Breaking barriers in ICD classification with a robust graph neural network for hierarchical coding by Suyang Xi, Jiesen Shi, Jiachen Yan, MingJing Lin, Xinyi Zhou, Yuan Cheng, Hong Ding, Chia Chao Kang

“…By leveraging a hierarchical structure to refine feature learning, our approach addresses the issue of over-smoothing in deep graph neural networks. …”
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Paired competing neurons improving STDP supervised local learning in spiking neural networks by Gaspard Goupy, Pierre Tirilly, Ioan Marius Bilasco

“…Direct training of Spiking Neural Networks (SNNs) on neuromorphic hardware has the potential to significantly reduce the energy consumption of artificial neural network training. …”
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GMFLDA: Improved Prediction of lncRNA-Disease Association via Graph Convolutional Network by Kwangsu Kim, Jihwan Ha

“…In this study, we present GMFLDA, an advanced machine learning framework for inferring lncRNA-disease associations (LDA) by synergizing graph convolutional networks (GCNs) with deep matrix factorization. Specifically, GCNs are leveraged to extract and encode high-fidelity feature representations of lncRNAs and diseases, while deep matrix factorization, implemented via a multi-layer perceptron, facilitates the discovery of potential disease-associated lncRNAs. …”
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OBTPN: A Vision-Based Network for UAV Geo-Localization in Multi-Altitude Environments by Nanxing Chen, Jiqi Fan, Jiayu Yuan, Enhui Zheng

“…In this paper, we propose a novel end-to-end network, OBTPN. In the initial phase of the model, we optimized the distribution of attention within the primary network, aiming to achieve a balance between self-attention and cross-attention. …”
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Event Camera Denoising Using Asynchronous Spatio-Temporal Event Denoising Neural Network by W. Wu, H. Yao, C. Zhai, Z. Dai, X. Zhu

“…Drawing upon principles from graph encoding and temporal convolutional networks, we incorporate spatiotemporal feature attention mechanisms to capture the temporal and spatial correlations between events. …”
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Comparative study of state-based neural networks for virtual analog audio effects modeling by Riccardo Simionato, Stefano Fasciani

“…In particular, we compare State-Space models and Linear Recurrent Units against the more common Long Short-Term Memory networks, with a variety of audio effects. We evaluate the performance and limitations of these models using multiple metrics, providing insights for future research and development. …”
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SBCS-Net: Sparse Bayesian and Deep Learning Framework for Compressed Sensing in Sensor Networks by Xianwei Gao, Xiang Yao, Bi Chen, Honghao Zhang

“…Compressed sensing is widely used in modern resource-constrained sensor networks. However, achieving high-quality and robust signal reconstruction under low sampling rates and noise interference remains challenging. …”
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Advanced Temporal Convolutional Network Framework for Intrusion Detection in Electric Vehicle Charging Stations by Ikram Benfarhat, Vik Tor Goh, Chun Lim Siow, It Ee Lee, Muhammad Sheraz, Eng Eng Ngu, Teong Chee Chuah

“…The proposed Temporal Convolutional Network (TCN)-based Intrusion Detection System (IDS) architecture integrates four key innovations: multi-receptive fields, a gating mechanism, iterative dilation, and a self-attention mechanism combined with a Squeeze-and-Excitation (SE) block to recalibrate feature responses by explicitly modeling interactions between different channels. …”
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Source-Free Model Transferability Assessment for Smart Surveillance via Randomly Initialized Networks by Wei-Cheng Wang, Sam Leroux, Pieter Simoens

“…In contrast, our method leverages embeddings generated by randomly initialized neural networks (RINNs) to construct task-agnostic reference embeddings without relying on pretraining. …”
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CrowNet: Open Source Modeling and Simulation of Pedestrian-2-X and Crowd Networks by Stefan Schuhback, Christina Maria Mayr, Jorg Ott, Lars Wischhof

“…One way to improve urban traffic is by networking road users. While V2X communication initially focused on vehicles, attention is now shifting towards pedestrian communication, particularly through wireless applications. …”
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Reconfiguration of brain network dynamics in bipolar disorder: a hidden Markov model approach by Xi Zhang, Lan Yang, Jiayu Lu, Yuting Yuan, Dandan Li, Hui Zhang, Rong Yao, Jie Xiang, Bin Wang

