Clinical characteristics of bronchopulmonary dysplasia and the risk of sepsis onset prediction via machine learning models by Yanhua Wang, Yanhua Wang, Yanhua Wang, Yanhua Wang, Yi Wang, Yi Wang, Yi Wang, Linhong Song, Jun Li, Jun Li, Jun Li, Yuanyuan Xie, Yuanyuan Xie, Yuanyuan Xie, Lei Yan, Lei Yan, Lei Yan, Siqi Hu, Siqi Hu, Siqi Hu, Zhichun Feng, Zhichun Feng, Zhichun Feng, Zhichun Feng

“…By including clinical features, ML algorithms can predict BPD with sepsis, and the random forest (RF) model (sorted by the mean AUC) performs the best. …”
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A Fine Agricultural Flood Segmentation Model for HJ-2E S-Band SAR Data by Mingyang Song, Lu Xu, Nanhuanuowa Zhu, Zihuan Guo, Haoxuan Duan, Jiahua Teng, Xia Lei, Lijun Zuo

“…In addition, a full-level channel–spatial interaction block is designed to enhance the modeling and extraction of features for small, fragmented flood areas in agricultural regions. …”
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A Multi-Input Neural Network Model for Accurate MicroRNA Target Site Detection by Mohammad Mohebbi, Amirhossein Manzourolajdad, Ethan Bennett, Phillip Williams

“…These features, rather than nucleotide sequences, enhance our model to generalize beyond specific sequence contexts and perform well on sequentially distant samples.…”
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Deep Residual Transfer Ensemble Model for mRNA Gene-Expression-Based Breast Cancer by Job Prasanth Kumar Chinta Kunta, Vijayalakshmi A. Lepakshi

“…Subsequently, ResNet101 and AlexNet deep networks were applied distinctly to extract respective features, which were later fused to yield composite ensemble feature. …”
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Using elastography-based multilayer perceptron model to evaluate renal fibrosis in chronic kidney disease by Ziman Chen, Tin Cheung Ying, Jiaxin Chen, Chaoqun Wu, Liujun Li, Hui Chen, Ting Xiao, Yongquan Huang, Xuehua Chen, Jun Jiang, Yingli Wang, Wuzhu Lu, Zhongzhen Su

“…The MLP classifier using a machine learning algorithm was used to construct a diagnostic model incorporating elastic values with clinical features. …”
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Mining prerequisite relations among learning objects by Kexin MA, Bifan WEI, Jie MA, Jun LIU, Yi HUANG, Min HU, Junlan FENG

“…Prerequisite relation refers to the learning dependency between learning objects.Most previous works mined prerequisite relations in a pipelined way and heavily relied on hyperlinks,which lead to the accumulation of errors.To address these issues,prerequisite relations among knowledge topics were analyzed,and the asymmetry feature of prerequisite relation was found out.An end-to-end prerequisite relation model for mining prerequisite relations from texts was proposed.Based on the hyponymy relations between terms extracted from texts,this model calculates the asymmetry of prerequisite relation among related terms of learning objects,and then predicts the prerequisite relation betweens learning objects.The experimental results show that the proposed method achieves the state-of-the-art performance.…”
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Transferable deep learning with coati optimization algorithm based mitotic nuclei segmentation and classification model by Amal Alshardan, Nazir Ahmad, Achraf Ben Miled, Asma Alshuhail, Yazeed Alzahrani, Ahmed Mahmud

“…At last, the classification procedure is performed using the bidirectional long short-term memory (BiLSTM) model. …”
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Modeling the Dynamics of a Heavy-Duty Mobile Robot Based on Gauss's Principle of Least Constraint by Amandyk Tuleshov, Assylbek Ozhiken, Kassymbek Ozhikenov, Moldir Kuatova, Gulama-Garip Alisher Ibrayev, Igor Gritsenko, Daniyar Kerimkulov, Assiya Boltaboyeva

“…This paper presents a novel approach to model the dynamics of a heavy-duty mobile robot using Gauss’s principle of least constraint. …”
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Synthesis, Antibacterial Evaluation and Molecular Modeling of Novel Chalcone Derivatives Incorporating the Diphenyl Ether Moiety by Shiyuan Li, Hong Jin

“…In particular, compound <b>5u</b>, which features two diphenyl ether moieties, displayed outstanding antibacterial performance, with MIC values of 25.23 μM for <i>S. aureus</i> and 33.63 μM for <i>E. coli</i>, <i>Salmonella</i>, and <i>P. aeruginosa</i>. …”
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SSMM-DS: A semantic segmentation model for mangroves based on Deeplabv3+ with swin transformer by Zhenhua Wang, Jinlong Yang, Chuansheng Dong, Xi Zhang, Congqin Yi, Jiuhu Sun

