An Optimization Coverage Strategy for Wireless Sensor Network Nodes Based on Path Loss and False Alarm Probability by Jianing Guo, Yunshan Sun, Ting Liu, Yanqin Li, Teng Fei

“…Based on this perception model, the optimization objective function of the WSN node optimization coverage problem is established, and then the intelligent optimization algorithm is used to solve the objective function and finally achieve the optimization coverage of sensor nodes. …”
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Machine learning predicts improvement of functional outcomes in spinal cord injury patients after inpatient rehabilitation by Mohammad Rasoolinejad, Irene Say, Peter B. Wu, Xinran Liu, Yan Zhou, Yan Zhou, Nathan Zhang, Emily R. Rosario, Daniel C. Lu, Daniel C. Lu, Daniel C. Lu

“…The ability to accurately predict functional outcomes for SCI patients is essential for optimizing rehabilitation strategies, guiding patient and family decision making, and improving patient care.MethodsWe conducted a retrospective analysis of 589 SCI patients admitted to a single acute rehabilitation facility and used the dataset to train advanced machine learning algorithms to predict patients' rehabilitation outcomes. …”
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Two-layer Optimization Scheduling for Off-grid Microgrids Based on Multi-agent Deep Policy Gradient by Huicong FAN, Zhiguo DUAN, Zhiyong CHEN, Shijia ZHU, Hang LIU, Wenxiao LI, Yang YANG

“…The lower-level model optimizes slow-regulating discrete devices based on mixed-integer second-order cone programming, while the upper-level model optimizes fast-regulating continuous devices using a multi-agent deep policy gradient algorithm. …”
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Sentence Embedding Generation Framework Based on Kullback–Leibler Divergence Optimization and RoBERTa Knowledge Distillation by Jin Han, Liang Yang

“…First, a sentence embedding generation method based on Kullback–Leibler Divergence (KLD) optimization is proposed, which enhances semantic differentiation between sentence vectors, thereby improving the accuracy of textual similarity computation. …”
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An enhanced moth flame optimization extreme learning machines hybrid model for predicting CO2 emissions by Ahmed Ramdan Almaqtouf Algwil, Wagdi M. S. Khalifa

“…The model integrates the Gaussian mutation and shrink mechanism-based moth flame optimization (GMSMFO) algorithm with an extreme learning machine (ELM). …”
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Exploration design for Q-learning-based adaptive linear quadratic optimal regulators under stochastic disturbances by Vina Putri Virgiani, Shiro Masuda

“…The decaying method gradually reduces the exploration signal over time, enabling effective initial exploration while stabilizing as the system approaches optimal performance. …”
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Optimized Controller Design Using Hybrid Real-Time Model Identification with LSTM-Based Adaptive Control by Yeon-Jeong Park, Joon-Ho Cho

“…The method is improved through a combination of numerical calculation, Genetic Algorithms, and LSTM networks, showing approximately 15% better performance compared to conventional methods. …”
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Optimizing a Dynamic Vehicle Routing Problem with Deep Reinforcement Learning: Analyzing State-Space Components by Anna Konovalenko, Lars Magnus Hvattum

“…<i>Background:</i> The dynamic vehicle routing problem (DVRP) is a complex optimization problem that is crucial for applications such as last-mile delivery. …”
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Local Structure Optimization Design of Floating Offshore Wind Turbine Platform Based on Response Surface Analysis by Yajun Ren, Mingxuan Huang, Jungang Hao, Jiazhi Wang, Shuai Li, Ling Zhu, Haisheng Zhao, Wei Shi

“…The optimization results indicate that the maximum stress of the optimized model is reduced by 22.12% compared to the original model, while maintaining the same mass, centroid, and other mass-related parameters. …”
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Balancing conflicting objectives in pre-salt reservoir development: A robust multi-objective optimization framework by Auref Rostamian, Amir Davari Malekabadi, Marx Vladimir De Souda Miranda, Vinicius Edurado Botechia, Denis José Schiozer

