Deep learning framework based on ITOC optimization for coal spontaneous combustion temperature prediction: a coupled CNN-BiGRU-CBAM model by Xuming Shao, Wenhao Liu, Gang Bai, Yan Chen, Yu Liu, Jiahe Guang

“…Based on these variables, a deep learning framework combining an Improved Tornado Optimization with Coriolis force (ITOC) strategy and a CNN-BiGRU-CBAM model is proposed. …”
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A hybrid 1DCNN-GRU deep learning framework for classifying caprine granulosa cell fertility potential using single-cell transcriptomics by Thanida Sananmuang, Denis Puthier, Kaj Chokeshaiusaha

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A Temporal–Spatial–Spectral Fusion Framework for Coastal Wetland Mapping on Time-Series Remote Sensing Imagery by Xiang Li, Shenfu Zhang, Qiang Liu, Liang Chen, Gang Yang, Rui Zhao, Weiwei Sun, Feng Shao, Xiangchao Meng

“…A hybrid transformer-convolution module enhances fine-grained feature extraction. …”
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A machine learning-based framework for predicting metabolic syndrome using serum liver function tests and high-sensitivity C-reactive protein by Bahareh Behkamal, Fatemeh Asgharian Rezae, Amin Mansoori, Rana Kolahi Ahari, Sobhan Mahmoudi Shamsabad, Mohammad Reza Esmaeilian, Gordon Ferns, Mohammad Reza Saberi, Habibollah Esmaily, Majid Ghayour-Mobarhan

“…The framework integrated diverse ML algorithms, including Linear Regression (LR), Decision Trees (DT), Support Vector Machine (SVM), Random Forest (RF), Balanced Bagging (BG), Gradient Boosting (GB), and Convolutional Neural Networks (CNNs). …”
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A novel soil moisture evaluation framework incorporating brightness temperature and a high-resolution 1 km summer brightness temperature dataset by Ziyue Zhu, Runze Zhang, Bin Fang, Hyunglok Kim, Hoang Hai Nguyen, Venkataraman Lakshmi

“…The results suggest that this approach, which integrates both physical models and machine learning, offers a more comprehensive and reliable framework for SM product evaluation, while the 1 km TB dataset provides valuable support for applications requiring finer spatial resolution.…”
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A data-efficient deep transfer learning framework for methane super-emitter detection in oil and gas fields using the Sentinel-2 satellite by S. Zhao, S. Zhao, Y. Zhang, Y. Zhang, S. Zhao, S. Zhao, X. Wang, X. Wang, D. J. Varon

“…Here, we propose a novel deep-transfer-learning-based methane plume detection framework. It consists of two components: an adaptive artifact removal algorithm (low-reflectance artifact detection, LRAD) to reduce artifacts in methane retrievals and a deep subdomain adaptation network (DSAN) to detect methane plumes. …”
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Assessment of landscape diversity in Inner Mongolia and risk prediction using CNN-LSTM model by Yalei Yang, Hong Wang, Xiaobing Li, Tengfei Qu, Jingru Su, Dingsheng Luo, Yixiao He

“…A Potential-Connectedness-Resilience framework was used to assess landscape diversity risks from 2010 to 2020, with a Convolutional Neural Network combined with a Long Short-Term Memory (CNN-LSTM) model predicting future risks for 2025. …”
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Breaking Barriers in Thyroid Cytopathology: Harnessing Deep Learning for Accurate Diagnosis by Seo Young Oh, Yong Moon Lee, Dong Joo Kang, Hyeong Ju Kwon, Sabyasachi Chakraborty, Jae Hyun Park

“…The first framework is a patch-level classifier referred as “TCS-CNN”, based on a convolutional neural network (CNN) architecture, to predict thyroid cancer based on the Bethesda System (TBS) category. …”
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C²SP-Net: Joint Compression and Classification Network for Epilepsy Seizure Prediction by Di Wu, Yi Shi, Ziyu Wang, Jie Yang, Mohamad Sawan

