Models and Algorithms of Conflict Detection and Scheduling Optimization for High-Speed Train Operations Based on MPC by Zhihui Wang, Yonghua Zhou, Deng Liu

“…Compared with the general-speed railway transportation, the high-speed railway transportation requires a timely and automatic adjustment capability in the centralized traffic control (CTC) system. In order to improve the capability, this paper mainly explores the models and algorithms of conflict detection and scheduling optimization of high-speed train operations. …”
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Optimized SVR with nature-inspired algorithms for environmental modelling of mycotoxins in food virtual-water samples by Abdullahi G. Usman, Sagiru Mati, Hanita Daud, Ahmad Abubakar Suleiman, Sani I. Abba, Hijaz Ahmad, Taha Radwan

“…This study proposed the use of a support vector regression (SVR) predictive model improved by two metaheuristic algorithms used for optimization namely, Harris Hawks Optimization (HHO) and Particle Swarm Optimization (PSO) to predict chromatographic retention time of various food mycotoxin groups. …”
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Enhancing Surgery Scheduling in Health Care Settings With Metaheuristic Optimization Models: Algorithm Validation Study by João Lopes, Tiago Guimarães, Júlio Duarte, Manuel Santos

“…By implementing these AI-driven strategies, hospitals can minimize patient wait times, maximize resource use, and enhance surgical outcomes through improved planning. This development highlights how AI algorithms can effectively adapt to changing health care environments, having a transformative impact.…”
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Automated weed and crop recognition and classification model using deep transfer learning with optimization algorithm by K. Gopalakrishnan, R. Sivaraj, M. Vijayakumar

“…The extensive outcome highlighted the improvements of the developed AWRC-DLMLO technique with other existing models.…”
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Exploring multiple optimization algorithms in transfer learning with EfficientNet models for agricultural insect classification by Hoang-Tu Vo, Nhon Nguyen Thien, Kheo Chau Mui, Huan Lam Le, Phuc Pham Tien

“…This study investigates the impact of multiple optimization algorithms within transfer learning, employing EfficientNet models for the classification of agricultural insects. …”
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Exploring multiple optimization algorithms in transfer learning with EfficientNet models for agricultural insect classification by Hoang-Tu Vo, Nhon Nguyen Thien, Kheo Chau Mui, Huan Lam Le, Phuc Pham Tien

“…This study investigates the impact of multiple optimization algorithms within transfer learning, employing EfficientNet models for the classification of agricultural insects. …”
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Optimization Management of Storage Location in Stereoscopic Warehouse by Integrating Genetic Algorithm and Particle Swarm Optimization Algorithm by Shuhong Zhang, Xianghui Zheng, Fan Xu, Suzhen Wang, Qixia Zhang, Yuan Cao

“…At the same time, the study introduced the GA-PSO algorithm to solve the mathematical model and optimize the goods location planning. …”
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Anomaly Detection and Performance Analysis With Exponential Smoothing Model Powered by Genetic Algorithms and Meta Optimization by Ali Kerem Guler, Huseyin Fuat Alsan, Taner Arsan

“…This study employs a genetic algorithm to optimize the parameters of the Third Order Exponential Smoothing model for predicting on the real-time traffic datasets of the Numenta Anomaly Benchmark (NAB). …”
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Application model of “Internet+Marketing service” virtual robot under dung beetle optimizer algorithm by HE Wei, ZHOU Yutian, YU Yang, KANG Yumeng, ZHU Meng, QIAN Xusheng

“…In order to deal with the situation of power marketing service under the new situation and improve the service of power grid in the internet era, a virtual robot model of "Internet+Marketing service" was designed by the dung beetle optimizer (DBO) algorithm. …”
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State-of-the-Art Review: Models and Algorithms for Optimal Power System Design, Stabilization, and Reliability Enhancement by Senele Njabulo Zwane, Bongumsa Mendu, Bessie Baakanyang Monchusi

