Machine Learning-Driven Prediction of Vitamin D Deficiency Severity with Hybrid Optimization by Usharani Bhimavarapu, Gopi Battineni, Nalini Chintalapudi

“…The improved whale optimization (IWOA) algorithm was used for feature selection, which optimized weight functions to improve prediction accuracy. …”
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An Improved Galerkin Framework for Solving Unsteady High-Reynolds Navier–Stokes Equations by Jinlin Tang, Qiang Ma

“…This error indicator guides an AMR algorithm that combines longest-edge bisection with local Delaunay re-triangulation, ensuring optimal mesh adaptation to complex flow features such as boundary layers and vortices. …”
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Accuracy–Efficiency Trade-Off: Optimizing YOLOv8 for Structural Crack Detection by Jiahui Zhang, Zoia Vladimirovna Beliaeva, Yue Huang

“…To address the accuracy–efficiency trade-off faced by deep learning models in structural crack detection, this paper proposes an optimized version of the YOLOv8 model. YOLO (You Only Look Once) is a real-time object detection algorithm known for its high speed and decent accuracy. …”
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Schur Complement Optimized Iterative EKF for Visual–Inertial Odometry in Autonomous Vehicles by Guo Ma, Cong Li, Hui Jing, Bing Kuang, Ming Li, Xiang Wang, Guangyu Jia

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Optimization of multi-structural parameters in metamaterials based on the DGN co-simulation method. by Shangyang Jin, Fuxing Chen, Jie Bai, Bingfei Liu

“…Then the global algorithm is combined with the local algorithm to solve the problem of poor convergence of the global optimization algorithm while ensuring the optimization quality of the local optimization algorithm. …”
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APG mergence and topological potential optimization based heuristic user association strategy by Zhirui HU, Meihua BI, Fangmin XU, Meilin HE, Changliang ZHENG

“…Therefore, it is reasonable to model the problem of improving network scalable degree as minimizing network coupling degree,and it is feasible to improve network scalable degree by reducing network coupling degree.2)The upper limit of computational complexity of the proposed algorithm is <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML"> <mi mathvariant="script">O</mi><mo stretchy="false">(</mo><mi>K</mi><mi>N</mi><msub> <mi>log</mi> <mn>2</mn> </msub> <mi>N</mi><mo>+</mo><msup> <mi>k</mi> <mn>2</mn> </msup> <mo>+</mo><mi>N</mi><mi>N</mi><msub> <mover accent="true"> <mi>N</mi> <mo>¯</mo> </mover> <mtext>p</mtext> </msub> <mo stretchy="false">)</mo></math></inline-formula>,while that of directly solving the optimization problem is<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML"><mi mathvariant="script">O</mi><mo stretchy="false">(</mo><msup> <mi>N</mi> <mrow> <msub> <mover accent="true"> <mi>N</mi> <mo>¯</mo> </mover> <mtext>u</mtext> </msub> <mi>K</mi></mrow> </msup> <mo stretchy="false">)</mo></math></inline-formula>.3)For theoretical analysis of the network scalable degree,take Fig.3 as an example.If AP2 changes,12 APs in Fig. 3(a)are affected and the network scalable degree is η₂=0.51,while 4 APs in Fig.3(c)are affected and the network scalable degree is η₂=0.79.4)Fig.5 shows the simulation results of network scalable degree.Compared with the traditional strategy,the network scalable degree is improved by 9.59% with 4.43% user rate loss.Compared with the strategy in[10],the network scalable degree is improved by 22.15% with 4.99% user rate loss. 5) The algorithm parameters, the threshold β₀of overlap rate and the upper limit number N₀of AP associated, effect the performance.As shown in Fig.6,with β₀or N₀decreases,η increases and the total user rate decreases. …”
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Networked Sensor-Based Adaptive Traffic Signal Control for Dynamic Flow Optimization by Xinhai Wang, Wenhua Shao

“…This method integrates the Webster algorithm with a proportional–integral–derivative (PID) controller, whose parameters are optimized using a genetic algorithm, thereby facilitating scientifically informed traffic signal timing strategies for enhanced traffic regulation. …”
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COD Optimization Prediction Model Based on CAWOA-ELM in Water Ecological Environment by Lili Jiang, Liu Yang, Yang Huang, Yi Wu, Huixian Li, XiYan Shen, Meng Bi, Lin Hong, Yiting Yang, Zuping Ding, Wenjie Chen

“…In order to detect high error rate and poor convergence of the water ecological chemical oxygen demand (COD) prediction model, combining the limit learning machine (ELM) model and whale optimization algorithm, CAWOA is improved by the sin chaos search strategy, while the ELM optimizes the parameters of the algorithm to improve convergence speed, thus improving the generalization performance of the ELM. …”
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Optimized Integral Super-Twisting Sliding Mode Control for Acute Leukemia Therapy by Muhammad Munir Butt, Azhar Iqbal Kashif Butt

