A preoperative pathological staging prediction model for esophageal cancer based on CT radiomics by Haojun Li, Shuoming Liang, Mengxuan Cui, Weiqiu Jin, Xiaofeng Jiang, Simiao Lu, Jicheng Xiong, Hainan Chen, Ziwei Wang, Guotai Wang, Jiming Xu, Linfeng Li, Yao Wang, Haomiao Qing, Yongtao Han, Xuefeng Leng

“…Conclusions This study established a non-invasive preoperative radiomics model that demonstrated good predictive performance in determining the pTNM staging, pT staging, vascular invasion, and perineural invasion for EC patients. …”
Get full text

An intelligent attention based deep convoluted learning (IADCL) model for smart healthcare security by J. Maruthupandi, S. Sivakumar, B. Lakshmi Dhevi, S. Prasanna, R. Karpaga Priya, Shitharth Selvarajan

Get full text

USS-Net: A neural network-based model for assisting flight route scheduling. by Yinlei Cheng, Qingfu Li

“…By constructing a symmetrical encoder-decoder structure, the model progressively extracts image features and performs multi-scale fusion to achieve high-precision pixel-level segmentation. …”
Get full text

Cervical cancer prediction using machine learning models based on routine blood analysis by Jie Su, Hui Lu, Ruihuan Zhang, Na Cui, Chao Chen, Qin Si, Biao Song

“…These features include age, red blood cell count (RBC), platelet distribution width (PDW), white blood cell count (WBC), Lymphocyte Percentage (LYMPH%), basophil count (BASO), Basophil Percentage (BASO%), Lymphocyte Absolute Value (LYMPH), Neutrophil Percentage (NEUT%), Hemoglobin (HGB), Mean Corpuscular Hemoglobin Concentration (MCHC), Red Cell Distribution Width (R-CV), Mean Platelet Volume (MPV), Plateletcrit (PCT), and Among the four models, the extreme gradient boosting (XGBoost) model achieved the highest predictive performance, with an area under the curve (AUC) of 0.964. …”
Get full text

Mortality prediction of inpatients with NSTEMI in a premier hospital in China based on stacking model. by Li Wang, Yu Zhang, Feng Li, Caiyun Li, Hongzeng Xu

“…Finally, a unique double-layer stacking model is designed to improve the performance of the algorithm. …”
Get full text

Surrogate Modeling for Building Design: Energy and Cost Prediction Compared to Simulation-Based Methods by Navid Shirzadi, Dominic Lau, Meli Stylianou

“…All three models exhibit strong predictive performance, with R² values exceeding 0.9 for both EUI and cost. …”
Get full text

Developing an innovative lung cancer detection model for accurate diagnosis in AI healthcare systems by Wang Jian, Amin Ul Haq, Noman Afzal, Shakir Khan, Hadeel Alsolai, Sultan M. Alanazi, Abu Taha Zamani

“…The CNN model extracts spatial features from lung CT images through convolutional and pooling layers. …”
Get full text

Large-scale-aware data augmentation for reduced-order models of high-dimensional flows by Philipp Teutsch, Mohammad Sharifi Ghazijahani, Florian Heyder, Christian Cierpka, Jörg Schumacher, Patrick Mäder

“…Convolutional autoencoders have proven to be an adequate tool to perform reduced-order modeling for high-dimensional nonlinear dynamical systems. …”
Get full text

Sensor-Based Bermudagrass Yield Prediction Models Using Random Forest Algorithm in Oklahoma by Gabriel Camargo de Campos Jezus, Lucas Freires Abreu, Daryl Brian Arnall, Lucas Martins Stolerman, Alexandre Caldeira Rocateli

“…The 4 WOR, all-features, all-cultivars model had the highest performance when evaluating the model using ten-fold cross-validation (R² = 0.75, MAPE = 26.79%, RMSE = 1.0 Mg ha⁻¹), with the laser having the highest feature importance score (65.5%). …”
Get full text

Derivation of surface models using satellite imagery deep learning architectures with explainable AI by Vivaldi Rinaldi, Francisco Gómez-Vela, Masoud Ghandehari

