DSMF-Net: Dual Semantic Metric Learning Fusion Network for Few-Shot Aerial Image Semantic Segmentation by Xiyu Qi, Yidan Zhang, Lei Wang, Yifan Wu, Yi Xin, Zhan Chen, Yunping Ge

“…To exploit multiscale global semantic context, we construct scale-aware graph prototypes from different stages of the feature layers based on graph convolutional networks (GCNs), while also incorporating prior-guided metric learning to further enhance context at the high-level convolution features. …”
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Conveyor belt deviation identification algorithm based on anchor point positioning and cross-layer correction by Zhe WANG, Zhe FU, Pengjun CAO, Qing LI, Gaoxiang ZHANG

“…Secondly, a cross-layer correction strategy is added during the training stage of the model. Different supervision weights are assigned to different training stages so as to increase the influence of the later training on the whole stage and enhance the visibility of the later stage for the correction of the previous stage. …”
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A Machine Vision Approach to Assessing Steel Properties through Spark Imaging by Goran Munđar, Miha Kovačič, Uroš Župerl

“…By capturing and analyzing sparks generated during grinding, the method offers a fast and cost-effective alternative to conventional testing. Using convolutional neural networks (CNNs), the proposed models demonstrate high reliability and adaptability across different steel types. …”
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Digital Processing and Deep Learning Techniques: A review of the Literature by Kifaa Hadi Thanoon, Abdulwahhb Fathee Shareef, Omar A. Alsaif

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A multi-class driver behavior dataset for real-time detection and road safety enhancementdoi: 10.5281/zenodo.14908802. by Arafat Sahin Afridi, Arafath Kafy, Ms. Nazmun Nessa Moon, Md. Shahriar Shakil

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Detection of Cardiovascular Diseases Using Predictive Models Based on Deep Learning Techniques: A Hybrid Neutrosophic AHP-TOPSIS Approach for Model Selection by Julio Barzola-Monteses, Rosangela Caicedo-Quiroz, Franklin Parrales-Bravo, Cristhian Medina-Suarez, Wendy Yanez-Pazmino, David Zabala-Blanco, Maikel Y. Leyva-Vazquez

“…In this work, three different models were proposed and compared: deep neural networks (DNN), convolutional neural networks (CNN), and multilayer perceptron (MLP). …”
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A Multi-kernel CNN model with attention mechanism for classification of citrus plants diseases by Shiny R M, Angelin Gladston, Khanna Nehemiah H

“…Initially, the input image is pre-processed for resizing the images as the images are obtained from different datasets. After resizing the image, the feature extraction process is carried out by the pretrained convolutional neural networks. …”
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Learning temporal granularity with quadruplet networks for temporal knowledge graph completion by Rushan Geng, Cuicui Luo

“…Simultaneously, it leverages Dynamic Convolutional Neural Networks (DCNNs) to extract representations of latent spaces across different temporal granularities. …”
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The Improved Kurdish Dialect Classification Using Data Augmentation and ANOVA-Based Feature Selection by Karzan J. Ghafoor, Sarkhel H. Karim, Karwan M. Hama Rawf, Ayub O. Abdulrahman

“…The ANOVA filter method ranks features based on the means from different dialect groups, which made FS better. To make dialect classification work better, a 1D convolutional neural network model was given a dataset that had ANOVA FS added to it. …”
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Deep Learning Utilization for In-Line Monitoring of an Additive Co-Extrusion Process Based on Evaluation of Laser Profiler Data by Valentin Lang, Christian Thomas Ernst Herrmann, Mirco Fuchs, Steffen Ihlenfeldt

“…The collected data are employed to train deep neural networks to classify the printed layers, aiming for the deep neural networks to be able to classify four different classes and identify layers with insufficient quality. …”
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A multi-scale temporal feature fusion framework for sheep voiceprint recognition by Xipeng Wang, Delong Wang, Weijiao Dai, Cheng Zhang, Yudongchen Liang, Yong Zhou, Juan Yao, Fang Tian

“…The model uses the feature pyramid network (FPN) structure and a one-dimensional convolutional block attention module (1D-CBAM) for feature fusion to enhance the classification ability of the model. …”
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Embedded solution to detect and classify head level objects using stereo vision for visually impaired people with audio feedback by Kevin Muñoz, Mario Chavarria, Luisa Ortiz, Silvan Suter, Klaus Schönenberger, Bladimir Bacca-Cortes

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Leveraging an ensemble of EfficientNetV1 and EfficientNetV2 models for classification and interpretation of breast cancer histopathology images by Mahdi Azmoodeh-Kalati, Hasti Shabani, Mohammad Sadegh Maghareh, Zeynab Barzegar, Reza Lashgari

“…Additionally, we propose two ensemble architectures that integrate different trained EfficientNet models. Our framework achieves a classification accuracy of 99.58%, outperforming conventional CNN models on the BreakHis dataset. …”
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Alternate encoder and dual decoder CNN-Transformer networks for medical image segmentation by Lin Zhang, Xinyu Guo, Hongkun Sun, Weigang Wang, Liwei Yao

“…To efficiently fuse different feature information from two sub-decoders during decoding, we introduce a channel attention module to reduce redundant feature information. …”
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Deep learning techniques for speech emotion recognition: A review by Silviana Widya Lestari, Saliyah Kahar, Trismayanti Dwi

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HEAT: Incorporating hierarchical enhanced attention transformation into urban road detection by Ting Han, Chuanmu Li, Siyu Chen, Zongyue Wang, Jinhe Su, Yundong Wu, Guorong Cai

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Enhancing Defect Detection on Surfaces Using Transfer Learning and Acoustic Non-Destructive Testing by Michele Lo Giudice, Francesca Mariani, Giosuè Caliano, Alessandro Salvini

“…This study presents a novel approach that leverages transfer learning techniques to improve the detection of debonding defects across different surface types using PICUS, an acoustic diagnostic device developed at Roma Tre University for the assessment of defects in heritage wall paintings. …”
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Towards scalable medical image compression using hybrid model analysis by Shunlei Li, Jiajie Lu, Yingbai Hu, Leonardo S. Mattos, Zheng Li

“…The performance of SDWTCNN is evaluated using different performance metrics, demonstrating its effectiveness in maintaining image quality at various compression rates. …”
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Biological Motion-Based Emotion Recognition Through a Deep Learning Approach by Amjaad T. Alotaibi, Suhare Solaiman

“…The dataset used in this study includes 2664 high-quality videos of emotional biological motion viewed from three different angles (frontal 0°, left 45°, and left 90°). …”
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DESIGN OF A SYSTEM FOR AUTOMATIC MONITORING OF MOBILE TERRITORIALLY SEPARATED OBJECTS by I.A. Kubasov, L.A. Lekar