Research on the Rapid Detection of Formaldehyde Emission From Wood-Based Panels Based on the AMSHKELM by Yinuo Wang, Huanqi Zheng, Hua Wang, Yucheng Zhou

“…The multi-strategy improved black-winged kite algorithm then optimizes key parameters of the successive variational mode decomposition (SVMD) and hybrid kernel extreme learning machine (HKELM). …”
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Design and Analysis of a Hybrid MPPT Method for PV Systems Under Partial Shading Conditions by Oğuzhan Timur, Bayram Kaan Uzundağ

“…The partial shading of PV modules is one of the most crucial factors that causes the performance degradation of PV systems. …”
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Enhanced NDVI prediction accuracy in complex geographic regions by integrating machine learning and climate data—a case study of Southwest basin by Zehui Zhou, Jiaxin Jin, Bin Yong, Weidong Huang, Lei Yu, Peiqi Yang, Dianchen Sun

“…The LSKRX model demonstrated significant improvements in prediction accuracy compared to single-model approaches, with the most notable enhancement in BIAS. …”
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Mathematical model of on-demand route formation for public transport based on individual passenger requests in low-density population area by Svetlana S. Titova, Andrey V. Ostroukh

“…A mathematical model was developed that accounts for the specifics of populated areas with low population density, including uneven distribution of demand, large distances between populated areas, and limited financial resources. Various route optimization algorithms were investigated, and the most suitable method was selected for solving the stated problem. …”
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A systematic review of deep learning applications in database query execution by Bogdan Milicevic, Zoran Babovic

“…We categorize these approaches into three groups based on how such models are applied: improving performance of index structures and consequently data manipulation algorithms, query optimization tasks, and externally controlling query optimizers through parameter tuning. …”
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Photovoltaic Power Generation Forecasting Based on Secondary Data Decomposition and Hybrid Deep Learning Model by Liwei Zhang, Lisang Liu, Wenwei Chen, Zhihui Lin, Dongwei He, Jian Chen

“…Empirical tests on a PV dataset from an Australian solar power plant show that the proposed CECSVB-LSTM model significantly outperforms traditional single models and combination models with different decomposition methods, improving R² by more than 7.98% and reducing the root mean square error (RMSE) and mean absolute error (MAE) by at least 60% and 55%, respectively.…”
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INFO-RF-based fault diagnosis and analysis method for busbars by Chen Xue, Jian Zhu, Haiou Cao, Yan Gu, Siyu Chen

“…A simulation model of a dual-busbar power system is first established, and key electrical quantities such as differential current, bus tie current, and voltage are extracted to quantify fault features using Root Mean Square (RMS) values. The RF model is then used to predict fault types and fault resistance, with the INFO algorithm iteratively optimizing the hyperparameters of the RF model to further improve prediction accuracy. …”
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Short-Term Load Forecasting for Electrical Power Distribution Systems Using Enhanced Deep Neural Networks by Shewit Tsegaye, Sanjeevikumar Padmanaban, Lina Bertling Tjernberg, Kinde Anlay Fante

“…This represents a 7.486% improvement over the prediction obtained using only LSTM model. …”
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Multi-strategy fusion binary SHO guided by Pearson correlation coefficient for feature selection with cancer gene expression data by Yu-Cai Wang, Hao-Ming Song, Jie-Sheng Wang, Xin-Ru Ma, Yu-Wei Song, Yu-Liang Qi

“…Firstly, the CEC-2022 test functions were used to test the performance of the multi-strategy fusion SHO, from which the best variant TanASSHO was selected, and then compared with other nine swarm intelligent optimization algorithms. Performance tests of various algorithm variants on 18 UCI datasets show that V1PTASSHO is the most effective binary version. …”
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Сontrol of Intelligent Transport System in Minsk by D. V. Kapskiy, D. V. Navoy, P. A. Pegin

“…The paper considers algorithms for searching a maximum traffic volume of road vehicles in a traffic light cycle with a distributed intensity pulse and optimization of shifts under coordinated traffic flow control. …”
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Interactive online learning method for students based on artificial intelligence by Cizhang Li, Wenfen Yin

“…This study proposes a novel approach that integrates the Dwarf Mongoose Optimization (DMO) algorithm with a Gated Recurrent Unit (GRU) neural network to develop an AI-powered interactive online learning model. …”
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New method for landslide susceptibility evaluation in alpine valley regions that considers the suitability of InSAR monitoring and introduces deformation rate grading by Dingyi Zhou, Zhifang Zhao, Wenfei Xi, Xin Zhao, Jiangqin Chao

