MONet: cancer driver gene identification algorithm based on integrated analysis of multi-omics data and network models by Yingzan Ren, Tiantian Zhang, Jian Liu, Fubin Ma, Jiaxin Chen, Ponian Li, Guodong Xiao, Chuanqi Sun, Yusen Zhang

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Multi-stream feature fusion of vision transformer and CNN for precise epileptic seizure detection from EEG signals by Qi Li, Wei Cao, Anyuan Zhang

“…Methods Our study proposes an epilepsy detection model, CMFViT, based on a Multi-Stream Feature Fusion (MSFF) strategy that fuses a Convolutional Neural Network (CNN) with a Vision Transformer (ViT). …”
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Gaze Estimation Network Based on Multi-Head Attention, Fusion, and Interaction by Changli Li, Fangfang Li, Kao Zhang, Nenglun Chen, Zhigeng Pan

“…Existing gaze estimation models typically utilize separate neural network branches to process data streams from both eyes and the face, failing to fully exploit their feature correlations. This study presents a gaze estimation network that integrates multi-head attention mechanisms, fusion, and interaction strategies to fuse facial features with eye features, as well as features from both eyes, separately. …”
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EEG-based epilepsy detection using CNN-SVM and DNN-SVM with feature dimensionality reduction by PCA by Yousra Berrich, Zouhair Guennoun

“…The models were evaluated on two benchmark EEG databases: Epileptic Seizure Recognition and BONN, to ensure robustness and generalization. The integration of Convolutional Neural Networks (CNN) and Deep Neural Networks (DNN) with Support Vector Machines (SVM) is explored, with a particular emphasis on the role of Principal Component Analysis (PCA) in simplifying feature dimensions. …”
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FDI-VSR: Video Super-Resolution Through Frequency-Domain Integration and Dynamic Offset Estimation by Donghun Lim, Janghoon Choi

“…We introduce two key modules: the Spatiotemporal Feature Extraction Module (STFEM), which employs dynamic offset estimation, spatial alignment, and multi-stage temporal aggregation using residual channel attention blocks (RCABs); and the Frequency–Spatial Integration Module (FSIM), which transforms deep features into the frequency domain to effectively capture global context beyond the limited receptive field of standard convolutions. …”
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HierbaNetV1: a novel feature extraction framework for deep learning-based weed identification by Justina Michael, Thenmozhi Manivasagam

“…Extracting the essential features and learning the appropriate patterns are the two core character traits of a convolution neural network (CNN). …”
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Incorporated flexible load forecasting based on non-intrusive load monitoring: a TCN-based meta learning approach by Yun Zhang, Quanyan Shu, Feng Ding, Feng Liu, Shuiming Jiang, Wenlong Wu

“…The enhanced performance of the proposed method is attributed to the integration of feature extraction and model adaptation within a meta-learning framework.Future research could explore the incorporation of contextual information to further enhance performance.…”
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A Novel Context-Aware Feature Pyramid Networks With Kolmogorov-Arnold Modeling and XAI Framework for Robust Lung Cancer Detection by Yajnaseni Dash, Sudhir C. Sarangi, Vinayak Gupta, Naween Kumar, Ajith Abraham

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Multi-Biometric Feature Extraction from Multiple Pose Estimation Algorithms for Cross-View Gait Recognition by Ausrukona Ray, Md. Zasim Uddin, Kamrul Hasan, Zinat Rahman Melody, Prodip Kumar Sarker, Md Atiqur Rahman Ahad

“…Subsequently, we employed a residual graph convolutional network (ResGCN) to extract features from the generated skeleton data. …”
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Interpretation and understanding of asphalt crack detection deep learning models using integrated gradient (I.G.) maps by Gihan P. Ruwanpathirana, Sadeep Thilakarathna, Shanaka Kristombu Baduge

“…In this study, we employed Integrated Gradient (I.G.) maps to elucidate the workings of these models and interpret CNN-based crack image voxels that contributed to the positive (cracked) output of CNN. …”
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Intrusion Detection and Mitigation Method for the Industrial Internet of Things Using Bidirectional Convolutional Long Short-Term Memory and Deep Recurrent Convolutional Q-Networks by Zhang Yan, Piyush Kumar Shukla, Prashant Kumar Shukla, Kanika Thakur, Anurag Sinha, Saifullah Khalid

