Application of Hybrid Algorithm Based on Ant Colony Optimization and Sparrow Search in UAV Path Planning by Yangyang Tian, Jiaxiang Zhang, Qi Wang, Shanfeng Liu, Zhimin Guo, Huanlong Zhang

“…At the same time, our research further verifies the effectiveness of improving heuristic algorithms by fusing different optimization strategies, providing new ideas and directions for future algorithm design and optimization.…”
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An equivalent modeling method for integrated water-wind-solar systems based on sparrow search algorithm by Yuanhong Lu, Jie Zhang, Jingyue Zhang, Libin Huang, Haiping Guo, Binjiang Hu, Tianyu Guo

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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

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Online Prediction Method for Power System Frequency Response Analysis Based on Swarm Intelligence Fusion Model by Lin Xu, Li Li, Meiying Wang, Xiangxu Wang, Yicong Li, Weidong Li, Kuanjiu Zhou

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Early detection of monkeypox: Analysis and optimization of pretrained deep learning models using the Sparrow Search Algorithm by Amna Bamaqa, Waleed M. Bahgat, Yousry AbdulAzeem, Hossam Magdy Balaha, Mahmoud Badawy, Mostafa A. Elhosseini

“…When PCR tests are unavailable, computational lesion detection offers a promising option. This research presents a non-invasive diagnostic method using the Sparrow Search Algorithm (SpaSA). …”
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Mine cable partial discharge denoising method based on multivariate variational mode decomposition and improved wavelet threshold by Jiyuan CAO, Yanwen WANG, Peng CHEN, Xuan ZHOU, Weixiong ZHU, Yihe ZHANG, Le WANG

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Research on bearing fault diagnosis based on improved northern goshawk algorithm optimizing SVM by WU Xiaojun, LI Quwei

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Prediction of Breakdown Voltage of Long Air Gaps Under Switching Impulse Voltage Based on the ISSA-XGBoost Model by Zisheng Zeng, Bin Song, Shaocheng Wu, Yongwen Li, Deyu Nie, Linong Wang

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Prediction of Coiled Tubing Erosion Rate Based on Sparrow Search Algorithm Back-Propagation Neural Network Model by Yinping Cao, Fengying Fang, Guowei Wang, Wenyu Zhu, Yijie Hu

“…To accurately predict the erosion rate of coiled tubing, this study studied the influence law of erosion rate through experiments, screened the main influencing factors of erosion rate by grey relational analysis (GRA), and established a back-propagation neural network (BPNN) model optimized by the sparrow search algorithm (SSA) to predict the erosion rate. …”
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Noise Reduction of Steam Trap Based on SSA-VMD Improved Wavelet Threshold Function by Shuxun Li, Qian Zhao, Jinwei Liu, Xuedong Zhang, Jianjun Hou

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Research on optimal selection of runoff prediction models based on coupled machine learning methods by Xing Wei, Mengen Chen, Yulin Zhou, Jianhua Zou, Libo Ran, Ruibo Shi

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The prediction of karst-collapse susceptibility levels based on the ISSA-ELM integrated model by Jiaxin Wang, Ying Yang, Xian Yang, Yulong Lu, Yang Liu, Da Hu, Yongjia Hu

“…To address the limitations of conventional prediction methods, in this study, we introduce an enhanced predictive model, the improved sparrow search algorithm-optimized extreme learning machine (ISSA-ELM), for accurate karst-collapse susceptibility assessment. …”
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Multi-objective optimal scheduling of cascade reservoirs in complex basin systems: Case study of the Jinsha River-Yalong River confluence basin in China by Zhaocai Wang, Zhihua Zhu, Hualong Luan, Tunhua Wu

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Enhanced securities investment strategy using ISSA–SVM: a hybrid model combining adaptive moving average, support vector machine, and multi-strategy sparrow search algorithm for im... by Wei Ni, Qingqing Chen, Xiaochen Guo, Yanan Liu

“…This study proposes a novel hybrid strategy, ISSA–SVM, that combines Adaptive Moving Average (AMA), Support Vector Machine (SVM), and an Improved Sparrow Search Algorithm (ISSA) to enhance CTA model performance in securities investment. …”
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A Hybrid Strategy-Improved SSA-CNN-LSTM Model for Metro Passenger Flow Forecasting by Jing Liu, Qingling He, Zhikun Yue, Yulong Pei

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Research on rock strength prediction model based on machine learning algorithm by Xiang Ding, Mengyun Dong, Wanqing Shen

“…By selecting different features, the optimal feature combination for predicting rock compressive strength was obtained, and the optimal parameters for different models were obtained through the Sparrow Search Algorithm (SSA). Finally, four regression evaluation indicators, including mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), and coefficient of determination (R2), were used to evaluate the predictive performance of the established regression models. …”
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Development of an Improved KOA Algorithm for Solving Task Allocation in Hilly Orchards With Weeding Robots by Xiaolin Xie, Hang Jin, Heng Wang, Man Xu, Cheng Zhang, Xin Jin, Zhihong Zhang

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CEEMDAN-IHO-SVM: A Machine Learning Research Model for Valve Leak Diagnosis by Ruixue Wang, Ning Zhao

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Photovoltaic Hosting Capacity Assessment of Distribution Networks Considering Source–Load Uncertainty by Chao Chen, Weifeng Peng, Cheng Xie, Shufeng Dong, Yibo Hua

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A data-driven approach utilizing machine learning (ML) and geographical information system (GIS)-based time series analysis with data augmentation for water quality assessment in M... by Abhijeet Das

“…To tackle this issue, we developed a new tool that harnesses optimization models, enhancing the reliability and accuracy of water quality assessments. Our research in Mahanadi River Basin, Odisha, presents an enhanced methodology based on data, specifically designed to be beneficial for Water Quality (WQ) based on Synthetic Pollution Index (SPI) and machine learning models such as Long Short-Term Memory (LSTM) and Sparrow Search Algorithm (SSA), for its analysis and interpretation of extensive, intricate data sets on water quality, as well as the allocation of pollution sources or contributing elements, in order to improve knowledge of the water quality and the planning of monitoring networks for efficient water resource management. …”
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