AI Machine Learning–Based Diabetes Prediction in Older Adults in South Korea: Cross-Sectional Analysis by Hocheol Lee, Myung-Bae Park, Young-Joo Won

“…ObjectiveThis study determined diabetes risk factors among older adults aged ≥60 years using machine learning algorithms and selected an optimized prediction model. …”
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Developing an Automatic Asbestos Detection Method Based on a Convolutional Neural Network and Support Vector Machine by Tomohito Matsuo, Mitsuteru Takimoto, Suzuyo Tanaka, Ayami Futamura, Hikari Shimadera, Akira Kondo

“…In this study, we developed a machine-learning model to automatically detect asbestos fibers in phase-contrast microscopy images. …”
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Predicting the permeability and compressive strength of pervious concrete using a stacking ensemble machine learning approach by Fan Yu, Wei Chu, Rui Zhang, Zhang Gao, Yunan Yang

“…The aim of this paper is to establish machine learning-based models for predicting permeability and compressive strength of pervious concrete. …”
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Exploring temperature-dependent photoluminescence dynamics of colloidal CdSe nanoplatelets using machine learning approach by Ivan P. Malashin, Daniil Daibagya, Vadim Tynchenko, Vladimir Nelyub, Aleksei Borodulin, Andrei Gantimurov, Alexandr Selyukov, Sergey Ambrozevich, Roman Vasiliev

“…Abstract The study explore machine learning (ML) techniques to predict temperature-dependent photoluminescence (PL) spectra in colloidal CdSe nanoplatelets (NPLs), leveraging polynomial regression models trained on experimental data from 85 to 270 K spanning temperatures to forecast PL spectra backward to 0 K and forward to 300 K. 6th-degree polynomial models with Tweedie regression were optimal for band energy ( $$B_1$$ ) predictions up to 300 K, while 9th-degree models with LassoLars and Linear Regression regressors were suitable for backward predictions to 0 K. …”
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Hybrid machine learning-based 3-dimensional UAV node localization for UAV-assisted wireless networks by Workeneh Geleta Negassa, Demissie J. Gelmecha, Ram Sewak Singh, Davinder Singh Rathee

“…The proposed model efficiently managed interference, adapted to UAV mobility, and ensured optimal throughput by dynamically optimizing UAV trajectories. …”
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Breeding of Solanaceous Crops Using AI: Machine Learning and Deep Learning Approaches—A Critical Review by Maria Gerakari, Anastasios Katsileros, Konstantina Kleftogianni, Eleni Tani, Penelope J. Bebeli, Vasileios Papasotiropoulos

“…Through advanced algorithms and predictive models, ML and DL facilitate the identification and optimization of key traits, including higher yield, improved quality, pest resistance, and tolerance to extreme climatic conditions. …”
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Performance Analysis and Improvement of Machine Learning with Various Feature Selection Methods for EEG-Based Emotion Classification by Sherzod Abdumalikov, Jingeun Kim, Yourim Yoon

“…Two different EEG datasets, EEG Emotion and DEAP Dataset, containing 2548 and 160 features, respectively, were evaluated using random forest (RF), logistic regression, XGBoost, and support vector machine (SVM). For both datasets, the experimented three feature selection methods consistently improved the accuracy of the models. …”
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Forecasting Wind Farm Production in the Short, Medium, and Long Terms Using Various Machine Learning Algorithms by Gökhan Ekinci, Harun Kemal Ozturk

“…These findings provide practical insights for optimizing wind energy forecasting models, which can improve energy trading strategies, enhance grid stability, and support informed decision making in renewable energy investments. …”
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Application of Computer Simulation to Evaluate Performance Parameters of the Selective Soldering Process by Maciej Dominik, Marek Kęsek

“…Ultimately, the study confirms that simulation modeling is a powerful and adaptable approach to production optimization, contributing to long-term strategic improvements and innovation in technologically advanced manufacturing environments.…”
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Integrating multimodal imaging and peritumoral features for enhanced prostate cancer diagnosis: A machine learning approach. by Huadi Zhou, Mei Xie, Hemiao Shi, Chenhan Shou, Meng Tang, Yue Zhang, Yue Hu, Xiao Liu

“…Three machine learning models-Random Forest, XGBoost, and Extra Trees-were then constructed and trained on four different feature combinations (tumor ADC, tumor T2, tumor ADC+T2, and tumor + peritumoral ADC+T2).…”
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Predicting central lymph node metastasis in papillary thyroid microcarcinoma: a breakthrough with interpretable machine learning by Weijun Zhou, Lijuan Li, Xiaowen Hao, Lanying Wu, Lifu Liu, Binyu Zheng, Yangzheng Xia, Yong Liu

