Advancing Rice Disease Detection in Farmland with an Enhanced YOLOv11 Algorithm by Hongxin Teng, Yudi Wang, Wentao Li, Tao Chen, Qinghua Liu

“…The algorithm offers significant advantages in lightweight design and real-time performance, outperforming other classical object detection algorithms and providing an optimal solution for real-time field diagnosis.…”
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Grouped Byzantine fault tolerant consensus algorithm based on aggregated signatures by Yong Wang, Qiancheng Wan, Yifan Wu, Lijie Chen

“…Abstract The Practical Byzantine Fault Tolerance consensus algorithm faces several challenges in large-scale networks, such as the simplistic primary node selection, high communication overhead, poor scalability, and low costs for malicious behavior. …”
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Bandit Algorithms for Efficient Toxicity Detection in Competitive Online Video Games by Jacob Morrier, Rafal Kocielnik, R. Michael Alvarez

“…This algorithm balances exploration and exploitation to optimize long-term performance and is designed intentionally for easy deployment in production environments. …”
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Wafer Defect Classification Algorithm With Label Embedding Using Contrastive Learning by Jeongjoon Hwang, Somi Ha, Dohyun Kim

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A coherent approach to quantum-classical optimization by Andrés N. Cáliz, Jordi Riu, Josep Bosch, Pau Torrente, Jose Miralles, Arnau Riera

“…In the particular case of large optimization problems, commonly found in real-world use cases, this strategy is almost mandatory to reduce the otherwise unfathomable execution costs and improve the quality of the results. …”
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Local Outlier Detection Method Based on Improved K-means by Yu ZHOU, Hao XIA, Xuezhen YUE, Peichong WANG

“…When there is no significant decrease in the cost function value with an increase in the number of cluster centers, the position of the “elbow” is observed to determine the optimal number of clusters. …”
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Automated Calibration of SWMM for Improved Stormwater Model Development and Application by Hossein Ahmadi, Durelle Scott, David J. Sample, Mina Shahed Behrouz

“…The tool also supports parallelized optimization algorithms and utilizes Application Programming Interfaces (APIs) to dynamically update SWMM model parameters, accelerating both model execution and convergence. …”
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Modeling and Solving the Multi-Objective Vehicle Routing Problem with Soft and Fuzzy Time Windows by Ailing Chen, Tianao Li

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An improved lightweight method based on EfficientNet for birdsong recognition by Haolun He, Hui Luo

“…Finally, we employ the Adam optimization algorithm to improve network convergence speed. …”
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Combined use of near infrared spectroscopy and chemometrics for the simultaneous detection of multiple illicit additions in wheat flour by Xinyi Dong, Ying Dong, Jinming Liu, Siting Wu

“…The model combines long short-term memory network (LSTM) data dimensionality reduction with partial least squares to detect multiple illicit additives in wheat flour. The Bayesian optimization algorithm was used to optimize the LSTM parameters. …”
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Guided Particle Swarm Optimization for Feature Selection: Application to Cancer Genome Data by Simone A. Ludwig

“…Feature selection helps in improving model performance by reducing overfitting, enhancing generalization, and decreasing computational cost. …”
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Identification of soil texture and color using machine learning algorithms and satellite imagery by Jiyang Wang

“…For future research, it is recommended to explore the combination of SVR with optimization techniques such as genetic algorithms to further improve the accuracy of soil texture and color predictions.…”
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Hydrogen Production Using PVs with MPPT Optimization by Kristián Ondrejička, Vladimír Goga, Šimon Berta, Michal Miloslav Uličný, Vladimír Kutiš

“…This study aims to enhance green hydrogen production by optimizing PV-PEMEL performance using commercial power electronics, while improving energy storage and reducing costs for sustainable hydrogen generation.…”
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A Ｒeinforcement Learning of Cloud Ｒesource Scheduling Algorithm Based on Adaptive Weight by LI Cheng-yan, SUN Wei, TANG Li-min

“…To solve the model，a reinforcement learning based on adaptive weight algorithm was presented. To improve the efficiency of the algorithm，we designed the state space and the action representation method based on one-dimensional array to compress the storage cost of algorithm. …”
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A comparative study of hybrid adaptive neuro-fuzzy inference systems to predict the unconfined compressive strength of rocks by Wei Cao

“…Hybrid models included support vector regression (SVR) combined with the Seahorse Optimizer (SVSH) and SVR combined with the COOT optimization algorithm (SVCO). …”
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Optimizing power network expansion with pumped hydro energy storage using a multi-objective enhanced spider wasp optimizer approach by Mohamed M. Refaat, Saad F. Al-Gahtani, Hussain Bassi, Ziad M. Ali, Shady H. E. Abdel Aleem

“…The problem is framed as a comprehensive multi-objective optimization model designed to minimize the cost of network expansion while maximizing the benefits of integrating various PHES technologies. …”
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Application of artificial intelligence technology in the economic development of urban intelligent transportation system by Ziming Zhao, Jinyu Chen

“…A traffic flow prediction model was established for traffic flow prediction and optimized scheduling algorithms were used to dispatch vehicles on congested urban roads intelligently. …”
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Flexible scheduling strategy for power systems considering source-load uncertainty by Qunmin YAN, Xiaoyu REN, Xiao SONG, Mengjue ZHAO, Chen AN

“…The two-stage robust model is transformed into relatively independent main problems and sub-problems，and the column constraint generation （C&CG） algorithm and strong dyadic theory are adopted to iterate repeatedly to approximate the optimal solution. …”
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Design of digital low-carbon system for smart buildings based on PPO algorithm by Yaohuan Wu, Nan Xie

“…The research results indicate that improving the near-end strategy optimization algorithm can reduce carbon emissions by 2354CO2e, while the lowest operating cost of the model is only 35,000 yuan. …”
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RRMSE-enhanced weighted voting regressor for improved ensemble regression. by Shikun Chen, Wenlong Zheng

“…This uniform weighting approach doesn't consider that some models may perform better than others on different datasets, leaving room for improvement in optimizing ensemble performance. To overcome this limitation, we propose the RRMSE (Relative Root Mean Square Error) Voting Regressor, a new ensemble regression technique that assigns weights to each base model based on their relative error rates. …”
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