Classification Modeling Method for Near-Infrared Spectroscopy of Tobacco Based on Multimodal Convolution Neural Networks by Lei Zhang, Xiangqian Ding, Ruichun Hou

“…The classification is performed by the combination of global and local training features. …”
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Ultrasound-based machine learning model to predict the risk of endometrial cancer among postmenopausal women by Yi-Xin Li, Yu Lu, Zhe-Ming Song, Yu-Ting Shen, Wen Lu, Min Ren

“…Three models were developed: (1) R model: radiomics-based machine learning (ML) algorithms; (2) CNN model: image-based CNN algorithms; (3) DLR model: a hybrid model combining radiomics and deep learning features with ML algorithms. …”
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FDC-TA-DSN Ship Classification Model and Dataset Construction Based on Complex-Valued SAR by Gui Gao, Yucong He, Jinghao Zhao, Sijie Li, Meixiang Wang, Gang Yang, Xi Zhang

“…First, this new deep SAR-Net (DSN) devises an FDC module to reduce the influence of SAR speckle noise and enhance the adaptability of the network for inputting features, and a TA module to suppress background sea clutter and capture important features. …”
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Integrated deep network model with multi-head twofold attention for drug–target interaction prediction by Angelin Jeba P, Tamilpavai G

“…This model leverages convolutional and recurrent neural networks to extract both local and sequential features from drug molecular structures and target protein sequences. …”
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Tumor tissue-of-origin classification using miRNA-mRNA-lncRNA interaction networks and machine learning methods by Ankita Lawarde, Ankita Lawarde, Masuma Khatun, Prakash Lingasamy, Prakash Lingasamy, Andres Salumets, Andres Salumets, Andres Salumets, Vijayachitra Modhukur, Vijayachitra Modhukur

“…Ensemble ML algorithms were trained and validated with stratified five-fold cross-validation for robust performance assessment across class distributions.ResultsOur models achieved an overall 99% classification accuracy, distinguishing 14 cancer types with high robustness and generalizability. …”
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A crop model based on dual attention mechanism for large area adaptive yield prediction by Wei Xiang, Long Long, Zichen Liu, Feng Dai, Yucheng Zhang, Hu Li, Lin Cheng

“…Although existing models have improved accuracy by increasing model complexity and coupling different deep learning models, their generalization performance is poor due to significant spatial differences in crop growth environments, making it difficult to explore common features of crop environments in different regions.To address this issue, this paper comprehensively considers crop growth cycles and environmental factors such as soil and weather, presenting a large-scale crop yield prediction model based on an attention mechanism.The model consists of two modules: time attention module and feature attention module. …”
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Integrated Explainable Diagnosis of Gear Wear Faults Based on Dynamic Modeling and Data-Driven Representation by Zemin Zhao, Tianci Zhang, Kang Xu, Jinyuan Tang, Yudian Yang

“…Then, a deep network incorporating Gradient-weighted Class Activation Mapping (Grad-CAM) enables visualized extraction of frequency-domain sensitive features. Bidirectional verification between the dynamic model and deep learning demonstrates enhanced meshing harmonics in wear faults, leading to a quantitative diagnostic index that achieves 0.9560 recognition accuracy for gear wear across four speed conditions, significantly outperforming comparative indicators. …”
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Comparative Analysis of Regression Models for Stock Price Prediction: Linear, Support Vector, Polynomial, and Lasso by Ștefan Rusu, Marcel Ioan Boloș, Marius Leordeanu

“…Evaluation metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and R-squared (R²) were employed to assess the models’ performance. Results show that linear regression and LASSO regression were the most performant models, reaching R² values of more than 0.95, with minimal error values. …”
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UnifiedCut: A Simple and Efficient Neural Model for Thai, Burmese and Khmer Word Segmentation by Yonghua Wen, Yantuan Xian, Yuehan Wang, Zhengtao Yu

“…Existing approaches demonstrate that models using fixed-length windowed context inputs can achieve high segmentation accuracy; however, they often rely on low-level character features or language-specific preprocessing. …”
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Modeling of settlement of shallow-founded rocking structures using explainable physics-guided machine learning by Sivapalan Gajan, Christopher Kantor

