Soybean Yield Estimation Using Improved Deep Learning Models With Integrated Multisource and Multitemporal Remote Sensing Data by Jian Li, Junrui Kang, Ji Qi, Jian Lu, Hongkun Fu, Baoqi Liu, Xinglei Lin, Jiawei Zhao, Hengxu Guan, Jing Chang, Zhihan Liu

“…Compared to the best traditional machine learning model (support vector regression), <italic>R</italic>² increased by 52.96% and RMSE decreased by 26.05%, and relative to the best deep learning baseline model (long short-term memory), <italic>R</italic>² and RMSE improved by 7.04% and 7.04%, respectively. …”
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Marine Mammals Classification using Acoustic Binary Patterns by Maheen NADIR, Syed Muhammad ADNAN, Sumair AZIZ, Muhammad Umar KHAN

“…Multi-class Support Vector Machines (SVM) classifier is employed to identify different classes of mammal sounds. …”
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Bioinformatics-based analysis of autophagy-related genes and prediction of potential Chinese medicines in diabetic kidney disease by Yufeng Xing, Zining Peng, Chaoyang Ye

“…Subsequently, the least absolute shrinkage and selection operator (LASSO) regression and support vector machine-recursive feature elimination (SVM-RFE) algorithms were adopted to select autophagy-related genes. …”
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Multiple factors affecting Ixodes ricinus ticks and associated pathogens in European temperate ecosystems (northeastern France) by Nathalie Boulanger, Delphine Aran, Armand Maul, Baba Issa Camara, Cathy Barthel, Marie Zaffino, Marie-Claire Lett, Annick Schnitzler, Pascale Bauda

“…The tick-borne pathogens responsible for Lyme borreliosis, anaplasmosis, and hard tick relapsing fever showed specific habitat preferences and associations with specific animal families. Machine learning algorithms identified soil related variables as the best predictors of tick and pathogen abundance.…”
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Association of microtubule-based processes gene expression with immune microenvironment and its predictive value for drug response in oestrogen receptor-positive breast cancer by Zhenfeng Huang, Minghui Zhang, Nana Zhang, Mengyao Zeng, Yao Qian, Meng Zhu, Xiangyan Meng, Ming Shan, Guoqiang Zhang, Feng Liu

“…Prognostic risk models were developed via random forest, support vector machines and the least absolute shrinkage and selection operator algorithm. …”
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Effects of urban sprawl on land use change in the peripheral villages of Tehran metropolis (case study: Tehran-Damavand axis) by ِAshkan Mohammadi, Naser Shafiei Sabet, Alireza Shakiba

“…After field operation and harvesting of samples with two-frequency GPS receivers and introducing it to the software, the classification of complications was performed by support vector machines with a mean total accuracy of 62.69% and a mean Kappa coefficient of 85.33%. …”
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Habitat suitability modeling to improve conservation strategy of two highly-grazed endemic plant species in saint Catherine Protectorate, Egypt by Mohamed M. El-Khalafy, Eman T. El-Kenany, Alshymaa Z. Al-Mokadem, Salma K. Shaltout, Ahmed R. Mahmoud

“…In our analysis, we included the incorporation of bioclimatic variables into the SDM modeling process using four main algorithms: generalized linear model (GLM), Random Forest (RF), Boosted Regression Trees (BRT), and Support Vector Machines (SVM) in an ensemble model. …”
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Integration of agronomic information, vegetation indices (VIs), and meteorological data for phenological monitoring and yield estimation of rice (Oryza sativa L.) by Jorge A. Fernandez-Jibaja, Nilton Atalaya-Marin, Yeltsin A. Álvarez-Robledo, Victor H. Taboada-Mitma, Juancarlos Cruz-Luis, Daniel Tineo, Malluri Goñas, Darwin Gómez-Fernández

“…Among the regression algorithms tested, support vector regression (SVR) demonstrated the highest predictive accuracy (R² = 0.81) for the Bellavista variety at the maximum tillering stage. …”
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A Construction and Representation Learning Method for a Traffic Accident Knowledge Graph Based on the Enhanced TransD Model by Xiaojia Liu, Haopeng Wu, Dexin Yu, Yunjie Chen, Hao Wu

“…This research provides a solid data foundation and algorithmic support for downstream traffic accident risk prediction and intelligent traffic safety management.…”
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A systematic review of AI-powered collaborative learning in higher education: Trends and outcomes from the last decade by Attila Kovari

“…Artificial intelligence tools, in particular machine learning, natural language processing and recommender algorithms, facilitate collaborative learning by enabling personalized learning through feedback and group work. …”
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Development of Ai-Based Crop Quality Grading Systems using Image Recognition by Dusi Prerna, Sharma Pooja