“…Although its FLOPs are slightly higher than SegFormer's (15.11G vs. 9.9G), SSMM-DS offers a balance between performance and efficiency. Experimental results highlight SSMM-DS's effectiveness in extracting mangrove features, making it a valuable tool for monitoring and managing these critical ecosystems.…”
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A Lightweight Cotton Field Weed Detection Model Enhanced with EfficientNet and Attention Mechanisms by Lu Zheng, Lyujia Long, Chengao Zhu, Mengmeng Jia, Pingting Chen, Jun Tie

“…Existing weed detection models perform poorly in the complex environment of cotton fields, where the visual features of weeds and crops are similar and often overlap, resulting in low detection accuracy. …”
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Highly Robust South Fork Zumbro River Flow Forecasting Based on Deep Temporal Modeling by Yingjun Sun, Peixia Wang, Chong Wang, Xiaodong Wang, Hailin Feng, Wei Wang, Kai Fang

“…BILSTM captures bidirectional features, enhancing the model’s capacity to learn complex flow sequences. …”
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A New Algorithm Model Based on Extended Kalman Filter for Predicting Inter-Well Connectivity by Liwen Guo, Zhihong Kang, Shuaiwei Ding, Xuehao Yuan, Haitong Yang, Meng Zhang, Shuoliang Wang

“…This study proposes a state variable-based dynamic capacitance (SV-DC) model that integrates artificial intelligence techniques with dynamic data and geological features to more accurately identify inter-well connectivity and its evolution. …”
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MSUD-YOLO: A Novel Multiscale Small Object Detection Model for UAV Aerial Images by Xiaofeng Zhao, Hui Zhang, Wenwen Zhang, Junyi Ma, Chenxiao Li, Yao Ding, Zhili Zhang

“…First, the model uses an attention scale sequence fusion mode to achieve more efficient multiscale feature fusion. …”
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A fuzzy-optimized hybrid ensemble model for yield prediction in maize-soybean intercropping system by Amna Ikram, Sunnia Ikram, El-Sayed M. El-kenawy, El-Sayed M. El-kenawy, Adil Hussain, Amal H. Alharbi, Marwa M. Eid, Marwa M. Eid

“…The model is validated using performance metrics such as MSE, MAE, and R2. …”
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Monitoring water quality parameters using multi-source data-driven machine learning models by Yubo Zhao, Mo Chen, Jinyu He, Yanping Ma

“…The results indicated that, compared to models using only spectral reflectance as features, the inclusion of environmental factors significantly enhanced the accuracy of the inversion of the three water quality parameters. …”
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Construction of a prediction model for moderate to severe perimenopausal syndrome based on machine learning algorithms by ZHANG Min, GU Tingting, GUAN Wei, LIU Xiangxiang, SHI Junyao

“…Logistic regression (LR), random forest (RF), support vector machine (SVM), and gradient boosting decision tree (GBDT) were constructed, and model performances were evaluated using accuracy, precision, recall, area under curve(AUC) of the receiver operating characteristic curve, and F1-score.Results A total of 856 perimenopausal women were included in the study, of which 557 were in the normal or mild PMS group and 299 were in the moderate to severe PMS group; 599 were in the training set and 257 were in the testing set. 9 features (employment status, exercise, age, menstrual condition, medical history, obesity, residence area, history of health education, household register) were selected as predictors for the final model using the Boruta algorithm and SHAP analysis. …”
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Risk factors and prediction model of metabolic disorders in adult patients with pituitary stalk interruption syndrome by Deyue Jiang, Shengjie Wang, Yan Xiao, Peng Zhi, Erhan Zheng, Zhaohui Lyu, Qinghua Guo

“…However, GH non-treatment may serve as the notable predictor of MAFLD in PSIS patients revealed by the Ridge regression model of machine learning model with the highest predictive performance of a mean area under the curve (AUC) of 0.82. …”
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Thyroid nodule segmentation in ultrasound images using transformer models with masked autoencoder pre-training by Yi Xiang, Rajendra Acharya, Quan Le, Jen Hong Tan, Chiaw-Ling Chng

“…However, overall improvements are modest: the model achieves Dice Similarity Coefficient (DSC) scores of 0.63, 0.64, and 0.65 on AIMI, TN3K, and DDTI, respectively, highlighting limitations under small-sample conditions. …”
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Chondrogenic Cancer Grading by Combining Machine and Deep Learning with Raman Spectra of Histopathological Tissues by Gianmarco Lazzini, Mario D’Acunto

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