Optimal Planning of Terminal Locations and Capacity of UHVDC Considering Constraints of Receiving-End Power Grid Support Capability by Linlin YU, Ge YAN, Xintong YAN, Yubin MAO, Shuyu CHEN, Tiantian LI, Yunfeng WEN

“…Furthermore, the model is decomposed into a upper layer for planning and a lower layer for operation checking, and the the Benders decomposition algorithm is used to realized the iterative solution of the complex optimization model. …”
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Incorporating soil moisture data into a machine learning framework improved the predictive accuracy of corn yields in the U.S. by Bishwoyog Bhattarai, Zachary Leasor, André Fróes de Borja Reis

“…Leveraging machine learning (ML) techniques capable of handling large-scale datasets offers a promising alternative for uncovering hidden patterns and generating actionable insights to improve crop yield. We hypothesize that incorporating soil moisture and temperature data from land surface models into a ML framework will enhance accuracy of corn yield predictions. …”
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Research on Long-Term Scheduling Optimization of Water–Wind–Solar Multi-Energy Complementary System Based on DDPG by Zixing Wan, Wenwu Li, Mu He, Taotao Zhang, Shengzhe Chen, Weiwei Guan, Xiaojun Hua, Shang Zheng

“…To address the challenges of high complexity in modeling the correlation of multi-dimensional stochastic variables and the difficulty of solving long-term scheduling models in continuous action spaces in multi-energy complementary systems, this paper proposes a long-term optimization scheduling method based on Deep Deterministic Policy Gradient (DDPG). …”
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Path planning of unmanned ships based on A* and dynamic window approach by FU Long, SHEN Zhen, TAO Hao, HAN Yunjun, DONG Xisong, XIONG Gang

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Optimizing Energy Efficiency in Cloud Data Centers: A Reinforcement Learning-Based Virtual Machine Placement Strategy by Abdelhadi Amahrouch, Youssef Saadi, Said El Kafhali

“…To address this issue, we propose a novel energy-efficient virtual machine (VM) placement strategy that integrates reinforcement learning (Q-learning), a Firefly optimization algorithm, and a VM sensitivity classification model based on random forest and self-organizing map. …”
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Optimizing Location of Car-Sharing Stations Based on Potential Travel Demand and Present Operation Characteristics: The Case of Chengdu by Yu Cheng, Xu Chen, Xiaohua Ding, Linting Zeng

“…This study aims to use different data source with statistical models and machine learning algorithm to help car-sharing operator to choose the optimal location of new stations and adjust the location of existing stations. …”
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Multi-scenario Dynamic Simulation and Optimization of Urban Ventilation Environment: A Case Study of Taiyuan Metropolitan Area by Junda HUANG, Yuncai WANG

“…Then, a prediction model is constructed based on the random forest algorithm. …”
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An Efficient Mutual Authentication and Fractional Lyrebird Optimization With Deep Learning–Based SIP-Based DRDoS Attack Detection by V. Sreenivasulu, C. V. Ravikumar

“…In this article, a deep learning model fractional lyrebird optimization algorithm–deep stack autoencoder (FLOA-DSA) is developed for the detection of SIP-based distributed reflection denial-of-service (DRDoS) attacks in SIP network. …”
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Leveraging ultrasonic-derived phenotypes and estimated breeding value to improve abdominal fat weight prediction in chickens throughout the egg laying period by Penghao Li, Zhengda Li, Fan Ying, Dan Zhu, Dawei Liu, Xianyi Song, Jie Wen, Guiping Zhao, Bingxing An

“…While, AFT measured by ultrasound improved the predictive ability of all the models (R² of KNN showed highest increase of 12.35 %). …”
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Reinforcement learning-based assimilation of the WOFOST crop model by Haochong Chen, Xiangning Yuan, Jian Kang, Danni Yang, Tianyi Yang, Xiang Ao, Sien Li

“…Our results demonstrate that the Time-Series Driven RL model achieved assimilation accuracy comparable to the SUBPLEX optimization algorithm, with an average MAE of 0.65 compared to 0.76 for SUBPLEX, and a slight decrease in RMSE, while significantly reducing the computational burden by 365 times. …”
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The Optimal Dispatch for a Flexible Distribution Network Equipped with Mobile Energy Storage Systems and Soft Open Points by Yu Ji, Ying Zhang, Lei Chen, Juan Zuo, Wenbo Wang, Chongxin Xu

