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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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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A Novel Model for Noninvasive Haemoglobin Detection Based on Visibility Network and Clustering Network for Multi-Wavelength PPG Signals by Lei Liu, Ziyi Wang, Xiaohan Zhang, Yan Zhuang, Yongbo Liang

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Corrosion rate prediction for long-distance submarine pipelines based on MWIWOA-SVM by Zhengshan LUO, Haipeng LYU, Jihao LUO

“…MethodsTo address these issues, Multi-Way Improved Whale Optimization Algorithm (MWIWOA) was proposed to optimize the SVM-based prediction model for the internal corrosion rate of long-distance submarine pipelines. …”
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Influence of artificial intelligence on higher education reform and talent cultivation in the digital intelligence era by Limin Qian, Weiran Cao, Lifeng Chen

“…Abstract In order to solve the problems of inefficient allocation of teaching resources and inaccurate recommendation of learning paths in higher education, this paper proposes a smart education optimization model (SEOM) by combining the improved random forest algorithm (RFA) based on adaptive enhancement mechanism and the Graph Neural Network (GNN) algorithm. …”
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Research on subway settlement prediction based on the WTD-PSR combination and GSM-SVR model by Miren Rong, Chao Feng, Yinping Pang, Hailong Wang, Ying Yuan, Wensong Zhang, Lanxin Luo

“…Furthermore, Particle Swarm Optimization (PSO), Gray Wolf Optimization (GWO), Marine Predators Algorithm (MPA), and Whale Optimization Algorithm (WOA) are introduced to optimize the SVR model, and the prediction performance is compared with that of the Long Short-Term Memory (LSTM) model. …”
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Machine learning analysis of pharmaceutical cocrystals solubility parameters in enhancing the drug properties for advanced pharmaceutical manufacturing by Tareq Nafea Alharby, Bader Huwaimel

“…Each model’s performance was assessed via measuring Root Mean Square Error (RMSE), R2, Mean Absolute Error (MAE), and Monte Carlo Cross-Validation (CV) scores using a Tabu Search method for optimization. …”
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Classification-based point cloud denoising and 3D reconstruction of roadways by Denghong CHEN, Ning PANG, Wen NIE, Juqiang FENG, Jiliang KAN, Jinjing ZHANG

“…By integrating a mean value method, an improved density-based spatial clustering of applications with noise (DBSCAN) algorithm, and an improved bilateral filtering algorithm, this study constructed a technical framework for classification processing. …”
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An integrated IKOA-CNN-BiGRU-Attention framework with SHAP explainability for high-precision debris flow hazard prediction in the Nujiang river basin, China. by Hao Yang, Tianlong Wang, Nikita Igorevich Fomin, Shuoting Xiao, Liang Liu

“…This study proposes an explainable deep learning framework, the Improved Kepler Optimization Algorithm-Convolutional Neural Network-Bidirectional Gated Recurrent Unit-Attention (IKOA-CNN-BiGRU-Attention) model, for precise debris flow hazard prediction in the Yunnan section of the Nujiang River Basin, China. …”
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Accurate extraction of electrical parameters in three-diode photovoltaic systems through the enhanced mother tree methodology: A novel approach for parameter estimation. by Mouncef El Marghichi, Abdelilah Hilali, Abdelkhalek Chellakhi, Mohamed Makhad, Azeddine Loulijat, Najib El Ouanjli, Abdelhak Essounaini, Vikash Kumar Saini, Ameena Saad Al-Sumaiti

“…This balance between exploration and exploitation allows the algorithm to dynamically and effectively identify optimal parameters. …”
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Domain Knowledge-Enhanced Process Mining for Anomaly Detection in Commercial Bank Business Processes by Yanying Li, Zaiwen Ni, Binqing Xiao

“…Additionally, we employed large language models (LLMs) for root cause analysis and process optimization recommendations. …”
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Prediction of Carbonate Reservoir Porosity Based on CNN-BiLSTM-Transformer by Yingqiang Qi, Shuiliang Luo, Song Tang, Jifu Ruan, Da Gao, Qianqian Liu, Sheng Li