“…These improvements highlight the potential of ISTSMC in optimizing chemotherapy administration, ensuring better patient outcomes while minimizing side effects.…”
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Optimizing the lifetime of wireless sensor networks via reinforcement-learning-based routing by Wenjing Guo, Cairong Yan, Ting Lu

“…Reinforcement-learning-based routing protocol takes advantage of the intelligent algorithm of reinforcement learning to search for the optimal routing path for data transmission. …”
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Research on Adaptive Planning of Three-Dimensional Trajectory for Uncrewed Aerial Vehicle Inspection Based on Nonlinear Weibull Algorithm by Zhuang Liu, Ning Yang, Jiaxing Fu, Huanqing Cai, Xuebei Wei

“…An S-shaped characteristic function-based nonlinear evolution factor is employed to balance global exploration and local exploitation of the optimal path, while a Weibull flight operator mutation strategy is designed to enable the algorithm to escape from locally optimal paths, enrich the search space, and improve convergence accuracy. …”
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Optimizing Fairness and Spectral Efficiency With Shapley-Based User Prioritization in Semantic Communication by Moirangthem Tiken Singh, Adnan Arif, Rabinder Kumar Prasad, Bikramjit Choudhury, Chandan Kalita, Sikdar Md. S. Askari

“…Notably, as the number of channels increases, S-SE stabilizes while fairness continues to improve, approaching optimal levels in diverse system configurations. …”
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Optimal scheduling of BIES with multi-energy flow coupling based on deep RL by XIA Xuhua, YANG Jiandi, SHI Yongtao

“…Building integrated energy systems （BIESs） can enhance energy efficiency ratio （EER） and reduce carbon emissions while meeting diverse user-side load demands. To further improve the energy dispatch capability of BIES， this paper proposes a low-carbon economic and optimal dispatch method for BIES with multi-energy flow coupling based on deep reinforcement learning （deep RL）. …”
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Deep Reinforcement Learning-Based Motion Control Optimization for Defect Detection System by Yuhuan Cai, Liye Zhao, Xingyu Chen, Zhenjun Li

“…A composite reward mechanism is introduced to mitigate potential motor instability, while CP-MPA is utilized to optimize the performance of the proposed m-TD3 composite controller. …”
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Two-Stage Integrated Optimization Design of Reversible Traction Power Supply System by Xiaodong Zhang, Wei Liu, Qian Xu, Zhuoxin Yang, Dingxin Xia, Haonan Liu

“…The parallel cheetah algorithm is employed to solve this complex optimization problem. …”
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Integrating Machine Learning and Multi-Objective Optimization in Biofuel Systems: A Review by Ivan P. Malashin, Dmitry A. Martysyuk, Vadim S. Tynchenko, Andrei P. Gantimurov, Vladimir A. Nelyub, Aleksei S. Borodulin

“…Studies have leveraged hybrid models, including Convolutional Neural Network - Gated Recurrent Unit (CNN-GRU) networks for emission control and Neutrosophic Fuzzy Optimization (NFO) for uncertainty handling. While existing models demonstrate improvements in predictive accuracy and optimization effectiveness, challenges remain in model generalization, computational complexity, and real-time adaptability. …”
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Decentralized Multi-Robot Navigation Based on Deep Reinforcement Learning and Trajectory Optimization by Yifei Bi, Jianing Luo, Jiwei Zhu, Junxiu Liu, Wei Li

“…Additionally, it introduces safety constraints through an artificial potential field (APF) to optimize these trajectories. Additionally, a constrained nonlinear optimization method further refines the APF-adjusted paths, resulting in the development of the GNN-RL-APF-Lagrangian algorithm. …”
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ZZ-YOLOv11: A Lightweight Vehicle Detection Model Based on Improved YOLOv11 by Zhe Zhang, Zhongyang Zhang, Gang Li, Chenxi Xia

“…Aiming at the problems of insufficient vehicle detection accuracy, high misdetection and omission rate, and heavy model computational burden caused by complex lighting conditions, target occlusion, and other factors in urban traffic scenarios, this paper proposes an improved lightweight detection network, ZZ-YOLO. Firstly, the current mainstream target detection algorithms lack components to improve the network’s focus on the edges of the objects, which can indirectly lead to unclear classification and localization. …”
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Optimization of Operation Strategy of Multi-Islanding Microgrid Based on Double-Layer Objective by Zheng Shi, Lu Yan, Yingying Hu, Yao Wang, Wenping Qin, Yan Liang, Haibo Zhao, Yongming Jing, Jiaojiao Deng, Zhi Zhang

“…The simulation results show that shared energy storage can optimize the allocation of multi-party resources by flexibly adjusting the control mode, improving the efficiency of resource utilization while improving the consumption of renewable energy, meeting the power demand of all parties, and realizing the sharing of energy storage resources. …”
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Development of an Optimized Ensemble Least Squares Model for Identifying Potential Deposit Customers by Firman Aziz, Mutia Maulida, Jafar Jafar, Nurafni Shahnyb, Norma Nasir, Ampauleng Ampauleng

“…This study presents the development and optimization of an Ensemble Least Squares (ELS) algorithm to enhance the classification of potential deposit customers. …”
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