Biotic interactions and stream network position affect body size of aquatic vertebrates across watersheds by Brooke E. Penaluna, Sherri L. Johnson, Amanda M. M. Pollock, Ivan Arismendi, Dana R. Warren

“…We reveal a biogeographic pattern in which body size peaks in middle stream‐network positions and plateaus or declines at lower and upper locations, proposing that stream network position also plays a role in determining body size in small watersheds. …”
Get full text

Spatiotemporal Evolution Mechanism and Spatial Correlation Network Effect of Resilience in Different Shrinking Cities in China by Weijun Yu, Siyu Zhang, Entao Pang, Huihui Wang, Yunsong Yang, Yuhao Zhong, Tian Jing, Hongguang Zou

“…Finally, this paper empirically examines the spatial correlation network effects of UR under various US scenarios using a social network analysis model. …”
Get full text

Fairness-Aware Graph Neural Networks for ICU Length of Stay Prediction in IoT-Enabled Environments by Angelos Christos Maroudis, Konstantina Karathanasopoulou, Charithea C. Stylianides, George Dimitrakopoulos, Andreas S. Panayides

“…Motivated by the concept of “fairness through unawareness,” our proposed framework employs a demographic feature exclusion strategy, preventing access to potentially discriminatory information, and thus enforcing an inductive bias that redirects learning toward non-discriminatory pattern dependencies. To address the loss of static information, we introduce a custom graph neural network that dynamically reconstructs patient relationships over time, adapting from static demographics to evolving inter-patient correlations via multi-modal embeddings (e.g., medications, procedures, vitals, conditions) and learned feature-driven edge formation. …”
Get full text

Brain Network Analysis Reveals Age-Related Differences in Topological Reorganization During Vigilance Decline by Lingyun Gao, Rui Zhang, Mengru Xu, Yi Sun, Gang Li, Yu Sun

“…Subsequently, EEG functional connectivity was constructed and the differences in network topological properties between the two groups were quantitatively evaluated based on global and nodal metrics. …”
Get full text

Subtypes detection of papillary thyroid cancer from methylation assay via Deep Neural Network by Andrea Colacino, Andrea Soricelli, Michele Ceccarelli, Ornella Affinito, Monica Franzese

“…Methods: To address this issue, we first performed a pan-cancer analysis to train a convolutional 1-D Neural Network (CNN) using supervised learning. Then, we evaluated the robustness of the net on an independent PTC dataset and assessed its ability to classify normal (N=56) versus tumor (N=461) samples and fvPTC (N=102) versus cvPTC (N=359). …”
Get full text

Spatial distribution and source apportionment of nitrogen in typical plain river networks and bacterial community response by Aiju You, Aiju You, Qiaoxi Zheng, Pengcheng Yao, Pengcheng Yao

“…IntroductionThe Yubei plain river network (YPRN) is confined and has poor hydrodynamics, resulting in the accumulation of pollutants. …”
Get full text

Convolutional Neural Network Based Vehicle Classification in Adverse Illuminous Conditions for Intelligent Transportation Systems by Muhammad Atif Butt, Asad Masood Khattak, Sarmad Shafique, Bashir Hayat, Saima Abid, Ki-Il Kim, Muhammad Waqas Ayub, Ahthasham Sajid, Awais Adnan

“…Based on better performance, ResNet architecture is further improved by adding a new classification block in the network. To ensure generalization, we fine-tuned the network on the public VeRi dataset containing 50,000 images, which have been categorized into six vehicle classes. …”
Get full text

Investigating the Complexity of Multidimensional Symptom Experiences in Patients With Cancer: Systematic Review of the Network Analysis Approach by Vincent Richard, Allison Gilbert, Emanuela Pizzolla, Giovanni Briganti

“…Among these, 3 evaluated the longitudinal changes in symptom networks across chemotherapy cycles, and 1 evaluated changes during radiotherapy. …”
Get full text

Identifying the effectiveness of face mask in a large population with a network-based fluid model. by Akshay Anand, Kourosh Shoele

“…It is argued that subtle variations in facial features, especially the zygomatic arch, significantly influence leakage patterns, emphasizing the importance of customized mask designs. …”
Get full text

DFANet: A Deep Feature Attention Network for Building Change Detection in Remote Sensing Imagery by Peigeng Lu, Haiyong Ding, Xiang Tian

