail Raut, Jan Saynisch‐Wagner, Matthias Schartner, Erik Schoenemann, Harald Schuh, M. Kiani Shahvandi, Benedikt Soja, Xiaoqing Su, Daniela Thaller, Maik Thomas, Guocheng Wang, Yuanwei Wu, CanCan Xu, Xueqing Xu, Xinyu Yang, Xin Zhao, Zhijin Zhou

“…Abstract Growing interest in Earth Orientation Parameters (EOP) resulted in various approaches to the EOP prediction algorithms, as well as in the exploitation of distinct input data, including the observed EOP values from various operational data centers and modeled effective angular momentum functions. …”
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A Quantitative Evaluation Method Based on Back Analysis and the Double-Strength Reduction Optimization Method for Tunnel Stability by Jinglai Sun, Fan Wang, Xinling Wang, Xu Wu

“…Compared with the traditional method, the proposed back analysis method can reduce errors in the predicted performance, and unlike the SRM, the ODSRM can avoid overestimating the safety factor with the same reduction factor. …”
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Explainable Machine Learning in the Prediction of Depression by Christina Mimikou, Christos Kokkotis, Dimitrios Tsiptsios, Konstantinos Tsamakis, Stella Savvidou, Lillian Modig, Foteini Christidi, Antonia Kaltsatou, Triantafyllos Doskas, Christoph Mueller, Aspasia Serdari, Kostas Anagnostopoulos, Gregory Tripsianis

“…The XGBoost classifier utilized the 15 most significant risk factors identified by the GA algorithm. Additionally, the SHAP analysis revealed that anxiety, education level, alcohol consumption, and body mass index were the most influential predictors of depression. …”
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CPO-VMD Combined With Multiscale Permutation Entropy for Noise Reduction in GNSS Vertical Time Series in Mining Areas by Xu Yang, Xinxin Yao, Xinjian Fang, Xuexiang Yu, Yi Wu, Shicheng Xie

“…The method uses the CPO algorithm to optimize the key parameters of the VMD, determines the high-frequency components with MPE values higher than a set threshold as noise components and removes them, and then reconstructs the remaining components in order to obtain the noise-reduced time series. …”
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PCA-FSA-MLR Model and Its Application in Runoff Forecast by GUO Cunwen, CUI Dongwen

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A Novel Method for Noise Reduction and Jump Correction of Maglev Gyroscope Rotor Signals Under Instantaneous Perturbations by Di Liu, Zhen Shi, Chenxi Zou, Ziyi Yang, Jifan Li

“…To solve this problem, we propose a novel noise reduction algorithm that integrates Moving Average Filtering with Autoregressive Integrated Moving Average (MAF-ARIMA), based on the noise characteristics of the rotor jump signal. …”
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Mining privacy-preserving association rules using transaction hewer allocator and facile hash algorithm in multi-cloud environments by D. Dhinakaran, S. Gopalakrishnan, D. Selvaraj, M.S. Girija, G. Prabaharan

“…The complexities involved in the mining of frequent itemsets led us to introduce the Apriori with Tid Reduction (ATid) algorithm considering scalability and computational operational improvements to the mining process due to the Tid Reduction concept. …”
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Evolutionary Algorithms for the Optimal Design of Robotic Cells: A Dual Approximation for Space and Time by Raúl-Alberto Sánchez-Sosa, Ernesto Chavero-Navarrete

“…In response, this study presents a dual approach to optimize both spatial design and traversal time in robotic cells, using bioinspired evolutionary algorithms. Initially, a genetic algorithm is employed to optimize the layout of the cell elements, reducing space usage and avoiding interferences between workstations. …”
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Robust Design Optimization of Viscoelastic Damped Composite Structures Integrating Model Order Reduction and Generalized Stochastic Collocation by Tianyu Wang, Chao Xu, Teng Li

“…Pareto optimal solutions are determined by combining the proposed MOR and gSC approaches with a well-established Non-dominated Sorting Genetic Algorithm II (NSGA-II) algorithm, which accounts for robustness in handling design variables, objectives, and constraints. …”
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Mahalanobis distance–based kernel supervised machine learning in spectral dimensionality reduction for hyperspectral imaging remote sensing by Jing Liu, Yulong Qiao

“…Spectral dimensionality reduction is a crucial step for hyperspectral image classification in practical applications. …”
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Alert Reduction and Telemonitoring Process Optimization for Improving Efficiency in Remote Patient Monitoring Programs: Framework Development Study by Job van Steenkiste, Niki Lupgens, Martijn Kool, Daan Dohmen, Iris Verberk-Jonkers

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An Approach to Data Reduction for Learning from Big Datasets: Integrating Stacking, Rotation, and Agent Population Learning Techniques by Ireneusz Czarnowski, Piotr Jędrzejowicz

“…We propose to use an agent-based population learning algorithm for data reduction in the feature and instance dimensions. …”
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The impact of quantitative platform on candidacy for bronchoscopic lung volume reduction: a multi-center retrospective cohort study by Max Wayne, Suchitra Pilli, Hee Jae Choi, Nathaniel Moulton, Praveen Chenna, Allen Cole Burks, Alexander Chen

“…Background: Bronchoscopic lung volume reduction (BLVR) can be an effective treatment for highly selected patients with severe emphysema but only half of carefully selected patients derive clinical benefit. …”
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Peak Arm Current Reduction of Modular Multilevel Converter Based on Particle Swarm Optimizer for Circulating Current Injections by Riyadh Toman Thahab, Tahani Hamodi Al-Mhana

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Lifetime Prediction of Power IGBT Module by WANG Yan-gang, Chamund Dinesh, LI Shi-ping, Jones Steve, DOU Ze-chun, XIN Lan-yuan, LIU Guo-you

“…The Weibull methodology for power cycling test data and some reported typical lifetime models were firstly discussed. Then, lifetime prediction procedures were presented including the conversion of mission profile to temperature profile, the temperature cycles counting by Rainflow algorithm, and lifetime calculating based on the fatigue linear accumulation damage theory and lifetime models. …”
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Research on Vibration Reduction Method of Nonpneumatic Tire Spoke Based on the Mechanical Properties of Domestic cat’s Paw Pads by Haichao Zhou, Huiyun Li, Ye Mei, Guolin Wang, Congzhen Liu, Lingxin Zhang

“…The three parameters, the asymmetric arc, the thickness, and the curvature of spokes, were used as design variables to maximize the vibration reduction. The orthogonal experimental, the Kriging approximate model, and the genetic algorithm were carefully selected for optimal solutions. …”
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Inverse Design of Broadband Artificial Magnetic Conductor Metasurface for Radar Cross Section Reduction Using Simulated Annealing by Haoda Xia, Xiaoyu Liang, Bowen Jia, Pei Shi, Zhihong Chen, Shi Pu, Ning Xu

“…The experimental results show a significant RCS reduction of 10 dB within the 7.6–15.5 GHz range, with the peak reduction reaching-28 dB at normal incidence. …”
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