Secret Key Generation Driven by Attention-Based Convolutional Autoencoder and Quantile Quantization for IoT Security in 5G and Beyond by Anas Alashqar, Ehsan Olyaei Torshizi, Raed Mesleh, Werner Henkel

“…To overcome these challenges, this paper introduces a deep learning–enhanced PSKG framework that effectively mitigates channel discrepancies and improves key generation reliability under imperfect channel state information (CSI). …”
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A High-Efficient Method for Synthesizing Multiple Antenna Array Radiation Patterns Simultaneously Based on Convolutional Neural Network by Shiyuan Zhang, Chuan Shi, Ming Bai

“…During training, the cost function is designed to represent the difference between each synthesized radiation pattern and the corresponding target radiation pattern, guiding self-learning. The main framework of the method is a convolutional neural network, where the convolutional layer is used to reduce the expansion of input parameters due to the simultaneous input of multiple mask matrices. …”
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A sorghum seed variety identification method based on image–hyperspectral fusion and an improved deep residual convolutional network by Xu Yang, Yihan Chen, Shaozhong Song, Zhimin Zhang

“…The network was enhanced by integrating depthwise separable convolution (DSC) and the Convolutional Block Attention Module (CBAM) into the ResNet50 framework.ResultsThe CBAM-ResNet50-DSC model demonstrated outstanding performance, achieving a classification accuracy of 94.84%, specificity of 99.20%, recall of 94.39%, precision of 94.52%, and an F1-score of 0.9438 on the fusion dataset.DiscussionThese results confirm that the proposed model can accurately and non-destructively classify sorghum seed varieties. …”
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MG6D: A Deep Fusion Approach for 6D Pose Estimation With Mamba and Graph Convolution Network by Jiaqi Zhu, Bin Li, Xinhua Zhao

“…This paper proposes a novel 6D pose estimation framework that addresses these limitations through a hybrid Mamba-Graph architecture. …”
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A Multi-Kernel Mode Using a Local Binary Pattern and Random Patch Convolution for Hyperspectral Image Classification by Wei Huang, Yao Huang, Zebin Wu, Junru Yin, Qiqiang Chen

“…In order to improve classification performance while reducing costs, this article proposes a multikernel method based on a local binary pattern and random patches (LBPRP-MK), which integrates a local binary pattern (LBP) and deep learning into a multiple-kernel framework. First, we use LBP and hierarchical convolutional neural networks to extract local textural features and multilayer convolutional features, respectively. …”
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Optimized Demand Forecasting for Bike-Sharing Stations Through Multi-Method Fusion and Gated Graph Convolutional Neural Networks by Hebin Guo, Kexin Li, Yutong Rou

“…By integrating three key edge-weight attributes—stations distance, travel duration, and correlation—into a multi-attribute graph framework, the model significantly improves predictive accuracy for user travel patterns. …”
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Discovery of novel TACE inhibitors using graph convolutional network, molecular docking, molecular dynamics simulation, and Biological evaluation. by Muhammad Yasir, Jinyoung Park, Eun-Taek Han, Jin-Hee Han, Won Sun Park, Mubashir Hassan, Andrzej Kloczkowski, Wanjoo Chun

“…We applied the GraphConvMol model within the DeepChem framework, which utilizes graph convolutional networks, to build a predictive model based on the DUD-E datasets. …”
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Enhanced diagnosis of planetary gear train faults based on bispectrum and attention mechanism deep convolutional generative adversarial networks by Dalian Yang, Yang Zhang, Renjie Li, Hui Long, Changzheng Huang

“…First, to enhance the sample quality generated by the Attention Mechanism Deep Convolutional Generative Adversarial Network (AMDCGAN), bispectral features are adopted as input samples, forming the proposed BAMDCGAN framework. …”
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DeepMoIC: multi-omics data integration via deep graph convolutional networks for cancer subtype classification by Jiecheng Wu, Zhaoliang Chen, Shunxin Xiao, Genggeng Liu, Wenjie Wu, Shiping Wang

“…Results To address the challenges of multi-omics research, our approach DeepMoIC presents a novel framework derived from deep Graph Convolutional Network (GCN). …”
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Fault detection and classification in overhead transmission lines through comprehensive feature extraction using temporal convolution neural network by Nadeem Ahmed Tunio, Ashfaque Ahmed Hashmani, Suhail Khokhar, Mohsin Ali Tunio, Muhammad Faheem

