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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Improving thermal state preparation of Sachdev–Ye–Kitaev model with reinforcement learning on quantum hardware by Akash Kundu

“…This paper addresses this challenge by integrating reinforcement learning (RL) with convolutional neural networks, employing an iterative approach to optimize the quantum circuit and its parameters. …”
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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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Research on leaf identification of table grape varieties based on deep learning by PAN Bowen, LIN Meiling, JU Yanlun, SU Baofeng, SUN Lei, FAN Xiucai, ZHANG Ying, ZHANG Yonghui, LIU Chonghuai, JIANG Jianfu, FANG Yulin

“…When ResNet-101 was used as the classification model, the optimized parameters were the learning rate of 0.005, the minimum batch of 32, and the number of iterations was 50. …”
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Haberleşme Sistemlerinde Turbo Kodlama ve Turbo İlkesinin Bazı Pratik Uygulamaları by Selman KULAÇ, Murat Hüsnü SAZLI

“…Temelde turbo kodlama kod çözme mimarisi, aralarında serpiştiricilerin de yer aldığı özyineli sistematik katlamalı kodlayıcıların (Recursive Systematic Convolutional Encoder, RSC) paralel sıralanması (concatenation), SISO (soft input, soft output – yumuşak giriş, yumuşak çıkış) kod çözücüler arasında döngülü (iterative) bilgi değişimine dayalı kod çözme yaklaşımı sunan bir yapı içermektedir. …”
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Multi-scale feature pyramid network with bidirectional attention for efficient mural image classification. by Shulan Wang, Siyu Liu, Mengting Jin, Pingmei Fan

“…Second, a bidirectional LSTM-driven attention module iteratively optimizes channel and spatial weights, enhancing detail perception for low-frequency categories. …”
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Research on the Method of Crop Pest and Disease Recognition Based on the Improved YOLOv7-U-Net Combined Network by Wenchao Xiang, Zitao Du, Xinran Liu, Zehui Lu, Yuna Yin

“…For the U-Net network, the CBAM attention module is added before decoder skip connections, and depth-separable convolutions replace traditional kernels to strengthen feature fusion and detail attention. …”
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