Hydropower Plant Available Energy Forecasting Using Artificial Neural Network and Particle Swarm Optimization by Suriya Kaewarsa, Vanhkham Kongpaseuth

“…The model was evaluated by using correlation coefficient (<i>r</i>), relative error (<i>RE</i>), root mean square error (<i>RMSE</i>), and Taylor diagram plots in comparison with popular single-algorithm approaches such as ANN, and NARX models. …”
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Energy-Adaptive SGHSMC: A Particle-Efficient Nonlinear Filter for High-Maneuver Target Tracking by Chang Ho Kang, Sun Young Kim

“…By integrating Hamiltonian Monte Carlo sampling with stochastic gradient techniques, our approach achieves a 40% reduction in computational overhead compared to traditional particle filters while maintaining particle diversity. …”
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Democratization of Crop Irrigation: A Socio-Technical Optimization Approach Using Particle Swarm Optimization by Hasna Elalaoui Elabdallaoui, Youssef Mourdi, Abdelaziz ElFazziki, Mohamed Sadgal

“…To do this, we propose a collective irrigation system using a Particle Swarm Optimization (PSO) algorithm, to accurately estimate crop water needs, a method of equitable distribution of water according to needs. …”
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Machine learning-based pattern recognition of Bender element signals for predicting sand particle-size by Yong-Hoon Byun, Juik Son, Jungmin Yun, Hyunwook Choo, Jongmuk Won

“…Nevertheless, the developed CNN model well classified the four sand types at a given vertical stress and cutoff frequency, implying that the unique pattern of each sand type can be satisfactorily captured by the CNN algorithm. Overall, the framework shown in this study demonstrates that the bender element (or pattern of receiving shear wave signals) with the CNN model can be used in monitoring real-time variation of sand particle size.…”
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Permeability Predictions for Tight Sandstone Reservoir Using Explainable Machine Learning and Particle Swarm Optimization by Jing-Jing Liu, Jian-Chao Liu

“…Based on the data preprocessing, global and local interpretations are performed according to the Shapley additive explanations (SHAP) analysis, and the redundant features in the data set are screened to identify the porosity, AC, CAL, and GR slope as the four most important features. The particle swarm optimization algorithm is then used to optimize the hyperparameters of the XGBoost model. …”
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Size optimization method of the Watt-II six-bar mechanism based on particle swarm optimization by H. Ye, H. Ye, Z. Niu, Z. Niu, D. Han, D. Han, X. Su, X. Su, W. Chen, W. Chen, G. Tu, G. Tu, T. Zhu, T. Zhu

“…<p>Aiming at the difficult problem of comprehensive scale design of the six-bar mechanism in engineering practice, kinematic and dynamic analysis and modeling of the Watt-II six-bar mechanism were carried out and combined with the particle swarm optimization (PSO) algorithm, the size optimization model of the Watt-II six-bar mechanism was established, and the size optimization of the Watt-II six-bar mechanism was completed. …”
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LVRT Measurement Model and Transient Parameter Identification of Wind Turbine Based on Chaotic Particle Swarm by Dan LI, Shiyao QIN, Shaolin LI, Jing HE

“…Secondly, based on part of the field measured LVRT data of doubly-fed wind turbines, the fault transient parameters are identified with the chaotic particle swarm optimization algorithm. Finally, the accuracy of the identification parameters are analyzed and verified based on the remaining measured data. …”
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Respirable particles from cutting and grinding ceramic tiles: A Scanning Electron Microscopy investigation by Silvia Contessi, Francesca Borghi, Francesca Graziosi, Francesco S. Violante

“…An automated microscopy workflow based on image analysis with a trained deep learning algorithm was applied. The findings revealed that a consistent proportion of the respirable particles were smaller than 1 μm. …”
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Distributed multi‐station target tracking based on unscented particle filter and Dempster‐Shafer theory by Haoxuan Du, Dazheng Feng, Meng Wang, Xuqi Shen, Duo Ye

“…This indicates that the proposed algorithm can improve estimation precision in dynamic and uncertain environments.…”
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Separating the albedo-reducing effect of different light-absorbing particles on snow using deep learning by L.-A. Chevrollier, A. Wehrlé, J. M. Cook, N. Pirk, L. G. Benning, A. M. Anesio, M. Tranter

“…This method includes a deep-learning emulator of a radiative transfer model (RTM) and an inversion algorithm. The emulator alone can be used as a fast and lightweight alternative to the full RTM with the possibility to add new features, such as new light-absorbing particles. …”
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Neural network-based link prediction algorithm by Yonghao PAN, Hongtao YU, Shuxin LIU

