YOLO-SW: A Real-Time Weed Detection Model for Soybean Fields Using Swin Transformer and RT-DETR by Yizhou Shuai, Jingsha Shi, Yi Li, Shaohao Zhou, Lihua Zhang, Jiong Mu

“…The research stands out for its novel integration of three key advancements: the Swin Transformer backbone, which leverages local window self-attention to achieve linear O(N) computational complexity for efficient global context capture; the CARAFE dynamic upsampling operator, which enhances small target localization through context-aware kernel generation; and the RTDETR encoder, which enables end-to-end detection via IoU-aware query selection, eliminating the need for complex post-processing. Additionally, a dataset of six common soybean weeds was expanded to 12,500 images through simulated fog, rain, and snow augmentation, effectively resolving data imbalance and boosting model robustness. …”
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A Novel Geometric-Descriptor Based Algorithm for Individual-Level Crop Monitoring using UAVs by Y. Goswami, Y. Goswami, N. Ramprasad, S. N. Omkar

“…Consistent, individual-level crop monitoring enhances yields and crop health by providing farmers with relevant insights for each plant, boosting overall productivity and minimizing waste. …”
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Machine learning with hyperparameter optimization applied in facies-supported permeability modeling in carbonate oil reservoirs by Watheq J. Al-Mudhafar, Alqassim A. Hasan, Mohammed A. Abbas, David A. Wood

“…This review considers the performance of six ML algorithms (LightGBM, CATBoost, XGBoost, Adaboost, random forest and gradient boosting) for permeability prediction from a high-quality dataset. …”
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Exploring the potential utilization of copigmented barberry anthocyanins in ice cream: Focusing on foaming aspects, and melting attributes by Arash Dara, Sara Naji-Tabasi, Javad Feizy, Ebrahim Fooladi, Ali Rafe

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Using machine learning to assist decision making in the assessment of mental health patients presenting to emergency departments by Oliver Higgins, Rhonda L. Wilson, Stephan K. Chalup

“…Six different ML models were tested: Random Forest, XGBoost, CatBoost, k-Nearest Neighbours (kNN), Explainable Boosting Machine (EBM) using InterpretML, and Support Vector Machine using Support Vector Classification (SVC). …”
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An Optimization Framework for Waste Treatment Center Site Selection Considering Nighttime Light Remote Sensing Data and Waste Production Fluctuations by Junbao Xia, Yanping Liu, Haozhong Yang, Guodong Zhu

“…Using Beijing as a case study, the gradient boosting regression algorithm yielded a prediction accuracy of 92%. …”
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Non-Iterative Reconstruction and Selection Network-Assisted Channel Estimation for mmWave MIMO Communications by Jing Yang, Yabo Guo, Xinying Guo, Pengpeng Wang

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An integrated framework for importance-performance analysis of product attributes and validation from online reviews and maintenance records by Mengyuan Shen, Aoxiang Cheng, Youyi Bi

“…The proposed framework enables automatic data processing and can support companies in making efficient design decisions with more comprehensive perspectives from multisource data.…”
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Elastic net with Bayesian Density Estimation model for feature selection for photovoltaic energy prediction by Venkatachalam Mohanasundaram, Balamurugan Rangaswamy

“…Considering comprehensive data preliminary processing, FS, and validation, ELNET-BDE outperforms existing methods. …”
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Utilizing Machine Learning Techniques for Cancer Prediction and Classification based on Gene Expression Data by Mariwan Mahmood Hama Aziz, Sozan Abdullah Mahmood

“…In addition, our model integrates a self-attention mechanism in the transformer layers to enhance the model’s focus on key features and employs an embedding layer for dimensionality reduction, improving the processing of gene statistics, preventing overfitting, and boosting generalization. …”
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A novel method to predict the haemoglobin concentration after kidney transplantation based on machine learning: prediction model establishment and method optimization by Songping He, Xiangxi Li, Fangyu Peng, Jiazhi Liao, Xia Lu, Hui Guo, Xin Tan, Yanyan Chen