“…ObjectiveTo develop and validate an interpretable machine learning (ML) model for the preoperative prediction of central lymph node metastasis (CLNM) in papillary thyroid microcarcinoma (PTMC).MethodsFrom December 2016 to December 2023, we retrospectively analyzed 710 PTMC patients who underwent thyroidectomies. …”
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Prediction of birthweight with early and mid-pregnancy antenatal markers utilising machine learning and explainable artificial intelligence by Manohar Pavanya, Krishnaraj Chadaga, Vennila J, Akhila Vasudeva, Bhamini Krishna Rao, Srikanth Prabhu, Shashikala K Bhat

“…Prediction of birthweight using machine learning (ML) models with antenatal data may help in better clinical management. …”
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Bagging Vs. Boosting in Ensemble Machine Learning? An Integrated Application to Fraud Risk Analysis in the Insurance Sector by Ruixing Ming, Osama Mohamad, Nisreen Innab, Mohamed Hanafy

“…Addressing the pressing challenge of insurance fraud, which significantly impacts financial losses and trust within the insurance industry, this study introduces an innovative automated detection system utilizing ensemble machine learning (EML) algorithms. The approach encompasses four strategic phases: 1) Tackling data imbalance through diverse re-sampling methods (Over-sampling, Under-sampling, and Hybrid); 2) Optimizing feature selection (Filtering, Wrapping, and Embedding) to enhance model accuracy; 3) employing binary classification techniques (Bagging and Boosting) for effective fraud identification; and 4) applying explanatory model analysis (Shapley Additive Explanations, Break-down plot, and variable-importance Measure) to evaluate the influence of individual features on model performance. …”
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Identifying novel biomarkers for biliary tract cancer based on volatile organic compounds analysis and machine learning by Jingrong Qian, Qi Liu, Jue Wang, Xuewei Zhuang, Jun Fang

“…In BTC and BBD patients, the diagnostic model was constructed based on six machine learning method. …”
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Leveraging machine learning to evaluate the effect of raw materials on the compressive strength of ultra-high-performance concrete by Mohamed Abdellatief, G. Murali, Saurav Dixit

“…The impact of 12 influential features on CS was evaluated to optimize the performance of the proposed models. Among the algorithms, XGB outperformed the others with an R² of 90.1 % and a lower RMSE of 11.52 MPa, surpassing RF (88.7 %), GB (89.2 %), and GPR (86.1 %). …”
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Integrative machine learning approach for forecasting lung cancer chemosensitivity: From algorithm to cell line validation by Jinghong Chen, Yonglin Yi, Chunqian Yang, Haoxuan Ying, Jian Zhang, Anqi Lin, Ting Wei, Peng Luo

“…Results: A model combining random forest and support vector machine algorithms exhibited superior performance in both the training and validation sets. …”
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"Numerical simulation and optimization of homogenization section screw based on response surface method " by WANG Yu-peng, XIN Jin-wei, MA Zi-chen, JIAN Ran-ran, MIAO Qing, ZENG Xian-kui

“…A multi-factor quadratic polynomial ma-thematical model of the homogenization section screw structure and extrusion effect of the rubber injection machine was established. …”
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Advancing Seaweed Cultivation: Integrating Physics Constraint in Machine Learning for Enhanced Biomass Forecasting in IMTA Systems by Alisa Kunapinun, William Fairman, Paul S. Wills, Dennis Hanisak, Bing Ouyang

“…The rationale behind choosing LSTM over other state-of-the-art models is presented in the paper. This study highlights the potential of integrating machine learning with physical models to optimize seaweed cultivation and support sustainable aquaculture practices. …”
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Machine learning-based estimation of CO2 footprint and environmental-mechanical performance of blended cement concrete by Felipe Vargas, Iván La Fé-Perdomo, Jorge A. Ramos-Grez, Ivan Navarrete

“…The models were developed using a dataset of 246 mixtures compiled from the literature and validated against 15 experimentally tested mixtures. …”
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Predicting the sonication energy for focused ultrasound surgery treatment of breast fibroadenomas using machine learning algorithms by Mengdi Liang, Yuelin Liu, Yue Huang, Ge Ma, Xu Han, Shuaikang Li, Jing Hang, Hui Xie, Lin Chen, Xiaoan Liu, Shui Wang, Tiansong Xia

“…At the optimal feature count, identified by the minimum RMSE, 33 features were selected for further modeling. …”
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