“…To improve the difference existed in the link prediction accuracy and adaptability of different topology structure similarity based methods,a neural network-based link prediction algorithm,which fused similarity indices by neural network was proposed.The algorithm uses neural network to study the numerical characteristics of different similarity indices,and uses particle swarm optimization to optimize the neural network,and calculates the fusion index by the optimized neural network model.The experiment on the real network data set shows that the prediction accuracy of the algorithm is obviously higher than that before the fusion,and the accuracy is better than the existing methods.…”
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LEADERS AND FOLLOWERS ALGORITHM FOR TRAVELING SALESMAN PROBLEM by Helen Yuliana Angmalisang, Syaiful Anam

“…Leaders and Followers algorithm is a metaheuristics algorithm. In solving continuous optimization, this algorithm is proved to be better than other well-known algorithms, such as Genetic Algorithm and Particle Swarm Optimization. …”
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Asynchronous perception algorithm based on energy detection by Pan YU, Bin LI, Cheng-lin ZHAO

“…In the future heterogeneous wireless networks,since primary user (PU) and cognitive secondary user (SU) are not coordinated to be synchronous,it will result in sense timing difference between PU’s transmitter and SU’s receiver.For this asynchronous sense case,a new asynchronous sensing algorithm based on Bayesian estimation theory was proposed.A unified dynamic state space model was first proposed to describe the observable energy relationship with dynamic PU state and unknown timing difference.Then,an iterative estimation scheme was designed using stochastic finite set and the rules of maximum posterior probability.Finally,approximated estimation results were obtained by using a particle filter.The simulation results show that the proposed asynchronous scheme significantly eliminates the uncertainty of the received signal information and thus improves the spectrum sensing performance by obtaining the time difference accurately.…”
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Optimizing XGBoost Hyperparameters for Credit Scoring Classification Using Weighted Cognitive Avoidance Particle Swarm by Atul Vikas Lakra, Sudarson Jena, Kaushik Mishra

“…Weighted cognitive avoidance particle swarm optimization for the XGBoost (WCAPSO-XGB) model has been proposed for credit score classification. …”
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Artificial intelligence-aided endoscopic in-line particle size analysis during the pellet layering process by Orsolya Péterfi, Nikolett Kállai-Szabó, Kincső Renáta Demeter, Ádám Tibor Barna, István Antal, Edina Szabó, Emese Sipos, Zsombor Kristóf Nagy, Dorián László Galata

“…A convolutional neural network-based instance segmentation algorithm was employed to detect particles in focus, ensuring that pellet size could be accurately determined despite the dense flow of the particles. …”
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Probing structural changes in single enveloped virus particles using nano-infrared spectroscopic imaging. by Sampath Gamage, Marquez Howard, Hiroki Makita, Brendan Cross, Gary Hastings, Ming Luo, Yohannes Abate

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Silicon Mode (de)Multiplexer Based on Cascaded Particle-Swarm-Optimized Counter-Tapered Couplers by Weiwei Chen, Hongxiang Li, Bohao Zhang, Pengjun Wang, Shixun Dai, Yuxiao Liu, Jun Li, Yan Li, Qiang Fu, Tingge Dai, Hui Yu, Jianyi Yang

“…In this paper, a design of a silicon mode (de)multiplexer based on cascaded counter-tapered couplers is proposed and investigated. By using the particle swarm optimization algorithm and finite difference time domain method, structural parameters of each counter-tapered coupler in our proposed mode (de)multiplexer are optimized so that high conversion efficiencies can be obtained and coupling lengths can be significantly shortened. …”
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Influencer autonomy: Navigating authenticity, agencies, and algorithms by SUNDET VILDE SCHANKE, STEEN-JOHNSEN KARI, ANDERSEN MADS MØLLER TOMMERUP

“…Our findings reveal that while influencer autonomy is always limited and partial, it remains a compelling narrative.…”
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QPSO-Based Adaptive DNA Computing Algorithm by Mehmet Karakose, Ugur Cigdem

“…This new approach aims to perform DNA computing algorithm with adaptive parameters towards the desired goal using quantum-behaved particle swarm optimization (QPSO). …”
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Research on the Application of Genetic Algorithm in Physical Education by Haibo Wang

“…An improved genetic algorithm is proposed to reduce the problem of slow convergence and partial convergence of the fundamental genetic algorithm for intelligent grouping systems. …”
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