“…In this paper, a case study uses a regional road network in Chengdu coupled with an IEEE 33-node standard grid, and the model is solved using the non-dominated sorting genetic algorithm III (NSGA-III) algorithm. …”
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Collaborative Optimization of Passenger Control Strategy and Train Operation Plan with Variable Formations for a Rail Transit Network by Yonghao Yin, Jun Chen, Wenrui Zhao

“…We proposed a passenger control strategy and train operation plan collaborative optimization (PCS&TOP) model to schedule the train operation that improves the efficiency of passengers’ travel and reduces the cost of train operation for a rail transit network. …”
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Optimal protection coordination for directional overcurrent relays in radial distribution networks with inverter-based distributed energy resources by Jie Li, Jingtong Feng, Min Huang, Shenquan Liu, Gang Wang

“…The Pareto-front of the proposed optimal protection coordination problem is obtained by the improved multi-objective particle swarm optimization, where a time–space section evaluation utilizes the K-means clustering method to improve the computational efficiency. …”
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A Wi-Fi Indoor Localization Strategy Using Particle Swarm Optimization Based Artificial Neural Networks by Nan Li, Jiabin Chen, Yan Yuan, Xiaochun Tian, Yongqiang Han, Mingzhe Xia

“…In this paper, we propose an indoor localization system using the affinity propagation (AP) clustering algorithm and the particle swarm optimization based artificial neural network (PSO-ANN). …”
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Combined Use of Spectral and Structural Features for Improved Early Detection of Pine Shoot Beetle Attacks in Yunnan Pines by Yujie Liu, Youqing Luo, Run Yu, Lili Ren, Qi Jiang, Shaoshun He, Xinqiang Chen, Guangzhao Yang

“…In conclusion, this study identified optimal spectral and structural variables for UAV monitoring and improved HRS model accuracy and thereby provided technical support for the IPM of PSB outbreaks.…”
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The authentication of Yanchi tan lamb based on lipidomic combined with particle swarm optimization-back propagation neural network by Qi Yang, Dequan Zhang, Chongxin Liu, Le Xu, Shaobo Li, Xiaochun Zheng, Li Chen

“…This study successfully combined widely targeted lipidomic with a back propagation (BP) neural network optimized based on a particle swarm algorithm to identify the authenticity of Yanchi Tan lamb. …”
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A New Hybrid Model for Underwater Acoustic Signal Prediction by Guohui Li, Wanni Chang, Hong Yang

“…In addition, an artificial bee colony (ABC) algorithm is used to optimize model performance by adjusting the parameters of SVR. …”
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Supply chain finance credit risk assessment using support vector machine–based ensemble improved with noise elimination by Ying Liu, Lihua Huang

“…The main characteristics of this approach include that (1) a novel noise filtering scheme that avoids the noisy examples based on fuzzy clustering and principal component analysis algorithm is proposed to remove both attribute noise and class noise to achieve an optimal clean set, and (2) support vector machine classifiers, based on the improved particle swarm optimization algorithm, are seen as component classifiers. …”
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Optimization of Cocoa Pods Maturity Classification Using Stacking and Voting with Ensemble Learning Methods in RGB and LAB Spaces by Kacoutchy Jean Ayikpa, Abou Bakary Ballo, Diarra Mamadou, Pierre Gouton

“…Next, we apply classification algorithms to identify the best performers. These algorithms are then combined via stacking and voting techniques, allowing our model to be optimized by taking advantage of the strengths of each method, thus guaranteeing more robust and precise results. …”
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The effect of using the algorithm of restrictive intraoperative fluid therapy, early immune enteral nutrition, and early patient mobilization on orthotopic liver transplantation ou... by A. M. Dzyadz'ko, M. L. Katin, O. A. Chugunova, A. F. Minov, E. G. Oganova, E. O. Santotskiy, A. E. Shcherba, O. O. Rummo, I. I. Kanus

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