Machine Learning‐Driven Extraction of Hybrid Compact Models Integrating Neural Networks and Berkeley Short‐Channel Insulated‐Gate Field‐Effect Transistor Model‐Common Multigate for... by Seungjoon Eom, Seunghwan Lee, Hyeok Yun, Kyeongrae Cho, Soomin Kim, Rockhyun Baek

“…This study presents a novel machine learning–based method to accelerate and enhance the accuracy of compact model generation for multiple devices simultaneously. …”
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Building and explaining data-driven energy demand models for Indian states by Kieran M R Hunt, Hannah C Bloomfield

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Price Forecast of Treasury Bond Market Yield: Optimize Method Based on Deep Learning Model by Weiying Ping, Yuwen Hu, Liangqing Luo

“…This paper integrates the ideas of improved multivariate time series sampling and deep learning prediction model structure optimization, and proposes an optimized deep learning model framework under the LASSO-SMLR-PCA machine learning method. …”
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Attention-driven hybrid deep learning and SVM model for early Alzheimer’s diagnosis using neuroimaging fusion by Arjun Kidavunil Paduvilan, Godlin Atlas Lawrence Livingston, Sampath Kumar Kuppuchamy, Rajesh Kumar Dhanaraj, Muthuvel Subramanian, Amal Al-Rasheed, Masresha Getahun, Ben Othman Soufiene

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Prediction of Freezing Time During Hydrogen Fueling Using Machine Learning by Ji-Ah Choi, Ji-Seong Jang, Sang-Won Ji

“…Currently, nitrogen and heating air are used for defrosting and frost reduction, which can be costly. The developed machine learning models are expected to enable prediction of both frost formation and defrosting timings, potentially allowing for more cost-effective management of defrosting and frost reduction strategies.…”
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Improving Climate Bias and Variability via CNN‐Based State‐Dependent Model‐Error Corrections by William E. Chapman, Judith Berner

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Optimizing Renewable Energy Integration Using IoT and Machine Learning Algorithms by Orken Mamyrbayev, Ainur Akhmediyarova, Dina Oralbekova, Janna Alimkulova, Zhibek Alibiyeva

“…The study also implemented a reinforcement learning-based grid optimization system. Results showed significant improvements in forecasting accuracy, with the LSTM model achieving a 59.1% reduction in Mean Absolute Percentage Error compared to the persistence model. …”
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Long-term natural streamflow forecasting under drought scenarios using data-intelligence modeling by Lavínia D. Balthazar, Felix Miranda, Vinícius B.R. Cândido, Priscila Capriles, Marconi Moraes, CelsoB.M. Ribeiro, Geane Fayer, Leonardo Goliatt

“…This study significantly contributes to the progress of predicting long-term river streamflow through the application of machine learning models. Moreover, this study offers valuable insights and recommendations for hydrologists to improve future research efforts.…”
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Enhanced slope stability prediction using ensemble machine learning techniques by Devendra Kumar Yadav, Swarup Chattopadhyay, Debi Prasad Tripathy, Pragyan Mishra, Pritiranjan Singh

“…This study presents a machine learning (ML) model for evaluating slope stability that meets high precision and speed criteria in slope engineering. …”
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Machine Learning for Earthquake Emergency Evacuation: Site Selection and Neighborhood Navigation by Amirmasoud Amiran, Behrouz Behnam, Sanaz Seyedin

“…This research is first to introduce a machine learning-based method to enhance the quality and speed of selecting emergency evacuation centers in Tehran, optimizing the use of the city’s current capacities. …”
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Tether Force Estimation Airborne Kite Using Machine Learning Methods by Akarsh Gupta, Yashwant Kashyap, Panagiotis Kosmopoulos

“…Through a series of controlled field experiments and the application of classical machine learning techniques, we achieved significant improvements in tether force estimation. …”
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Enhancing landslide susceptibility modelling through a novel non-landslide sampling method and ensemble learning technique by Chao Zhou, Yue Wang, Ying Cao, Ramesh P. Singh, Bayes Ahmed, Mahdi Motagh, Yang Wang, Ling Chen, Guangchao Tan, Shanshan Li

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Integrating Machine Learning and Material Feeding Systems for Competitive Advantage in Manufacturing by Müge Sinem Çağlayan, Aslı Aksoy

“…The research employs six machine learning (ML) algorithms—logistic regression (LR), decision trees (DT), random forest (RF), support vector machines (SVM), K-nearest neighbors (K-NN), and artificial neural networks (ANN)—to develop a multi-class classification model for material feeding system selection. …”
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Predicting axillary lymph node metastasis in breast cancer using a multimodal radiomics and deep learning model by Fuyu Guo, Shiwei Sun, Xiaoqian Deng, Yue Wang, Wei Yao, Peng Yue, Shaoduo Wu, Junrong Yan, Junrong Yan, Junrong Yan, Xiaojun Zhang, Xiaojun Zhang, Xiaojun Zhang, Yangang Zhang, Yangang Zhang, Yangang Zhang

“…Various machine learning algorithms were used to construct radiomics, deep learning, and combined models. …”
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An ensemble-driven machine learning framework for enhanced water quality classification by Preet Singh, Taniya Hasija, Salil Bharany, Hafiza Nazra Tun Naeem, B. Chinna Rao, Seada Hussen, Ateeq Ur Rehman

“…Its accurate definition helps identify health risks, optimize resource consumption, and feed sustainable practices. This study applies machine learning (ML) models to classify water quality using an integrated dataset from Telangana, India. …”
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Determination of cervical vertebral maturation using machine learning in lateral cephalograms by Shahab Kavousinejad, Asghar Ebadifar, Azita Tehranchi, Farzan Zakermashhadi, Kazem Dalaie

“…This study aimed to develop a semi-automated approach using machine learning based on cervical vertebral dimensions (CVD) for determining skeletal maturation status. …”
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About the trustworthiness of physics-based machine learning – considerations for geomechanical applications by D. Degen, D. Degen, D. Degen, M. Ziegler, M. Ziegler, O. Heidbach, O. Heidbach, A. Henk, K. Reiter, F. Wellmann, F. Wellmann

“…To overcome the challenge of trustworthiness, we propose the usage of a novel hybrid machine learning method, namely the non-intrusive reduced-basis method, as a surrogate model. …”
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Prediction of dialysis adequacy using data-driven machine learning algorithms by Yi-Chen Liu, Ji-Ping Qing, Rong Li, Juan Chang, Li-Xia Xu

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Advanced Methodology for Fraud Detection in Energy Using Machine Learning Algorithms by Silviu Gresoi, Grigore Stamatescu, Ioana Făgărășan

“…This approach integrates multiple machine learning models—k-nearest neighbors (kNN), decision trees, random forest, and artificial neural networks (ANNs)—to improve detection accuracy and efficiency. …”
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A Comprehensive Review of Machine Learning Approaches for Flood Depth Estimation by Bo Liu, Yingbing Li, Minyuan Ma, Bojun Mao

“…This review reveals the potential of applying machine learning models in flood depth estimation, providing directions for future research and reliable support for disaster prevention and reduction efforts.…”
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