Thermal errors in high-speed motorized spindle: An experimental study and INFO-GRU modeling predictions by Zhaolong Li, Kai Zhao, Haonan Sun, Yongqiang Wang, Bangxv Wang, JunMing Du, Haocheng Zhang

“…The novelty of this study lies in two improvements: firstly, the number of temperature measurement points is optimized by combining a clustering algorithm with a correlation coefficient method, reducing the amount of calculation and the risk of data coupling in the prediction; secondly, the GRU model optimized by the INFO algorithm is applied to the field of electric spindles for the first time, effectively analyzing the dynamic relationship between temperature and thermal expansion. …”
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Hybrid-driven modeling using a BiLSTM–AdaBoost algorithm for diameter prediction in the constant diameter stage of Czochralski silicon single crystals by Yu-Yu Liu, Ding Liu, Shi-Hai Wu, Yi-Ming Jing

“…In this paper, a hybrid-driven modeling method integrating Bidirectional Long Short-Term Memory network (BiLSTM) and Adaptive Boosting (AdaBoost) algorithm is proposed, aiming to improve the accuracy and stability of crystal diameter prediction in the medium diameter stage of the SSC growth by the Czochralski (CZ) method. …”
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A novel two stage neighborhood search for flexible job shop scheduling problem considering reconfigurable machine tools by Yanjun Shi, Chengjia Yu, Shiduo Ning

“…This paper focuses on the flexible job shop scheduling problem with machine reconfigurations (FJSP-MR) and proposes an improved genetic algorithm with a two-stage neighborhood search (IGA-TNS) to minimize total weighted tardiness (TWT). …”
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SLPDBO-BP: an efficient valuation model for data asset value by Cuiping Zhou, Shaobo Li, Cankun Xie, Panliang Yuan, Zihao Liao

“…Secondly, in an attempt to comprehensively evaluate the optimization performance of SLPDBO, a series of numerical optimization experiments are carried out with 20 test functions and with popular optimization algorithms and dung beetle optimizer (DBO) algorithms with different improvement strategies. …”
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MULTI-MODEL STACK ENSEMBLE DEEP LEARNING APPROACH FOR MULTI-DISEASE PREDICTION IN HEALTHCARE APPLICATION by Bhaskar Adepu, T. Archana

“…This model integrates pre-processing techniques and employs the Tuna Swarm Optimization (TSO) Algorithm for feature selection in executing multi-label disease prediction. …”
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Comparative analysis of machine learning models for the detection of fraudulent banking transactions by Pedro María Preciado Martínez, Ricardo Francisco Reier Forradellas, Luis Miguel Garay Gallastegui, Sergio Luis Náñez Alonso

“…Using data from 565,000 real-world transfers, models based on algorithms such as Random Forest, Neural Networks and Naive Bayes were built and tested. …”
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Research on IoT network performance prediction model of power grid warehouse based on nonlinear GA-BP neural network by Pang Lele, Xia Bo, Cheng Zhanfeng, Ren Zhiqiang, Shen Hao, Li Pengfei

“…This study introduces a novel approach for forecasting network performance prediction in power grid warehouses, employing a nonlinear Genetic Algorithm (GA)-optimized backpropagation (BP) neural network model. …”
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Correlation learning based multi-task model and its application by XU Wei, LUO Jianping, LI Xia, CAO Wenming

“…The experimental results verify the effectiveness of the proposed multi-task learning model based on the correlation layer. Meanwhile, the proposed multi-task learning network as a proxy model is applied to the Bayesian optimization algorithm, which not only reduces the evaluation times of model to target problem, but also enlarges the number of training data exponentially and further improves the model accuracy.…”
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Explainable AutoML models for predicting the strength of high-performance concrete using Optuna, SHAP and ensemble learning by Muhammad Salman Khan, Tianbo Peng, Tianbo Peng, Muhammad Adeel Khan, Asad Khan, Mahmood Ahmad, Mahmood Ahmad, Kamran Aziz, Mohanad Muayad Sabri Sabri, N. S. Abd EL-Gawaad

“…This study introduces four explainable Automated Machine Learning (AutoML) models that integrate Optuna for hyperparameter optimization, SHapley Additive exPlanations (SHAP) for interpretability, and ensemble learning algorithms such as Random Forest (RF), Extreme Gradient Boosting (XGB), Light Gradient Boosting Machine (LGB), and Categorical Gradient Boosting (CB). …”
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Development of IIOT-Based Pd-Maas Using RNN-LSTM Model with Jelly Fish Optimization in the Indian Ship Building Industry by PNV Srinivasa Rao, PVY Jayasree

