A dual path graph neural network framework for dementia diagnosis by Denghui Zhang, Chenxuan Zhu

“…We then performed multi-scale graph convolution to analyze brain connectivity at varying resolutions-from fine-grained to more extensive patterns, and ultimately employed an attention mechanism to enhance features across different domains. …”
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A BEARING DEEP LEARNING TRANSFER DIAGNOSIS METHOD BASED ON OPTIMIZATION OF SYMMETRIC POLAR COORDINATES by WU DingHai, WANG HuaiGuang, SONG Bin, ZHANG YunQiang

“…The bearing dataset of Case Western Reserve University which includes different rotational speeds and load is used to verify this method and a good recognition effect has been achieved.…”
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Online evaluation method for MMC submodule capacitor aging based on CapAgingNet by Xinlan Deng, Youhan Deng, Liang Qin, Weiwei Yao, Min He, Kaipei Liu

“…Subsequently, the CapAgingNet model is introduced, incorporating key technical modules to enhance performance: the Deep Stem module, which extracts larger receptive fields through multiple convolution layers and mitigates the impact of data sparsity in capacitor aging on feature extraction; the efficient channel attention (ECA) module, utilizing one-dimensional convolution for dynamic weighting to adjust the importance of each channel, thereby enhancing the ability of the model to process high-dimensional features in capacitor aging data; and the multiscale feature fusion (MSF) module, which integrates capacitor aging information across different scales by combining fine-grained and coarse-grained features, thus improving the capacity of the model to capture high-frequency variation characteristics. …”
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ASHM-YOLOv9: A Detection Model for Strawberry in Greenhouses at Multiple Stages by Yan Mo, Shaowei Bai, Wei Chen

“…Strawberry planting requires different amounts of soil water-holding capacity and fertilizer at different growth stages. …”
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Tomato ripeness detection and fruit segmentation based on instance segmentation by Jinfan Wei, Yu Sun, Yu Sun, Lan Luo, Lingyun Ni, Mengchao Chen, Minghui You, Minghui You, Ye Mu, Ye Mu, He Gong, He Gong

“…The method proposes two innovative modules: the Adaptive and Oriented Feature Refinement module (AOFRM) and the Custom Multi-scale Pooling module (CMPRD) with Residuals and Depth. By deformable convolution and multi-directional asymmetric convolution, the AOFRM module adaptively extracts the shape and direction features of tomatoes to solve the problems of occlusion and overlap. …”
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Influence of chronic prenatal hypoxia on the specialized contact apparatus of rat heart ventricles during ontogeny by N. S. Petruk

“…The increasing complexity of the geometry of the intercalated disc is characterized by slower growth of convolution index.…”
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Efficient Implementation of Matrix-Based Image Processing Algorithms for IoT Applications by Sorin Zoican, Roxana Zoican

“…Two implementations are illustrated: one using the Blackfin processor with 32-bit fixed-point representation and the second using the convolutional neural network (CNN) accelerator in the MAX78000 microcontroller. …”
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Enhancement of Underwater Images through Parallel Fusion of Transformer and CNN by Xiangyong Liu, Zhixin Chen, Zhiqiang Xu, Ziwei Zheng, Fengshuang Ma, Yunjie Wang

“…Subsequently, to extract global features, both temporal and frequency domain features are incorporated to construct the convolutional neural network. Finally, the image’s high and low frequency information are utilized to fuse different features. …”
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Towards the Development of the Clinical Decision Support System for the Identification of Respiration Diseases via Lung Sound Classification Using 1D-CNN by Syed Waqad Ali, Muhammad Munaf Rashid, Muhammad Uzair Yousuf, Sarmad Shams, Muhammad Asif, Muhammad Rehan, Ikram Din Ujjan

“…This study presents a Clinical Decision Support System (CDSS) for the early detection of respiratory disorders using a one-dimensional convolutional neural network (1D-CNN) model. The ICBHI 2017 Breathing Sound Database, which contains samples of different breathing sounds, was used in this research. …”
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Deep learning on medical image analysis by Jiaji Wang, Shuihua Wang, Yudong Zhang