“…ADASYN data augmentation is used to address class imbalance, while entropy analysis and statistical techniques are used to extract key features. The intrusion detection phase uses a combination of deep convolutional neural networks (DCNN) and bidirectional long short-term memory (BI-LSTM) networks to capture both spatial and temporal relationships in the data, while a hybrid feature selection technique improves the model’s performance. …”
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A Novel Framework for Improving Soil Organic Carbon Mapping Accuracy by Mining Temporal Features of Time-Series Sentinel-1 Data by Zhibo Cui, Bifeng Hu, Songchao Chen, Nan Wang, Defang Luo, Jie Peng

“…Compared to models using single-date (R² = 0.23) and multi-date (R² = 0.33) data, the R² increased by 0.57 and 0.47, respectively. (3) The newly developed vertical–horizontal maximum and mean annual cumulative indices made a significant contribution (17.93%) to mapping SOC. Therefore, integrating the optimal monitoring period, feature selection, and deep learning model offers significant potential for enhancing the accuracy of digital SOC mapping.…”
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Intelligent recognition algorithm and application of coal mine overhead passenger device based on multiscale feature fusion by Beijing XIE, Heng LI, Hang DONG, Zheng LUAN, Ben ZHANG, Xiaoxu LI

“…The CLC−PAN−CA module effectively integrated multi-scale features and improved the accuracy of cmopd recognition. …”
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Rolling Bearing Fault Diagnosis Based on Recurrence Plot by Zheming Chen, Bin Xu, Zhong Zhang

“…For the prediction model, the traditional convolutional neural network is enhanced by integrating bidirectional gated recurrent unit and multi-head attention mechanism, allowing it to capture temporal features alongside the spatial features typically extracted by convolutional neural network. …”
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An Integrated CNN-BiLSTM-Transformer Framework for Improved Anomaly Detection Using Surveillance Videos by Sarfaraz Natha, Mohammad Siraj, Fareed Ahmed, Majid Altamimi, Muslim Syed

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Research on Underwater Acoustic Target Recognition Based on a 3D Fusion Feature Joint Neural Network by Weiting Xu, Xingcheng Han, Yingliang Zhao, Liming Wang, Caiqin Jia, Siqi Feng, Junxuan Han, Li Zhang

“…This paper proposes a novel deep neural network model for underwater target recognition, which integrates 3D Mel frequency cepstral coefficients (3D-MFCC) and 3D Mel features derived from ship audio signals as inputs. …”
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Multiscale Attention Feature Fusion Based on Improved Transformer for Hyperspectral Image and LiDAR Data Classification by Aili Wang, Guilong Lei, Shiyu Dai, Haibin Wu, Yuji Iwahori

“…The classification results indicate that the proposed framework, by fully utilizing spatial context information and effectively integrating feature information, significantly outperforms state-of-the-art classification methods.…”
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Feature Graph Construction With Static Features for Malware Detection by Binghui Zou, Chunjie Cao, Longjuan Wang, Yinan Cheng, Chenxi Dang, Ying Liu, Jingzhang Sun

“…In MFGraph, we construct a feature graph using static features extracted from binary PE files, then apply a deep graph convolutional network to learn the representation of the feature graph. …”
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Deformable Feature Fusion and Accurate Anchors Prediction for Lightweight SAR Ship Detector Based on Dynamic Hierarchical Model Pruning by Yue Guo, Shiqi Chen, Ronghui Zhan, Wei Wang, Jun Zhang

“…Specifically, DWDCN is integrated into the backbone network to adapt convolutional positions to the shape of ship targets, thereby enhancing feature extraction and improving detection accuracy in complex scenarios. …”
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Spatiotemporal Deformation Prediction Model for Retaining Structures Integrating ConvGRU and Cross-Attention Mechanism by Yanyong Gao, Zhaoyun Xiao, Zhiqun Gong, Shanjing Huang, Haojie Zhu

“…Therefore, this study proposes a novel deep learning framework, CGCA (Convolutional Gated Recurrent Unit with Cross-Attention), which integrates ConvGRU and cross-attention mechanisms. …”
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