“…It also integrate Convolutional Neural Networks (CNN), Transfer Learning, Support Vector Machines (SVM) and Random Forest algorithms to label crop images into pre defined categories. …”
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Theoretical Analysis and Experiment of the Five DOF Hybrid Robot P(RPR/RP)RR by Xuejian Ma, Xiaoyu He, Yundou Xu, Jiantao Yao, Yongsheng Zhao

“…Finally, the research team constructed a prototype of the robot, and zero-point error parameter calibration and accuracy testing were completed using the closed-loop vector method. This research provides significant technical support for the precision machining of aluminum alloy structures in new energy vehicles.…”
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Computed tomography-based radiomic features combined with clinical parameters for predicting post-infectious bronchiolitis obliterans in children with adenovirus pneumonia: a retro... by Li Zhang, Ling He, Guangli Zhang, Xiaoyin Tian, Haoru Wang, Fang Wang, Xin Chen, Yinglan Zheng, Man Li, Yang Li, Zhengxiu Luo

“…Combined models based on radiomic and clinical features were established via logistic regression (LR), random forest (RF), and support vector machine (SVM) algorithms. Model performance was evaluated via the area under the receiver operating characteristic curve (AUC). …”
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Method for Determining Igneous Rock Mineral Content Using Element Logging Data Based on Variational AutoEncoder by JIA Ruilong, PAN Baozhi, WANG Qinghui, LI Yan, GUAN Yao, WANG Xinru

“…The model validation reveals that the proposed model has a smaller mean absolute error and mean square error compared to three typical methods: BP (Back Propagation) neural networks, ridge regression and support vector machines. Furthermore, the model is applied to a section of buried hill igneous rock well in the South China Sea. …”
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Estimation of the water content of needles under stress by Erannis jacobsoni Djak. via Sentinel-2 satellite remote sensing by Jiaze Guo, Xiaojun Huang, Xiaojun Huang, Xiaojun Huang, Debao Zhou, Junsheng Zhang, Gang Bao, Gang Bao, Siqin Tong, Siqin Tong, Yuhai Bao, Yuhai Bao, Dashzebeg Ganbat, Dorjsuren Altanchimeg, Davaadorj Enkhnasan, Mungunkhuyag Ariunaa

“…Needle leaf water content exhibits a clear response to these changes and is highly sensitive in reflecting the degree of tree damage.MethodsIn this work, we combine vegetation indices with machine learning algorithms to estimate the water content of needles at a large scale. …”
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Triphasic CT Radiomics Model for Preoperative Prediction of Hepatocellular Carcinoma Pathological Grading by Huang H, Pan X, Zhang Y, Yang J, Chen L, Zhao Q, Huang L, Lu W, Deng Y, Huang Y, Ding K

“…Key features were selected using minimum redundancy maximum relevance (mRMR), SelectKBest, and least absolute shrinkage and selection operator (LASSO) algorithms. Logistic regression and support vector machine (SVM) classifiers were employed to develop individual phase-specific models and a triphasic fusion model. …”
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Identification of Fake Comments in E-Commerce Based on Triplet Convolutional Twin Network and CatBoost Model by Juanjuan Peng

“…The benchmark experimental results show that the proposed TriCNN-CatBoost model significantly outperforms traditional Naive Bayes, Support Vector Machines, and Random Forest models in terms of accuracy, recall, and F1 score, demonstrating stronger false comment recognition ability and generalization performance. …”
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Identification and validation of TUBB, CLTA, and FBXL5 as potential diagnostic markers of postmenopausal osteoporosis by Yue Tan, Yujing Wang, Qin Zhu, Yan Xue, Xuhao Ji, Zhenkun Li, Jiawen Shen, Chengming Sun, Shiqi Ren, Chenlin Zhang, Jianfeng Chen

“…Additionally, we intersected the clusters to identify differentially expressed genes (DEGs) and analyzed potential diagnostic markers for PMOP using support vector machine recursive feature elimination (SVM-RFE), LASSO, and random forest (RF) algorithms, which were subsequently validated in the GSE56116 dataset. …”
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Bioinformatic identification of signature miRNAs associated with fetoplacental vascular dysfunction in gestational diabetes mellitus by Yulan Lu, Chunhong Liu, Xiaoxia Pang, Xinghong Chen, Chunfang Wang, Huatuo Huang

“…Then, the least absolute shrinkage and selection operator (LASSO) and support vector machine (SVM) were used as the other algorithms for screening candidate signature miRNA genes. …”
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Exhaled volatile organic compounds as novel biomarkers for early detection of COPD, asthma, and PRISm: a cross-sectional study by Jiaxin Tian, Qiurui Zhang, Minhua Peng, Leixin Guo, Qianqian Zhao, Wei Lin, Sitong Chen, Xuefei Liu, Simin Xie, Wenxin Wu, Yijie Li, Junqi Wang, Jin Cao, Ping Wang, Min Zhou

“…Subsequently, classification models were established by machine learning algorithms, based on these VOC markers along with baseline characteristics. …”
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