Design Optimization of Parameters for Resolver Software Decoding Based on Surrogate Model Management by DENG Yu, LING Yuelun, WU Zuolai, PENG Zaiwu, WANG Jun

“…In order to improve the adaptability of the resolver software decoding system to the electric drive of commercial vehicles and to suppress torque and speed fluctuations during motor operation, this paper proposes a design optimization method for resolver software decoding parameters based on update management using a surrogate model. …”
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Prediction of Telkomsel 4G LTE Card Sales using The K-Nearest Neighbor Algorithm by Alfiana Fontes Martins, Yasinta Oktaviana Legu Rema, Debora Chrisinta, Alejandro Jr. V. Matute, Krisantus Jumarto Tey Seran

“…Accurate sales prediction is a critical challenge in business decision-making, as factors such as data imbalance, outliers, and overfitting may compromise the reliability of predictive models. This study aims to develop a precise model for predicting card sales using the K-Nearest Neighbor (KNN) algorithm and to offer recommendations for improving prediction quality by addressing issues related to data imbalance and overfitting. …”
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Advanced predictive disease modeling in biomedical IoT using the temporal adaptive neural evolutionary algorithm by Chandragandhi S, Arvind C, Srihari K

“…TANEA leverages temporal data patterns, adapts to dynamic changes in sensor readings, and optimizes feature selection through an evolutionary mechanism, resulting in a more precise and reliable predictive model. …”
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Research and application of intelligent learning path optimization based on LSTM-Transformer model by Jinling Wang, Wandong Chai

“…Experimental comparison shows that compared with the traditional learning path recommendation algorithm, the optimization strategy based on the LSTM-Transformer model has achieved remarkable results, with the learner's knowledge mastery rate greatly increased from 75 % to 95 %, the learning time shortened by about 25 %, and the learning satisfaction also increased from 70 % to 90 %, which verifies the research hypothesis and fully proves that the LSTM-Transformer model has high application value in intelligent learning path optimization.…”
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Multiradar Collaborative Task Scheduling Algorithm Based on Graph Neural Networks with Model Knowledge Embedding by Haoqing LI, Dian YU, Changchun PAN, Wenxian YU, Dongying LI

“…A key innovation of this algorithm is its capability to capture critical model knowledge using low-complexity calculations, which helps to further optimize the GNN model. …”
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Multibranch semantic image segmentation model based on edge optimization and category perception. by Zhuolin Yang, Zhen Cao, Jianfang Cao, Zhiqiang Chen, Cunhe Peng

“…Second, a category perception module is used to learn category feature representations and guide the pixel classification process through an attention mechanism to optimize the resulting segmentation accuracy. Finally, an edge optimization module is used to integrate the edge features into the middle and the deep supervision layers of the network through an adaptive algorithm to enhance its ability to express edge features and optimize the edge segmentation effect. …”
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A New Algorithm Model Based on Extended Kalman Filter for Predicting Inter-Well Connectivity by Liwen Guo, Zhihong Kang, Shuaiwei Ding, Xuehao Yuan, Haitong Yang, Meng Zhang, Shuoliang Wang

“…Given that more and more oil reservoirs are reaching the high water cut stage during water flooding, the construction of an advanced algorithmic model for identifying inter-well connectivity is crucial to improve oil recovery and extend the oilfield service life cycle. …”
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A combined model of shoot phosphorus uptake based on sparse data and active learning algorithm by Tianli Wang, Yi Zhang, Haiyan Liu, Fei Li, Dayong Guo, Ning Cao, Yubin Zhang

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Development of an Optimized Ensemble Least Squares Model for Identifying Potential Deposit Customers by Firman Aziz, Mutia Maulida, Jafar Jafar, Nurafni Shahnyb, Norma Nasir, Ampauleng Ampauleng

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Bayesian optimization of hybrid quantum LSTM in a mixed model for precipitation forecasting by Yumin Dong, Huanxin Ding

