A reliability model to predict failure behaviour of overlying strata in groundwater-rich coal mine by Ruirui Li, Xiaowei Hou, Luwang Chen, Yingxin Wang, Fuyou Huang, Lanting Wang

“…In this study, a reliability model with consideration of spatial variability and uncertainty of strength parameters was proposed to predict the failure behaviour of overlying strata during coal mining in groundwater-rich coalfields. …”
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YOLO-HPSD: A high-precision ship target detection model based on YOLOv10. by Manlin Zhu, Dezhi Han, Bing Han, Xiaohu Huang

“…These results indicate that the model not only ensures high detection speed but also delivers high-accuracy ship target detection. …”
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A Deep Transfer NOx Emission Inversion Model of Diesel Vehicles with Multisource External Influence by Zhenyi Xu, Ruibin Wang, Yu Kang, Yujun Zhang, Xiushan Xia, Renjun Wang

“…The comprehensive experiment results show that our method can achieve the feature distribution alignment of emission data under different vehicle working conditions and improve the prediction performance of the NOx inversion model given a little amount of NOx emission monitoring data.…”
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Applying deep learning model to aerial image for landslide anomaly detection through optimizing process by Chwen-Huan Wang, Li Fang, Chiung-Yun Hu

“…The process employing the GANomaly deep learning model to enhance landslide anomaly detection using high-resolution (25 cm) aerial imagery. …”
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Employing Streaming Machine Learning for Modeling Workload Patterns in Multi-Tiered Data Storage Systems by Edson Ramiro Lucas Filho, George Savva, Lun Yang, Kebo Fu, Jianqiang Shen, Herodotos Herodotou

“…Modern multi-tiered data storage systems optimize file access by managing data across a hybrid composition of caches and storage tiers while using policies whose decisions can severely impact the storage system’s performance. Recently, different Machine-Learning (ML) algorithms have been used to model access patterns from complex workloads. …”
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MIESTC: A Multivariable Spatio-Temporal Model for Accurate Short-Term Wind Speed Forecasting by Shaohan Li, Min Chen, Lu Yi, Qifeng Lu, Hao Yang

“…To address these issues, this study introduces a novel short-term wind speed forecasting model named as MIESTC. The proposed model employs an independent encoder to extract features from each meteorological variable, mitigating the issues of noise that are caused by variable mixing. …”
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MODELING HOUSE SELLING PRICES IN JAKARTA AND SOUTH TANGERANG USING MACHINE LEARNING PREDICTION ANALYSIS by Sugha Faiz Al Maula, Nicoletta Almira Dyah Setiawan, Elly Pusporani, Sa'idah Zahrotul Jannah

“…The analysis focused on key predictors like land area, building area, bedrooms, and carports, with R² and Mean Squared Error (MSE) metrics evaluating model performance. Results showed that LGBM and Random Forest outperformed others with 0.8 R2 and low MSE, with building and land area as the most significant factors influencing prices. …”
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LatentResNet: An Optimized Underwater Fish Classification Model with a Low Computational Cost by Muhab Hariri, Ercan Avsar, Ahmet Aydın

“…This paper presents LatentResNet, a computationally lightweight deep learning model involving two key innovations: (i) using the encoder from the proposed LiteAE, a lightweight autoencoder for image reconstruction, as input to the model to reduce the spatial dimension of the data and (ii) integrating a DeepResNet architecture with lightweight feature extraction components to refine encoder-extracted features. …”
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An Enhanced TimesNet-SARIMA Model for Predicting Outbound Subway Passenger Flow with Decomposition Techniques by Tianzhuo Zuo, Shaohu Tang, Liang Zhang, Hailin Kang, Hongkang Song, Pengyu Li

“…The results show that the VMD preprocessing effectively extracts features, enhancing prediction performance (13.25% MAE, 19.7% RMSE improvements). …”
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Advanced Deep Learning Models for Corn Leaf Disease Classification: A Field Study in Bangladesh by Sachi Nandan Mohanty, Hritwik Ghosh, Irfan Sadiq Rahat, Chirra Venkata Rami Reddy