“…Abstract Medical image analysis plays an irreplaceable role in diagnosing, treating, and monitoring various diseases. Convolutional neural networks (CNNs) have become popular as they can extract intricate features and patterns from extensive datasets. …”
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Thyroid nodule classification in ultrasound imaging using deep transfer learning by Yan Xu, Mingmin Xu, Zhe Geng, Jie Liu, Bin Meng

“…Following model fusion, the AUC improved to 0.783 (95% CI: 0.724–0.841). The difference in performance between the fusion model and the traditional machine learning model was statistically significant (p = 0.036). …”
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Fault Diagnosis Method for Main Pump Motor Shielding Sleeve Based on Attention Mechanism and Multi-Source Data Fusion by Nengqing Liu, Xuewei Xiang, Hui Li, Zhi Chen, Peng Jiang

“…Then, on the basis of the AM-MSCNN, a convolutional neural network structure based on the attention mechanism for multi-scale and multi-source data fusion (AM-MSMDF-CNN) is proposed to further fuse the primary fusion features of different channels of torque, rotational speed, voltage, and current. …”
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Racism Detection by Analyzing Differential Opinions Through Sentiment Analysis of Tweets Using Stacked Ensemble GCR-NN Model by Ernesto Lee, Furqan Rustam, Patrick Bernard Washington, Fatima El Barakaz, Wajdi Aljedaani, Imran Ashraf

“…Social media opinions and remarks provocating racial differences have been regarded as a serious threat to social, political, and cultural stability and have threatened the peace of different countries. …”
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Research on the identification and simulation of the earth-rock dam leakage inversion based on a high-density electrical method by Yang Duo, Huang Xixi, Zheng Jianguo, Sun Hao, Zhang Dingfei, Wang Shilin, Liu Tao

“…The law was summarized according to the inversion results, and the detection ability of the device for leakage regions of different sizes, quantities and distances was analyzed. …”
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Deep learning models for deriving optimised measures of fat and muscle mass from MRI by Belvin Thomas, M. Adam Ali, Fatima M. H. Ali, Anthony Chung, Manjiri Joshi, Sophia Maiguma-Wilson, Gabrielle Reiff, Hadil Said, Pardis Zalmay, Michael Berks, Matthew D. Blackledge, James P. B. O’Connor

“…There was negligible difference in the accuracy of human observers and all deep learning models in delineating SF or EM. …”
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Connected and automated vehicle control at unsignalized intersection based on deep reinforcement learning in vehicle-to-infrastructure environment by Juan Chen, Vijayan Sugumaran, Peiyan Qu

“…In order to reduce the number of vehicle collisions and average travel time when vehicles pass through an unsignalized intersection with connected and automated vehicle, an improved Double Dueling Deep Q Network method with Convolutional Neutral Network and Long Short-Term Memory is presented in this article. …”
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Identification of cucumber leaf diseases using deep learning and small sample size for agricultural Internet of Things by Jingyao Zhang, Yuan Rao, Chao Man, Zhaohui Jiang, Shaowen Li

“…To overcome this shortcoming, one approach, based on small sample size and deep convolutional neural network, was proposed for conducting the recognition of cucumber leaf diseases under field conditions. …”
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Automation and Optimization of Food Process Using CNN and Six-Axis Robotic Arm by Youngjin Kim, Sangoh Kim

“…As an application of FPRIS coffee roasting, this system uses a Convolutional Neural Network (CNN) to classify coffee beans inside the roaster and control the roaster in real time, avoiding obstacles and empty spaces. …”
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Fault Diagnosis Method for UHVDC Transmission Based on Deep Learning under Cloud-Edge Architecture by Shihao Zhou, Benren Pan, Dongbin Lu, Yiming Zhong, Guannan Wang

“…It has the highest diagnosis accuracy under different fault types, and its performance is better than the other three comparison methods.…”
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An Efficient and Low-Complexity Transformer-Based Deep Learning Framework for High-Dynamic-Range Image Reconstruction by Josue Lopez-Cabrejos, Thuanne Paixão, Ana Beatriz Alvarez, Diodomiro Baldomero Luque

“…In this context, various architectures with different approaches exist, such as convolutional neural networks, diffusion networks, generative adversarial networks, and Transformer-based architectures, with the latter offering the best quality but at a high computational cost. …”
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