Dynamic Error Modeling and Predictive Compensation for Direct-Drive Turntables Based on CEEMDAN-TPE-LightGBM-APC Algorithm by Manzhi Yang, Hao Ren, Shijia Liu, Bin Feng, Juan Wei, Hongyu Ge, Bin Zhang

“…Our methodology comprises four key stages: Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN)-based decomposition of historical error data, development of component-specific prediction models using Tree-structured Parzen Estimator (TPE)-optimized Light Gradient Boosting Machine (LightGBM) algorithms for each Intrinsic Mode Function (IMF), integration of component predictions to generate initial values, and application of the Adaptive Prediction Correction (APC) module to produce final predictions. …”
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ADMET evaluation in drug discovery: 21. Application and industrial validation of machine learning algorithms for Caco-2 permeability prediction by Dong Wang, Jieyu Jin, Guqin Shi, Jingxiao Bao, Zheng Wang, Shimeng Li, Peichen Pan, Dan Li, Yu Kang, Tingjun Hou

“…We believe that the model developed in this study could represent a reliable tool for assessing Caco-2 permeability during early-stage drug discovery and the chemical transformation rules derived here could provide insights for optimizing Caco-2 permeability. Scientific contribution A comprehensive validation of various machine learning algorithms combined with diverse molecular representations on a large dataset for predicting Caco-2 permeability was reported. …”
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Study on risk factors of impaired fasting glucose and development of a prediction model based on Extreme Gradient Boosting algorithm by Qiyuan Cui, Jianhong Pu, Wei Li, Yun Zheng, Jiaxi Lin, Lu Liu, Peng Xue, Jinzhou Zhu, Mingqing He

“…Feature selection, parameter optimization, and model construction were performed in the training set, while the validation set was used to evaluate the predictive performance of the models. …”
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Crops Disease Detection, from Leaves to Field: What We Can Expect from Artificial Intelligence by Youssef Lebrini, Alicia Ayerdi Gotor

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Identifying key factors influencing maize stalk lodging resistance through wind tunnel simulations with machine learning algorithms by Guanmin Huang, Ying Zhang, Shenghao Gu, Weiliang Wen, Xianju Lu, Xinyu Guo

“…Climate change has intensified maize stalk lodging, severely impacting global maize production. While numerous traits influence stalk lodging resistance, their relative importance remains unclear, hindering breeding efforts. …”
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Disaggregated and Distributed Near-Real-Time RIC for Large Scale User-Centric RAN by Amr Amrallah, Yu Tsukamoto, Takahide Murakami, Akio Ikami, Hiroyuki Shinbo, Yoshiaki Amano

“…Our proposed divide-and-conquer (D&C) and heuristic-greedy (HG)-based algorithms significantly reduce computational complexity while maintaining near-optimal performance. …”
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Development of a PYTHON library for a mathematical model of nuclear reactor kinetics by Aleksei G. Goryunov, Danil D. Kachanov, Kirill D. Kachanov

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Individual health passport storage, sharing and verification scheme based on blockchain by GAO Hancheng, JI Haoyu, GAO Chenhao, HUANG Haiping

“…The scheme also refined the weak consensus mechanism, with an improved algorithm that achieved consensus in merely two stages. …”
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Crossover operators for molecular graphs with an application to virtual drug screening by Nico Domschke, Bruno J. Schmidt, Thomas Gatter, Richard Golnik, Paul Eisenhuth, Fabian Liessmann, Jens Meiler, Peter F. Stadler

“…Abstract Genetic algorithms are a powerful method to solve optimization problems with complex cost functions over vast search spaces that rely in particular on recombining parts of previous solutions. …”
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Fine-Grained Classification of Pressure Ulcers and Incontinence-Associated Dermatitis Using Multimodal Deep Learning: Algorithm Development and Validation Study by Alexander Brehmer, Constantin Seibold, Jan Egger, Khalid Majjouti, Michaela Tapp-Herrenbrück, Hannah Pinnekamp, Vanessa Priester, Michael Aleithe, Uli Fischer, Bernadette Hosters, Jens Kleesiek

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Identification of fresh leaves of Anji White Tea: S-YOLOv10-ASI algorithm fusing asymptotic feature pyra-mid network. by Chunhua Yang, Wenxia Yuan, Qiang Zhao, Zejun Wang, Bowu Song, Xianqiu Dong, Yuandong Xiao, Shihao Zhang, Baijuan Wang