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An integrated IKOA-CNN-BiGRU-Attention framework with SHAP explainability for high-precision debris flow hazard prediction in the Nujiang river basin, China. by Hao Yang, Tianlong Wang, Nikita Igorevich Fomin, Shuoting Xiao, Liang Liu

“…This study proposes an explainable deep learning framework, the Improved Kepler Optimization Algorithm-Convolutional Neural Network-Bidirectional Gated Recurrent Unit-Attention (IKOA-CNN-BiGRU-Attention) model, for precise debris flow hazard prediction in the Yunnan section of the Nujiang River Basin, China. …”
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Research on Lightweight Small Object Detection Algorithm Based on Context Representation by Li Qiang, Cui Jianghui

“…This paper proposes a lightweight framework algorithm based on a contextual semantic fusion model, which is built upon the YOLOv7 model. …”
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An ensemble deep learning model for author identification through multiple features by Yuan Zhang

“…The proposed research will improve the accuracy and stability of authorship identification by creating a new deep learning framework that combines the features of various types in a self-attentive weighted ensemble framework. …”
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Enhancing semi‐supervised contrastive learning through saliency map for diabetic retinopathy grading by Jiacheng Zhang, Rong Jin, Wenqiang Liu

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Heterogeneous AI Music Generation Technology Integrating Fine-Grained Control by Hongtao Wang, Li Gong

“…This integrated model was subsequently paired with a Transformer architecture, creating a sophisticated framework capable of fine-grained control and heterogeneous music generation. …”
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Towards an Energy Consumption Index for Deep Learning Models: A Comparative Analysis of Architectures, GPUs, and Measurement Tools by Sergio Aquino-Brítez, Pablo García-Sánchez, Andrés Ortiz, Diego Aquino-Brítez

“…Furthermore, the inclusion of the Swin Transformer, a state-of-the-art and modern non-convolutional model, highlights the adaptability of the framework to diverse architectural paradigms. …”
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Sensor Infused Quantum CNN for Diabetes Disease Prediction and Diet Recommendation by Jameer Kotwal, Pravin Futane, Gurunath Chavan, Archana Chaudhari, Jithina Jose, Vajid Khan

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Multi-Scale 3D Cephalometric Landmark Detection Based on Direct Regression with 3D CNN Architectures by Chanho Song, Yoosoo Jeong, Hyungkyu Huh, Jee-Woong Park, Jun-Young Paeng, Jaemyung Ahn, Jaebum Son, Euisung Jung

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A Comprehensive Drift-Adaptive Framework for Sustaining Model Performance in COVID-19 Detection From Dynamic Cough Audio Data: Model Development and Validation by Theofanis Ganitidis, Maria Athanasiou, Konstantinos Mitsis, Konstantia Zarkogianni, Konstantina S Nikita

“…Machine learning models, particularly convolutional neural networks, are promising in this regard. …”
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Machine and Deep Learning–driven Angular Momentum Inference from BHEX Observations of the n = 1 Photon Ring by Joseph Farah, Jordy Davelaar, Daniel Palumbo, Michael Johnson, Jonathan Delgado

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Approach of target tracking combining particle filter and metric learning by Hongyan WANG, Libin ZHANG, Guoqiang CHEN, Zumin WANG, Zhiyuan GUAN

“…Focusing on the issue of the significant degradation of target tracking performance caused by adverse factors in complex environment, a target tracking method based on particle filtering and metric learning was proposed.First of all, a convolutional neural network (CNN) was offline-trained via the proposed method to effectively obtain the target characteristics.After that, the distance measurement matrix optimization model to minimize the prediction error could be constructed on the basis of the metric learning for kernel regression (MLKR) method, and the resultant model could be handled via using the gradient descent approach to obtain the optimal solution of the candidate target.Moreover, based on the predicted value of the optimal candidate target, the reconstruction error was calculated to construct the target observation model.Finally, a long-short-term update strategy was introduced to achieve the effective target tracking under the particle filter tracking framework.The experiment results show that the proposed method has higher tracking accuracy and better robustness in complex environments.…”
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