“…The hyperparameters of the model are optimized using the Bayesian optimization algorithm to obtain the best performance. …”
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A multi-algorithm prognostic model combining inflammatory indices and surgical features in distal cholangiocarcinoma by Yi Yin, Yi Yin, Luyuan Bai, Xinyue Mu, Xinyue Mu, Shan Zhang, Panpan Zhai, Panpan Zhai

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Model Optimization for High-Yield Biocrude in Co-Hydrothermal Liquefaction of Municipal Sludge by Botian HAO, Yunfei DIAO, Ya WEI, Donghai XU

“…After training with the Levenberg-Marquardt algorithm, the model′s R2 significantly improved to 0.9989, demonstrating the superiority of neural networks in modeling nonlinear complex systems. …”
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Online Vulnerability Assessment in Cascading Failure Analysis Using an Intelligence Monitoring Model by Saber Armaghani, Zahra Moravej

“…Thus, in this paper, Demand Response modeling will be based on determining the cost of electric energy consumption in the emergency of the network in such a way as to cause a shift of consumption to improve the performance of the network. …”
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An integrated vehicle routing model to optimize agricultural products distribution in retail chains by W. Madushan Fernando, Amila Thibbotuwawa, H. Niles Perera, Peter Nielsen, Deniz Kenan Kilic

“…It introduces an integrated bi-objective VRP model that concurrently optimizes resource allocation, order scheduling, and route planning. …”
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Bearing fault diagnosis for high-speed train based on improved VMD and APSO-SVM by ZHANG Qingsong, ZHANG Bing, QIN Yi

“…The experimental results show that traditional SVM has better effect on the diagnosis of rolling element fault and composite type fault, but the diagnosis effect on cage fault is relatively poor. Therefore, APSO algorithm was used to optimize the core parameters of the SVM, which further improved the recognition accuracy of cage fault and realized the effective identification for the fault bearings of high-speed train.…”
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HSoMLSDP: A Hybrid Swarm-Optimized Machine Learning Framework for Software Defect Prediction by Madhusmita Das, Biju R. Mohan, Ram Mohana Reddy Guddeti

“…In pursuit of enhancing the defect prediction accuracy of the SoMLDP model, this paper designed two novel hybrid swarm-optimization algorithms (SOAs) referred to as gravitational force grasshopper optimization algorithm-artificial bee colony (GFGOA-ABC), and levy flight grasshopper optimization algorithm-artificial bee colony (LFGOA-ABC) algorithms. …”
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MULTI-OBJECTIVE ROBUST OPTIMIZATION DESIGN OF COMPLIANT HINGE BASED ON BP NEURAL NETWORK (MT) by WU JianJun, LI JiaHui

“…In order to improve the robustness of the compliant hinge, genetic algorithm and BP neural network methods are introduced to optimize the parameters of the compliant mechanism. …”
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Frequency Dependent Spencer Modeling of Magnetorheological Damper Using Hybrid Optimization Approach by Ali Fellah Jahromi, Rama B. Bhat, Wen-Fang Xie

“…The parameters of the model are identified using an experimental data based hybrid optimization approach which is a combination of Genetic Algorithm and Sequential Quadratic Programming approach. …”
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A New System Reliability Optimization Model Based on Swapping Existing Components by Yuxiong Li, Xianzhen Huang, Xinong En, Pengfei Ding

“…Replacing failed components with other functioning components properly in the original system can be an attractive way for improving system reliability. This paper proposes a new system reliability optimization model to achieve optimal component reliability and the ideal component-swapping strategy under a certain set of constraints. …”
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Differential Evolution for Optimizing Model Parameters in Simulation of Direct Dimethyl Carbonate Synthesis by Outi Ruusunen, Riitta Keiski, Mika Ruusunen

“…The Differential Evolution (DE) algorithm was applied to optimize parameters of the model. …”
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