MEFA-Net: Multilevel Feature Extraction and Fusion Attention Network for Infrared Small-Target Detection by Jingcui Ma, Nian Pan, Dengyu Yin, Di Wang, Jin Zhou

“…Specifically, the dilated direction-sensitive convolution block (DDCB) is devised to collaboratively extract local detail features, contextual features, and Gaussian salient features via ordinary convolution, dilated convolution and parallel strip convolution. …”
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ZoomHead: A Flexible and Lightweight Detection Head Structure Design for Slender Cracks by Hua Li, Fan Yang, Junzhou Huo, Qiang Gao, Shusen Deng, Chang Guo

“…Second, Detail Enhanced Convolution (DEConv) replaces traditional convolution kernels, and shared convolution is adopted to reduce redundant structures, which enhances the ability to capture details and improves the detection performance for small objects. …”
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Fidex and FidexGlo: From Local Explanations to Global Explanations of Deep Models by Guido Bologna, Jean-Marc Boutay, Damian Boquete, Quentin Leblanc, Deniz Köprülü, Ludovic Pfeiffer

“…We first used FidexGlo with ensembles and support vector machines (SVMs) to show that its performance on three benchmark problems is competitive in terms of complexity, fidelity and accuracy. The most challenging part was then to apply it to convolutional neural networks. …”
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GLN-LRF: global learning network based on large receptive fields for hyperspectral image classification by Mengyun Dai, Tianzhe Liu, Youzhuang Lin, Zhengyu Wang, Yaohai Lin, Changcai Yang, Riqing Chen

“…In the decoder phase, to further extract rich semantic information, we propose a multi-scale simple attention (MSA) block, which extracts deep semantic information using multi-scale convolution kernels and fuses the obtained features with SimAM. …”
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Novel Multi-Scale Attention Generative Adversarial Network for Photovoltaic Solar Cell Defect Inspection Using Electroluminescence Images by Yuanjun Guan, Yang Liu, Jiayi Wang, Tao Wang, Qianchuan Yi, Wenxin Jiang, Xiaopu Gu, Yichen Zhang, Li Zhang, Tianyan Han, Binbing Huang, Lilei Hu

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The hybrid feature extraction method for classification of adolescence idiopathic scoliosis using Evolving Spiking Neural Network by Nurbaity Sabri, Haza Nuzly Abdull Hamed, Zaidah Ibrahim, Kamalnizat Ibrahim, Mohd Adham Isa, Norizan Mat Diah

“…Scoliosis is the most commonly occurring spinal condition in adolescents. …”
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Evolution of deep learning tooth segmentation from CT/CBCT images: a systematic review and meta-analysis by Wai Ying Kot, Sum Yin Au Yeung, Yin Yan Leung, Pui Hang Leung, Wei-fa Yang

“…Convolutional models with U-Net architecture were the most commonly used deep learning algorithms. …”
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Estimating Age and Sex from Dental Panoramic Radiographs Using Neural Networks and Vision–Language Models by Salem Shamsul Alam, Nabila Rashid, Tasfia Azrin Faiza, Saif Ahmed, Rifat Ahmed Hassan, James Dudley, Taseef Hasan Farook

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The Algorithm for Preparing a Set of Data for Teaching Neural Networks on the Example of the Task to Analyze the Radiological Images of Lungs by A. A. Kosareva

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Cultivation strategies of English thinking ability in the environment of Internet of Things by Shuling Yang, Yan Hou

“…With the widespread use of the Internet of Things (IoT) and from the perspective of deep learning, the Local Similar Convolutional Neural Network (LSNN) recommendation model is designed by adding adjustment layers to the Convolutional Neural Network (CNN). …”
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Comparison of Preprocessing Method Impact on the Detection of Soldering Splashes Using Different YOLOv8 Versions by Peter Klco, Dusan Koniar, Libor Hargas, Marek Paskala

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Occlusion‐invariant face recognition using simultaneous segmentation by Dan Zeng, Raymond Veldhuis, Luuk Spreeuwers, Richard Arendsen

“…Abstract When using convolutional neural network (CNN) models to extract features of an occluded face, the occluded part will inevitably be embedded into the representation just as with other facial regions. …”
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Development and Optimization of a Novel Deep Learning Model for Diagnosis of Quince Leaf Diseases by A. Naderi Beni, H. Bagherpour, J. Amiri Parian

“…However, the physical condition of the expert such as eyesight, fatigue, and work pressure can affect their decision-making capability. Today, deep convolutional neural networks (DCNNs), a novel approach to image classification, have become the most crucial detection method. …”
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A Stock Prediction Method Based on Deep Reinforcement Learning and Sentiment Analysis by Sha Du, Hailong Shen

“…Most previous stock investing methods were unable to predict newly listed stocks because they did not have historical data on newly listed stocks. …”
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Patient-Specific Detection of Atrial Fibrillation in Segments of ECG Signals using Deep Neural Networks by Jeyson A. Castillo, Yenny C. Granados, Carlos Augusto Fajardo Ariza

“… Atrial Fibrillation (AF) is the most common cardiac arrhythmia worldwide. It is associated with reduced quality of life and increases the risk of stroke and myocardial infarction. …”
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Face Identification Based on K-Nearest Neighbor by Ni Kadek Ayu Wirdiani, Praba Hridayami, Ni Putu Ayu Widiari, Komang Diva Rismawan, Putu Bagus Candradinata, I Putu Deva Jayantha

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Quality over quantity: how to get the best results when using docking for repurposing by Lenin Domínguez-Ramírez, Maricruz Anaya-Ruiz, Paulina Cortés-Hernández

“…Molecular docking is among the fastest and most readily available computational tools to explore protein–ligand interactions. …”
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MDPruner: Meta-Learning Driven Dynamic Filter Pruning for Efficient Object Detection by Lingyun Zhou, Xiaoyong Liu

“…Filter pruning is a potent technique for diminishing the computational demands of Convolutional Neural Networks (CNNs), while effectively retaining model performance in image categorization tasks. …”
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Learning Frequency-Aware Spatial Attention by Reconstructing Images With Different Frequency Responses by Keisuke Sano, Tsubasa Hirakawa, Takayoshi Yamashita, Hironobu Fujiyoshi

“…Convolutional Neural Networks are widely used in various real-world applications due to their exceptional performance. …”
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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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