A novel model for mapping soil organic matter: Integrating temporal and spatial characteristics by Xinle Zhang, Guowei Zhang, Shengqi Zhang, Hongfu Ai, Yongqi Han, Chong Luo, Huanjun Liu

“…CNN-LSTM-CAWR demonstrates faster convergence, thereby improving mapping precision and effectively utilizing temporal information from features to enhance overall model performance. This study underscores the significant potential of the hybrid CNN-LSTM with CAWR model, highlighting the valuable information for SOM mapping contained within Sentinel-2 time series data.…”
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Development and validation of a deep learning-enhanced prediction model for the likelihood of pulmonary embolism by Yu Tian, Yu Tian, Jingjie Liu, Shan Wu, Yucong Zheng, Rongye Han, Qianhui Bao, Lei Li, Lei Li, Tao Yang

“…Our prediction model uses a convolutional neural network (CNN), enhanced with three custom-designed modules for better performance. …”
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MRI-based deep transfer learning models for predicting progesterone receptor expression in meningioma by Song Gao, Li Zhao, Nan Li, Xiaoming Zhou, Chongfeng Duan

“…Decision curve analysis (DCA) curves were drawn to evaluate the clinical usefulness of the nomogram.ResultsA total of 2048 DTL features were extracted, and 35 features were selected for model construction. …”
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State-of-the-art performance of deep learning methods for pre-operative radiologic staging of colorectal cancer lymph node metastasis: a scoping review by David Jayne, Aaron Quyn, Benjamin Keel, Samuel David Relton

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The development of a multimodal prediction model based on CT and MRI for the prognosis of pancreatic cancer by Zheng Dou, Jiaxi Lin, Chenghao Lu, Xiaoting Ma, Ruoxu Zhang, Jinzhou Zhu, Songbing Qin, Chao Xu, Jinli Li

“…These models included a radiomics model based on contrast-enhanced CT, a radiomics model based on MRI, a clinical model, 3 bimodal models combining two types of features, and a multimodal model combining radiomics features with clinical features. …”
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Non-invasive acoustic classification of adult asthma using an XGBoost model with vocal biomarkers by Yi Lyu, Quan-Cheng Jiang, Shuai Yuan, Jing Hong, Chun-Feng Chen, Hai-Mei Wu, Yi-Qin Wang, Yu-Jing Shi, Hai-Xia Yan, Jin Xu

“…The top-performing model, Extreme Gradient Boosting, was further assessed through ten-fold cross-validation, external validation, and feature analysis using SHapley Additive exPlanations and Local Interpretable Model-Agnostic Explanations. …”
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Deep learning for network security: an Attention-CNN-LSTM model for accurate intrusion detection by Abdullah Mujawib Alashjaee

“…Here, CNN works with extraction of spatial features, LSTM works with modelling of temporal sequence. …”
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Device-independent Wi-Fi fingerprinting indoor localization model based on domain adaptation by Zenghua ZHAO, Yuefan TONG, Jiayang CUI

“…In real-world large-scale deployments of indoor localization, Wi-Fi fingerprinting approaches suffer from device diversity problem which impacts the localization accuracy significantly.A device-independent Wi-Fi fingerprint indoor localization model DeviceTransfer was proposed.Based on the domain adaptation theory of deep learning, the device type of the smartphone was taken as the domain, the task-related and device-independent Wi-Fi data features were extracted through adversarial training, and the learned source domain location information was transferred to the target domain.Pre-training and joint training were employed to improve model training stability and to accelerate convergence.The performance of DeviceTransfer was evaluated using four types of smartphones in two real-world indoor environments: a school building and a shopping mall.The experimental results show that DeviceTransfer effectively extracts device-independent Wi-Fi fingerprint features.Using only one type of phone to collect Wi-Fi fingerprints, online localization using other types still achieves high localization accuracy, thus reducing localization cost significantly.…”
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Device-independent Wi-Fi fingerprinting indoor localization model based on domain adaptation by Zenghua ZHAO, Yuefan TONG, Jiayang CUI

