A Novel Lightweight Algorithm for Sonar Image Recognition by Gang Wan, Qi He, Qianqian Zhang, Hanren Wang, Huanru Sun, Xinnan Fan, Pengfei Shi

“…Secondly, the original 1 × 1 convolution is replaced with the redesigned multi-scale convolution Res2Net in the MV2 module to enhance the ability of the algorithm to learn global and local features. …”
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The impacts of training data spatial resolution on deep learning in remote sensing by Christopher Ardohain, Songlin Fei

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Implementation of real time root crop leaf classification using CNN on raspberry-Pi microprocessor by M.D. Rakesh, M. Jeevankumar, S.B. Rudraswamy

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Deep learning modeling of manufacturing and build variations on multistage axial compressors aerodynamics by Giuseppe Bruni, Sepehr Maleki, Senthil K. Krishnababu

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Boost Cyclone Intensity Estimation via Processing Motion Information and Embedding Rotation Invariance by Xiaoping Cheng, Xinxin Kong, Jianfang Fei, Xiaogang Huang

“…To ensure that the feature extraction module of the network possesses rotation invariance, each basic convolution operation is replaced with a rotation invariant convolution operation. …”
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Hybrid CNN-GCN Network for Hyperspectral Image Classification by Cuiping Shi, Diling Liao, Liguo Wang

“…Unlike CNN, graph convolutional networks (GCNs) can well handle the intrinsic manifold structures of hyperspectral images (HSIs). …”
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Identification of line status changes using phasor measurements through deep learning networks by N. E. Gotman, G. P. Shumilova

“…The combination of different CNN architectures and the number of time slices from the moment of line status change are used to detect the power grid topology. …”
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Deep-Learning-Based Solar Flare Prediction Model: The Influence of the Magnetic Field Height by Lei Hu, Zhongqin Chen, Long Xu, Xin Huang

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Fish oocyte morphology detection using neural networks: a comparison of YOLO architectures by Yanna Leidy Ketley Fernandes Cruz, Ewaldo Eder Carvalho Santana, Isa Rosete Araujo Nascimento, Antonio Fhillipi Maciel Silva, Raimunda Nonata Fortes Carvalho Neta, José Ribamar de Souza Torres-Junior

“…The research uses an image database with 5,680 oocytes with different maturation stadiums (PV - pre-vitellogenesis, VI - early vitellogenesis and VF - late vitellogenesis), in histological images, divided into training, testing and validation, and detection performed by YOLOv3, YOLOv4, and YOLOv5 architectures. …”
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Detection of single and dual pulmonary diseases using an optimized vision transformer by Almughamisi Naif, Abosamra Gibrael, Albar Adnan, Saleh Mostafa

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Estimating the effect of age-induced defocus on object detection performance of automotive cameras by Amit Pandey, Stephan Kühn, Marcel Kettelgerdes, Bernhard Wunderle, Gordon Elger

“…This work investigates the effect of defocus on safety-critical object detection by comparing various pre-trained object detection models, primarily convolutional neural networks. These models are compared in their performance against static scenes with different levels of defocus. …”
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Deep Learning Algorithm Analysis of Potato Disease Classification for System on Chip Implementation by John Adebisi, Sesham Srinu, Varqa Mitonga

“…The broad range of crops has witnessed setbacks in different capacities due to climate change among other factors, hence leading to diseases and infections; thereby leading to negatively impacted nutrition. …”
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Deep Learning-Based Multistage Fire Detection System and Emerging Direction by Tofayet Sultan, Mohammad Sayem Chowdhury, Mejdl Safran, M. F. Mridha, Nilanjan Dey

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A Unified Approach to Voice Classification: Leveraging Spectrograms, Mel Spectrograms, and Statistical Features by Muhammad Talha, Huma Ghafoor, Seung Yeob Nam

“…These findings confirm the effectiveness of the proposed multimodal architecture for accurate voice and sound classification across different datasets.…”
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PLANT DISEASE DETECTION USING DEEP LEARNING by Amrita, Sanjay Kumar Nayak, Rajiv Kumar, Sakshi Tomar

“…In this paper, several deep learning (DL) models are proposed to recognize the multiple classes of diseases present in plants from the images of leaves taken under various resolutions and different environmental conditions. Employing a Deep Convolutional Neural Network (CNN) in multi-class classification for detecting plant diseases can be beneficial in the early identification of these diseases and also in dealing with the negative impact of these diseases on agriculture. …”
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Triplet-Style Dynamic Graph Network With Transformer Encoder for Scam Detection in Cryptocurrency Transactions by Min-Woo Nam, Hyeon-Ju Lee, Seok-Jun Buu

“…TD-GCN’s performance gains stem from its dynamic updates for evolving networks and Triplet Learning for disentangling subtly different patterns. These features enable TD-GCN to significantly bolster cryptocurrency security by effectively detecting scams and minimizing false positives in dynamic transaction networks.…”
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High sensitivity in spontaneous intracranial hemorrhage detection from emergency head CT scans using ensemble-learning approach by Juuso Takala, Heikki Peura, Riku Pirinen, Katri Väätäinen, Sergei Terjajev, Ziyuan Lin, Rahul Raj, Miikka Korja

“…The DL solution included four base convolutional neural networks (CNNs), which were trained using 300 head CT scans. …”
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SLG-Net: Small-Large-Global Feature-Based Multilevel Feature Extraction Network for Ultrasound Image Segmentation by Xinya Fan, Jianwen Hu, Kai Hu

“…Specifically, the CNN encoder improves the representation and interaction of fine feature and large-scale context feature for targets of different sizes by large-small kernel attention (LSKA) modules. …”
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Assessment of a Smartphone-Based Neural Network Application for the Risk Assessment of Skin Lesions under Real-World Conditions by Teresa Maria Kränke, Philipp Efferl, Katharina Tripolt-Droschl, Rainer Hofmann-Wellenhof

“… Introduction: The diagnostic performance of convolutional neural networks (CNNs) in diagnosing different types of skin cancer has been quite promising. …”
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Application of Hyperspectral Imaging for Identification of Melon Seed Variety Using Deep Learning by Zhiqi Hong, Chu Zhang, Wenjian Song, Xiangbo Nie, Hongxia Ye, Yong He

“…The image features of the corresponding seeds were manually extracted from the RGB images. Five different datasets were formed using the spectral features and RGB images of the seeds, including seed spectral features, manually extracted seed image features, seed images, the fusion of seed spectral features with manually extracted features, and the fusion of seed spectral features with seed images. …”
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