Multi-branch LSTM encoded latent features with CNN-LSTM for Youtube popularity prediction by Neeti Sangwan, Vishal Bhatnagar

“…These latent features train the fused Convolutional Neural Network (CNN) with LSTM to predict the popularity of unseen videos on the trained deep learning network. …”
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Vocal performance evaluation of the intelligent note recognition method based on deep learning by Dongyun Chang

“…Firstly, the basic theory of music is analyzed. Secondly, the convolutional neural network (CNN) in deep learning (DL) is selected to integrate gated recurrent units for optimization. …”
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ORD-YOLO: A Ripeness Recognition Method for Citrus Fruits in Complex Environments by Zhaobo Huang, Xianhui Li, Shitong Fan, Yang Liu, Huan Zou, Xiangchun He, Shuai Xu, Jianghua Zhao, Wenfeng Li

“…First, the standard convolution operations are replaced with Omni-Dimensional Dynamic Convolution (ODConv) to improve feature extraction capabilities. …”
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Application of DEO Method to Solving Fuzzy Multiobjective Optimal Control Problem by Latafat A. Gardashova

“…On the other hand, the number of the criteria is not small and most of them are integral criteria. Due to the above mentioned aspects, solving the considered problem by using convolution of criteria into one criterion would lead to loss of information and also would be counterintuitive and complex. …”
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A new approach to the room impulse response simulation by H. Łopacz, R. Marczuk

“…The most important problem of room acoustics is the evaluation of the acoustic quality of projected and modernized rooms. …”
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Daily soil temperature prediction using hybrid deep learning and SHAP for sustainable soil management by Meysam Alizamir, Kaywan Othman Ahmed, Salim Heddam, Sungwon Kim, Jeong Eun Lee

“…At Chamchamal station, the hybrid deep learning model that combines bidirectional gated recurrent unit (BiGRU) and convolutional neural network (CNN), denoted as BiGRU-CNN achieved the best result for the 05 cm depth (RMSE = 1.298°C), while the hybrid model based on gated recurrent unit (GRU) and convolutional neural network (CNN), referred to as GRU–CNN yielded the best performance at 10 cm (RMSE = 1.333°C). …”
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Creating interpretable deep learning models to identify species using environmental DNA sequences by Samuel Waggoner, Jon Donnelly, Rose Gurung, Laura Jackson, Chaofan Chen

“…Our results show that reducing reliance on the convolutional output increases both interpretability and accuracy.…”
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A review on deep learning methods for heart sound signal analysis by Elaheh Partovi, Ankica Babic, Ankica Babic, Arash Gharehbaghi

“…Implementation of the observed methods along with the related results is pervasively represented and compared.Results and discussionIt is observed that convolutional neural networks and recurrent neural networks are the most commonly used ones for discriminating abnormal heart sounds and localization of heart sounds with 67.97% and 33.33% of the related papers, respectively. …”
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A PCC-Ensemble-TCN model for wind turbine icing detection using class-imbalanced and label-missing SCADA data by Shenyi Ding, Zhijie Wang, Jue Zhang, Fang Han, Xiaochun Gu, Guangxiao Song

“…Aiming at the class-imbalance problem, this article constructs multiple class-balanced subsets from the original dataset by under-sampling the normal data. Temporal convolutional networks are trained to extract features and make predictions on each subset. …”
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A Deep Learning Model with Axial Attention for Radar Echo Extrapolation by Yu-Mei Xie, Ying-Liang Zhao, Shu-Yan Huang

“…The experimental results show that the performance of the proposed SA-TrajGRU model is comparable to other convolutional recurrent neural network models. HSS and CSI scores of the SA-TrajGRU model are higher than scores of other models under the radar echo threshold of 25 dBZ, indicating that the SA-TrajGRU model has the most accurate prediction results under this threshold.…”
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AI-driven thermography-based fault diagnosis in single-phase induction motor by Muhammad Atif, Shoaib Azmat, Faisal Khan, Fahad R. Albogamy, Adam Khan

“…Among various faults, the most common mechanical faults in SIMs are bearing faults. …”
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Hierarchical Knowledge Transfer: Cross-Layer Distillation for Industrial Anomaly Detection by Junning Xu, Sanxin Jiang

“…There are two problems with traditional knowledge distillation methods in industrial anomaly detection: first, traditional methods mostly use feature alignment between the same layers. …”
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A Configurable Accelerator for CNN-Based Remote Sensing Object Detection on FPGAs by Yingzhao Shao, Jincheng Shang, Yunsong Li, Yueli Ding, Mingming Zhang, Ke Ren, Yang Liu

“…The results show that, under INT16 or INT8 precision, the system achieves remarkable throughput in most convolutional layers of the network, with an average performance of 153.14 giga operations per second (GOPS) or 301.52 GOPS, which is close to the system’s peak performance, taking full advantage of the platform’s parallel computing capabilities.…”
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Efficient BFCN for Automatic Retinal Vessel Segmentation by Yun Jiang, Falin Wang, Jing Gao, Wenhuan Liu

“…Retinal vessel segmentation has high value for the research on the diagnosis of diabetic retinopathy, hypertension, and cardiovascular and cerebrovascular diseases. Most methods based on deep convolutional neural networks (DCNN) do not have large receptive fields or rich spatial information and cannot capture global context information of the larger areas. …”
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Identification of diabetic retinopathy lesions in fundus images by integrating CNN and vision mamba models. by Zenglei Liu, Ailian Gao, Hui Sheng, Xueling Wang

“…Considering these analyses, this work presents a comprehensive deep learning model that combines convolutional neural network and vision mamba models. …”
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Usage of Neural-Based Predictive Modeling and IIoT in Wind Energy Applications by Adrian-Nicolae Buturache, Stelian Stancu

“…At the time of this study, no prior research studies have presented a direct comparison between feedforward, recurrent, and convolutional neural networks ‒ these being the most important in the field of supervised learning.…”
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Image-Based Malware Detection Using Deep CNN Models by hawraa omran musa, Muhanad Tahrir Younis

“…This study presents a model based on deep learning with Convolutional Neural Network (CNN) for malware classification. …”
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University proceedings. Volga region. Technical sciences by V.I. Volchikhin, A.I. Ivanov, A.V. Bezyaev, I.A. Filipov

“…Replacing classical statistical criteria with their equivalent binary neurons provides significant redundancy of the output code of the neural network, which isconvolved with error elimination. The mechanism of convolution of code redundancy can be improved if the most informative part of the neuron response is not quantized. …”

Impact of increased anthropogenic Amazon wildfires on Antarctic Sea ice melt via albedo reduction by Sudip Chakraborty, Maloy Kumar Devnath, Atefeh Jabeli, Chhaya Kulkarni, Gehan Boteju, Jianwu Wang, Vandana P. Janeja

“…This study also employs the matrix profile and convolution operation of the Convolution Neural Network (CNN) to detect anomalous events in sea ice loss. …”
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WSN intrusion detection method using improved spatiotemporal ResNet and GAN by Yang Jing

“…Then, an improved spatiotemporal residual network model is designed, in which the spatial and temporal features of the data are extracted and fused through multi-scale one-dimensional convolution modules and gated loop unit modules, and identity maps are added based on the idea of residual networks to avoid network degradation and other issues. …”
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