Hybrid transformer and convolution iteratively optimized pyramid network for brain large deformation image registration by Xinxin Cui, Yuee Zhou, Caihong Wei, Guodong Suo, Fengqing Jin, Jianlan Yang

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RETRACTED ARTICLE: Attention Pyramid Convolutional Neural Network Optimized with Big Data for Teaching Aerobics by Chunmei Chen

Subjects: “…Attention pyramid convolutional neural network…”
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BCDCNN: breast cancer deep convolutional neural network for breast cancer detection using MRI images by D. E. Martina Jaincy, V. Pattabiraman

“…It is a recent nature-inspired metaheuristic that converges to an optimal solution in fewer iterations compared to conventional methods. …”
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Spatial-Spectral Adaptive Graph Convolutional Subspace Clustering for Hyperspectral Image by Yuqi Liu, Enshuo Zhu, Qinghe Wang, Junhong Li, Shujun Liu, Yaowen Hu, Yuhang Han, Guoxiong Zhou, Renxiang Guan

“…However, existing methods focus on using graph convolution techniques to design feature extraction functions, ignoring the mutual optimization of the graph convolution operator and the self-expression coefficient matrix, leading to suboptimal clustering results. …”
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Iterative segmentation and classification for enhanced crop disease diagnosis using optimized hybrid U-Nets model by Malathi Chilakalapudi, Sheela Jayachandran

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Efficient and secure multi-party computation protocol supporting deep learning by Shancheng Zhang, Gang Qu, Zongyang Zhang, Minzhe Huang, Haochun Jin, Liqun Yang

“…Moreover, we introduce optimized protocols for two crucial deep learning operations: convolution and Softmax function computation. …”
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Research progress in globular fruit picking recognition algorithm based on deep learning by LI Hui, ZHANG Jun, YU Shuochen, LI Zhixin

“…With the continuous research by domestic scholars, YOLO algorithm is also continuously iteratively optimized, and its ability to detect the objects of different sizes and shapes is significantly improved, which can adapt to the maturity degree, size and occlusion of fruits, and improve the detection performance in complex environments.…”
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Limited-angle x-ray nano-tomography with machine-learning enabled iterative reconstruction engine by Chonghang Zhao, Mingyuan Ge, Xiaogang Yang, Yong S. Chu, Hanfei Yan

“…To tackle this challenge, we propose an approach dubbed Perception Fused Iterative Tomography Reconstruction Engine, which integrates a convolutional neural network (CNN) with perceptional knowledge as a smart regularizer into an iterative solving engine. …”
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Exploring spatial reasoning performances of CNN on linear layout dataset by Jelena Pejic, Marko Petkovic, Sandra Klinge

“…Linear layout generation has broad applicability and is of fundamental importance in design and optimization. To benchmark dataset, we develop LinLayCNN, a generic data-driven method that applies shallow, one-dimensional convolutional neural network (CNN), to generate linear layouts in an iterative process. …”
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Speech emotion recognition based on a stacked autoencoders optimized by PSO based grass fibrous root optimization by Chi Zeng, Jialing Li, Abbas Habibi

“…The model’s performance is evaluated on a standard emotion recognition dataset, comparing with some state-of-the-art models, including Convolutional Neural Network (CNN), Support Vector Machine (SVM), Deep Learning (DL), CNN and Iterative Neighborhood Component Analysis (CNN/INCA), VGG-16 achieving high accuracy in identifying various emotional states.…”
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Application of Machine Learning for Bulbous Bow Optimization Design and Ship Resistance Prediction by Yujie Shen, Shuxia Ye, Yongwei Zhang, Liang Qi, Qian Jiang, Liwen Cai, Bo Jiang

“…To solve the problem of insufficient accuracy in the single surrogate model, this study proposes a CBR surrogate model that integrates convolutional neural networks with backpropagation and radial basis function models. …”
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Opt-CoInfer: Optimal collaborative inference across IoT and cloud for fast and accurate CNN inference by Zhanhua Zhang, Hanqiao Yu, Fangzhou Wang

“…For fast and accurate Convolutional Neural Network (CNN) inference of massive Internet of Things (IoT) data, Collaborative Inference (CI) based on partition and compression techniques needs to carefully select the collaboration scheme considering both application scenario and inference requirement. …”
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COMQ: A Backpropagation-Free Algorithm for Post-Training Quantization by Aozhong Zhang, Zi Yang, Naigang Wang, Yingyong Qi, Jack Xin, Xin Li, Penghang Yin

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Multi-fault diagnosis and damage assessment of rolling bearings based on IDBO-VMD and CNN-BiLSTM by Lihai Chen, Xiaolong Bai, Yonghui He, Dong Jia, Yican Li, Zhenshui Li

“…It combines IDBO (Improved Dung beetle optimizer) optimised VMD (Variational mode decomposition) and CNN-BiLSTM (convolutional neural network-Bi-directional Long Short-Term Memory) to achieve rolling bearing conformity fault diagnosis and damage assessment. …”
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TWD-DepNet: a deep network enhanced by three-way decisions for EEG-based depression detection by Chunlei Shi, Shouzhu Yan, Wei He, Haiyan Wang

“…Then, a lightweight convolutional backbone (DepNet) with multi-scale convolution is designed, depthwise separable layers, and dynamic channel attention to capture rich spatiotemporal patterns efficiently. …”
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Semantic ECG hash similarity graph by Yixian Fang, Shilin Zhang, Yuwei Ren

“…Additionally, to ensure the maintenance of semantic similarity, we propose an iterative optimization approach in the orthogonal domain for generating hash representations. …”
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Deep Learning Model of Image Classification Using Machine Learning by Qing Lv, Suzhen Zhang, Yuechun Wang

“…Secondly, based on the existing convolution neural network model, the noise reduction and parameter adjustment were carried out in the feature extraction process, and an image classification depth learning model was proposed based on the improved convolution neural network structure. …”
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SBCS-Net: Sparse Bayesian and Deep Learning Framework for Compressed Sensing in Sensor Networks by Xianwei Gao, Xiang Yao, Bi Chen, Honghao Zhang

“…This framework innovatively expands the iterative process of sparse Bayesian compressed sensing using convolutional neural networks and Transformer. …”
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Personalized trajectory inference framework integrating driving behavior recognition and temporal dependency learning. by Jinhao Yang, Junwen Cao, Mingyu Fang

“…The model achieves a mean RMSE of 4.46 and NLL of 3.89 across varying prediction horizons, with 35.8% error reduction attained after 100 hyperparameter optimization iterations. Comparative analysis with baseline models (LSTM, Social-LSTM, Social-Velocity-LSTM, Convolutional-Social-LSTM) reveals particularly enhanced accuracy in long-term predictions. …”
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FED-GEM-CN: A federated dual-CNN architecture with contrastive cross-attention for maritime radar intrusion detection by Md. Alamgir Hossain

“…The proposed architecture integrates dual parallel convolutional neural network (CNN) pipelines to independently process network and radar modality features, which are subsequently fused via a multi-head cross-attention mechanism to capture intricate inter-modal dependencies. …”
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