Towards Transparent Diabetes Prediction: Combining AutoML and Explainable AI for Improved Clinical Insights by Raza Hasan, Vishal Dattana, Salman Mahmood, Saqib Hussain

Subjects: “…AutoML…”
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Predicting largest expected aftershock ground motions using automated machine learning (AutoML)-based scheme by Xiaohui Yu, Meng Wang, Chaolie Ning, Kun Ji

Subjects: “…Automated machine learning(AutoML)…”
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AutoML based workflow for design of experiments (DOE) selection and benchmarking data acquisition strategies with simulation models by Xukuan Xu, Donghui Li, Jinghou Bi, Michael Moeckel

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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

“…The resulting interpretable AutoML models O-RF, O-XGB, O-LGB, and O-CB are applied to predict the compressive and tensile strengths of HPC. …”
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Interpretable flash flood susceptibility mapping in Yarlung Tsangpo River Basin using H2O Auto-ML by Fei He, Suxia Liu, Xingguo Mo, Zhonggen Wang

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Integrated AutoML-based framework for optimizing shale gas production: A case study of the Fuling shale gas field by Tianrui Ye, Jin Meng, Yitian Xiao, Yaqiu Lu, Aiwei Zheng, Bang Liang

“…Specifically, it harnesses the power of Automated Machine Learning (AutoML) to construct an ensemble model to predict the estimated ultimate recovery (EUR) of shale gas wells. …”
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Developing an Hourly Water Level Prediction Model for Small- and Medium-Sized Agricultural Reservoirs Using AutoML: Case Study of Baekhak Reservoir, South Korea by Jeongho Han, Joo Hyun Bae

“…This study focuses on developing an hourly water level prediction model for small- and medium-sized agricultural reservoirs using the Tree-based Pipeline Optimization Tool (TPOT), an automated machine learning (AutoML) technique. The study area is the Baekhak Reservoir in South Korea, and various precipitation-related and reservoir water storage data were collected. …”
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Risk Prediction of Liver Injury in Pediatric Tuberculosis Treatment: Development of an Automated Machine Learning Model by Zeng Y, Lu H, Li S, Shi QZ, Liu L, Gong YQ, Yan P

“…After the features were screened by univariate risk factor analysis, AutoML technology was used to establish predictive models. …”
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Advanced automated machine learning framework for photovoltaic power output prediction using environmental parameters and SHAP interpretability by Muhammad Paend Bakht, Mohd Norzali Haji Mohd, Babul Salam KSM Kader Ibrahim, Nuzhat Khan, Usman Ullah Sheikh, Ab Al-Hadi Ab Rahman

“…This study establishes an advanced and powerful framework combining Auto-ML and explainable AI for predictive modeling of PV power output. …”
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Automatic Synthesis of Recurrent Neurons for Imitation Learning From CNC Machine Operators by Hoa Thi Nguyen, Roland Olsson, Oystein Haugen

“…This adds another layer to the AutoML framework, targeting the internal structure of neurons. …”
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A systematic literature review and taxonomy proposition of machine learning techniques in smart manufacturing by Frederico de Oliveira Santos, Ivanete Schneider Hahn

“…The main findings show that machinery was the industry subsector with the major implementations regarding machine learning; process improvement is the main concern (interest) of all implementations; random forest was the most specific machine learning technique used; and diverse technologies associated with this context were identified such as: the industrial internet of things, digital twin, sensor technologies (soft, optical, barometric, ultrasonic), software technologies (Python, MATLAB, LabView, Google AutoML Platform) and equipment technologies (robotic, PLC, CNC). …”
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Machine and deep learning performance in out-of-distribution regressions by Assaf Shmuel, Oren Glickman, Teddy Lazebnik

“…We extensively ( $n \gt 40\,000$ runs) compare the ID versus OOD performance of XGBoost, random forest, K-nearest-neighbors, support vector machine, and linear regression models, as well as AutoML models (Tree-based Pipeline Optimization Tool and AutoKeras). …”
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Integrating AutoGluon for Real-Time Monitoring and Classification of Dental Equipment Performance by Muxiu Yang, Fengzhou Li, Wenfeng Qiu

“…This study aims to introduce AutoGluon, an automated machine learning (AutoML) framework that monitors and classifies the performance of dental equipment in real time. …”
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Predict Diabetes Using Voting Classifier and Hyper Tuning Technique by Chra Ali Kamal, Manal Ali Atiyah

“…In the first phase, two different hyper parameter techniques (Randomized Search and TPOT(autoML)) were used to increase the accuracy level for each algorithm. …”
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Model of forecasting of material resources and estimated cost at early stages of life cycle of construction objects by M. V. Gureev, A. N. Makarov

“…The training and research were conducted using the automated machine learning (AutoML) method. Based on a comparison of the coefficient of determination R2 and the standard deviation (RMSE), ensembles of models were selected that form a forecast for the volume of material resources and equipment, as well as for the estimated cost with an error range of ±8 %. …”
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Developing a semi-automated technique of surface water quality analysis using GEE and machine learning: A case study for Sundarbans by Sheikh Fahim Faysal Sowrav, Sujit Kumar Debsarma, Mohan Kumar Das, Khan Mohammad Ibtehal, Mahfujur Rahman, Noshin Tabassum Hridita, Atika Afia Broty, Muhammad Sajid Anam Hoque

“…The predictive framework leverages Google Earth Engine (GEE) and AutoML, utilizing deep learning libraries to create dynamic, adaptive models that enhance prediction accuracy. …”
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