Detection of litchi fruit maturity states based on unmanned aerial vehicle remote sensing and improved YOLOv8 model by Changjiang Liang, Changjiang Liang, Dandan Liu, Dandan Liu, Weiyi Ge, Weiyi Ge, Wenzhong Huang, Wenzhong Huang, Yubin Lan, Yubin Lan, Yubin Lan, Yongbing Long, Yongbing Long, Yongbing Long, Yongbing Long

“…The improved model demonstrated robust performance in different application scenarios. …”
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Optimization Strategy of Microgrid Hierarchical Scheduling Considering Electric Vehicles User Satisfaction Degree by Huiqun YU, Shen YIN, Hao ZHANG, Shanshan SHI, Daogang PENG, Guoshun CAI

“…Renewable energy is used to support the load of the microgrid in the source storage layer, and the excess part is absorbed by the dispatchable electric vehicle, which makes the comprehensive cost of the microgrid minimized. The improved ant lion algorithm is used to solve the model of the source storage layer. …”
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An Improved Maximum Power Point Tracking Control Scheme for Photovoltaic Systems: Integrating Sparrow Search Algorithm-Optimized Support Vector Regression and Optimal Regulation fo... by Mingjun He, Ke Zhou, Yutao Xu, Jinsong Yu, Yangquan Qu, Xiankui Wen

“…The sparrow search algorithm (SSA), recognized for its excellent global search capability, was employed to optimize the hyperparameters of SVR to further increase the prediction accuracy. …”
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Machine learning model design for high performance cloud computing & load balancing resiliency: An innovative approach by Nilayam Kumar Kamila, Jaroslav Frnda, Subhendu Kumar Pani, Rashmi Das, Sardar M.N. Islam, P.K. Bharti, Kamalakanta Muduli

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Unified total body CT image with multiple organ specific windowings: validating improved diagnostic accuracy and speed in trauma cases by Naoki Okada, Shusuke Inoue, Chang Liu, Sho Mitarai, Shinichi Nakagawa, Yohsuke Matsuzawa, Satoshi Fujimi, Goshiro Yamamoto, Tomohiro Kuroda

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Improving Forest Above-Ground Biomass Estimation Accuracy Using Multi-Source Remote Sensing and Optimized Least Absolute Shrinkage and Selection Operator Variable Selection Method by Er Wang, Tianbao Huang, Zhi Liu, Lei Bao, Binbing Guo, Zhibo Yu, Zihang Feng, Hongbin Luo, Guanglong Ou

“…Compared to the optimal SGBoost model with the Lasso variable selection method (R² of 0.69, RMSE of 18.63 Mg/ha), the VIF-Lasso method improves R² by 0.06 and reduces RMSE by 2.15 Mg/ha, while the Lasso-GA method improves R² by 0.04 and reduces RMSE by 1.93 Mg/ha. …”
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Enhancing Lidar-Based 3D Classification Through an Improved Deep Learning Framework With Residual Connections by Shih-Lin Lin, Jun-Yi Wu

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Genetic algorithm application in multi-objective optimization of structural parameters and PID controller parameters on bus driver seat's suspension system by Pham Ngoc Dai

“…The elastic element's stiffness kd (N/m) and the damper's damping coefficient cd (Ns/m) are optimized by the Pareto Method with the objective function J(x) designed by the Weighted Square Sum method, and the min value of J(x) is found with the genetic algorithm (GA). A PID controller is integrated into the system to improve the working efficiency, and GA optimizes the controller parameters KP, KI, KD, and N. …”
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Dynamic Grouping Control of BESS for Remaining Useful Life Extension and Overall Energy Efficiency Improvement in Smoothing Wind Power Fluctuations by Yang Yu, Dongyang Chen, Yuwei Wu, Boxiao Wang, Yuhang Huo, Wentao Lu, Zengqiang Mi

“…Second, a model to optimize capacity allocation for three battery groups (BGs) in BESS is established considering LL and OEE, and it is solved by the designed improved beetle swarm antennae search algorithm. …”
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Fine-tuning text-to-SQL models with reinforcement-learning training objectives by Xuan-Bang Nguyen, Xuan-Hieu Phan, Massimo Piccardi

