Unlocking Dynamic Subtle Stimuli Tactile Perception: A Deep Learning‐Enhanced Super‐Resolution Tactile Sensor Array with Rapid Response by Shuyao Zhou, Depeng Kong, Mengke Wang, Baocheng Wang, Yuyao Lu, Honghao Lyu, Zhangli Lu, Yong Tao, Kaichen Xu, Geng Yang

“…Here, a 130 μm‐thick flexible tactile sensor array is designed, with spatial resolution enhanced by a tailored deep learning model, multistage attention‐based adaptive spatial–temporal graph convolutional networks (MS‐AASTGCN), simultaneously achieving a dynamic response of ≈30 ms and a super‐resolution factor of 75.19. …”
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Research on entity recognition and alignment of APT attack based on Bert and BiLSTM-CRF by Xiuzhang YANG, Guojun PENG, Zichuan LI, Yangqi LYU, Side LIU, Chenguang LI

“…Compared with CNN-CRF, which integrates convolutional neural networks, the F1-score of the proposed model is increased by 6.92%. …”
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Research on entity recognition and alignment of APT attack based on Bert and BiLSTM-CRF by Xiuzhang YANG, Guojun PENG, Zichuan LI, Yangqi LYU, Side LIU, Chenguang LI

“…Compared with CNN-CRF, which integrates convolutional neural networks, the F1-score of the proposed model is increased by 6.92%. …”
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AI-driven discovery of synergistic drug combinations against pancreatic cancer by Mohsen Pourmousa, Sankalp Jain, Elena Barnaeva, Wengong Jin, Joshua Hochuli, Zina Itkin, Travis Maxfield, Cleber Melo-Filho, Andrew Thieme, Kelli Wilson, Carleen Klumpp-Thomas, Sam Michael, Noel Southall, Tommi Jaakkola, Eugene N. Muratov, Regina Barzilay, Alexander Tropsha, Marc Ferrer, Alexey V. Zakharov

“…Of the 88 tested, 51 show synergy, with graph convolutional networks achieving the best hit rate and random forest the highest precision. …”
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A Survey on Anti-Money Laundering Techniques in Blockchain Systems by Leyuan Liu, Xiangye Li, Tian Lan, Yakun Cheng, Wei Chen, Zhixin Li, Sheng Cao, Weili Han, Xiaosong Zhang, Hongfeng Chai

“…It categorizes existing AML techniques into three primary approaches: rule-based methods, such as transaction parameter threshold setting, address-entity association analysis, and cross-chain association analysis; machine learning-based approaches, including support vector machines, logistic regression, decision trees, random forests, k-means clustering, and combining off-chain information; and deep learning-based methodologies, encompassing convolutional neural networks, recurrent neural networks, graph neural networks, and transformer-based models. …”
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SS-EMERGE - self-supervised enhancement for multidimension emotion recognition using GNNs for EEG by Chirag Ahuja, Divyashikha Sethia

“…Therefore, this study introduces a hybrid SSL framework: Self-Supervised Enhancement for Multidimension Emotion Recognition using Graph Neural Networks (SS-EMERGE). This model enhances cross-subject EEG-based emotion recognition by incorporating Causal Convolutions for temporal feature extraction, Graph Attention Transformers (GAT) for spatial modelling, and Spectral Embedding for spectral domain analysis. …”
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Hadron Identification Prospects with Granular Calorimeters by Andrea De Vita, Abhishek, Max Aehle, Muhammad Awais, Alessandro Breccia, Riccardo Carroccio, Long Chen, Tommaso Dorigo, Nicolas R. Gauger, Ralf Keidel, Jan Kieseler, Enrico Lupi, Federico Nardi, Xuan Tung Nguyen, Fredrik Sandin, Kylian Schmidt, Pietro Vischia, Joseph Willmore

“…This motivates further work required to combine high- and low-level feature analysis, e.g., using convolutional and graph-based neural networks, and extending the study to a broader range of particle energies and types.…”
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A spatiotemporal model for urban taxi Origin–Destination prediction based on Multi-hop GCN and Hierarchical LSTM by Jiang Rong, Wangtu Xu, Yanjie Wen

“…We develop a Multi-hop Spatial-Hierarchical Temporal (MS-HT) block that leverages Chebyshev polynomial-based k-hop Graph Convolutions Networks(GCNs) to extract long-range spatial dependencies, which alleviates over-smoothing resulting from stacked GCNs. …”
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Recent Advancement in Small Traffic Sign Detection: Approaches and Dataset by R. Suresha, N. Manohar, G. Ajay Kumar, M. Rohit Singh

“…This review comprehensively examines the performance of state-of-the-art deep learning models, including YOLO (You Only Look Once), SSD (Single Shot MultiBox Detector), and various RCNN (Region-based Convolutional Neural Network) variants, assessing their strengths and weaknesses for small traffic sign detection through detailed tables and bar graphs. …”
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Research on a Joint Extraction Method of Track Circuit Entities and Relations Integrating Global Pointer and Tensor Learning by Yanrui Chen, Guangwu Chen, Peng Li

“…First, a multi-layer dilate gated convolutional neural network with residual connections is used to extract key features and fuse the weighted information from the 12 different semantic layers of the RoBERTa-wwm-ext model, fully exploiting the performance of each encoding layer. …”
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A computational framework for IoT security integrating deep learning-based semantic algorithms for real-time threat response by Ripal Ranpara, Shobhit K. Patel, Om Prakash Kumar, Fahad Ahmed Al-Zahrani

“…The proposed research framework integrates Convolutional Neural Networks for spatial anomaly detection and Recurrent Neural Networks for sequential pattern recognition. …”
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Development of interpretable intelligent frameworks for estimating river water turbidity by Amin Gharehbaghi, Salim Heddam, Saeid Mehdizadeh, Sungwon Kim

“…Categorical Boosting (CatBoost), Light Gradient-Boosting Machine (LightGBM), eXtreme Gradient Boosting (XGBoost), and a deep learning method named Convolutional Neural Networks (CNN). To evaluate the performance of proposed models, two gauging river stations situated in United States (i.e. …”
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