Ultrafine‐Resolution Urban Climate Modeling: Resolving Processes Across Scales by Chenghao Wang, Yongling Zhao, Qi Li, Zhi‐Hua Wang, Jiwen Fan

“…Addressing these limitations requires advances in computational techniques, numerical schemes, and the integration of diverse observational data. Machine learning presents new opportunities by emulating certain computationally expensive processes, enhancing data assimilation, and improving model accessibility for decision‐making. …”
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Categorizing E-cigarette-related tweets using BERT topic modeling by D. Murthy, S. Keshari, S. Arora, Q. Yang, A. Loukas, S.J. Schwartz, M.B. Harrell, E.T. Hébert, A.V. Wilkinson

“…In contrast, unsupervised machine learning approaches, such as topic modeling, allow for efficient analysis of large datasets, uncovering patterns and trends that manual methods cannot achieve at scale. …”
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AstroM3: A Self-supervised Multimodal Model for Astronomy by M. Rizhko, J. S. Bloom

“…While machine-learned models are now routinely employed to facilitate astronomical inquiry, model inputs tend to be limited to a primary data source (namely images or time series) and, in the more advanced approaches, some metadata. …”
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Data-driven intelligent productivity prediction model for horizontal fracture stimulation by Qian Li, Yiyong Sui, Mengying Luo, Bin Guan, Lu Liu, Yuan Zhao

“…Under the assumption of similar characteristics and mechanisms, correlation analysis was conducted for each fracturing interval category to identify the dominant controlling factors affecting post-fracturing productivity in each reservoir type. Machine learning algorithms were used to establish intelligent models describing the relationships between post-fracturing production enhancement effects, dominant factors, and production time for each reservoir category. …”
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Deep learning on routine full-breast mammograms enhances lymph node metastasis prediction in early breast cancer by Daqu Zhang, Looket Dihge, Pär-Ola Bendahl, Ida Arvidsson, Magnus Dustler, Julia Ellbrant, Kim Gulis, Malin Hjärtström, Mattias Ohlsson, Cornelia Rejmer, David Schmidt, Sophia Zackrisson, Patrik Edén, Lisa Rydén

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Using AI for Optimizing Packing Design and Reducing Cost in E-Commerce by Hayder Zghair, Rushi Ganesh Konathala

“…In the second phase, a random forest (RF) machine learning model was developed to predict optimal packaging configurations using key product features: weight, volume, and fragility. …”
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Modeling, optimization, and thermal management strategies of hydrogen fuel cell systems by Abubakar Unguwanrimi Yakubu, Liu Qingsheng, Meng Kai, Chen Jinwei, Omer Abbaker Ahmed Mohammed, Jiahao Zhao, Qi Jiang, Xuanhong Ye, Junyi Liu, Qinglong Yu, Muhammad Aurangzeb, Shusheng Xiong

“…The review also explores hybrid physical-AI models, CFD-based surrogate models, and predictive machine-learning methods like LSTM and CNN. …”
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Advanced graph embedding for intelligent heating, ventilation, and air conditioning optimization: An ensemble learning-based recommender system by Shouliang Lai, Xiyu Yi, Peiling Zhou, Lu Peng, Wentao Liu, Shi Sun, Binrong Huang

“…Utilizing advanced graph embedding techniques combined with ensemble learning models, we developed a recommender system tailored for Heating, Ventilation, and Air Conditioning (HVAC) optimization in Shenzhen Qianhai Smart Community. …”
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Reduced-Order Models and Conditional Expectation: Analysing Parametric Low-Order Approximations by Hermann G. Matthies

“…Similarly, in the field of machine learning, a function mapping the parameter set to the image space of the machine learning model is learned from a training set of samples, typically minimising the mean square error. …”
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Internet of things driven hybrid neuro-fuzzy deep learning building energy management system for cost and schedule optimization by Deepshikha Shrivastava, Deepshikha Shrivastava, Prerna Goswami

“…The data collected was preprocessed, cleaned, transformed and used for training a machine learning model. Based on the previous literature, a hybrid DL model was developed using artificial neural networks and fuzzy logic by integrating fuzzy layers in the deep neural architecture. …”
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Intelligent deep learning for human activity recognition in individuals with disabilities using sensor based IoT and edge cloud continuum by Mohammed Maray