“…Taking the Dongchuan district, the most typical high mountain valley area in southwest China, as the research object, the SAR is quantitatively simulated and analyzed in this study using the improved R-index shadow layover method. …”
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SABO-ELM model for remaining life prediction of lithium-ion batteries under multiple health factors by Jiabo LI, Zhonglin SUN, Di TIAN, Zhixuan WANG

“…The SABO algorithm optimizes the weights and bias thresholds of the ELM model, which effectively reduces the risk of local optima and improves its predictive performance and stability. …”
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Higher-order aberrations and spherical aberration in various age groups after LASIK and cataract surgery by Robert Edward T. Ang, Patricia L. Lee, Ryan S. Torres, Michelle Marie Q. Araneta, Emerson M. Cruz, Maria Socorro M. Torno

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Advanced Machine Learning Methodology for Earthquake Magnitude Forecasting Using Comprehensive Seismic Data by Subhieh El-Salhi, Bashar Igried, Sari Awwad

“…Feature selection was performed using Genetic Algorithm, Particle Swarm Optimization, and Simulated Annealing, while ten machine learning models were implemented — ranging from Linear Regression and Decision Trees to Gradient Boosting, XGBoost, LightGBM, and Long Short-Term Memory (LSTM) networks. …”
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Fractional-Order Swarming Intelligence Heuristics for Nonlinear Sliding-Mode Control System Design in Fuel Cell Hybrid Electric Vehicles by Nabeeha Qayyum, Laiq Khan, Mudasir Wahab, Sidra Mumtaz, Naghmash Ali, Babar Sattar Khan

“…This framework integrates moth flame optimization (MFO) with the gravitational search algorithm (GSA) and Fractal Heritage Evolution, implemented through three spiral-based variants: MFOGSAPSO-A (Archimedean), MFOGSAPSO-H (Hyperbolic), and MFOGSAPSO-L (Logarithmic). …”
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Analytical Solution and Analysis of Aerodynamic Noise Induced by the Turbulent Flow Interaction of a Plate with Double-Wavelength Bionic Serration Leading Edges by Chenye Tian, Xiaomin Liu, Lei Wang, Yuefei Li, Yandong Wu

“…Especially when the slits are embedded at the roots of the optimized leading-edge serrated structures, the improved design further promotes an additional noise reduction level of 0.7 dB for the flat plate. …”
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Energy storage efficiency modeling of high-entropy dielectric capacitors using extreme learning machine and swarm-based hybrid support vector regression computational methods by Yas Al-Hadeethi, Taoreed O. Owolabi, Mouftahou B. Latif, Bahaaudin M. Raffah, Ahmad H. Milyani, Saheed A. Tijani

“…The developed sigmoid (SG) activation function-based ELM (SG-ELM) shows performance improvement over sine (SI) function-based ELM (SI-ELM) model and PS-SVR model with an improvement of 79.25 % and 89.4 % using root mean square error (RMSE) performance measuring parameter. …”
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Dynamic Deformation Analysis of Super High-Rise Buildings Based on GNSS and Accelerometer Fusion by Xingxing Xiao, Houzeng Han, Jian Wang, Dong Li, Cai Chen, Lei Wang

“…To accurately capture the dynamic displacement of super-tall buildings under complex conditions, this study proposes a data fusion algorithm that integrates NRBO-FMD optimization with Adaptive Robust Kalman Filtering (ARKF). …”
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Enhancing fever of unknown origin diagnosis: machine learning approaches to predict metagenomic next-generation sequencing positivity by Zhi Gao, Zhi Gao, Zhi Gao, Yongfang Jiang, Yongfang Jiang, Yongfang Jiang, Mengxuan Chen, Mengxuan Chen, Mengxuan Chen, Weihang Wang, Weihang Wang, Weihang Wang, Qiyao Liu, Qiyao Liu, Qiyao Liu, Jing Ma, Jing Ma, Jing Ma

“…Using the SHAP method, the five most important factors for predicting mNGS-positive results were albumin, procalcitonin, blood culture, disease type, and sample type.ConclusionThe validated LightGBM-based predictive model could have practical clinical value in enhancing the application of mNGS in the etiological diagnosis of FUO, representing a powerful tool to optimize the timing of mNGS.…”
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