“…Moreover, the temporal convolutional neural network (TCN) is used for fault classification in 500 kV transmission network due to its robust framework. …”
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Fusion of Multimodal Spatio-Temporal Features and 3D Deformable Convolution Based on Sign Language Recognition in Sensor Networks by Qian Zhou, Hui Li, Weizhi Meng, Hua Dai, Tianyu Zhou, Guineng Zheng

“…In this paper, we firstly propose a Multi-Stream Spatio-Temporal Graph Convolutional Network (MSGCN) that relies on three modules: a decoupling graph convolutional network, a self-emphasizing temporal convolutional network, and a spatio-temporal joint attention module. …”
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Multi-Sensor Information Fusion with Multi-Scale Adaptive Graph Convolutional Networks for Abnormal Vibration Diagnosis of Rolling Mill by Rongrong Peng, Changfen Gong, Shuai Zhao

“…To solve this issue, a multi-sensor information fusion with multi-scale adaptive graph convolutional networks (M²AGCNs) framework is proposed to model graph data and multi-sensor information fusion in a unified in-depth network and then to achieve abnormal vibration diagnosis. …”
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Lung and Colon Cancer Classification Using Multiscale Deep Features Integration of Compact Convolutional Neural Networks and Feature Selection by Omneya Attallah

“…To this end, the present research introduces a CAD system that integrates several lightweight convolutional neural networks (CNNs) with dual-layer feature extraction and feature selection to overcome the aforementioned constraints. …”
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Real-Time Dolphin Whistle Detection on Raspberry Pi Zero 2 W with a TFLite Convolutional Neural Network by Rocco De Marco, Francesco Di Nardo, Alessandro Rongoni, Laura Screpanti, David Scaradozzi

“…This study bridges artificial intelligence innovation with ecological stewardship, providing a scalable framework for deploying machine learning in resource-constrained settings while addressing urgent conservation challenges.…”
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Examining the complex and cumulative effects of environmental exposures on noise perception through interpretable spatio-temporal graph convolutional networks by Liuyi Song, Mei-Po Kwan, Yang Liu

“…To address this gap, this study employs noise exposure as a case study and utilizes an interpretable spatio-temporal graph convolutional network (ST-GCN) framework to model the perception process in urban environments. …”
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Reduced-order estimation and optimisation of claw-shaped throttling notch via CFD analysis, surrogate models, and CCMO algorithm by Yuhang Zhang, Beichen Ding, Guishan Yan

“…The pressure drop of the CSN valve port reduces by as much as 13.7% after optimisation. The suggested framework can strengthen the CSN hydraulic spool valve optimisation efficiency and can be performed to diverse kinds of high-order notches flexibly.…”
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Taking a look at your speech: identifying diagnostic status and negative symptoms of psychosis using convolutional neural networks by Gleb Melshin, Anthony DiMaggio, Nadia Zeramdini, Michael MacKinley, Lena Palaniyappan, Alban Voppel

“…Our results suggest that spectrogram-based CNN analyses of short conversational segments can robustly detect both schizophrenia-spectrum disorders and ascertain burden of negative symptoms. This interpretable framework underscores how time–frequency feature maps of natural speech may facilitate more nuanced tracking and detection of negative symptoms in schizophrenia.…”
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Integrating Multiscale Spatial–Spectral Shuffling Convolution With 3-D Lightweight Transformer for Hyperspectral Image Classification by Qinggang Wu, Mengkun He, Qiqiang Chen, Le Sun, Chao Ma

“…This network directly captures 3-D structural features throughout the entire feature extraction process, thereby enhancing HSI classification performance even at small sampling rates within a lightweight framework. Specifically, we first design a multiscale spatial–spectral shuffling convolution to comprehensively refine spatial–spectral feature granularities and enhance feature interactions by shuffling multiscale features across different groups. …”
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PIPNet: A Deep Convolutional Neural Network for Multibaseline InSAR Phase Unwrapping Based on Pure Integer Programming by Hui Liu, Ke Zheng, Changwei Miao, Xuemei Liu, Xianlin Liu, Lin Li, Yongguang Zhang, Longhai Xiong

“…This PIPNet is built on the U-Net framework, incorporating Transformer modules, upsampling, and dense connections mechanisms to achieve powerful feature extraction capabilities. …”
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Efficient 3D convolutional neural networks for Sentinel-2 land cover classification with limited ground truth data by Gabriel A. Carneiro, Jan Svoboda, António Cunha, Joaquim J. Sousa, Přemysl Štych

“…This paper focuses on an innovative application of deep learning (DL) techniques, particularly 3D convolutional neural networks (CNNs), for land cover classification using multispectral Sentinel-2 (S-2) data. …”
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