“…The validation of the proposed predictive maintenance model optimization with different types of deep learning algorithms shows that our proposed methodology gives an improved accuracy of 98.9336% which is higher than any other models.   …”
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A machine learning model with crude estimation of property strategy for performance prediction of perovskite solar cells based on process optimization by Dan Li, Ernie Che Mid, Shafriza Nisha Basah, Xiaochun Liu, Jian Tang, Hongyan Cui, Huilong Su, Qianliang Xiao, Shiyin Gong

“…The model’s evaluation metrics improved by utilizing excess non-stoichiometric components (Ensc) and perovskite additive compounds (Pac) as CEP. …”
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EDECO: An Enhanced Educational Competition Optimizer for Numerical Optimization Problems by Wenkai Tang, Shangqing Shi, Zengtong Lu, Mengying Lin, Hao Cheng

“…On the one hand, the estimation of distribution algorithm enhances the global exploration ability and improves the population quality by establishing a probabilistic model based on the dominant individuals provided by EDECO, which solves the problem that the algorithm is unable to search the neighborhood of the optimal solution. …”
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Simplifying the calibration of ecological models by using the parameter estimation tool (PEST): The Curonian Lagoon case by Burak Kaynaroglu, Mindaugas Zilius, Rasa Idzelytė, Artūras Razinkovas-Baziukas, Georg Umgiesser

“…However, subjective and time-consuming manual (trial-and-error) calibration methods cannot ensure optimal parameter match.To address this, we automated the calibration of a newly developed ecological model to improve the simulation of nutrient dynamics as ammonia, nitrate, and phosphate in the estuarine system (Curonian Lagoon). …”
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Enhancing Last-Mile Logistics: AI-Driven Fleet Optimization, Mixed Reality, and Large Language Model Assistants for Warehouse Operations by Saverio Ieva, Ivano Bilenchi, Filippo Gramegna, Agnese Pinto, Floriano Scioscia, Michele Ruta, Giuseppe Loseto

“…However, existing approaches often treat these aspects in isolation, missing opportunities for optimization and operational efficiency gains through improved information visibility across different roles in the logistics workforce. …”
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Dynamic Reactive Power Optimization Strategy for AC/DC Hybrid Power Grid Considering Different Wind Power Penetration Levels by Nan Feng, Yuyao Feng, Yun Su, Yajun Zhang, Tao Niu

“…Considering the nonlinearity and non-convexity of the optimization model, trajectory sensitivity method and whale optimization algorithm are adopted to enhance the solution efficiency. …”
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A Dual-Strategy Framework for Cyber Threat Detection in Imbalanced, High-Dimensional Data Across Heterogeneous Networks by T. Saranya, S. Indra Priyadharshini

“…Second, the Cauchy-Gaussian Genetic-Arithmetic Optimizer (CG-GAO) addresses the challenge of high-dimensional data by combining a genetic algorithm (GA) and an arithmetic optimization algorithm (AOA), enhancing exploration and preventing premature convergence. …”
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Optimization of fractional PI controller parameters for enhanced induction motor speed control via indirect field-oriented control by I. I. Alnaib, A. N. Alsammak

“…The novelty of the work consists of a proposal for a driving cycle model for testing the control system of electric vehicles in Mosul City (Iraq), and using a Complex Fractional Order Proportional Integral (CFOPI) controller to control IMs via IFOC strategies, the Artificial Bee Colony (ABC) algorithm was applied, which is considered to be highly efficient in finding the values of controllers. …”
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Predicting suicidal behavior outcomes: an analysis of key factors and machine learning models by Mohammad Bazrafshan, Kourosh Sayehmiri

“…A combination of statistical models for feature selection and machine learning algorithms for prediction was used, with Random Forest showing the best performance. …”
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Improving Acceptance to Sensory Substitution: A Study on the V2A-SS Learning Model Based on Information Processing Learning Theory by Kyeong Deok Moon, Yun Kyung Park, Moo Seop Kim, Chi Yoon Jeong

“…This improvement is significantly higher than the gain of 2.72% achieved by optimizing the V2A-SS algorithm with Mel-Scaled Frequency Mapping. …”
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Radial Basis Function Coupling with Metaheuristic Algorithms for Estimating the Compressive Strength and Slump of High-Performance Concrete by Amir Reza Taghavi Khangah, Erfan Khajavi, Hasti Azizi, Amir Reza Alizade Novin

“…The following study represents an important step toward developing novel hybrid models for predicting CS and SL. The contribution in this paper proposes the following: the radial basis function (RBF) model will be enhanced by using two optimization algorithms, namely Horse Herd Optimization (HHO) and Wild Geese Algorithm (WGA). …”
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