“…Second, we introduce a GatedConv module to improve the network’s capability for building edge detection. Finally, Transformer is introduced to capture long-range dependencies across bitemporal images, enabling the network to better understand feature change patterns and the relationships between different regions and land cover categories. …”
Get full text

GNNSeq: A Sequence-Based Graph Neural Network for Predicting Protein–Ligand Binding Affinity by Somanath Dandibhotla, Madhav Samudrala, Arjun Kaneriya, Sivanesan Dakshanamurthy

“…To overcome these limitations, we developed GNNSeq, a novel hybrid machine learning model that integrates a Graph Neural Network (GNN) with Random Forest (RF) and XGBoost. …”
Get full text

Generative Adversarial Network for Real‐Time Flash Drought Monitoring: A Deep Learning Study by Ehsan Foroumandi, Keyhan Gavahi, Hamid Moradkhani

“…Comparative assessments reveal the proposed GAN's superior ability to replicate SSI values over U‐Net and Naïve models. Evaluation metrics further underscore that the developed GAN successfully identifies both fine‐ and coarse‐scale spatial drought patterns and abrupt changes in the SSI temporal patterns that is important for flash drought identification.…”
Get full text

Efficient IDS for IoT Networks Using Host-Based Data Aggregation and Multi-Entropy Analysis by Yusei Katsura, Arata Endo, Ismail Arai, Kazutoshi Fujikawa

Get full text

Spatiotemporal Flood Hazard Classification in Bangkok Using Graph Convolutional Network and Temporal Fusion Transformer by Pakpoom Chaimook, Nirattaya Khamsemanan, Cholwich Nattee, Alice Sharp

“…Traditional flood prediction models often fail to capture spatial correlations across districts and the temporal patterns within different types of features. To address this problem, this study proposes a hybrid deep learning framework combining Graph Convolution Network (GCN) and the Temporal Fusion Transformer (TFT) for predicting flood hazard levels in 50 Bangkok districts. …”
Get full text

Detection of Student Engagement via Transformer-Enhanced Feature Pyramid Networks on Channel-Spatial Attention by A. Naveen, I. Jeena Jacob, Ajay Kumar Mandava

“…Traditional techniques for evaluating student engagement are often time-consuming and subjective. …”
Get full text

Hierarchical data modeling: A systematic comparison of statistical, tree-based, and neural network approaches by Marzieh Amiri Shahbazi, Nasibeh Azadeh-Fard

“…The evaluation framework integrated quantitative metrics and qualitative factors, including analyses across varying sample sizes, simplified hierarchies, and a separate intensive-care dataset. …”
Get full text

Detection of Steel Reinforcement in Concrete Using Active Microwave Thermography and Neural Network-Based Analysis by Barbara Szymanik, Maja Kocoń, Sam Ang Keo, Franck Brachelet, Didier Defer

“…These images served as training data for a deep neural network designed to identify and localize rebar positions based on thermal patterns. …”
Get full text

GNSTAM: Integrating Graph Networks With Spatial and Temporal Signature Analysis for Enhanced Android Malware Detection by Yogesh Kumar Sharma, Deepak Singh Tomar, R. K. Pateriya, Surendra Solanki

“…To model the intricate relationships between applications, an efficient Graph Neural Network (GNN) process is utilized. Incorporating transformers, sequences of system and API calls are analyzed, harnessing this ability to discern patterns indicative of malicious activities. …”
Get full text

Investigation of a transformer-based hybrid artificial neural networks for climate data prediction and analysis by Shangke Liu, Ke Liu, Zheng Wang, Yuanyuan Liu, Bin Bai, Rui Zhao

“…Firstly, the Transformer model is introduced to capture the complex patterns in cimate data time series through its powerful sequence modeling capabilities. …”
Get full text

Spotting Leaders in Organizations with Graph Convolutional Networks, Explainable Artificial Intelligence, and Automated Machine Learning by Yunbo Xie, Jose D. Meisel, Carlos A. Meisel, Juan Jose Betancourt, Jianqi Yan, Roberto Bugiolacchi

“…In addition, behavioral theory posits that leaders can be distinguished based on their daily conduct, while social network analysis provides valuable insights into behavioral patterns. …”
Get full text