Efficient Gearbox Fault Diagnosis Based on Improved Multi-Scale CNN with Lightweight Convolutional Attention by Bin Yuan, Yaoqi Li, Suifan Chen

“…In this paper, we propose an intelligent diagnosis framework based on Empirical Mode Decomposition and multimodal feature co-optimization and innovatively construct a fault diagnosis model by fusing a multi-scale convolutional neural network and a lightweight convolutional attention model. …”
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GP-DGECN: Geometric Prior Dynamic Group Equivariant Convolutional Networks for Specific Emitter Identification by Yu Han, Xiang Chen, Manxi Wang, Long Shi, Zhongming Feng

“…This framework combines group-equivariant convolutional layers and dynamic convolution kernel strategies to resolve the limitation of traditional CNN models that only possess translational equivariance. …”
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Spatiotemporal Flood Hazard Classification in Bangkok Using Graph Convolutional Network and Temporal Fusion Transformer by Pakpoom Chaimook, Nirattaya Khamsemanan, Cholwich Nattee, Alice Sharp

“…To address this problem, this study proposes a hybrid deep learning framework combining Graph Convolution Network (GCN) and the Temporal Fusion Transformer (TFT) for predicting flood hazard levels in 50 Bangkok districts. …”
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Color Night-Light Remote Sensing Image Fusion With Two-Branch Convolutional Neural Network by Jie Wang, Yanling Lu, Yuefeng Wang, Jianwu Jiang

“…To address the low-resolution limitation of NLRSI, this study proposes a multisource remote sensing image fusion framework based on the two-branch convolutional neural network (TbCNN), which fuses Landsat-8 and NPP/VIIRS data to generate high-resolution color night-light remote sensing imagery (CNLRSI). …”
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Using deep convolutional networks combined with signal processing techniques for accurate prediction of surface quality by Mohammad Zangane, Mohammad Shahbazi, Seyed Ali Niknam

“…Abstract This paper uses deep learning techniques to present a framework for predicting and classifying surface roughness in milling parts. …”
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Grape Leaf Diseases Identification System Using Convolutional Neural Networks and LoRa Technology by Zinon Zinonos, Socratis Gkelios, Ala F. Khalifeh, Diofantos G. Hadjimitsis, Yiannis S. Boutalis, Savvas A. Chatzichristofis

“…To achieve this objective, the framework utilizes a combination of on-site and simulation experiments along with different LoRa parameters and Convolutional Neural Model (CNN) model fine-tuning. …”
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Inferring free surface disturbance properties from Kelvin wakes using convolutional neural network by Xuanting Hao

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Complementary performances of convolutional and capsule neural networks on classifying microfluidic images of dividing yeast cells. by Mehran Ghafari, Justin Clark, Hao-Bo Guo, Ruofan Yu, Yu Sun, Weiwei Dang, Hong Qin

“…This work lays a useful framework for sophisticated deep-learning processing of microfluidic-based assays of yeast replicative aging.…”
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Lung Nodule Malignancy Prediction From Longitudinal CT Scans With Siamese Convolutional Attention Networks by Benjamin P. Veasey, Justin Broadhead, Michael Dahle, Albert Seow, Amir A. Amini

“…<italic>Goal:</italic> We propose a convolutional attention-based network that allows for use of pre-trained 2-D convolutional feature extractors and is extendable to multi-time-point classification in a Siamese structure. …”
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Employing convolutional neural networks and explainable artificial intelligence for the detection of seizures from electroencephalogram signal by Tamilarasi Kathirvel Murugan, Anush Kameswaran

“…Evaluation criteria like specificity and accuracy are used to assess the models' performance. This framework's objective is to create simple seizure detection systems that assist early epilepsy patient identification and individualized treatment plans. …”
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Improving Performance of the Convolutional Neural Networks for Electricity Theft Detection by using Cheetah Optimization Algorithm by Hassan Ghaedi, Seyed Reza Kamel Tabbakh, Reza Ghaemi

“…Today, one of the most widely used methods is convolutional neural networks (CNNs). These networks contain a large number of hyper-parameters.  …”
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Enhancing Brain Tumor Detection Through Custom Convolutional Neural Networks and Interpretability-Driven Analysis by Kavinda Ashan Kulasinghe Wasalamuni Dewage, Raza Hasan, Bacha Rehman, Salman Mahmood