“…We performed a quantitative evaluation using metrics such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and R-Squared, and a qualitative analysis to identify the best performing model. …”
Get full text

A Lightweight Model for Small-Target Pig Eye Detection in Automated Estrus Recognition by Min Zhao, Yongpeng Duan, Tian Gao, Xue Gao, Guangying Hu, Riliang Cao, Zhenyu Liu

“…To enable non-contact, automated estrus detection, this study proposes an improved algorithm, Enhanced Context-Attention YOLO (ECA-YOLO), based on YOLOv11. The model utilizes ocular appearance features—eye’s spirit, color, shape, and morphology—across different estrus stages as key indicators. …”
Get full text

TE-LSTM: A Prediction Model for Temperature Based on Multivariate Time Series Data by Kang Zhou, Chunju Zhang, Bing Xu, Jianwei Huang, Chenxi Li, Yifan Pei

“…As a result, model performance can suffer, particularly in prediction tasks with specific time requirements. …”
Get full text

Deep learning model for precise and rapid prediction of tomato maturity based on image recognition by Muhammad Waseem, Muhammad Muzzammil Sajjad, Laraib Haider Naqvi, Yaqoob Majeed, Tanzeel Ur Rehman, Tayyaba Nadeem

“…Our objective is to address the dual challenge of maintaining high accuracy while enabling real-time performance on low-power edge devices. Then, these models were deployed on an edge device to investigate their performance for tomato maturity classification. …”
Get full text

Bayesian compositional generalized linear mixed models for disease prediction using microbiome data by Li Zhang, Xinyan Zhang, Justin M. Leach, A. K. M. F. Rahman, Carrie R. Howell, Nengjun Yi

“…Current approaches in this area often assume a high-dimensional sparse setting, where only a small subset of microbiome features is considered relevant to the outcome. However, in real-world data, both large and small effects frequently coexist, and acknowledging the contribution of smaller effects can significantly enhance predictive performance. …”
Get full text

MMTransformer: a multivariate time-series resource forecasting model for multi-component applications by Guangzhang Cui, Tao Hu, Wei Zhang, Hujun Bao

“…To evaluate the model’s performance, we constructed workload datasets for courseware production and digital human video creation systems using real-world application scenarios, with three key performance metrics established by monitoring core resource states. …”
Get full text

Segmentation and Classification of Interstitial Lung Diseases Based on Hybrid Deep Learning Network Model by Surendra Reddy Vinta, B. Lakshmi, M. Aruna Safali, G. Sai Chaitanya Kumar

“…Due to the stage-by-stage improvement in the DL method performance, the proposed hybrid deep learning network model’s performance has significantly increased.…”
Get full text

ED-Swin Transformer: A Cassava Disease Classification Model Integrated with UAV Images by Jing Zhang, Hao Zhou, Kunyu Liu, Yuguang Xu

“…These experiments demonstrate the superior performance of the ED-Swin Transformer model in cassava classification networks.…”
Get full text

A Hybrid Model Based on Deep Convolutional Network for Medical Named Entity Recognition by Tingzhong Wang, Yongxin Zhang, Yifan Zhang, Hao Lu, Bo Yu, Shoubo Peng, Youzhong Ma, Deguang Li

“…Finally, extensive experimental results show that the performance of the proposed model is effectively improved compared with other models.…”
Get full text

Clinical prediction of intravenous immunoglobulin-resistant Kawasaki disease based on interpretable Transformer model. by Gahao Chen, Ziwei Yang

“…The top-performing model was subsequently subjected to interpretability analysis through Shapley Additive Explanations (SHAP) to elucidate feature contributions. …”
Get full text

Diagnostic framework to validate clinical machine learning models locally on temporally stamped data by Maximilian Schuessler, Scott Fleming, Shannon Meyer, Tina Seto, Tina Hernandez-Boussard

“…This variability, if not addressed, can result in data shifts with potentially poor model performance. Presently, there are few easy-to-implement, model-agnostic diagnostic frameworks to vet machine learning models for future applicability and temporal consistency. …”
Get full text