Optimizing PEMFC parameter identification using improved pufferfish algorithm and CNN by Ji Li, Changiz Bastani

“…In this research, a novel approach has been proposed for enhancing the accuracy of proton exchange membrane fuel cell (PEMFC) models based on convolutional neural networks (CNNs) and an improved optimization method (called the improved pufferfish optimization algorithm). …”
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ViTAU: Facial paralysis recognition and analysis based on vision transformer and facial action units by Jia GAO, Wenhao CAI, Junli ZHAO, Fuqing DUAN

“…These maps are then processed through a pyramid convolutional neural network interpreter to generate heatmaps. …”
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STHFD: Spatial–Temporal Hypergraph-Based Model for Aero-Engine Bearing Fault Diagnosis by Panfeng Bao, Wenjun Yi, Yue Zhu, Yufeng Shen, Boon Xian Chai

“…Accurate fault diagnosis in aerospace transmission systems is essential for ensuring equipment reliability and operational safety, especially for aero-engine bearings. However, current approaches relying on Convolutional Neural Networks (CNNs) for Euclidean data and Graph Convolutional Networks (GCNs) for non-Euclidean structures struggle to simultaneously capture heterogeneous data properties and complex spatio-temporal dependencies. …”
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Wavelet-Enhanced Deep Learning Ensemble for Accurate Stock Market Forecasting: A Case Study of Nifty 50 Index by Priya Singh, Manoj Jha, Harshita Patel

“…This research proposes an ensemble model that integrates Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), and Temporal Convolutional Networks (TCN) for effective stock market prediction. …”
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A method for short-term wind power forecasting under extreme weather conditions based on meteorological factor interpretability and hybrid deep learning algorithms by Bo Wang, Shu Wang, Zheng Wang, Yingying Zheng, Xin Li

“…Finally, a hybrid deep learning model, the convolutional neural network (CNN)-bidirectional long short-term memory (BiLSTM) network-attention mechanism (AM), is constructed. …”
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Obtaining Rotational Stiffness of Wind Turbine Foundation from Acceleration and Wind Speed SCADA Data by Jiazhi Dai, Mario Rotea, Nasser Kehtarnavaz

“…First, a convolutional neural network model is applied to map acceleration and wind speed data within a moving window to corresponding moment and tilt values. …”
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Semantic Segmentation of Brain Tumors Using a Local–Global Attention Model by Shuli Xing, Zhenwei Lai, Junxiong Zhu, Wenwu He, Guojun Mao

“…Therefore, researchers are trying to develop an automated and accurate segmentation model. Currently, many segmentation models in deep learning rely on Convolutional Neural Network or Vision Transformer. …”
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Application of Deep Learning Techniques in Uranium Microparticle Fission Track Detection by ZHAO Xiong, REN Fangda, SHEN Yan

“…The YOLOv5 algorithm, a deep learning approach, was employed as the foundational network model. To address the issue of long-distance dependencies in convolutional operations, a window multi-head attention mechanism (swin transformer) was integrated to design the uranium microparticle detection network. …”
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Exploratory development of human–machine interaction strategies for post-stroke upper-limb rehabilitation by Kang Xia, Xue-Dong Chang, Chong-Shuai Liu, Yu-Hang Yan, Han Sun, Yi-Min Wang, Xin-Wei Wang

“…The active patient-in-charge strategy incorporates data acquisition matrices and a new deep learning model, which is developed based on Convolutional Neural Network (CNN) and Transformer structure, aims to capture subtle motion intentions. …”
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Machine Learning-Driven D-Glucose Prediction Using a Novel Biosensor for Non-Invasive Diabetes Management by Pardis Sadeghi, Shahriar Noroozizadeh, Rania Alshawabkeh, Nian Xiang Sun

“…Advanced models, such as Convolutional Neural Networks and Recurrent Neural Networks, were used to analyze resistance signals, while classical algorithms served as benchmarks. …”
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Irrigated rice-field mapping in Brazil using phenological stage information and optical and microwave remote sensing by Andre Dalla Bernardina Garcia, MD Samiul Islam, Victor Hugo Rohden Prudente, Ieda Del’Arco Sanches, Irene Cheng

