Assessment of Deep Neural Network Models for Direct and Recursive Multi-Step Prediction of PM10 in Southern Spain by Javier Gómez-Gómez, Eduardo Gutiérrez de Ravé, Francisco J. Jiménez-Hornero

“…The models were also assessed here for recursive multi-step prediction over different forecast periods in three different situations: background concentration, a strong dust event, and an extreme dust event. …”
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Leveraging Deep Learning for Robust Structural Damage Detection and Classification: A Transfer Learning Approach via CNN by Burak Duran, Saeed Eftekhar Azam, Masoud Sanayei

“…Then, this acceleration time-history series was transformed into grayscale images, serving as inputs for a Convolutional Neural Network developed to detect and classify structural damage states. …”
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A Representation-Learning-Based Graph and Generative Network for Hyperspectral Small Target Detection by Yunsong Li, Jiaping Zhong, Weiying Xie, Paolo Gamba

“…Experiments on different hyperspectral data sets demonstrate the advantages of the proposed architecture.…”
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An UNet3+ Network based on global pyramid aggregation for change detection in optical remote-sensing imagesGosNIIASLEarning, VIsion and Remote sensing laboratory by Yanbo Sun, Wenxing Bao, Wei Feng, Kewen Qu, Xuan Ma, Xiaowu Zhang

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A novel network with enhanced edge information for left atrium segmentation from LGE-MRI by Ze Zhang, Zhen Wang, Xiqian Wang, Kuanquan Wang, Yongfeng Yuan, Qince Li

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Enhanced Conformer-Based Speech Recognition via Model Fusion and Adaptive Decoding with Dynamic Rescoring by Junhao Geng, Dongyao Jia, Zihao He, Nengkai Wu, Ziqi Li

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Multi-S3P: Protein Secondary Structure Prediction With Specialized Multi-Network and Self-Attention-Based Deep Learning Model by M. M. Mohamed Mufassirin, M. A. Hakim Newton, Julia Rahman, Abdul Sattar

“…Also, predicting secondary structures in the boundary regions between different types of SS is challenging. This study presents Multi-S3P, which employs bidirectional Long-Short-Term-Memory (BILSTM) and Convolutional Neural Networks (CNN) with a self-attention mechanism to improve the secondary structure prediction using an effective training strategy to capture the unique characteristics of each type of secondary structure and combine them more effectively. …”
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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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Predicting trajectories of coastal area vessels with a lightweight Slice-Diff self attention by Jinxu Zhang, Jin Liu, Xiliang Zhang, Lai Wei, Zhongdai Wu, Junxiang Wang

“…Firstly, the trajectory slice difference encoder (TSDE) utilizes slice embedding (SE) to enrich the cross dimensional dependencies contained in the input sequence, and then combines Slice-Diff self attention (SDSA) and fine-grained convolution (FGC) to comprehensively capture sequence-specific positional and directional information. …”
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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

“…A dedicated convolutional neural network is designed taking into account various factors such as layer architecture, data pre-processing and optimization methods. …”
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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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