High data retrieval rates with SwissPedData for paediatric traumatic brain injuries in children’s hospitals: a multicentre, point-prevalence study by Michael von Rhein, Niklaus Krayenbühl, Andreas Meyer-Heim, Sergio Manzano, Gianluca Gualco, Renato Gualtieri, Fabrizio Romano, Alexander Mack, Larissa Hauser-Angst, Michelle Seiler

“… STUDY AIMS: SwissPedNet aims to improve the quality of multicentre research through standardised documentation of routine healthcare data. …”
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Advancements in image classification for environmental monitoring using AI by Jinjing Zhu, Ling Li

“…MABEC-Net integrates multi-scale feature extraction, which enables the model to analyze both fine-grained local textures and broader environmental patterns. …”
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Fault analysis of chemical equipment based on an improved hybrid model. by Wu Huiyong, Kuan Jiang, Wang Yanyu

“…The pre-activation mechanism introduced in the residual blocks of ResNet improves training efficiency and model stability. …”
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Research on algorithm for improving imaging accuracy of CFRP low speed impact damage by WU Xiangnan, CHENG Xiaojin, LI Qixin, SHANG Jianhua

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Real-Time Q-Band OFDM-RoF Systems With Optical Heterodyning and Envelope Detection for Downlink Transmission by Ming Chen, Jianjun Yu, Xin Xiao

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Convolutional block attention gate-based Unet framework for microaneurysm segmentation using retinal fundus images by C. B. Vanaja, P. Prakasam

“…The proposed architecture takes advantage of the U-Net’s encoder-decoder structure, which allows for perfect segmentation by gathering both high- and low-level information. …”
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Computational Neuroscience’s Influence on Autism Neuro-Transmission Research: Mapping Serotonin, Dopamine, GABA, and Glutamate by Victoria Bamicha, Pantelis Pergantis, Charalabos Skianis, Athanasios Drigas

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Transformer Dil-DenseUnet: An Advanced Architecture for Stroke Segmentation by Nesrine Jazzar, Besma Mabrouk, Ali Douik

“…The dilated convolutional blocks, placed before each DenseNet module, expand the receptive field, capturing broader contextual information essential for accurate segmentation. …”
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Fusion of Visual Attention and Scene Descriptions With Deep Reinforcement Learning for AAV Indoor Autonomous Navigation by Hussein Samma, Ahmed Abubaker, Mubarak Badamasi Aremu, Mohammed Abdel-Nasser, Sami El-Ferik

“…Both the scene and attention models contribute significantly. Compared to ResNet, EfficientNet, DenseNet, and transformers, the CNN-based attention model performed better in accuracy and F1-score. …”
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Optimizing knee osteoarthritis severity prediction on MRI images using deep stacking ensemble technique by Punita Panwar, Sandeep Chaurasia, Jayesh Gangrade, Ashwani Bilandi, Dayananda Pruthviraja

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Sparse Attention-Based Residual Joint Network for Aspect-Category-Based Sentiment Analysis by Jooan Kim, Hyunyoung Kil

“…SPA-RJ Net incorporates two aspect-guided sparse attentions—sparse aspect-category attention and sparse aspect-sentiment attention—that introduce sparsity in attention via a sparse distribution transformation function, enabling the model to selectively focus on aspect-related information. …”
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Predicting peak ground acceleration using the ConvMixer networkKey points by Mona Mohammed, Omar M. Saad, Arabi Keshk, Hatem M. Ahmed

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Local pattern aware 3D video swin transformer with masked autoencoding for realtime augmented reality gesture interaction by Suli Wang

“…Experimental results demonstrate that the proposed model’s accuracy, F1 score, and MIoU on the 4 GTEA sub-dataset surpass those of traditional CNN, Transformer, MobileNet, and DenseNet models, particularly on small datasets. …”
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Multi-target personnel tracking algorithm for coal mine based on improved YOLOv7 and ByteTrack by Pengcheng QU, Jingzhao LI, Zechao LIU

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IM- LTS: An Integrated Model for Lung Tumor Segmentation using Neural Networks and IoMT by Jayapradha J, Su-Cheng Haw, Naveen Palanichamy, Kok-Why Ng, Senthil Kumar Thillaigovindhan

“…The semantic features of lung images are extracted based on texture, intensity, and shape to provide information to the training network. • In this work, the transfer learning technique is incorporated, and the pre-trained NN was used as an encoder for the U-NET model for segmentation. …”
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Estimation Model of Corn Leaf Area Index Based on Improved CNN by Chengkai Yang, Jingkai Lei, Zhihao Liu, Shufeng Xiong, Lei Xi, Jian Wang, Hongbo Qiao, Lei Shi

“…This model can provide information technology support for the phenotypic monitoring of field crops.…”
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Explainable MRI-Based Ensemble Learnable Architecture for Alzheimer’s Disease Detection by Opeyemi Taiwo Adeniran, Blessing Ojeme, Temitope Ezekiel Ajibola, Ojonugwa Oluwafemi Ejiga Peter, Abiola Olayinka Ajala, Md Mahmudur Rahman, Fahmi Khalifa

“…The study explores a few commonly used perturbation-based interpretability (LIME) and gradient-based interpretability (Saliency and Grad-CAM) approaches for visualizing and explaining the dataset, models, and decisions of MRI image-based Alzheimer’s disease identification using the diagnostic and predictive strengths of an ensemble framework comprising Convolutional Neural Networks (CNNs) architectures (Custom multi-classifier CNN, VGG-19, ResNet, MobileNet, EfficientNet, DenseNet), and a Vision Transformer (ViT). …”
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Automated detection of submarine pipelines in the Yellow River Estuary: a deep learning approach for side-scan sonar data in dynamic deltaic systems by Min Wei, Min Wei, Yongqing Yu, Xing Du, Yupeng Song, Lifeng Dong, Qikun Zhou, Linfeng Wang, Longying Zhang, Yamei Wang

“…The integrity of submarine pipelines and cables is crucial for safeguarding marine oil, gas, and information transmission, as well as ecological security. …”
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Advancing Skin Disease Diagnosis: A Multimodal Approach Utilizing Telegram Api Token Chatbot for Text and Image Analysis in Skin Disease Classification by Modigari Narendra, T. S. Harshini, L. Jani Anbarasi

“…The chatbot’s self-learning capabilities allow it to improve its comprehension over time in response to user input, which makes it more adept at personalising queries. Integrating DenseNet169 and ResNet50 strengthens the feature reuse by connecting each layer to every other layer in a dense manner for efficient gradient flow thus reducing the number of parameters. …”
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Efficient and Effective NDVI Time-Series Reconstruction by Combining Deep Learning and Tensor Completion by Ang Li, Menghui Jiang, Dong Chu, Xiaobin Guan, Jie Li, Huanfeng Shen

“…Multidimensional gradient constraints are introduced in the loss function to retain critical information. The experiments conducted on moderate resolution imaging spectroradiometer NDVI data show that the NIT-Net framework is superior to most of the comparison methods. …”
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