Parameter estimation of submarine power cables in offshore applications using machine learning-based methods by Felipe P. de Albuquerque, Rafael Nascimento, Gabriel de Castro Biage, Rooney R.A. Coelho, Ronaldo F. Ribeiro Pereira, Eduardo C. Marques da Costa, Mario L. Pereira Filho, Cassio G. Lopes, José R. Cardoso

“…Contrarily of conventional techniques, the proposed methodology is based on supervised machine learning models trained on realistic simulations, which incorporate the physical and geometric characteristics of the power cable, with its seven propagation modes. …”
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ExAutoGP: Enhancing Genomic Prediction Stability and Interpretability with Automated Machine Learning and SHAP by Yao Rao, Lilian Zhang, Lutao Gao, Shuran Wang, Linnan Yang

“…Machine learning has attracted much attention in the field of genomic prediction due to its powerful predictive capabilities, yet the lack of an explanatory nature in modeling decisions remains a major challenge. …”
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Unveiling Spatiotemporal Differences and Responsive Mechanisms of Seamless Hourly Ozone in China Using Machine Learning by Jiachen Fan, Tijian Wang, Qingeng Wang, Mengmeng Li, Min Xie, Shu Li, Bingliang Zhuang, Ume Kalsoom

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Urban Thermal Archetype Classification in the Context of Urban Development Transformation Using Machine Learning Techniques by Yan Deng, Huimin Liu

“…This study collected 21 indicators of urban composition, urban form, urban function, and socio-economy to provide a more complete and detailed portrayal of real-world built environments in Wuhan. Then, five machine learning approaches, including <italic>k-means</italic>, agglomerative hierarchical clustering, Gaussian mixture model, density-based spatial clustering of applications with noise, and autoencoder combined with <italic>k-means</italic> (AE+<italic>k-means</italic>) were used to recognize urban thermal archetypes in large volumes of geospatial data. …”
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An Optimization Method for Green Permutation Flow Shop Scheduling Based on Deep Reinforcement Learning and MOEA/D by Yongxin Lu, Yiping Yuan, Adilanmu Sitahong, Yongsheng Chao, Yunxuan Wang

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Diagnostic systematic review and meta-analysis of machine learning in predicting biochemical recurrence of prostate cancer by Chenyang Ling, Ning Tao, Abudukeyoumu Maimaitiyimin, Yifan Zhang, Miao Yao, Hongyu Pu, Xiaodong Li, Yujie Wang, Hengqing An

“…Future studies should optimize and validate these models through large-scale clinical trials.…”
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Parkinson disease detection based on in-air dynamics feature extraction and selection using machine learning by Jungpil Shin, Abu Saleh Musa Miah, Koki Hirooka, Md. Al Mehedi Hasan, Md. Maniruzzaman

“…To overcome this problem, we proposed an optimized PD detection methodology that incorporates newly developed dynamic kinematic features and machine learning (ML)—based techniques to capture movement dynamics during handwriting tasks. …”
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EnsembleXAI-Motor: A Lightweight Framework for Fault Classification in Electric Vehicle Drive Motors Using Feature Selection, Ensemble Learning, and Explainable AI by Md. Ehsanul Haque, Mahe Zabin, Jia Uddin

“…We further optimize the models and their combination to a hybrid Soft Voting Classifier. …”
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Unbiased High-Precision Cloud Detection for Advanced Himawari Imager Using Automatic Machine Learning by Bochun Liu, Jinming Ge, Qingyu Mu, Chi Zhang, Xiaoyu Hu, Jiajing Du, Yanyan Wu, Bo Wang, Xiang Li, Jianping Huang, Qiang Fu

“…In this study, we integrate three deviation elimination schemes for cloud cover differences and various surface type characterization modes to develop an unbiased, high-precision cloud detection algorithm for the advanced Himawari imager (AHI) onboard Himawari-8, leveraging automatic machine learning (AutoML) techniques. In addition, we provide a new perspective on model interpretability by incorporating concepts from cooperative game theory. …”
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A study on forest fire risk assessment in jiangxi province based on machine learning and geostatistics by Jinping Lu, Mangen Li, Yaozu Qin, Niannan Chen, Lili Wang, Wanzhen Yang, Yuke Song, Yisu Zheng

