A Rolling-Bearing-Fault Diagnosis Method Based on a Dual Multi-Scale Mechanism Applicable to Noisy-Variable Operating Conditions by Jing Kang, Taiyong Wang, Ye Wei, Usman Haladu Garba, Ying Tian

“…This approach targets the intrinsic mode functions (IMFs), which capture information across multiple scales, to obtain the most precise denoised signal possible. Subsequently, we introduce the Dynamic Weighted Multi-Scale Feature Convolutional Neural Network (DWMFCNN) model, which integrates two structures: multi-scale feature extraction and dynamic weighting of these features. …”
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Comparisons of different deep learning-based methods on fault diagnosis for geared system by Bing Han, Xiaohui Yang, Yafeng Ren, Wanggui Lan

“…The comprehensive deep neural network model is the most effective one in gear fault recognition.…”
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Quantum‐inspired Arecanut X‐ray image classification using transfer learning by Praveen M. Naik, Bhawana Rudra

“…A comparative analysis of transfer learning‐based classification, employing both a traditional convolutional neural network (CNN) and an advanced quantum convolutional neural network (QCNN) approach is conducted. …”
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Pengembangan Deep Learning untuk Sistem Deteksi Dini Komplikasi Kaki Diabetik Menggunakan Citra Termogram by Medycha Emhandyksa, Indah Soesanti, Rina Susilowati

“…Selain arsitektur deep convolutional neural network model 4, kontribusi penelitian yang didapatkan dari penelitian ini adalah penggunaan variasi ukuran filter 3x3, 2x2, dan 1x1 dengan jumlah convolutional layer yang tetap dan pengurangan jumlah hidden layer pada struktur algoritma mampu menurunkan jumlah parameter model dengan tetap mempertahankan kemampuan deteksi yang tinggi. …”
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Evaluation of machine learning and deep learning algorithms for fire prediction in Southeast Asia by Aditya Eaturu, Krishna Prasad Vadrevu

“…In this study, we utilize Visible Infrared Imaging Radiometer Suite (VIIRS) satellite-derived fire data alongside six machine learning (ML) and deep learning (DL) models, Simple Persistence, Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), CNN-Long Short-Term Memory (CNN-LSTM), and Convolutional Long Short-Term Memory (ConvLSTM) to determine the most effective fire prediction model. …”
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Multi-branch LSTM encoded latent features with CNN-LSTM for Youtube popularity prediction by Neeti Sangwan, Vishal Bhatnagar

“…This leads to the way for more content-driven videos, which can generate revenue. YouTube is the most popular platform which shared the revenue from advertisement to video publisher. …”
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Vocal performance evaluation of the intelligent note recognition method based on deep learning by Dongyun Chang

“…The attention mechanism-gated recurrent convolutional neural network (A-GRCNN) model performs best on all indicators. …”
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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

“…Additionally, at Darbandikhan station, BiLSTM-CNN generated the most accurate predictions at 50 cm depth (RMSE = 1.506°C). …”
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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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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 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

“…The above two issues restrict the performance of most current data-driven models. In order to solve the label missing problem, this article proposes a Pearson correlation coefficient–based algorithm for measuring the degree of blade icing, which calculates the similarity between the unlabeled data and the icing data as its label. …”
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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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Transformers for Neuroimage Segmentation: Scoping Review by Maya Iratni, Amira Abdullah, Mariam Aldhaheri, Omar Elharrouss, Alaa Abd-alrazaq, Zahiriddin Rustamov, Nazar Zaki, Rafat Damseh

“…The most developed were those of hybrid convolutional neural network-transformer architectures (n=57, 85.07%), where the vision transformer is the most frequently used type of transformer (n=37, 55.22%). …”
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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

“…Empirical findings demonstrate that the suggested methodology surpasses the most advanced algorithms on the datasets that are accessible openly. …”
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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

“…Malware or malicious software represents one of the most remarkable threats to cybersecurity, as it compromises the integrity, confidentiality, and availability of computer systems and networks. …”
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