“…The study focuses on maximizing expected monetary value (EMV) and the net present value of RM4 considering economic uncertainty (NPVeco of RM4), of the most pessimistic scenario among the RMs. The optimization variables are location, type (injection or production), and number of wells, while the non-dominated sorting genetic algorithm II (NSGA-II) is employed for multi-objective optimization. …”
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A lightweight and optimized deep learning model for detecting banana bunches and stalks in autonomous harvesting vehicles by Duc Tai Nguyen, Phuoc Bao Long Do, Doan Dang Khoa Nguyen, Wei-Chih Lin

“…It is built upon the YOLOv8n (You Only Look Once version 8 nano) baseline and includes enhancements to improve accuracy while preserving a lightweight architecture. …”
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Real-Time Optimization of Discrete Element Models for Studying Asphalt Mixture Compaction Characteristics at the Meso-Scale by Xue Wang, Zifang Wang, Xuanye Luo

“…Among these, discrete element modeling (DEM) has been widely adopted due to its effectiveness in addressing particle-scale problems. However, improving simulation accuracy while maintaining computational efficiency remains a challenge. …”
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RM-MOCO: A Fast-Solving Model for Neural Multi-Objective Combinatorial Optimization Based on Retention by Huiqing Wei, Fei Han, Qing Liu, Henry Han

“…An industry-standard deep reinforcement learning algorithm is used to train RM-MOCO. Experimental results show that, while ensuring the quality of problem solving,the proposed method significantly outperforms some other methods in terms of the speed of solving MOCO problems.…”
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Prediction and optimization of hardness in AlSi10Mg alloy produced by laser powder bed fusion using statistical and machine learning approaches by İnayet Burcu Toprak

“…This study highlights the importance of integrating Machine Learning and statistical analysis methods for the effective modeling and optimization of LPBF processes. The findings contribute significantly to the literature and serve as a valuable reference for future research aimed at improving LPBF process efficiency and performance.…”
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A stochastic optimization method for economic microgrid operation based on "source-load operation" mode classification by ZHOU Hang, MA Lihong, LI Zhongzhong, WANG Haisheng, LI Sifan, ZHANG Changjun, FU Linbei

“…Finally， the adaptive weighted particle swarm optimization （AW-PSO） is employed to solve the stochastic optimization model for microgrid operations. …”
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CFD-based optimization of dynamic cyclones with variable vortex length using GMDH artificial neural network by Hamed Safikhani, Somayeh Davoodabadi Farahani, Lakhbir Singh Brar, Faroogh Esmaeili

“…The final step involves optimizing the cyclone designs through the non-dominated sorting genetic algorithm (NSGA). …”
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A deep neural network approach for optimizing charging behavior for electric vehicle ride-hailing fleet by Kaizhe Chen, Jin Liu, Wenjing Lyu, Tianyuan Wang, Jinxi Wen

“…While extensive research has been conducted on AI’s role in transportation innovation, there remains a significant gap in empirical studies focusing on optimizing the charging behavior of operational EV fleets, particularly within ride-hailing services. …”
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Maize Classification in Arid Regions via Spatiotemporal Feature Optimization and Multi-Source Remote Sensing Integration by Guang Yang, Jun Wang, Zhengyuan Qi

“…The recursive feature elimination random forest (RF-RFE) algorithm was employed for feature selection and model optimization. …”
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Application of Metaheuristics for Optimizing Predictive Models in iHealth: A Case Study on Hypotension Prediction in Dialysis Patients by Felipe Cisternas-Caneo, María Santamera-Lastras, José Barrera-Garcia, Broderick Crawford, Ricardo Soto, Cristóbal Brante-Aguilera, Alberto Garcés-Jiménez, Diego Rodriguez-Puyol, José Manuel Gómez-Pulido

“…Particle Swarm Optimization, Grey Wolf Optimizer, Pendulum Search Algorithm, and Whale Optimization Algorithm were employed to reduce the feature space, removing approximately 45% of clinical and analytical variables while maintaining high recall for the minority class of patients experiencing hypotension. …”
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Gray Wolf Optimizer With Communication Strategy Based on DV-Hop for Nodes Location of Wireless Sensor Networks by Dan Yu, Ting Yuan, Haiyin Qing, Wenwu Xie, Peng Zhu

“…To address this deficiency, this paper introduces an improved Grey Wolf Optimizer with a communication strategy (IGWO-CS). …”
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