SCF-CIL: A Multi-Stage Regularization-Based SAR Class-Incremental Learning Method Fused with Electromagnetic Scattering Features by Yunpeng Zhang, Mengdao Xing, Jinsong Zhang, Sergio Vitale

“…First, for the feature extractor, we fuse the convolutional neural network features with the scattering center features using a cross-attention feature fusion structure, ensuring both the plasticity and stability of the extracted features. …”
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Application of Deep Learning in Food Safety Detection and Risk Early Warning by DING Haohan, WANG Long, HOU Haoke, XIE Zhenqi, HAN Yu, CUI Xiaohui

“…This paper first introduces the basic concept of deep learning and its current development in the field of food safety, and discusses the application of convolutional neural network (CNN), recursive neural network (RNN), transformer architecture and graph neural network (GNN) in food safety detection and risk prediction. …”
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SiCRNN: A Siamese Approach for Sleep Apnea Identification via Tracheal Microphone Signals by Davide Lillini, Carlo Aironi, Lucia Migliorelli, Leonardo Gabrielli, Stefano Squartini

“…Our proposed SiCRNN processes Mel spectrograms using a Siamese approach, integrating a convolutional neural network (CNN) backbone and a bidirectional gated recurrent unit (GRU). …”
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Historical Blurry Video-Based Face Recognition by Lujun Zhai, Suxia Cui, Yonghui Wang, Song Wang, Jun Zhou, Greg Wilsbacher

“…To approach this problem, we first propose a trunk–branch concatenated multi-task cascaded convolutional neural network (TB-MTCNN), which efficiently extracts facial features from blurry historical films by combining the trunk with branch networks and employing various sizes of kernels to enrich the multi-scale receptive field. …”
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On Explainability of Reinforcement Learning-Based Machine Learning Agents Trained with Proximal Policy Optimization That Utilizes Visual Sensor Data by Tomasz Hachaj, Marcin Piekarczyk

“…Through its use, it is possible to estimate the causes of specific decisions made by the neural network due to the current state of the observed environment. …”
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Enhancing practicality and efficiency of deepfake detection by Ismael Balafrej, Mohamed Dahmane

“…Furthermore, some key considerations were identified to significantly reduce the size of the core convolutional neural network. The experiment yielded competitive results when evaluated on two second-generation deepfake datasets, namely Celeb-DFv2 and DFDC, while requiring only a fraction of the typical computational cost and resources.…”
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Reference frame list optimization algorithm in video coding by quality enhancement of the nearest picture by Junyan HUO, Ruipeng QIU, Yanzhuo MA, Fuzheng YANG

“…Interframe prediction is a key module in video coding, which uses the samples in the reference frames to predict those in the current picture, thus helps to represent the complex video by transmitting a small amount of the prediction residual.In lossy video coding, the qualities of reference frames are affected by the quantization distortion, which lead to poor prediction accuracy and performance degradation.Targeted at the low latency video services, a reference frame list optimization algorithm was proposed, which enhanced the quality of the nearest reference frame by a deep learning-based convolutional neural network, and integrated the enhanced reference frame into the reference frame list to improve the accuracy of interframe prediction.Compared with H.265/HEVC reference software HM16.22, the proposed algorithm provides BD-rate savings of 9.06%, 14.92% and 13.19% for Y, Cb and Cr components, respectively.…”
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Medical image diagnosis and auxiliary analysis based on deep learning by Abulikemu Ainikaerjiang

“…This paper introduces the concept and development of deep learning, as well as the development process of deep learning models, convolutional neural networks, and deep belief network models, and reviews the current status of their application research in medical image analysis. …”
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An Anomaly Detection Method for Industrial System Cybersecurity Based on GGL-WAVE-CNN by Bing Zou, Ke jun Zhang, Xin Ying Yu, Yu han Jin, Jun Wang, Ling yu Liu

“…This paper introduces a novel two-level anomaly detection framework that combines the generalized graph Laplacian (GGL), wavelet decomposition (WAVE), and an enhanced convolutional neural network (CNN). In the first level, the proposed method employs the GGL to efficiently identify abnormal windows in industrial time series data. …”
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Human Activity Recognition and Location Based on Temporal Analysis by Hongjin Ding, Faming Gong, Wenjuan Gong, Xiangbing Yuan, Yuhui Ma

