DAMAGE IDENTIFICATION METHOD BASED KRIGING MODEL by MA JingJing, YIN Hong, PENG ZhenRui, DONG KangLi

“…To improve damage identification efficiency,a damage identification method based on Kriging model was proposed. …”
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Sentence Embedding Generation Framework Based on Kullback–Leibler Divergence Optimization and RoBERTa Knowledge Distillation by Jin Han, Liang Yang

“…First, a sentence embedding generation method based on Kullback–Leibler Divergence (KLD) optimization is proposed, which enhances semantic differentiation between sentence vectors, thereby improving the accuracy of textual similarity computation. …”
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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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Machine Learning Application for Improving Customer and Postal Logistics Operator Satisfaction in Urban Areas – A Review by Adisa MEDIĆ, Amel KOSOVAC, Ermin MUHAREMOVIĆ, Muhamed BEGOVIĆ

“…The significance of this research is highlighted through the identification of shortcomings in existing literature, offering guidelines for future research in developing new machine learning model for optimal operator selection. This model aims to achieve improvements in both customer and operator satisfaction simultaneously.…”
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Online Estimation and Control Method of Optimal Slip Ratio for Vehicle With an Electric Power-Assisted Brake System by Lei Chen, Yuchao He, Tangtang Ma, Shengshi Zhong

“…Current algorithms for estimating the optimal slip ratio is still room for improving the accuracy and efficiency, especially under the changing road surface, introducing significant lag. …”
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Local Structure Optimization Design of Floating Offshore Wind Turbine Platform Based on Response Surface Analysis by Yajun Ren, Mingxuan Huang, Jungang Hao, Jiazhi Wang, Shuai Li, Ling Zhu, Haisheng Zhao, Wei Shi

“…The response surface model was then optimized using a gradient descent algorithm. …”
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A Deep Learning-Based Framework for Bearing RUL Prediction to Optimize Laser Shock Peening Remanufacturing by Yuchen Liang, Yuqi Wang, Anping Li, Chengyi Gu, Jie Tang, Xianjuan Pang

“…The fruit fly optimization (FFO) algorithm was employed to optimize the remanufacturing parameters; a 29.33% improvement was achieved in fitness compared to the baseline. (3) The DNN model predictions were validated against Finite Element Analysis (FEA) simulations, with a low relative error of 2.5% to 5.8%; the model showed good accuracy in capturing the effects of optimized LSP parameters on bearing life extension.…”
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Investigation on Durability Behaviour and Optimization of Concrete with Triple-Admixtures Subjected to Freeze-Thaw Cycles in Salt Solution by Xuejiao Li, Wensheng Wang, Zhiqing Zhu, Kunkun Zheng

“…After that, based on genetic algorithm, an optimization of mixture proportion was proposed, which only requires less trial mixes and accessible optimization process. …”
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Optimizing Random Forest Parameters with Hyperparameter Tuning for Classifying School-Age KIP Eligibility in West Java by Silfiana Lis Setyowati, Asyifah Qalbi, Rafika Aristawidya, Bagus Sartono, Aulia Rizki Firdawanti

“…Random Forest is an ensemble learning algorithm that combines multiple decision trees to generate a more stable and accurate classification model. …”
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Optimization of Hand Gesture Object Detection Using Fine-Tuning Techniques on an Integrated Service of Smart Robot by Faikul Umam, Hanifudin Sukri, Ach Dafid, Firman Maolana, Mycel Natalis Stopper Ndruru

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Carbon Emission Reduction in Traffic Control: A Signal Timing Optimization Method Based on Rainbow DQN by Juan Lv, Zhaowei Wang, Jianxiao Ma

“…To improve intersection traffic flow and reduce vehicle energy consumption and emissions at intersections, a signal optimization method based on deep reinforcement learning (DRL) is proposed. …”
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Improved U-Net for Precise Gauge Dial Segmentation in Substation Inspection Systems: A Study on Enhancing Accuracy and Robustness by Wan Zou, Yiping Jiang, Wenlong Liao, Songhai Fan, Yueping Yang, Jin Hou, Hao Tang

