A review of advances in electrical discharge machining: From flow field to multi-physics coupled simulations by Yifan Yu, Shuliang Dong, Feilong Liu, Zixuan Cui, Xianhui Hou, Libao An

“…Finally, it evaluates the current limitations of flow field simulation and suggests future research directions, such as refining multi-physics models, investigating nanoscale phenomena, and leveraging artificial intelligence for smart process optimization.…”
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Multi-Index Assessment and Machine Learning Integration for Drought Monitoring Using Google Earth Engine by Xulong Duan, Rana Waqar Aslam, Syed Ali Asad Naqvi, Dmitry E. Kucher, Zohaib Afzal, Danish Raza, Rana Muhammad Zulqarnain, Yahia Said

“…The framework’s AI-driven error correction and multisensor synergy provide a scalable model for drought applications, such as ecosystem resilience monitoring (integrating thermal and optical analysis) and hydrological modelling (fusing soil moisture, precipitation, and vegetation datasets). …”
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Psoriatic arthritis in psoriasis: optimizing the current screening system for psoriatic arthritis based on serum data from U.S. and Chinese populations by Zheng Lin, Si-yi Pan, Yue-yi Shi, Xuan Wu, Yuan Dou, Ping Lin, Yi Cao

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Soft computing applications in asphalt pavement: A comprehensive review of data-driven techniques using response surface methodology and machine learning by Nura Shehu Aliyu Yaro, Muslich Hartadi Sutanto, Mohd Rosli Hainin, Noor Zainab Habib, Aliyu Usman, Muhammad Sani Bello, Surajo Abubakar Wada, Abiola Usman Adebanjo, Ahmad Hussaini Jagaba

“…As a result of this shift, there is a stronger emphasis on advanced statistical approaches like optimization tools like response surface methodology (RSM) and machine learning (ML) techniques. …”
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Program semantic analysis model for code reuse detection by GUO Xi, WANG Pan

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Application of Machine Tool Thermal Error Compensation in Digital Twin-based System by MA Chi, LI Minging, LIU Jialan, HE Jialong, HUA Chunlei, WANG Liang

“…Thermal error significantly affects machining accuracy, demanding careful control. A robust system integrating a highly accurate thermal error model is the key to this control. …”
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Selective opposition based constrained barnacle mating optimization: Theory and applications by Marzia Ahmed, Mohd Herwan Sulaiman, Md. Maruf Hassan, Md. Atikur Rahaman, Masuk Abdullah

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A Hybrid Machine Learning Framework for Early Fault Detection in Power Transformers Using PSO and DMO Algorithms by Mohammed Alenezi, Fatih Anayi, Michael Packianather, Mokhtar Shouran

“…This study introduces a novel machine learning framework that integrates Particle Swarm Optimization (PSO) and Dwarf Mongoose Optimization (DMO) algorithms for feature selection and hyperparameter tuning, combined with advanced classifiers such as Decision Trees (DT), Random Forests (RF), and Support Vector Machines (SVM). …”
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Comprehensive Investigation of Machine Learning and Deep Learning Networks for Identifying Multispecies Tomato Insect Images by Chittathuru Himala Praharsha, Alwin Poulose, Chetan Badgujar

“…The best optimizer-based CNN architecture results were compared with these machine learning models. …”
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Review on System Identification, Control, and Optimization Based on Artificial Intelligence by Pan Yu, Hui Wan, Bozhi Zhang, Qiang Wu, Bohao Zhao, Chen Xu, Shangbin Yang

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Perfect Labelling: A Review and Outlook of Label Optimization Techniques in Dynamic Earth Observation by Sarah Hauser, Lena Augner, Andreas Schmitt

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Performance Investigation of Coated Carbide Tools in Milling Procedures by Paschalis Charalampous

“…The findings provide insights into the advantages and limitations of both methodologies, guiding the optimization of coated carbide tool performance. The outcomes of this study contribute to the advancement of predictive modeling in machining processes, offering a data-driven approach for improved tool wear assessment.…”
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Hospital Length-of-Stay Prediction Using Machine Learning Algorithms—A Literature Review by Guilherme Almeida, Fernanda Brito Correia, Ana Rosa Borges, Jorge Bernardino

“…The main objective is to identify the most effective machine learning algorithm for building a predictive model capable of predicting hospital length of stay. …”
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Innovative machine learning-based prediction of early airway hyperresponsiveness using baseline pulmonary function parameters by Hua Yang, Xingru Zhao, Zhuochang Chen, Lihong Yang, Guihua Zhao, Chenxiao Xu, Jinyi Xu

“…Model fit was evaluated using the Akaike Information Criterion (AIC), and a logistic regression model was constructed along with a nomogram.ResultsThe optimal model (Model C, AIC = 310.44) included FEV1/FVC%, MEF75%, PEF%, and MMEF75-25%, which demonstrated superior discriminative capacity in both the training (AUC = 0.790, cut-off = 0.354, 95% CI: 0.724–0.760) and validation cohorts (AUC = 0.756, cut-off = 0.404, 95% CI: 0.600–0.814). …”
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Predicting and interpreting key features of refractory Mycoplasma pneumoniae pneumonia using multiple machine learning methods by Yuhan Jiang, Xu Wang, Li Li, Yifan Wang, Xuelin Wang, Yingxue Zou

“…Ultimately, the optimal predictive model was selected using multidimensional metric assessments, and SHAP analysis identified key predictive factors related to RMPP. …”
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Finite Element Method-Based Modeling of a Novel Square Photonic Crystal Fiber Surface Plasmon Resonance Sensor with a Au–TiO₂ Interface and the Relevance of Artificial I... by Ayushman Ramola, Amit Kumar Shakya, Arik Bergman

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Design and Optimization of Brushless Hybrid Wound Rotor Vernier Motor for Variable Speed Applications by Kapeel Kumar, Qasim Ali, Sheeraz Ahmed, Muhammad Ilyas, Hanlin Zhan, Mudassir Raza Siddiqi

“…This paper presents the optimized design and electromagnetic performance analysis of a brushless hybrid wound rotor vernier motor (BL-HWRVM) for automatic washing machine applications. …”
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Prediction of tablet disintegration time based on formulations properties via artificial intelligence by comparing machine learning models and validation by Mohammed Ghazwani, Umme Hani

“…Grey Wolf Optimization (GWO) was utilized for model optimization to obtain optimal combinations of hyper-parameters. …”
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Reliability-Based Design Optimization of Bearing Hub Preform for Minimizing Defects Considering Manufacturing Tolerance in Hot Forging Process by Minseong Oh, Jinkuk Kim, Juhyun Cho, Mincheol Kim, Mansoo Joun, Seokmoo Hong

“…These results were derived using an approximate model based on the Kriging method, providing an optimal design that is practical and effective in actual manufacturing processes.…”
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Study on lithology identification using a multi-objective optimization strategy to improve integrated learning models: a case study of the Permian Lucaogou Formation in the Jimusae... by Xili Deng, Jiahong Li, Junkai Chen, Cheng Feng

“…Then, by combining the multi-objective optimization strategy Artificial Rabbit Optimization (ARO) with the Light Gradient Boosting Machine (LightGBM) model, a new intelligent lithology identification model (ST-ARO-LightGBM) is proposed, aimed at solving the problem of non-optimal hyperparameter settings in the model. …”
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