“…The model extracts curve features through the CNN layer, captures both short- and long-term neighborhood information via the BiLSTM layer, and utilizes the Transformer layer with a self-attention mechanism to focus on temporal information and input features, effectively capturing global dependencies. The Adam optimization algorithm is employed to update the network’s weights, and hyperparameters are adjusted based on feedback from network accuracy to achieve precise porosity prediction in highly heterogeneous carbonate reservoirs. …”
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Image Mosaic Based on Local Guidance and Dark Channel Prior by Chong Zhang, Fang Xu, Dejiang Wang, He Sun

“…First of all, the KAZE algorithm is utilized for rough feature matching. Secondly, a local fixed point asymptotic method is introduced to optimize the global objective and eliminate mismatched point pairs. …”
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Deep neural network approach integrated with reinforcement learning for forecasting exchange rates using time series data and influential factors by T. Soni Madhulatha, Dr. Md. Atheeq Sultan Ghori

“…The algorithm leverages the strengths of both deep learning and reinforcement learning to achieve improved predictive accuracy and adaptability. …”
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Constrained total least squares localization using angle of arrival and time difference of arrival measurements in the presence of synchronization clock bias and sensor position er... by Ruirui Liu, Ding Wang, Jiexin Yin, Ying Wu

“…Based on measurements of angle of arrival and time difference of arrival, a method is proposed to improve the accuracy of localization with imperfect sensors. …”
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Hyperspectral Imaging for Non-Destructive Moisture Prediction in Oat Seeds by Peng Zhang, Jiangping Liu

“…Subsequently, a dual-optimized neural network model, termed Bayes-ASFSSA-BP, was developed by incorporating Bayesian optimization and the Adaptive Spiral Flight Sparrow Search Algorithm (ASFSSA). …”
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Optimising the Selection of Input Variables to Increase the Predicting Accuracy of Shear Strength for Deep Beams by Mohammed Majeed Hameed, Faidhalrahman Khaleel, Mohamed Khalid AlOmar, Siti Fatin Mohd Razali, Mohammed Abdulhakim AlSaadi

“…The study found that all applied models were significantly improved by the presence of the GAITH algorithm, except for the MLR model. …”
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Medium- Long-Term Runoff Forecasting Using Interpretable Hybrid Machine Learning Model for Data-Scarce Regions by YOU Yu-jun, BAI Yun-gang, LU Zhen-lin, ZHANG Jiang-hui, CAO Biao, LI Wen-zhong, YU Qi-ying

“…[Methods] Based on historical precipitation, temperature, and runoff sequences from the Yulongkashi River, a Convolutional Neural Network-Bidirectional Gated Recurrent Unit-Attention (CNN-BiGRU-Attention) model was developed. An Improved Particle Swarm Optimization (IPSO) algorithm was used to optimize this model, forming the IPSO-CNN-BiGRU-Attention hybrid model. …”
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Apple Trajectory Prediction in Orchards: A YOLOv8-EK-IPF Approach by Jinxing Niu, Zhengyi Liu, Shuo Wang, Jiaxi Huang, Junlong Zhao

“…To address the challenge of accurate apple harvesting by orchard robots, which is hindered by dynamic changes in apple position due to wind interference and branch swaying, this study proposes an optimized prediction algorithm based on an integration of the extended Kalman filter (EKF) and an improved particle filter (IPF), built upon initial apple detection and recognition using YOLOv8. …”
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Broad learning system based on attention mechanism and tracking differentiator by LIAO Lüchao, ZOU Weidong, YANG Jialong, LU Huihuang, XIA Yuanqing, GAO Jianlei

“…In terms of model structure, A-TD-BLS introduced self-attention mechanism to the original BLS, and further fused and transformed the extracted features through attention weighting to improve the feature learning ability.In terms of model training methods, a weight optimization algorithm based on tracking differentiator was designed.This method effectively alleviates the overfitting phenomenon of the original BLS by limiting the size of the weight values, significantly reduces the influence of the number of hidden layer nodes on model performance and makes the generalization performance more stable.Moreover, the training algorithm was extended to the BLS incremental learning framework, so that the model can improve performance by dynamically adding hidden layer nodes.Multiple experiments conducted on some benchmark datasets show that compared to the original BLS, the classification accuracy of A-TD-BLS is increased by 1.27% on average on classification datasets and the root mean square error of A-TD-BLS is reduced by 0.53 on average on regression datasets.Besides, A-TD-BLS is less affected by the number of hidden layer nodes and has more stable generalization performance. …”
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