“…This approach contributes a highly accurate and interpretable framework for brain tumor detection, with the potential to significantly enhance diagnostic accuracy and personalized treatment planning in neuro-oncology.…”
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Improved convolutional neural network for precise exercise posture recognition and intelligent health indicator prediction by He Chen, Rongchang Fan

“…Abstract This paper presents a novel framework for accurate exercise posture recognition and health indicator prediction based on improved convolutional neural networks. …”
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Lightweight Transformer traffic scene semantic segmentation algorithm integrating multi-scale depth convolution by Gang XIE, Quanyi WANG, Xinlin XIE, Jian’an WANG

“…Aiming at the problems of discontinuous segmentation of thin strip objects that were easy to blend into the surrounding background and a large number of model parameters in the semantic segmentation algorithm of traffic scenes, a lightweight Transformer traffic scene semantic segmentation algorithm integrating multi-scale depth convolution was proposed.First, a multi-scale strip feature extraction module (MSEM) was constructed based on deep convolution to enhance the representation ability of thin strip target features at different scales.Secondly, a spatial detail auxiliary module (SDAM) was designed using the convolutional inductive bias feature in the shallow network to compensate for the loss of deep spatial detail information to optimize object edge segmentation.Finally, an asymmetric encoding-decoding network based on the Transformer-CNN framework (TC-AEDNet) was proposed.The encoder combined Transformer and CNN to alleviate the loss of detail information and reduce the amount of model parameters; while the decoder adopted a lightweight multi-level feature fusion design to further model the global context.The proposed algorithm achieves the mean intersection over union (mIoU) of 78.63% and 81.06% respectively on the Cityscapes and CamVid traffic scene public datasets.It can achieve a trade-off between segmentation accuracy and model size in traffic scene semantic segmentation and has a good application prospect.…”
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Blink Detection Using 3D Convolutional Neural Architectures and Analysis of Accumulated Frame Predictions by George Nousias, Konstantinos K. Delibasis, Georgios Labiris

“…The cropped eye regions are organized as three-dimensional (3D) input with the third dimension spanning time of 300 ms. Two different 3D convolutional neural networks are utilized (a simple 3D CNN and 3D ResNet), as well as a 3D autoencoder combined with a classifier coupled to the latent space. …”
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Temporal representation learning enhanced dynamic adversarial graph convolutional network for traffic flow prediction by Linlong Chen, Linbiao Chen, Hongyan Wang, Jian Zhao

“…Additionally, we design an adversarial graph convolutional framework, which optimizes the loss through adversarial training, thereby reducing the trend discrepancy between predicted and actual values. …”
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Knowledge based convolutional transformer for joint estimation of PM2.5 and O3 concentrations by Ying Ren, Siyuan Wang, Bisheng Xia, Biesheng Xia

“…In addition, the joint estimation framework for pollutants proposed in this study can be applied to multivariate retrieval or estimation in multiple fields.…”
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Topological Attention-Based Convolution Neural Networks in Analyzing and Predicting Particulate Matter Pollution Level by Zixin Lin, Nur Fariha Syaqina Zulkepli, Mohd Shareduwan Mohd Kasihmuddin, R. U. Gobithaasan

“…Methods The proposed framework combines CNNs, self-attention mechanisms, and persistent homology-derived topological features from three key environmental variables. …”
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Node-Based Graph Convolutional Network With SLIC Method for Breast Cancer Ultrasound Images Classification by Kien Trang, Fung Fung Ting, Bao Quoc Vuong, Chee-Ming Ting

“…This research presents a novel node-based Graph Convolutional Network (GCN) approach for the classification of breast cancer from ultrasound images. …”
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Optimizing non small cell lung cancer detection with convolutional neural networks and differential augmentation by Vahiduddin Shariff, Chiranjeevi Paritala, Krishna Mohan Ankala

“…The study concludes that the novel CNN + DA architecture provides a robust, accurate, and computationally efficient framework for lung cancer detection, positioning it as a valuable tool for clinical applications and paving the way for future research in medical image diagnostics.…”
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