Modeling and Optimization of Beam Pumping System Based on Intelligent Computing for Energy Saving by Xiaohua Gu, Taifu Li, Zhiqiang Liao, Liping Yang, Ling Nie

“…It firstly employs the general regression neural network (GRNN) algorithm to obtain the best model of the beam pumping system, and secondly searches the optimal operation parameters with improved strength Pareto evolutionary algorithm (SPEA2). …”
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Precision evaluation criteria for simulation algorithms in infinite systems: A network model-based approach by Yonglong Ding

“…As the particle count escalates, the computational demands of diverse simulation algorithms surge, paralleled by a marked improvement in accuracy. …”
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A SA-ANN-Based Modeling Method for Human Cognition Mechanism and the PSACO Cognition Algorithm by Shuting Chen, Dapeng Tan

“…Firstly, the relationship between SA processing procedure and cognition knowledge evolution is analyzed, and a SA-ANN-based inference model is set up. Then, based on the inference model, a Powell SA with combinatorial optimization (PSACO) algorithm is proposed to improve the clustering efficiency and recognition accuracy for the cognition process. …”
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Robust techno-economic optimization of energy hubs under uncertainty using active learning with artificial neural networks by Aya M. A. Heikal, Shady H. E. Abdel Aleem, Ragab A. El-Sehiemy, Almoataz Y. Abdelaziz

“…The AL algorithm enhances the ANN model’s predictive ability, resulting in a 57.9% decrease in operating costs and a 0.010682 loss of energy supply probability (LESP) value. …”
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Postpartum depression risk prediction using explainable machine learning algorithms by Xudong Huang, Lifeng Zhang, Chenyang Zhang, Jing Li, Chenyang Li

“…Feature selection was performed using LASSO regression and the Boruta algorithm. Eight machine learning algorithms were then employed to construct the prediction models. …”
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Determination of Sequential Well Placements Using a Multi-Modal Convolutional Neural Network Integrated with Evolutionary Optimization by Seoyoon Kwon, Minsoo Ji, Min Kim, Juliana Y. Leung, Baehyun Min

“…This complex multi-million-dollar problem involves optimizing multiple parameters using computationally intensive reservoir simulations, often employing advanced algorithms such as optimization algorithms and machine/deep learning techniques to find near-optimal solutions efficiently while accounting for uncertainties and risks. …”
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Review of Icing Prediction Model and Algorithm for Overhead Transmission Lines Considering Time Cumulative Effects by Chuanqi WANG, Liwen WU, Zhibin DENG, Weifeng DENG, Bin YANG

“…Secondly, the combination and intercrossing mode of support vector machine, hybrid swarm intelligence optimization algorithm, genetic algorithm in the model focus on the identification and modeling of icing process. …”
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COD Optimization Prediction Model Based on CAWOA-ELM in Water Ecological Environment by Lili Jiang, Liu Yang, Yang Huang, Yi Wu, Huixian Li, XiYan Shen, Meng Bi, Lin Hong, Yiting Yang, Zuping Ding, Wenjie Chen

“…In order to detect high error rate and poor convergence of the water ecological chemical oxygen demand (COD) prediction model, combining the limit learning machine (ELM) model and whale optimization algorithm, CAWOA is improved by the sin chaos search strategy, while the ELM optimizes the parameters of the algorithm to improve convergence speed, thus improving the generalization performance of the ELM. …”
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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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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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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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Tackling the Optimal Phasor Measurement Unit Placement and Attack Detection Problems in Smart Grids by Incorporating Machine Learning by Ramzi Al-Sharawi, Abdelfatah Ali, Mostafa Shaaban, Nasser Qaddoumi, Mohamed S. Abdalzaher

“…Existing research primarily addresses cybersecurity by focusing on the optimal placement of phasor measurement units (PMUs) to ensure topological observability and minimize system costs, followed by developing AI-based attack detection algorithms. …”
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Phase Sequence Adjustment Method for Three-Phase Load Imbalance in Station Area Based on Improved Pigeon-inspired Optimization by Mingyue HU, Huifen ZHANG, Shuping MIAO, Fan YANG, Zhenming QU

“…The unbalanced load commutation control strategy is used to establish an optimal commutation mathematical model with the goal of minimizing the three-phase current imbalance on the low-voltage side of the distribution transformers; the adaptive parameters and Cauchy disturbance are introduced to improve the original pigeon-inspired optimization (PIO) algorithm. …”
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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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Optimal energy management for multi-energy microgrids using hybrid solutions to address renewable energy source uncertainty by M. Siva Ramkumar, Jaganathan Subramani, M. Sivaramkrishnan, Arunkumar Munimathan, Goh Kah Ong Michael, Mohammad Mukhtar Alam

“…In this study, a new hybrid algorithm is used for system modelling and low-cost, optimal management of Micro Grid (MG) networked systems. …”
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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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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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Detection Model for Cotton Picker Fire Recognition Based on Lightweight Improved YOLOv11 by Zhai Shi, Fangwei Wu, Changjie Han, Dongdong Song, Yi Wu

“…The improved detection algorithm maintains high accuracy while achieving faster inference speed and fewer model parameters. …”
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Exploring the effectiveness of adaptive randomized sine cosine algorithm in wind integrated scenario based power system optimization with FACTS devices by Sunilkumar P. Agrawal, Pradeep Jangir, Arpita, Sundaram B. Pandya, Anil Parmar, Ahmad O. Hourani, Bhargavi Indrajit Trivedi, Mohammad Khishe

“…The results of these experiments show faster convergence and consistent solution accuracy compared to benchmark algorithms such as Sine Cosine Algorithm (SCA), Improved Grey Wolf Optimization (IGWO), Whale Optimization Algorithm (WOA), and others. …”
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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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