“…We utilized resting-state functional magnetic resonance imaging (rs-fMRI) data from 43 BD patients and 51 controls to evaluate the altered dynamic spatiotemporal architecture of the whole-brain network and identify unique activation patterns in BD. …”
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Constructing a prediction model for acute pancreatitis severity based on liquid neural network by Jie Cao, Shike Long, Huan Liu, Fu’an Chen, Shiwei Liang, Haicheng Fang, Ying Liu

“…This study aimed to develop a novel liquid neural network (LNN) model for predicting SAP. This study retrospectively analyzed the data of AP patients admitted to the Second Affiliated Hospital of Guilin Medical University between January 2020 and June 2024. …”
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Prediction of carbon dioxide phase at bottomhole by adaptive factorization network considering well geometry by Sungil Kim, Tea-Woo Kim, Yongjun Hong, Hoonyoung Jeong

“…This study addresses these challenges by introducing a deep learning framework based on the adaptive factorization network (AFN), which enhances CO2 phase prediction accuracy by leveraging feature interactions. …”
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Comparative Analysis of Multi-Omics Integration Using Graph Neural Networks for Cancer Classification by Fadi Alharbi, Aleksandar Vakanski, Boyu Zhang, Murtada K. Elbashir, Mohanad Mohammed

“…However, challenges related to addressing the high dimensionality and complexity in integrating multi-omics data, as well as in constructing graph structures that effectively capture the interactions between nodes, remain active areas of research. This study evaluates graph neural network architectures for multi-omics (MO) data integration based on graph-convolutional networks (GCN), graph-attention networks (GAT), and graph-transformer networks (GTN). …”
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Measuring transit-oriented development (TOD) network complementarity based on TOD node typology by Runjie Huang, Anna Grigolon, Mafalda Madureira, Mark Brussel

“…However, we argue that having such a TOD level value is not sufficient to understand the role each transit node plays within a TOD network. In other words, a transit node may have a low performance when evaluating its individual TOD level, but it may serve an important role within the TOD network, for example, as a feeder node. …”
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Integrated neural network framework for multi-object detection and recognition using UAV imagery by Mohammed Alshehri, Tingting Xue, Tingting Xue, Ghulam Mujtaba, Yahya AlQahtani, Nouf Abdullah Almujally, Ahmad Jalal, Ahmad Jalal, Hui Liu, Hui Liu, Hui Liu

“…Deep SORT is employed for efficient vehicle tracking, while CSRNet facilitates high-density vehicle counting. LSTM networks are integrated to predict vehicle trajectories based on temporal patterns, and a combination of DenseNet and SuperPoint is utilized for robust feature extraction. …”
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VSA-GCNN: Attention Guided Graph Neural Networks for Brain Tumor Segmentation and Classification by Kambham Pratap Joshi, Vishruth Boraiah Gowda, Parameshachari Bidare Divakarachari, Paramesh Siddappa Parameshwarappa, Raj Kumar Patra

“…Then, the segmented outputs are processed into feature extraction, where an AlexNet model is used to reduce the dimensionality. …”
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The outcome prediction method of football matches by the quantum neural network based on deep learning by Yang Sun, Hongyang Chu

“…The data are cleaned, standardized, and feature-engineered to meet the input requirements of neural network models. …”
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Multi-Step Peak Passenger Flow Prediction of Urban Rail Transit Based on Multi-Station Spatio-Temporal Feature Fusion Model by Jianan Sun, Xiaofei Ye, Xingchen Yan, Tao Wang, Jun Chen

“…A combination of a graph convolutional neural network and a Transformer is used. The model separately inputs land use (point of interest, POI) and station adjacency information as features into the BGCSTFFN model, using the Pearson correlation coefficient matrix, which is evaluated on real passenger flow dataset from 1 to 25 January 2019 in Hangzhou. …”
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An Objective Evaluation Method for Image Sharpness Under Different Illumination Imaging Conditions by Huan He, Benchi Jiang, Chenyang Shi, Yuelin Lu, Yandan Lin

“…Then, a particle swarm optimization-based general regression neural network (PSO-GRNN) is established to train the above extracted feature values, and the final blurred image evaluation result is determined. …”
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