“…We applied a modified version of the Fusion Adaptive Patch Network (FAPNET), named as Patch Layer Adaptive Network (PLANET) convolutional neural network (CNN) to obtain binary rice mapping, which was evaluated using the traditional Mean Intersection over Union (MIoU) and Dice coefficient. …”
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State-of-Health Estimation for Lithium-Ion Batteries via Incremental Energy Analysis and Hybrid Deep Learning Model by Yan Zhang, Anxiang Wang, Chaolong Zhang, Peng He, Kui Shao, Kaixin Cheng, Yujie Zhou

“…This paper proposes a novel SOH estimation method for lithium-ion batteries, utilizing incremental energy features and a hybrid deep learning model that combines Convolutional Neural Network (CNN), Kolmogorov–Arnold Network (KAN), and Bidirectional Long Short-Term Memory (BiLSTM) (CNN-KAN-BiLSTM). …”
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Air Quality and Healthy Ageing: Predictive Modeling of Pollutants Using CNN Quantum-LSTM by Fareena Naz, Muhammad Fahim, Adnan Ahmad Cheema, Bradley D. E. Mcniven, Tuan-Vu Cao, Ruth Hunter, Trung Q. Duong

“…In this work, we proposed a hybrid model referred as AirVCQnet, which combines the variational mode decomposition (VMD) method with a convolutional neural network (CNN) and a quantum long short-term memory (QLSTM) network for the prediction of air pollutants. …”
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A New Modeling Method for Meteorological Information of Regional Distributed Photovoltaic Power Generation Based on Multi‐Source Information Fusion by Yuhang Wang, Dengxuan Li, Wenwen Ma, Xi Zhang, Honglu Zhu

“…These metrics outperform those of convolutional neural network (CNN), back propagation (BP), support vector machines (SVM), and theoretical calculation models, demonstrating excellent seasonal adaptability and calculation accuracy.…”
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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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FIDC-YOLO: Improved YOLO for Detecting Pine Wilt Disease in UAV Remote Sensing Images via Feature Interaction and Dependency Capturing by Zekun Xu, Yipeng Zhou, Shiting Wen, Weipeng Jing

“…However, directly applying these generic detectors to detect PWD results in suboptimal performance due to insufficient utilization of local features. Although current PWD detection methods use the attention mechanisms to improve the recognition of infected targets, the limitations of convolutional neural networks (CNNs) in capturing long-range dependencies hinder their ability to separate targets from the background. …”
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Kidney Ensemble-Net: Enhancing Renal Carcinoma Detection Through Probabilistic Feature Selection and Ensemble Learning by Zaib Akram, Kashif Munir, Muhammad Usama Tanveer, Atiq Ur Rehman, Amine Bermak

“…Our approach begins by acquiring spatial features from contrast-enhanced images using a Convolutional Neural Network (CNN) effectively capturing intricate patterns and structures characteristic of different carcinoma subtypes. …”
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Hyperdimensional Intelligent Sensing for Efficient Real-Time Audio Processing on Extreme Edge by Sanggeon Yun, Ryozo Masukawa, Hanning Chen, Sungheon Jeong, Wenjun Huang, Arghavan Rezvani, Minhyoung Na, Yoshiki Yamaguchi, Mohsen Imani

“…Utilizing a Fast Fourier Transform (FFT) module, convolutional neural network (CNN) layers, and HyperDimensional Computing (HDC), our model excels in low-energy, rapid inference, and online learning. …”
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Attention-Module-Guided Time-Lapse Leakage Plume Imaging Driven by LeakInv-CUNet GPR Inversion Framework by Honghua Wang, Shan Wang, Fei Zhou, Yi Lei, Bin Zhang

“…However, achieving spatiotemporal leakage imaging remains challenging for current deterministic or probabilistic inversions due to the signal complexity, environmental interference, and computational burden. …”
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Segmentation-Assisted Fusion-Based Classification for Automated CXR Image Analysis by Shilu Kang, Dongfang Li, Jiaxin Xu, Aokun Mei, Hua Huo

“…The method involves two stages: first, we use a lightweight segmentation model, Partial Convolutional Segmentation Network (PCSNet) designed based on an encoder–decoder architecture, to accurately obtain lung masks from CXR images. …”
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