URFM: A general Ultrasound Representation Foundation Model for advancing ultrasound image diagnosis by Qingbo Kang, Qicheng Lao, Jun Gao, Wuyongga Bao, Zhu He, Chenlin Du, Qiang Lu, Kang Li

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An Innovative Inversion Method of Potato Canopy Chlorophyll Content Based on the AFFS Algorithm and the CDE-EHO-GBM Model by Xiaofei Yang, Qiao Li, Honghui Li, Hao Zhou, Jinyan Zhang, Xueliang Fu

“…Gradient Boosting Machine (GBM) model parameters were optimized using a hybrid strategy improved Elephant Herd Optimization (EHO) algorithm (CDE-EHO) that combines Differential Evolution (DE) and Cauchy Mutation (CM). …”
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Multiple Regression Analysis and Non-Dominated Sorting Genetic Algorithm II Optimization of Machining Carbon-Fiber-Reinforced Polyethylene Terephthalate Glycol Parts Fabricated via... by Anastasios Tzotzis, Nikolaos Efkolidis, Kai Cheng, Panagiotis Kyratsis

“…It was determined that feed and the corresponding interactions contributed more than 45% to the model’s <i>R</i>², followed by the depth of cut and the machining condition. …”
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Combined Forecasting Schemes Based on Random Forest Regression and Meta-heuristic Algorithms for Maximum Dry Density by Huaxuan Lu

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Intratumoral Heterogeneity Scores as Predictors of Invasiveness in Lung Adenocarcinoma Presenting as Pure Ground-Glass Nodules: Insights from Explainable Machine Learning-Based Ter... by Wang Peng BS, Wanyin Qi BS, Yunhua Li BS, Sanhong Zhang BS, Juan Long BS

“…We subsequently applied binary classification models to various tasks derived from the optimal ternary classification model to sequentially address the discordant classifications. …”
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Associations between urinary and blood heavy metal exposure and heart failure in elderly adults: Insights from an interpretable machine learning model based on NHANES (2003–2020) by Yang Yuting, Deng Shan

“…Our findings emphasize the need for continued investigation into the mechanisms of these associations and highlight the importance of monitoring and regulatory measures to mitigate heavy metal exposure in populations at risk. Methods: Five machine learning models were evaluated, with Gradient Boosting Decision Trees (GBDT) selected as the optimal model based on accuracy, interpretability, and ability to capture nonlinear relationships. …”
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Depth-specific soil moisture estimation in vegetated corn fields using a canopy-informed model: A fusion of RGB-thermal drone data and machine learning by Milad Vahidi, Sanaz Shafian, William Hunter Frame

“…Sophisticated regression models such as Gradient Boosting Machines (GBM), Least Absolute Shrinkage and Selection Operator (Lasso), and Support Vector Machines (SVM) were employed to analyze the effects of spectral indices, land surface temperature (LST), and structural canopy variables on soil moisture estimation accuracy. …”
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The prediction of karst-collapse susceptibility levels based on the ISSA-ELM integrated model by Jiaxin Wang, Ying Yang, Xian Yang, Yulong Lu, Yang Liu, Da Hu, Yongjia Hu

“…To address the limitations of conventional prediction methods, in this study, we introduce an enhanced predictive model, the improved sparrow search algorithm-optimized extreme learning machine (ISSA-ELM), for accurate karst-collapse susceptibility assessment. …”
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Preoperative lymph node metastasis risk assessment in invasive micropapillary carcinoma of the breast: development of a machine learning-based predictive model with a web-based cal... by Yan Zhang, Nan Wang, Yuxin Qiu, Yingxiao Jiang, Peiyan Qin, Xiaoxiao Wang, Yang Li, Xiangdi Meng, Furong Hao

“…The study aimed to identify predictors of LNM and to develop a machine learning (ML)-based risk prediction model for patients with breast IMPC. …”
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Development and validation of a machine learning model for central compartmental lymph node metastasis in solitary papillary thyroid microcarcinoma via ultrasound imaging features... by Haiyang Han, Heng Sun, Chang Zhou, Li Wei, Liang Xu, Dian Shen, Wenshu Hu

