A dual-branch deep learning model based on fNIRS for assessing 3D visual fatigue by Yan Wu, Yan Wu, Yan Wu, TianQi Mu, SongNan Qu, XiuJun Li, XiuJun Li, XiuJun Li, Qi Li, Qi Li, Qi Li

“…Given the time-series nature of fNIRS data and the variability of fatigue responses across different brain regions, a dual-branch convolutional network was constructed to separately extract temporal and spatial features. …”
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Digital Biomarkers for Parkinson Disease: Bibliometric Analysis and a Scoping Review of Deep Learning for Freezing of Gait by Wenhao Qi, Shiying Shen, Chaoqun dong, Mengjiao Zhao, Shuaiqi Zang, Xiaohong Zhu, Jiaqi Li, Bin Wang, Yankai Shi, Yongze Dong, Huajuan Shen, Junling Kang, Xiaodong Lu, Guowei Jiang, Jingsong Du, Eryi Shu, Qingbo Zhou, Jinghua Wang, Shihua Cao

“…In addition, 31 (78%) studies indicated that the best models were primarily convolutional neural networks or convolutional neural networks–based architectures. …”
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CSA-Net: Complex Scenarios Adaptive Network for Building Extraction for Remote Sensing Images by Dongjie Yang, Xianjun Gao, Yuanwei Yang, Minghan Jiang, Kangliang Guo, Bo Liu, Shaohua Li, Shengyan Yu

“…The HFE obtains high-level semantic information at different levels and fuses it with low-level detailed information by skipping connections to enhance the reasoning and perception ability of building structure in complex scenes. …”
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A novel method for soil organic carbon prediction using integrated ‘ground-air-space’ multimodal remote sensing data by Yilin Bao, Xiangtian Meng, Huanjun Liu, Mengyuan Xu, Mingchang Wang

“…We also evaluated the performance of various algorithms (e.g., Random Forest (RF), Convolutional Neural Networks (CNN), Graph Neural Networks (GNN), and Multi-Layer Perceptron (MLP)) across these models. …”
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Ubiquitous UWB Ranging Error Mitigation With Application to Infrastructure-Free Cooperative Positioning by Maija Makela, Martta-Kaisa Olkkonen, Martti Kirkko-Jaakkola, Toni Hammarberg, Tuomo Malkamaki, Jesperi Rantanen, Sanna Kaasalainen

“…This ranging error can be corrected with machine learning (ML) methods, such as convolutional neural networks (CNNs). However, these ML models often generalize poorly between different environments. …”
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A prototype-based rockburst types and risk prediction algorithm considering intra-class variance and inter-class distance of microseismic data by Xiufeng Zhang, Guoying Li, Yang Chen, Hao Wang, Haikuan Zhang, Haitao Li, Weisheng Du, Xiao Li, Xuewei Xu, Yuze He

“…The results show that the distribution features may be different for the same type of microseismic (MS) and rockburst events, and different types of events may show similar distribution features. …”
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HyDA-Net: A Hybrid Dense Attention Network for Remote Sensing Multi-Image Super-Resolution by Mohamed Ramzy Ibrahim, Robert Benavente, Daniel Ponsa, Felipe Lumbreras

“…Extensive experiments using real-captured satellite datasets, namely PROBA-V and MuS2, show that HyDA-Net outperforms state-of-the-art models in different spectral bands. Moreover, a cross-dataset experiment is conducted to further evaluate the robustness and generalizability of the proposed HyDA-Net.…”
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An improved ShuffleNetV2 method based on ensemble self-distillation for tomato leaf diseases recognition by Shuiping Ni, Yue Jia, Mingfu Zhu, Mingfu Zhu, Yizhe Zhang, Wendi Wang, Shangxin Liu, Yawei Chen

“…Based on the fused feature map that integrates the intermediate feature maps of ShuffleNetV2 and shallow models, a depthwise separable convolution layer is introduced to further extract more effective feature information. …”
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Automated classification of mandibular canal in relation to third molar using CBCT images [version 1; peer review: 2 approved] by Yogesh Chhaparwal, Veena Mayya, Sharath S, Neil Abraham Barnes, Roopitha C H, Winniecia Dkhar

“…Methods This retrospective study was conducted using 434 CBCT images. 3D slicer software was used to annotate and classify the data into lingual, buccal, and inferior categories. Two convolution neural network models, AlexNet and ResNet50, were developed to classify this relationship. …”
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Influence of cognitive networks and task performance on fMRI-based state classification using DNN models by Murat Kucukosmanoglu, Javier O. Garcia, Justin Brooks, Kanika Bansal

“…Here, we employ two different and complementary DNN models, a one-dimensional convolutional neural network (1D-CNN) and a bidirectional long short-term memory network (BiLSTM), to classify cognitive task states from fMRI data, focusing on the cognitive underpinnings of the classification. …”
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AI-Assisted identification of sex-specific patterns in diabetic retinopathy using retinal fundus images. by Parsa Delavari, Gulcenur Ozturan, Eduardo V Navajas, Ozgur Yilmaz, Ipek Oruc