“…To address these challenges, we propose an improved U-Net segmentation algorithm. The key innovation of our approach is the insertion of a layer-hopping connection module between the Encoder and Decoder to capture feature information across multiple scales, enhancing semantic expressiveness and optimizing feature fusion. …”
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Artificial Intelligence-Based Tools for Process Optimization: Case Study—Bromocresol Green Decolorization with Active Carbon by Gabriel Dan Suditu, Elena Niculina Drăgoi, Alexandra Georgiana Apostică, Andra Maria Mănăilă, Veronica Mădălina Radu, Adrian Cătălin Puițel, Mircea Teodor Nechita

“…In addition, a modified bacterial foraging optimization algorithm was applied as an alternative optimizer in combination with artificial neural networks in order to determine multiple combinations of parameters that can lead to maximum process efficiency. …”
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Multi-objective optimization of the heating oil circuit inside the calendering roller for the lithium battery pole piece by Shuangqiu Sun, Junqiang Wang, Rongying Zhang, Zhenchao Si, Zhijie Yang

“…Then, the improved NSGA-II algorithm is selected to execute the optimization loop. …”
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Automatic Detection and Classification of Dental Anomalies and Tooth Types Using Transformer-Based Yolo With GA Optimization by Sanabam Bineshwor Singh, Anuradha Laishram, Khelchandra Thongam, Khumanthem Manglem Singh

“…In addition, the system employs Genetic Algorithm (GA) optimization for hyperparameter tuning, which fine-tunes model parameters to maximize performance metrics and enhance accuracy and robustness in dental diagnostics. …”
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GRADIENT DESCENT METHOD AND PARAMETER SELECTION FOR IMAGE RESTORATION PROBLEM by Nguyen Dinh Dung

“…Image restoration is often considered one of the data processing steps before the training process for machine learning models is performed. Image restoration problems are often solved by iterative algorithms, where the choice of iteration parameters plays an important role in improving the algorithm's convergence rate. …”
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Double-layer stacking optimization for electricity theft detection considering data incompleteness and intra-class imbalance by Leijiao Ge, Jingjing Li, Tianshuo Du, Luyang Hou

“…This enables balanced synthesis of theft samples using these patterns as conditional supervisory terms for TimeGAN. To concurrently optimize the combination of the electricity theft detector (ETD) model and its hyperparameters, an integrated two-layer Stacking optimization framework is developed. …”
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Enhancing shear strength predictions of UHPC beams through hybrid machine learning approaches by Sanjog Chhetri Sapkota, Ajad Shrestha, Moinul Haq, Satish Paudel, Waiching Tang, Hesam Kamyab, Daniele Rocchio

“…This study proposes hybrid ML models that integrate three nature inspired metaheuristic algorithms—Giant Armadillo Optimization (GOA), Spotted Hyena Optimization (SHO) and Leopard seal optimization (LSA)- Extreme Gradient Boosting (XGB) to predict the shear strength of UHPC beams. …”
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A TD3-Based Reinforcement Learning Algorithm With Curriculum Learning for Adaptive Yaw Control in All-Wheel-Drive Electric Vehicles by Reza Jafari, Pouria Sarhadi, Amin Paykani, Shady S. Refaat, Pedram Asef

“…The proposed model-free torque vectoring algorithm performs based on the interaction of an agent with an environment to learn the optimal policy in a reward-driven manner and obtain the ability to dynamically adapt to varying conditions, such as different roads and vehicle speeds. …”
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Improved YOLO-Goose-Based Method for Individual Identification of Lion-Head Geese and Egg Matching: Methods and Experimental Study by Hengyuan Zhang, Zhenlong Wu, Tiemin Zhang, Canhuan Lu, Zhaohui Zhang, Jianzhou Ye, Jikang Yang, Degui Yang, Cheng Fang

“…The method constructs a lightweight model with a small-object detection layer, integrates the GhostNet backbone to reduce parameter count by 67.2%, and employs the GIoU loss function to optimize neck ring localization accuracy. …”
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