“…Conclusion A machine learning-based model combining ultrasound radiomics and clinical variables shows promise for the preoperative risk stratification of CCLNM in patients with PTMC. …”
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Clinical characteristics and prediction model of re-positive nucleic acid tests among Omicron infections by machine learning: a real-world study of 35,488 cases by Ying Cao, Tianhua Yao, Ronghao Li, Liang Tan, Zhixiong Zhang, Junsheng Qi, Rui Zhang, Yazhou Wu, Zhiqiang Chen, Changlin Yin

“…KNN was the most effective machine learning model to predict TR occurrence in isolation.…”
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Risk prediction of QTc prolongation occurrence in cancer patients treated with commonly used oral tyrosine kinase inhibitors: machine learning modeling or conventional statistical... by Hsiang-Wen Lin, Tien-Chao Lin, Chien-Ning Hsu, Tzu-Pei Yeh, Yu-Chieh Chen, Liang-Chih Liu, Chen-Yuan Lin

“…This study aimed to develop an optimal prediction model for QTc prolongation risk and estimate its risk probability in cancer patients treated with oral tyrosine kinase inhibitors (TKIs). …”
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A novel early stage drip irrigation system cost estimation model based on management and environmental variables by Masoud Pourgholam-Amiji, Khaled Ahmadaali, Abdolmajid Liaghat

“…Support vector machine (SVM) and optimization algorithms (Wrapper) were found to be the best learner and feature selection techniques, respectively, out of all the available feature selection algorithms. …”
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Enhanced random vector functional link based on artificial protozoa optimizer to predict wear characteristics of Cu-ZrO2 nanocomposites by Mamdouh I. Elamy, Mohamed Abd Elaziz, Mohammed Azmi Al-Betar, A. Fathy, M. Elmahdy

“…Owing to the absence of scientific methods for predicting nanocomposites' wear rates, a freshly updated machine learning method that uses an Artificial Protozoa Optimizer (APO) to forecast the tribological performance of Cu-ZrO2 nanocomposites was proposed. …”
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BAHGRF3: Human gait recognition in the indoor environment using deep learning features fusion assisted framework and posterior probability moth flame optimisation by Muhammad Abrar Ahmad Khan, Muhammad Attique Khan, Ateeq Ur Rehman, Ahmed Ibrahim Alzahrani, Nasser Alalwan, Deepak Gupta, Saima Ahmed Rahin, Yudong Zhang

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Heart Disease Prediction Using a Hybrid Feature Selection and Ensemble Learning Approach by Isha Gupta, Anu Bajaj, Manav Malhotra, Vikas Sharma, Ajith Abraham

“…This study leverages the UCI heart disease dataset to assess the effectiveness of various Machine Learning models in predicting heart diseases. …”
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Harnessing Artificial Intelligence to Revolutionize Microalgae Biotechnology: Unlocking Sustainable Solutions for High-Value Compounds and Carbon Neutrality by Yijian Wu, Lei Shan, Weixuan Zhao, Xue Lu

“…Recent advancements in artificial intelligence (AI), particularly machine learning (ML) and automation, have provided innovative solutions to these challenges. …”
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A mini review of transforming dementia care in China with data-driven insights: overcoming diagnostic and time-delayed barriers by Pinya Lu, Xiaolu Lin, Xiaofeng Liu, Mingfeng Chen, Caiyan Li, Hongqin Yang, Yuhua Wang, Xuemei Ding

“…Artificial intelligence (AI) and machine learning models built on dementia data are succinctly reviewed in terms of the roadmap of dementia care from home, community to hospital settings. …”
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Hybrid Gaussian Process Regression Models for Accurate Prediction of Carbonation-Induced Steel Corrosion in Cementitious Mortars by Teerapun Saeheaw

“…This study presents a comprehensive framework integrating domain expertise with advanced machine learning for carbonation-induced corrosion prediction. …”
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Reinforcement Learning in Energy Finance: A Comprehensive Review by Spyros Giannelos

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