“…This study integrated multiple factors, including topography, climate, vegetation, and human activities, and employed machine learning models, specifically random forest (RF), support vector machine (SVM), and back-propagation neural network (BPNN), to predict forest fire occurrence in Jiangxi. …”
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Machine learning and the nomogram as the accurate tools for predicting postoperative malnutrition risk in esophageal cancer patients by Zhenmeng Lin, Zhenmeng Lin, Hao He, Mingfang Yan, Xiamei Chen, Hanshen Chen, Jianfang Ke

“…Among machine learning models, the Random Forest (RF) model demonstrated optimal performance, achieving area under the receiver operating characteristic curve (AUC) values of 0.820 (95% CI: 0.796–0.845) and 0.805 (95% CI: 0.771–0.839) in the development and validation cohorts, respectively. …”
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Machine learning-based identification of efficient and restrictive physiological subphenotypes in acute respiratory distress syndrome by Gabriela Meza-Fuentes, Iris Delgado, Mario Barbé, Ignacio Sánchez-Barraza, Mauricio A. Retamal, René López

“…The generation of clustering and prediction models by machine learning involving clinical, ventilatory mechanics, and gas exchange variables allowed for more accurate stratification of patients. …”
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Machine learning approach in predicting water saturation using well data at “TM” Niger Delta by Oluwakemi Y. Adeogun, Mukthar O. Abdulwaheed, Lukumon Adeoti, Olawale J. Allo, Olawunmi O. Fasakin, Oluwafemi O. Okunowo

“…After preprocessing and correcting inconsistencies in the data, the five ML models were trained and hyperparameters tuned to optimize performance. …”
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Explainable Machine Learning for Radio Environment Mapping: An Intelligent System for Electric Field Strength Monitoring by Yiannis Kiouvrekis, Theodor Panagiotakopoulos, Efthymia Nousi, Ioannis Filippopoulos, Agapi Ploussi, Ellas Spyratou, Efstathios P. Efstathopoulos

“…We evaluate multiple machine learning models&#x2014;kNN, neural networks, decision trees, random forests, XGBoost, and LightGBM&#x2014;using a two-semester split for training and assessment. …”
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Weaning performance prediction in lactating sows using machine learning, for precision nutrition and intelligent feeding by Jiayi Su, Xiangfeng Kong, Wenliang Wang, Qian Xie, Chengming Wang, Bie Tan, Jing Wang

“…The models integrate farm management practices and feed nutrient composition, providing a data-driven framework for optimizing performance. …”
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Bayesian optimization of radical polymerization reactions in a flow synthesis system by Shogo Takasuka, Sho Ito, Shunto Oikawa, Yosuke Harashima, Tomoaki Takayama, Aniruddha Nag, Araki Wakiuchi, Tsuyoshi Ando, Tetsunori Sugawara, Miho Hatanaka, Tomoyuki Miyao, Takamitsu Matsubara, Yu-Ya Ohnishi, Hiroharu Ajiro, Mikiya Fujii

“…The role of each variable in the radical polymerization reaction was elucidated by assessing the extensive array of processing conditions while evaluating several broad trends. The proposed model confirms that specific monomer proportions can be produced in a copolymer using machine learning while investigating the reaction mechanism. …”
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Phase–Frequency Cooperative Optimization of HMDV Dynamic Inertial Suspension System with Generalized Ground-Hook Control by Yihong Ping, Xiaofeng Yang, Yi Yang, Yujie Shen, Shaocong Zeng, Shihang Dai, Jingchen Hong

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Control Optimization of a Hybrid Magnetic Suspension Blood Pump Controller Based on the Finite Element Method by Teng Jing, Yu Yang, Weimin Ru

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Research on digital twin modeling method for combustion process based on model reduction by Yue Zhang, Jiale Li

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Optimization of Rock-Cutting Tools: Improvements in Structural Design and Process Efficiency by Yuecao Cao, Qiang Zhang, Shucheng Zhang, Ying Tian, Xiangwei Dong, Xiaojun Song, Dongxiang Wang

“…The numerical methods for modeling rock–tool interactions are introduced, including discrete element method (DEM) simulations, smoothed particle hydrodynamics (SPH) methods, and machine learning (ML)-enhanced predictive models. …”
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