“…In this study, a state-of-the-art review of models and algorithms for optimal power system design, stabilization, and reliability enhancement is conducted. …”
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Multiscale Modeling and Reconstruction of Joint Motion: Finite Element Optimization Based on Particle Swarm Algorithm by Jiaju Zhu, Zhong Zhang, Runnan Liu, Meixue Ren, Guodong Ma

“…High-resolution CT and MRI scans of the knee joint were utilized to construct an accurate 3D reconstruction model, while the particle swarm algorithm was implemented to optimize joint positioning. …”
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Modelling and optimization of well hole cleaning using artificial intelligence techniques by Nageswara Rao Lakkimsetty, Hassan Rashid Ali Al Araimi, G. Kavitha

“…This study aims to improve the accuracy and practicality of hole cleaning assessment by applying Artificial Intelligence (AI) techniques, specifically Artificial Neural Networks (ANN) and Genetic Algorithms (GA), to predict downhole parameters and optimize drilling processes. …”
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MSKT: multimodal data fusion for improved nursing management in hemorrhagic stroke by Ting Zhou, Dandan Li, Jingfang Zuo, Aihua Gu, Li Zhao

“…These results highlight its potential to enhance nursing decision-making, optimize personalized plans, and improve patient outcomes.…”
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An Improved Particle Swarm Optimization and Adaptive Neuro-Fuzzy Inference System for Predicting the Energy Consumption of University Residence by Stephen Oladipo, Yanxia Sun, Oluwatobi Adeleke

“…To address this problem, the velocity update equation of the original PSO algorithm is modified by incorporating a dynamic linear decreasing inertia weight, which improves the PSO algorithm’s convergence behaviour and aids both local and global search. …”
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Integrated energy microgrids participating in voltage regulation ancillary services: An improved ADMM based distributed optimization approach by Hao Xiao, Wei Pei, Daoxin Han, Xiaowei Pu, Jiarui Wang

“…Moreover, an improved accelerated consensus alternating direction method of multipliers algorithm is proposed to accelerate distributed optimization solutions of the model while protecting user privacy. …”
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Improving Forecasts of the EGARCH Model Using Artificial Neural Network and Fuzzy Inference System by Geleta T. Mohammed, Jane A. Aduda, Ananda O. Kube

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Intelligent irrigation strategy model for farmland using dung beetle optimization-random forest algorithms by Wenwen Hu, Yong Liu, Jun An, Shipu Xu, Zhiwen Zhou, Mingming An, Xiaokun Guo, Xiang Ma, Wenfei Jiang, Yunsheng Wang

“…This study proposes an optimized machine learning prediction model using the Dung Beetle Optimization-Random Forest (DBO-RF) algorithm, thus improving irrigation predictability. …”
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Improvement of Assemble-to-Order Model Processes With Process Mining: Dynamic Analysis and Hybrid Approaches by Ufuk Celik, Yuksel Yurtay

“…Insights derived from the analysis informed actionable recommendations to optimize production workflows. This study demonstrates the potential of hybrid process mining techniques for understanding and improving ATO systems, offering a dynamic and scalable framework for addressing inefficiencies in similar production models.…”
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State of Health Prediction for Lithium-Ion Batteries Based on Gated Temporal Network Assisted by Improved Grasshopper Optimization by Xiankun Wei, Silun Peng, Mingli Mo

“…The experimental results demonstrate that the proposed IGOA-GGNN-TCN framework offers a novel and effective approach for state-of-health (SOH) estimation in lithium-ion batteries. By integrating improved grasshopper optimization (IGOA) with hybrid graph-temporal modeling, the method achieves superior prediction accuracy compared to conventional techniques, providing a promising tool for battery management systems in real-world applications.…”
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Noise Elimination for Wide Field Electromagnetic Data via Improved Dung Beetle Optimized Gated Recurrent Unit by Zhongyuan Liu, Xian Zhang, Diquan Li, Shupeng Liu, Ke Cao

“…Experiments demonstrate that the optimization capacity of the IDBO algorithm is conspicuously superior to other intelligent optimization algorithms, and the IDBO-GRU algorithm surpasses the probabilistic neural network (PNN) and the GRU algorithm in the denoising accuracy of WFEM data. …”
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