“…Objective To optimize the process of constructing a clinical prediction model based on machine learning and improve related technologies. …”
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Small-world network properties and cortical responses of Tai Chi Yunshou: Insights from fNIRS by Jin Fan, Ruijun He, Dongling Zhong, Xiaobo Liu, Huan Liu, Zhi Chen, Qinjian Dong, Yuxi Li, Chen Xue, Jiaming Zhang, Cheng Xie, XianJun Xiao, Xiaoshen Hu, Xi Wu, Juan Li, Rongjiang Jin

“…Conclusion: The motion of Tai Chi Yunshou enhances regulatory capacity in the dorsolateral prefrontal cortex and frontopolar area, boosts local brain processing, and improves network integration. …”
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A novel double machine learning approach for detecting early breast cancer using advanced feature selection and dimensionality reduction techniques by Suganya Athisayamani, Tamilazhagan S, A. Robert Singh, Jae-Yong Hwang, Gyanendra Prasad Joshi

“…The second model pairs eXtreme Gradient Boosting (XGBoost), a highly efficient boosting algorithm for tabular data, with an Artificial Neural Network (ANN). …”
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Modeling residue formation from crude oil oxidation using tree-based machine learning approaches by Mohammad-Reza Mohammadi, Seyyed-Mohammad-Mehdi Hosseini, Behnam Amiri-Ramsheh, Saptarshi Kar, Ali Abedi, Abdolhossein Hemmati-Sarapardeh, Ahmad Mohaddespour

“…Finally, the leverage method demonstrated that only 2.14% of the data were identified as suspected, with no out-of-leverage points detected, underscoring the reliability of the CatBoost model and the gathered experimental data. Effective management of fuel consumption and residue formation is crucial for maintaining the ISC process, and the CatBoost model has demonstrated strong predictive capabilities that support this objective.…”
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Detection of insect-damaged sunflower seeds using near-infrared hyperspectral imaging and machine learning by Bright Mensah, Jarrad Prasifka, Brent Hulke, Ewumbua Monono, Xin Sun

“…Machine learning techniques, specifically multilayer perceptron (MLP), support vector machine (SVM), random forest (RF), light gradient boosting machine (LGBM), extreme gradient boosting (XGB), gradient boosting (GB), and partial least squares discriminant analysis (PLS-DA), were trained and evaluated based on the spectral features. …”
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Weight optimization of steel lattice transmission towers based on Differential Evolution and machine learning classification technique by Tran-Hieu Nguyen, Anh-Tuan Vu

“…A classification model based on the Adaptive Boosting algorithm is developed in order to eliminate unpromising candidates during the optimization process. …”
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Improving Random Forest Algorithm for University Academic Affairs Management System Platform Construction by Jinyang Dai

“…Combining the advantages of three data-driven prediction algorithms, namely, random forest, extreme gradient boosting (XGBoost), and gradient boosting decision tree (GBDT), a model based on improved random forest algorithm is proposed. …”
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Iron Ore Information Extraction Based on CNN-LSTM Composite Deep Learning Model by Haili Chen, Mengxiang Xia, Yaping Zhang, Ruonan Zhao, Bingran Song, Yang Bai

“…In the mining, processing, and use of minerals, iron ore information identification is crucial. …”
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Global de-trending significantly improves the accuracy of XGBoost-based county-level maize and soybean yield prediction in the Midwestern United States by Yuanchao Li, Hongwei Zeng, Miao Zhang, Bingfang Wu, Xingli Qin

“…In our study, we utilized extreme gradient boosting (XGBoost) to scrutinize the effects of no trend processing (NTP), input year as a feature (IYF), input average yield as a feature (IAYF), input linear yield as a feature (ILYF), and the global detrending method (GDT) on the yield prediction of maize and soybean in the Midwestern United States. …”
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A new method for internal urinary metabolite exposure and dietary exposure association assessment of 3-MCPD and glycidol and their esters based on machine learning by Yimei Tian, Sunan Gao, Fan Zhang, Xuzhi Wan, Wei Jia, Jingjing Jiao, Yilei Fan, Yu Zhang

“…Among these, generalized additive model and extreme gradient boosting exhibited the strongest correlation and highest accuracy in predicting the associations. …”
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