“…Here we examine whether DR manifests differently in male and female patients, using a dataset of retinal images and leveraging convolutional neural networks (CNN) integrated with explainable artificial intelligence (AI) techniques. …”
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Coalmine image super-resolution reconstruction via fusing multi-dimensional feature and residual attention network by Jian CHENG, Lifei MI, Hao LI, Heping LI, Guangfu WANG, Yongzhuang MA

“…First, a multi-branch network is employed to parallelly integrate dynamic convolution and channel attention mechanisms, capturing different spatial statistical characteristics through “horizontal-channel” and “vertical-channel” interactions. …”
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Combined influence of quantum iterative reconstruction level and kernel sharpness on image quality in photon counting CT angiography of the upper leg by Kristina Krompaß, Florian Andreas Goldbrunner, Viktor Hartung, Süleyman Ergün, Dominik Peter, Robin Hendel, Henner Huflage, Theresa Sophie Patzer, Jan-Lucca Hennes, Thorsten Alexander Bley, Jan-Peter Grunz, Philipp Gruschwitz

“…Abstract Aim was to evaluate the influence of different quantum iterative reconstruction (QIR) levels on the image quality of femoral photon-counting CT angiographies (PCD-CTA). …”
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Deep Fuzzy Credibility Surfaces for Integrating External Databases in the Estimation of Operational Value at Risk by Alejandro Peña, Lina M. Sepúlveda-Cano, Juan David Gonzalez-Ruiz, Nini Johana Marín-Rodríguez, Sergio Botero-Botero

“…Following the above, this paper develops and analyzes a Deep Fuzzy Credibility Surface model (DFCS), which allows the integration in a single structure of different loss event databases for the estimation of an operational value at risk (OpVar), overcoming the limitations imposed by the low frequency with which a risk event occurs within an organization (sparse data). …”
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Handwritten Urdu Characters and Digits Recognition Using Transfer Learning and Augmentation With AlexNet by Aqsa Rasheed, Nouman Ali, Bushra Zafar, Amsa Shabbir, Muhammad Sajid, Muhammad Tariq Mahmood

“…The purpose of this research is to present a classification framework for automatic recognition of handwritten Urdu character and digits with higher recognition accuracy by utilizing theory of transfer learning and pre-trained Convolution Neural Networks (CNN). The performance of transfer learning is evaluated in different ways: by using pre-trained AlexNet CNN model with Support Vector Machine (SVM) classifier, and fine-tuned AlexNet for extracting features and classification. …”
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A Lightweight Method for Detecting Bearing Surface Defects Based on Deep Learning and Ontological Reasoning by Xiaolin Shi, Haisong Xu, Han Zhang, Yi Li, Xinshuo Li, Fan Yang

“…First, the dynamic convolution is fused with the Ghost module and the combined structure is embedded into the C3 module, thus constructing a new module named C3-GhostDynamicConv (C3-GDConv) module, which achieves network lightweighting while maintaining efficient computation. …”
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DAU-YOLO: A Lightweight and Effective Method for Small Object Detection in UAV Images by Zeyu Wan, Yizhou Lan, Zhuodong Xu, Ke Shang, Feizhou Zhang

“…To enhance feature extraction, a Receptive-Field Attention (RFA) module is introduced in the backbone, allowing adaptive convolution kernel adjustments across different local regions, thereby addressing the challenge of dense object distributions. …”
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Design and synthesis of reversible Vedic multiplier using cadence 180 nm technology for low-power high-speed applications by Narayanan Mageshwari, Periyasamy Sakthivel, Ramasamy Seetharaman

“…Thus, the proposed work can be applied to the most promising fields such as Microprocessors to design MAC units, to find the convolution in Digital signal processing applications, Communication, RF sensing applications, etc.…”
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D³-YOLOv10: Improved YOLOv10-Based Lightweight Tomato Detection Algorithm Under Facility Scenario by Ao Li, Chunrui Wang, Tongtong Ji, Qiyang Wang, Tianxue Zhang

“…However, under the facility scenario, existing detection algorithms still have challenging problems such as weak feature extraction ability for occlusion conditions and different fruit sizes, low accuracy on edge location, and heavy model parameters. …”
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Technical note: Impact of tissue section thickness on accuracy of cell classification with a deep learning network by Ida Skovgaard Christiansen, Rasmus Hartvig, Thomas Hartvig Lindkær Jensen

“…Method: From HE-stained digitized sections of liver cut manually at 5 thicknesses and on an automated microtome (DS), hepatocytes and non-hepatocytes were manually annotated and loaded into a DL convolutional neural network (ResNet). The network was trained at different settings to identify the thickness with optimal relation between number of training cells and validation accuracy. …”
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