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. …”
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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. …”
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Dietary habits and complex food relations in Northwest China: a population-based network analysis by Xinhua Wang, Duolao Wang, Shaonong Dang, Baibing Mi, Hong Yan, Yuhong Zhang, Yijun Kang, Jianghong Dai, Jing Hui, Samuel Chacha, Huang Yan, Zongkai Li, Jiaxin Cai, Fuchang Ma

“…The staple food-related food network indicated that the intake of rice, whole grains and beans, and potatoes was positively correlated with the intake of most other foods, while intake of wheat was negatively correlated with foods of animal source of food, milk and dairy products.Conclusions Northwest China’s diet exhibits irrational patterns, highlighting the importance of assessing overall dietary patterns in nutritional evaluation.…”
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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. …”
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Detection of Student Engagement via Transformer-Enhanced Feature Pyramid Networks on Channel-Spatial Attention by A. Naveen, I. Jeena Jacob, Ajay Kumar Mandava

“…The proposed approach automatically analyses student engagement patterns, such as body posture, eye contact, and head position, from visual data streams by integrating cutting-edge deep learning and computer vision techniques. …”
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Hierarchical data modeling: A systematic comparison of statistical, tree-based, and neural network approaches by Marzieh Amiri Shahbazi, Nasibeh Azadeh-Fard

“…Results demonstrate that tree-based approaches consistently outperform alternatives in predictive accuracy and explanation of variance while maintaining computational efficiency. These performance patterns remain generally consistent across sample sizes, simplified hierarchies, and the external dataset. …”
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Exploring Applications of Convolutional Neural Networks in Analyzing Multispectral Satellite Imagery: A Systematic Review by Antonia Ivanda, Ljiljana Šerić, Maja Braović

“…For 4D-CNN, there are limited number of studies, and all of them use segmentation; (3) This study shows that 2D-CNNs prevail in all application domains, but 3D-CNNs prove to be better for spatio-temporal pattern recognition, more specifically in agricultural and environmental monitoring applications. 1D-CNNs are less common compared to 2D-CNNs and 3D-CNNs, but they show good performance in spectral analysis tasks. 4D-CNNs are more complex and still underutilized, but they have potential for complex data analysis. …”
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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. …”
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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. …”
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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. …”
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Attention-enhanced hybrid CNN–LSTM network with self-adaptive CBAM for COVID-19 diagnosis by Fatin Nabilah Shaari, Aimi Salihah Abdul Nasir, Wan Azani Mustafa, Wan Aireene Wan Ahmed, Abdul Syafiq Abdull Sukor

“…Additionally, this study introduces five pre-trained-LSTM models, leveraging transfer learning to enhance CXR pattern recognition and serving as comparative models for the proposed Dual-Attention CNN-LSTM. …”
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Classifying IoT Botnet Attacks With Kolmogorov-Arnold Networks: A Comparative Analysis of Architectural Variations by Phuc Hao do, Tran Duc Le, Truong Duy Dinh, van Dai Pham

“…The rapid expansion of devices on the Internet of Things (IoTs) has led to a significant rise in IoT botnet attacks, creating an urgent need for advanced detection and classification methods. This study aims to evaluate the effectiveness of Kolmogorov-Arnold Networks (KANs) and their architectural variations in classifying IoT botnet attacks, comparing their performance with traditional machine learning and deep learning models. …”
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Forecasting Residential Energy Consumption with the Use of Long Short-Term Memory Recurrent Neural Networks by Zurisaddai Severiche-Maury, Carlos Eduardo Uc-Rios, Wilson Arrubla-Hoyos, Dora Cama-Pinto, Juan Antonio Holgado-Terriza, Miguel Damas-Hermoso, Alejandro Cama-Pinto

“…This dual structure enhances accuracy by capturing both device-specific consumption patterns and overall household energy use, facilitating informed decision-making at multiple levels. …”
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Explaining neural networks for detection of tropical cyclones and atmospheric rivers in gridded atmospheric simulation data by T. Radke, S. Fuchs, C. Wilms, I. Polkova, I. Polkova, I. Polkova, M. Rautenhaus, M. Rautenhaus

“…For ARs, relevant patterns include elongated bands of high TMQ and eastward winds. …”
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Penetration Testing and Machine Learning-Driven Cybersecurity Framework for IoT and Smart City Wireless Networks by Tamara Zhukabayeva, Zulfiqar Ahmad, Aigul Adamova, Nurdaulet Karabayev, Yerik Mardenov, Dina Satybaldina

“…Anomalies were identified using an optimized Isolation Forest model, revealing patterns such as unusual activity involving the Tenda_476300 WiFi network. …”
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GNNSeq: A Sequence-Based Graph Neural Network for Predicting Protein–Ligand Binding Affinity by Somanath Dandibhotla, Madhav Samudrala, Arjun Kaneriya, Sivanesan Dakshanamurthy

“…While existing sequence-based machine learning models for binding affinity prediction have shown potential, they lack accuracy and robustness in pattern recognition, which limits their generalizability across diverse and novel binding complexes. …”
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vBerlinV2N: Recreating a Cellular Network Measurement Campaign With Simulations by Christian L. Vielhaus, Mauri Seidel, Vincent Latzko, Alexander Grob, Peter Sossalla, Martin Reisslein, Frank H. P. Fitzek

“…System-level simulations play a crucial role in evaluating the behaviors of cellular networks, yet most studies rely on synthetic simulation scenarios rather than reproducing realistic network deployments. …”
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Integration of Artificial Neural Network Regression and Principal Component Analysis for Indoor Visible Light Positioning by Negasa Berhanu Fite, Getachew Mamo Wegari, Heidi Steendam

“…In this study, user movement is simulated using a constructed dataset with systematically varied receiver positions, reflecting realistic motion patterns rather than real-time movement. While the experimental setup considers a fixed obstacle scenario, the training and testing datasets incorporate position variations to emulate user displacement. …”
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Resilience evolution and optimization strategies of ecological networks in the Three Gorges Reservoir Area: A scenario-based simulation approach by Haohua Wang, Lulu Zhou, Kangchuan Su, Yun Zhou, Qingyuan Yang

“…This study focuses on the Three Gorges Reservoir Area (TGRA), addressing human-land relationship conflicts by constructing an EN system and analyzing its spatiotemporal evolution from 2001 to 2023. To evaluate network resilience, this study employs node attack simulation methods, we dynamically assessed EN resilience through four functional and structural indicators. …”
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Reinforced liquid state machines—new training strategies for spiking neural networks based on reinforcements by Dominik Krenzer, Martin Bogdan, Martin Bogdan, Martin Bogdan

“…IntroductionFeedback and reinforcement signals in the brain act as natures sophisticated teaching tools, guiding neural circuits to self-organization, adaptation, and the encoding of complex patterns. This study investigates the impact of two feedback mechanisms within a deep liquid state machine architecture designed for spiking neural networks.MethodsThe Reinforced Liquid State Machine architecture integrates liquid layers, a winner-takes-all mechanism, a linear readout layer, and a novel reward-based reinforcement system to enhance learning efficacy. …”
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