“…Our research is novel due to the integration of transfer learning and image augmentation, enhancing the models’ generalization capabilities. A hybrid model combining ResNet50 and VGG16 features achieved a remarkable 99.65% accuracy, validating our approach. …”
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Domain shifts in industrial condition monitoring: a comparative analysis of automated machine learning models by P. Goodarzi, A. Schütze, T. Schneider

“…However, the overall performance significantly decreases when faced with domain shifts, such as transferring the trained model from one machine to another. …”
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Scalable multimodal approach for face generation and super-resolution using a conditional diffusion model by Ahmed Abotaleb, Mohamed W. Fakhr, Mohamed Zaki

“…SLF consists of two main components: a feature vector generator (encoder), and an image generator (decoder) utilizing a conditional diffusion model. …”
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A data-informed cascading consequence modeling framework for hurricane-induced petrochemical facility disruptions by Kendall M. Capshaw, Jamie E. Padgett

“…The NHERI DesignSafe Cyberinfrastructure is leveraged to reuse prior hindcast storm datasets, develop and share a petrochemical infrastructure performance database, conduct statistical analyses for model development, and perform case study regional risk analyses. …”
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Refining Parkinson’s syndrome symptom forecasting using incorporated ocular imaging and the SymptoSense model by V. Jayasudha, N. Deepa, Devi Thiyagarajan

“…The SymptoSense Model shows superior performance, achieving 94.2% accuracy and 0.943 precision, alongside an impressive average word segmentation time of 0.129 s. …”
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TreePseCo: Scaling Individual Tree Crown Segmentation using Large Vision Models by J. Lungo Vaschetti, E. Arnaudo, C. Rossi

“…Our approach implements a three-stage pipeline: (1) tree center detection using a modified Segment Anything Model (SAM) decoder that generates probability heatmaps, (2) instance mask generation through prompt-guided segmentation utilizing SAM's visual features, and (3) boundary refinement via specialized classification to eliminate false positives. …”
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Digital pathology-based artificial intelligence model to predict microsatellite instability in gastroesophageal junction adenocarcinomas by Zhenqian Li, JingQi Chen, Miaomiao Sun, Daoming Li, Kuisheng Chen

“…Simple Vit and ResNet18 Neural networks were trained and tested on models developed from patch-level images. A whole-slide image (WSI)-level AI model was constructed by integrating deep learning- generated pathological features with six machine learning algorithms.ResultsThe MLP model showed demonstrated the highest performance in predicting MSI-H in the test cohort, achieving an AUC of 93.3%, a sensitivity of 0.841, and a specificity of 0.952. …”
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CADTrans: A code tree-guided CAD generative transformer model with regularized discrete codebooks by Xufei Guo, Xiao Dong, Juan Cao, Zhonggui Chen

“…Finally, the code tree is used as global information to guide the sketch-and-extrude method to recover the corresponding geometric information, thereby reconstructing the complete CAD model. Extensive experiments demonstrate that CADTrans achieves state-of-the-art performance, generating higher-quality, more varied, and complex models. …”
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Research on Prediction and Optimization of Airport Express Passenger Flow Based on Fusion Intelligence Network Model by Jin He, Yinzhen Li, Yuhong Chao

“…The experimental results show that the model achieves an excellent performance, with 0.0160, 0.0947, 0.0160, 0.1255, 18.40, and 0.7788 in key indicators such as loss value (Loss Value), mean absolute error (MAE), mean squared error (MSE), root mean square error (RMSE), mean absolute percentage error (MAPE), and coefficient of determination (R-Squared). …”
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Development and application of an early prediction model for risk of bloodstream infection based on real-world study by Xiefei Hu, Shenshen Zhi, Yang Li, Yuming Cheng, Haiping Fan, Haorong Li, Zihao Meng, Jiaxin Xie, Shu Tang, Wei Li

“…Based on the optimal combination, six machine learning algorithms were used to construct an early BSI risk prediction model. The best model was selected by models’ performance, and the Shapley Additive Explanations (SHAP) method was used to explain the model. …”
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Interpretable prediction model for hand-foot-and-mouth disease incidence based on improved LSTM and XGBoost by Xiao LI, Shuyu HE, Yan PENG, Rongxin YANG, Lu TAO, Tingqi LOU, Wenqi HE

“…Meanwhile, the SHAP method is used to analyze the feature importance and enhance the interpretability of the proposed model. …”
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