“…It presents valuable insights into the health, fitness, and overall wellness of individuals outside of hospital settings. Therefore, the machine learning (ML) model is mostly used for the growth of the HAR system to discover the models of human activity from the sensor data. …”
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In vivo electrophysiology recordings and computational modeling can predict octopus arm movement by Nitish Satya Sai Gedela, Ryan D. Radawiec, Sachin Salim, Julianna Richie, Cynthia Chestek, Anne Draelos, Galit Pelled

“…For kinematic analysis, deep learning models and unsupervised dimensionality reduction identified a consistent set of features that could be used to distinguish different types of arm movements. …”
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Analysis of the state of geometrization development and digital modeling in open-pit mining enterprises by M.S. Kunytska, D.S. Polishchyk, O.V. Shapochnikov

“…It was determined that their integrated application provides an increase in the accuracy of reserve assessment by 15–20 % and a reduction in operating costs by 10–15 %. Key development trends are identified: integration of digital technologies, improvement of methods of collecting and processing geospatial data, introduction of machine learning algorithms for risk prediction. …”
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Privacy-Preserving Glycemic Management in Type 1 Diabetes: Development and Validation of a Multiobjective Federated Reinforcement Learning Framework by Fatemeh Sarani Rad, Juan Li

“… Abstract BackgroundEffective diabetes management requires precise glycemic control to prevent both hypoglycemia and hyperglycemia, yet existing machine learning (ML) and reinforcement learning (RL) approaches often fail to balance competing objectives. …”
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Adaptive Management of Multi-Scenario Projects in Cybersecurity: Models and Algorithms for Decision-Making by Vadim Tynchenko, Alexander Lomazov, Vadim Lomazov, Dmitry Evsyukov, Vladimir Nelyub, Aleksei Borodulin, Andrei Gantimurov, Ivan Malashin

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Predictive modeling of response to repetitive transcranial magnetic stimulation in treatment-resistant depression by Lindsay L. Benster, Cory R. Weissman, Federico Suprani, Kamryn Toney, Houtan Afshar, Noah Stapper, Vanessa Tello, Louise Stolz, Mohsen Poorganji, Zafiris J. Daskalakis, Lawrence G. Appelbaum, Jordan N. Kohn

“…Leveraging electronic medical records (EMR), this retrospective cohort study applied supervised machine learning (ML) to sociodemographic, clinical, and treatment-related data to predict depressive symptom response (>50% reduction on PHQ-9) and remission (PHQ-9 < 5) following rTMS in 232 patients with TRD (mean age: 54.5, 63.4% women) treated at the University of California, San Diego Interventional Psychiatry Program between 2017 and 2023. …”
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A framework for continual learning in real-time traffic forecasting utilizing spatial–temporal graph convolutional recurrent networks by Mariam Labib Francies, Abeer Twakol Khalil, Hanan M. Amer, Mohamed Maher Ata

“…The rapid increase in traffic data, along with the inherently dynamic characteristics of urban traffic, poses considerable challenges for traditional Machine Learning (ML) models, which often find it difficult to efficiently handle large-scale datasets. …”
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Self‐Supervised Pre‐Training and Few‐Shot Finetuning for Gas‐Bearing Prediction by Long Han, Xinming Wu, Renjie Chen, Yunhua Shi, Zhanxuan Hu, Huijing Fang

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Green Video Transcoding in Cloud Environments Using Kubernetes: A Framework With Dynamic Renewable Energy Allocation and Priority Scheduling by B. M. Beena, Prashanth Cheluvasai Ranga, A. Vinitha Chowdary, Rohan Gamidi, M. Hemasri, Tejaswi Muppala

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Deep-Transfer-Learning Strategies for Crop Yield Prediction Using Climate Records and Satellite Image Time-Series Data by Abhasha Joshi, Biswajeet Pradhan, Subrata Chakraborty, Renuganth Varatharajoo, Shilpa Gite, Abdullah Alamri

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