“…For this work, we used a multilayer convolutional neural network (CNN) to extract features. …”
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S_I_LSTM: stock price prediction based on multiple data sources and sentiment analysis by Shengting Wu, Yuling Liu, Ziran Zou, Tien-Hsiung Weng

“…Then, we use the sentiment analysis method based on convolutional neural network for the non-traditional data, which can calculate the investors' sentiment index. …”
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A Dual-Stream Dental Panoramic X-Ray Image Segmentation Method Based on Transformer Heterogeneous Feature Complementation by Tian Ma, Jiahui Li, Zhenrui Dang, Yawen Li, Yuancheng Li

“…Firstly, we construct a parallel architecture comprising a Transformer semantic parsing branch and a Convolutional Neural Network (CNN) detail capturing pathway, achieving collaborative optimization of global context modeling and local feature extraction. …”
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Towards Transparent AI in Medicine: ECG-Based Arrhythmia Detection with Explainable Deep Learning by Oleksii Kovalchuk, Oleksandr Barmak, Pavlo Radiuk, Liliana Klymenko, Iurii Krak

“…Second, we proposed an arrhythmia classification method utilizing a modified convolutional neural network (CNN) architecture with additional convolutional and batch normalization layers. …”
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Attention-Guided Multi-Task Learning for Prostate Cancer Pelvic Lymph Node Metastasis Prediction by ZHANG Zhiyuan, HU Jisu, ZHANG Yueyue, QIAN Xusheng, ZHOU Zhiyong, DAI Yakang

“…First, within the tumor segmentation network, a multi-branch anisotropic large kernel attention module is introduced, where a larger receptive field is obtained through different branches and anisotropic large convolutional kernels, effectively capturing both local and global tumor information. …”
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Lightweight human activity recognition method based on the MobileHARC model by Xingyu Gong, Xinyang Zhang, Na Li

“…In recent years, Human activity recognition (HAR) based on wearable devices has been widely applied in health applications and other fields. Currently, most HAR models are based on the Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), or their combination. …”
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Federated Learning and EEL-Levy Optimization in CPS ShieldNet Fusion: A New Paradigm for Cyber–Physical Security by Nalini Manogaran, Yamini Bhavani Shankar, Malarvizhi Nandagopal, Hui-Kai Su, Wen-Kai Kuo, Sanmugasundaram Ravichandran, Koteeswaran Seerangan

“…This involves the incorporation of the Federated Residual Convolutional Network into an optimization method called EEL-Levy Fusion Optimization. …”
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ZZ-YOLOv11: A Lightweight Vehicle Detection Model Based on Improved YOLOv11 by Zhe Zhang, Zhongyang Zhang, Gang Li, Chenxi Xia

“…Aiming at the problems of insufficient vehicle detection accuracy, high misdetection and omission rate, and heavy model computational burden caused by complex lighting conditions, target occlusion, and other factors in urban traffic scenarios, this paper proposes an improved lightweight detection network, ZZ-YOLO. Firstly, the current mainstream target detection algorithms lack components to improve the network’s focus on the edges of the objects, which can indirectly lead to unclear classification and localization. …”
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Region-Focusing Data Augmentation via Salient Region Activation and Bitplane Recombination for Target Detection by Huan Zhang, Xiaolin Han, Weidong Sun

“…As the performance of a convolutional neural network is logarithmically proportional to the amount of training data, data augmentation has attracted increasing attention in recent years. …”
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Research on Intelligent Detection and Segmentation of Rock Joints Based on Deep Learning by Lei Peng, Haibo Wang, Chun Zhou, Feng Hu, Xiaoyang Tian, Zhu Hongtai

“…To address these concerns, this paper presents an intelligent recognition and segmentation algorithm based on Mask R-CNN (mask region-based convolutional neural network) for detecting joint targets on tunnel face images and automatically segmenting them, thereby improving detection efficiency and objectivity of the results. …”
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GCN-based unsupervised community detection with refined structure centers and expanded pseudo-labeled set. by Bing Guo, Liping Deng, Tao Lian

“…Considering them as pseudo-labeled nodes, graph convolutional network (GCN) is recently exploited to realize unsupervised community detection. …”
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