“…For this reason, in this paper we explore the use of reinforcement learning to improve the performance of models of more conservative size, which can fit within standard user hardware. …”
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Grey modeling method for approximate exponential sequence of optimizing initial condition by Yun YUE, Guangyue LU

“…Grey GM（1,1）prediction method is only suitable for the prediction model of the original sequence which satisfies the characteristic of the approximate exponential through the accumulated generating operation.In order to widen the application range of the traditional grey prediction model,a new method,dubbed DGM（1,1,c,β）model（direct grey model）,was proposed to improve the accuracy of grey GM（1,1）prediction by optimizing initial conditions.DGM（1,1,c,β）model was established for the original sequence conforming to the approximate exponential and the model parameters were obtained by the particle swarm optimization algorithm.Both the simulation and analysis of the example demonstrate that the proposed method is more effective and practical.…”
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Optimal Decision-making Model for Power Grid Maintenance Scheduling Considering Comprehensive Supply-Demand Factors by Hui LIU, Qianjun JIANG, Qianjin GUI, Lei WANG, Hongqiang TIAN, Jingjing WANG, Hejun YANG

“…Finally, a real power grid case is used for simulation analysis, which has verified the correctness and effectiveness of the proposed model and algorithm.…”
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Machine Learning Models for Artist Classification of Cultural Heritage Sketches by Gianina Chirosca, Roxana Rădvan, Silviu Mușat, Matei Pop, Alecsandru Chirosca

“…Models start from common Faster R-CNN architectures, reinforcement learning, and vector extraction tools. …”
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An algorithm for generation of DEMs from contour lines considering geomorphic features by Xiao-Ping Rui, Xue-Tao Yu, Jin Lu, Muhammad Aqeel Ashraf, Xian-Feng Song

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Framework for truck–RPAS hybrid models in last-mile delivery by Armin Mahmoodi, Leila Hashemi, Jeremy Laliberte

“…Extensive computational experiments demonstrate that the proposed hybrid truck–RPAS system achieves notable operational improvements compared to traditional truck-only models. …”
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Techno-Economic Enhancement of Distribution Network by Optimal DG Allocation Along with Network Reconfiguration Considering Different Load Models and Levels by Chintan D. Patel, Tarun Kumar Tailor, Samyak S. Shah, Gulshan Sharma, Pitshou N. Bokoro

“…CAEL minimization by DG allocation and NR under multiple load models is addressed for the first time in this study. …”
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Load balancing method of service cluster based on mean-variance by Xiaoan BAO, Xue WEI, Lei CHEN, Guoheng HU, Na ZHANG

“…When a large number of concurrent requests are allocated,the load scheduling mechanism is to achieve the load balancing of nodes in the network by minimizing the response time and maximizing the utilization ratio of nodes.In the load balancing algorithm based on genetic algorithm,the fitness function is designed to have an important influence on the load balancing efficiency.A service cluster load balancing method based on mean-variance was proposed to optimize the fitness function.The investment portfolio selection model mean-variance was used to minimize the response time,which was used to get the weight of each server's resource utilization,so as to obtain the optimal allocation combination.This method improves the accuracy and efficiency of the fitness function.Compared with other models in different service environment,the simulation results show that the load balancing algorithm makes the service cluster get a better balance performance in terms of node utilization and response time.…”
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The Utilization of a Naïve Bayes Model for Predicting the Energy Consumption of Buildings by Behnam Sadaghat, Ali Javadzade Khiavi, Babak Naeim, Erfan Khajavi, Hadi Sadaghat, Amir Reza Taghavi Khanghah

“…These algorithms are employed to improve HVAC system control, equipment sizing, energy management, and cost reduction. …”
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Optimizing high-speed train tracking intervals with an improved multi-objective grey wolf by Lin Yue, Meng Wang, Peng Wang, Jinchao Mu

“…To achieve multi-objective dynamic optimization, a novel train tracking operation calculation method is proposed, utilizing the improved grey wolf optimization algorithm (MOGWO). …”
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Highway subgrade stability prediction model based on depth separation convolutional fusion network by Yubian Wang

“…The research results of this paper further improve the efficiency of the neural network model and maintain the accuracy of data, which not only meets the needs of highway subgrade detection but also promotes the application of large-scale image processing technology. …”
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