“…The performances of PGML models are compared with the performances of purely data-driven ML models, the PBM outputs, and results obtained from an empirical relationship. …”
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Spatiotemporal fusion knowledge tracking model based on spatiotemporal graph and fourier graph neural network by Yinquan Liu, Weidong Ji, Guohui Zhou

“…The model constructs spatiotemporal graphs by integrating information from the time dimension and the spatial dimension through information extracted from the cognitive and behavioral perspectives, resolves the compatibility problem between the two, and processes spatiotemporal dependencies features in the frequency domain through Fourier Graph Neural Network (FourierGNN) to capture complex spatiotemporal relationships, and improve computational efficiency and accurate modeling of spatiotemporal features. …”
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A Predictive Model for Secondary Posttonsillectomy Hemorrhage in Pediatric Patients: An 8‐Year Retrospective Study by Yuting Ge, Wenchuan Chang, Lixiao Xie, Yan Gao, Yue Xu, Huie Zhu

“…The XGBoost model yielded the best performance in the validation set. …”
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Development and validation of a risk prediction model for depression in patients with chronic obstructive pulmonary disease by Tong Feng, PeiPei Li, Ran Duan, Zhi Jin

“…Nine machine learning models were trained and evaluated, with performance assessed via accuracy, area under the curve (AUC), calibration, and clinical utility metrics. …”
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Performance evaluation and comparative analysis of different machine learning algorithms in predicting postnatal care utilization: Evidence from the ethiopian demographic and healt... by Daniel Niguse Mamo, Agmasie Damtew Walle, Eden Ketema Woldekidan, Jibril Bashir Adem, Yosef Haile Gebremariam, Meron Asmamaw Alemayehu, Ermias Bekele Enyew, Shimels Derso Kebede

“…Among the four experiments, tenfold cross-validation with balancing using Synthetic Minority Over-sampling Technique was outperformed. From fifteen models, the MLP Classifier (f1 score = 0.9548, AUC = 0.99), Random Forest Classifier (f1 score = 0.9543, AUC = 0.98), and Bagging Classifier (f1 score = 0.9498, AUC = 0.98) performed excellently, with a strong ability to differentiate between classes. …”
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Comparison of four MRI diffusion models to differentiate benign from metastatic retropharyngeal lymph nodes by Jun Liu, Li Hua, Fei Wang, Ming Chen, Yinan Sun, Zhi Hu, Luqing Shu, Andong He, Mengxiao Liu, Qing Yang, Juan Zhu, Yinfeng Qian

“…Logistic regression analysis was performed to identify the best diffusion indicator for classification and to develop a multiparameter model combining morphological features. …”
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SparkNet–A Solar Panel Fault Detection Deep Learning Model by Rohith G, Dikshithula Sai Manish, Rahul Narasimhan A, Anurag Uttam Dhavale, Rohan Reji John

“…SparkNet, when implemented with a dataset of images of solar panels and different weather conditions, achieves an average of 95% in the quantitative performance metrics, including accuracy, precision, recall, and F1 score, better than the other state-of-the-art models. …”
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Deep Learning Algorithm for Automatic Classification of Power Quality Disturbances by Fatema A. Albalooshi, M. R. Qader

“…The results demonstrate promising classification performance in terms of simplicity and accuracy, highlighting the potential of this approach to improve PQ analysis and disturbance identification.…”
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Research on Emotion Classification Based on Multi-modal Fusion by zhihua Xiang, Nor Haizan Mohamed Radzi, Haslina Hashim

“…Multiple group experiments performed on MOSI datasets show that the emotion recognition model constructed based on the framework proposed here in this paper can better utilize the more complex complementary information between different modal data. …”
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Risk Prediction of Liver Injury in Pediatric Tuberculosis Treatment: Development of an Automated Machine Learning Model by Zeng Y, Lu H, Li S, Shi QZ, Liu L, Gong YQ, Yan P

“…The TreeShap algorithm showed that Cmax of rifampicin and BMI were important features that affect the AutoML model’s performance.Conclusion: The GBM model established by AutoML technology shows high predictive accuracy and interpretability for ATB-DILI in children. …”
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From Prediction to Explanation: Using Explainable AI to Understand Satellite-Based Riot Forecasting Models by Scott Warnke, Daniel Runfola

“…Analysis revealed minimal alignment between the model-identified features and traditional land use metrics, suggesting that deep learning captures unique patterns not represented in existing GIS datasets. …”
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