“…Intersection over Union (IoU) is used to optimize the loss function calculation. The slice-assisted super-reasoning algorithm is integrated to improve the recognition ability of YOLOv10 network for long-distance and small-target tea. …”
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Application of CycleGAN-based low-light image enhancement algorithm in foreign object detection on belt conveyors in underground mines by Anxin Zhao, Qiuhong Zheng, Liang Li

“…For the discriminator network, a global-local discriminator structure is designed to optimize overall illumination while adaptively enhancing shadow and highlight regions. …”
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Ecological and Statistical Evaluation of Genetic Algorithm (GARP), Maximum Entropy Method, and Logistic Regression in Predicting Spatial Distribution of Astragalus sp. by Amir Ghahremanian, Abbas Ahmadi, Hamid Toranjzar, Javad Varvani, Nourollah Abdi

“…This study aims to evaluate the potential habitat of Astragalus sp. using three different species distribution modeling methods: the maximum entropy (MaxEnt) model, the Genetic Algorithm for Rule-Set Production (GARP), and logistic regression. …”
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Long-term electricity demand forecasting of a university campus based on advanced deep learning artificial neural network algorithms by Saad A. Alsamraee, Sanjeev Khanna

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Unmanned trackless rubber wheeler based on LD improved Cartographer mapping algorithm underground SLAM autonomous navigation method and test by Fangwei LI, Jiusheng BAO, Chen WANG, Xiaoming YUAN, Yan YIN, Shirong GE, Maosen WANG, Yuhan LV

“…Firstly, a two-dimensional raster map of the underground roadway environment is constructed based on the Cartographer algorithm, and the Lazy Decision (LD) algorithm is introduced to optimize it, which solves the overlapping and fuzzy phenomena during Cartographer mapping and improves the mapping accuracy. …”
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LSTM-JSO framework for privacy preserving adaptive intrusion detection in federated IoT networks by Shaymaa E. Sorour, Mohammed Aljaafari, Amany M. Shaker, Ahmed E. Amin

“…The framework dynamically optimizes hyperparameters using the Joint Strategy Optimization (JSO) algorithm, ensuring efficient model adaptation across heterogeneous IoT networks. …”
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A Hybrid Approach Incorporating WSO-HO and the Newton-Raphson Method to Enhancing Photovoltaic Solar Model Parameters Optimisation by Jeridi Ahmed, Moulahi Mohamed Hedi, Khaterchi Hechmi, Zaafouri Abderrahmen

“…This study introduces a novel hybrid approach, WSO-HO, which integrates the strengths of the war strategy optimization (WSO) and Hippopotamus Optimization (HO) algorithms, enhanced by the Newton-Raphson (NR) method, to achieve precise parameter estimation for PV models. …”
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Fuzzy deep learning architecture for cucumber plant disease detection and classification by Anas Bilal, Junaid Ali Khan, Abdulkareem Alzahrani, Khalid Almohammadi, Maha Alamri, Xiaowen Liu

“…At the same time, the ReLU transfer function ensures robustness, mainly when dealing with noisy or incomplete image segments. Feature vector optimization is performed using a chaotic particle swarm algorithm, enhancing the model’s overall accuracy, reliability, and ease of implementation. …”
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Dual Function Radar and Communication Waveform Design Based on Sub-pulse Hybrid Modulation by Yu LIU, Junhao ZHANG, Xue YAO, Xianxiang YU, Guolong CUI

“…To tackle the resulting nonconvex and nondeterministic polynomial-hard optimization problem, the Spectral Majorization Minimization (SMM) algorithm is employed to monotonically decrease the objective function value. …”
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Strategic Implementation of Super-Agents in Heterogeneous Multi-Agent Training for Advanced Military Simulation Adaptability by Huseyin Oktay Altun, Huseyin Furkan Ceran, Korkut Kutay Metin, Tolga Erol, Emre Fisne

“…This study focuses on the application of reinforcement learning in tactical military simulation environments involving heterogeneous multi-agent systems. Optimizing Heterogeneous Multi-Agent Training (HMAT) through scenario-specific adjustments to the Proximal Policy Optimization (PPO) algorithm, we tackle the complexity of tactical simulations. …”
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