“…In real-world large-scale deployments of indoor localization, Wi-Fi fingerprinting approaches suffer from device diversity problem which impacts the localization accuracy significantly.A device-independent Wi-Fi fingerprint indoor localization model DeviceTransfer was proposed.Based on the domain adaptation theory of deep learning, the device type of the smartphone was taken as the domain, the task-related and device-independent Wi-Fi data features were extracted through adversarial training, and the learned source domain location information was transferred to the target domain.Pre-training and joint training were employed to improve model training stability and to accelerate convergence.The performance of DeviceTransfer was evaluated using four types of smartphones in two real-world indoor environments: a school building and a shopping mall.The experimental results show that DeviceTransfer effectively extracts device-independent Wi-Fi fingerprint features.Using only one type of phone to collect Wi-Fi fingerprints, online localization using other types still achieves high localization accuracy, thus reducing localization cost significantly.…”
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MolNexTR: a generalized deep learning model for molecular image recognition by Yufan Chen, Ching Ting Leung, Yong Huang, Jianwei Sun, Hao Chen, Hanyu Gao

“…In addition, it employs a series of novel augmentation algorithms to significantly enhance the robustness and performance of the model.…”
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Optimizing Pre-Trained Models for Medical Dataset Classification with a Fine-Tuning Approach by N. Kumar, T. Christopher

“…Traditional analytical techniques prove inadequate when dealing with extraction from features and performance of classifiers in this specific setting. …”
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Predictive Modeling of Heart Failure Outcomes Using ECG Monitoring Indicators and Machine Learning by Jia Liu, Dan Zhu, Lingzhi Deng, Xiaoliang Chen

“…SHAP interpretation reinforced the dominant influence of ECG‐related indices and cholesterol on individual risk estimates. Conclusion An RF model leveraging ECG features demonstrated excellent performance for HF risk prediction and highlighted key physiologic markers. …”
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HGTFM: Hierarchical Gating-Driven Transformer Fusion Model for Robust Multimodal Sentiment Analysis by Chengcheng Yang, Zhiyao Liang, Dashun Yan, Zeng Hu, Ting Wu

“…The model first encodes audio and video features using LSTM and CNN, respectively, while employing BERT to encode text features. …”
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Enhanced safety assessment on tunnel excavation via refined rock mass parameter identification by Hongwei Huang, Tongjun Yang, Jiayao Chen, Zhongkai Huang, Chen Wu, Jianhong Man

“…The integration of contact measurement data and surrounding environmental parameters leads to a proposal for rock mass quality prediction, utilizing integrated machine learning techniques. Subsequently, a 3D model is established by incorporating tunnel face features and environmental data. …”
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CNN–Patch–Transformer-Based Temperature Prediction Model for Battery Energy Storage Systems by Yafei Li, Kejun Qian, Qiuying Shen, Qianli Ma, Xiaoliang Wang, Zelin Wang

“…Subsequently, a patch-embedding mechanism was introduced, endowing the model with the ability to extract local temporal features from segments within the long historical look-back windows. …”
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Deep Learning-Enabled Dynamic Model for Nutrient Status Detection of Aquaponically Grown Plants by Mohamed Farag Taha, Hanping Mao, Samar Mousa, Lei Zhou, Yafei Wang, Gamal Elmasry, Salim Al-Rejaie, Abdallah Elshawadfy Elwakeel, Yazhou Wei, Zhengjun Qiu

“…These features were used as input to an LSTM model to classify lettuce grown across a gradient of nutrient levels. …”
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Enhancing runoff simulation by combining superflex with deep learning methods in China's Qinghai Lake Basin, Northeast Tibetan Plateau by Kaixun Liu, Na Li, Sihai Liang

“…New hydrological insights for the region: Compared to hydrological model, hybrid models significantly improve performance by incorporating internal hydrological variables and meteorological data as input features, reducing the error by over 50 % in Buha River Basin. …”
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An ensemble model for predicting dyslipidemia using 3-years continuous physical examination data by Naiwen Zhang, Xiaolong Guo, Xiaxia Yu, Zhen Tan, Zhen Tan, Feiyue Cai, Feiyue Cai, Ping Dai, Jing Guo, Guo Dan

“…Among them, HbA1c and CEA are key indicators for model construction.ConclusionsOur results suggest that the proposed ensemble model has good predictive performance and has the potential to become an effective tool for personal health management.…”
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Multi-scale time series prediction model based on deep learning and its application. by Zhifei Yang, Jia Zhang, Zeyang Li

“…Extensive experiments are conducted on a dataset from the California Performance Measurement System (PEMS), and the results show that the proposed MSCALSTM model outperforms the state-of-the-art models. …”
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Cross-View Correspondence Modeling for Joint Representation Learning Between Egocentric and Exocentric Videos by Zhehao Zhu, Yoichi Sato

“…Our model incorporates self-attention to enhance intra-view context and cross-view attention to align features across space and time. …”
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