Revealing Depression Through Social Media via Adaptive Gated Cross-Modal Fusion Augmented With Insights From Personality Traits by Gede Aditra Pradnyana, Wiwik Anggraeni, Eko Mulyanto Yuniarno, Mauridhi Hery Purnomo

“…To bridge this gap, we introduce DeXMAG, a novel personalized depression detection framework that integrates a Cross-Modal Attention mechanism with an Adaptive Gated Fusion strategy. …”
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A Novel Framework for Whole-Slide Pathological Image Classification Based on the Cascaded Attention Mechanism by Dehua Liu, Bin Hu

“…We developed a framework incorporating a cascaded attention mechanism, enhancing meaningful pattern recognition while suppressing irrelevant background information. …”
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Neuropsychological Performance: How Mental Health Drives Attentional Function in University-Level Football Athletes by Sacha Assadourian, Dima Daher, Catherine Leclerc, Antony Branco Lopes, Arnaud Saj

“…QEEG data revealed patterns associated with burnout, chronic pain, and insomnia among the athletes. …”
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An interpretable XAI deep EEG model for schizophrenia diagnosis using feature selection and attention mechanisms by Ahmad Almadhor, Stephen Ojo, Thomas I. Nathaniel, Shtwai Alsubai, Abdullah Alharthi, Abdullah Al Hejaili, Gabriel Avelino Sampedro

“…The study proposes a novel automated technique for diagnosing Schizophrenia based on electroencephalogram (EEG) sensor data, aiming to enhance interpretability and prediction performance.MethodsThis research utilizes Deep Learning (DL) models, including the Deep Neural Network (DNN), Bi-Directional Long Short-Term Memory-Gated Recurrent Unit (BiLSTM- GRU), and BiLSTM with Attention, for the detection of Schizophrenia based on EEG data. …”
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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

“…To address this, we propose Dual-Attention CNN-LSTM, an innovative hybrid deep learning model designed to enhance COVID-19 detection from CXR images. …”
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OXSeg: Multidimensional Attention UNet-Based Lip Segmentation Using Semi-Supervised Lip Contours by Hanie Moghaddasi, Christina Chambers, Sarah N. Mattson, Jeffrey R. Wozniak, Claire D. Coles, Raja Mukherjee, Michael Suttie

“…A further challenge with lip segmentation is its reliance on image quality, lighting, and skin tone, leading to inaccuracies in the detected boundaries. To address these challenges, we propose a sequential lip segmentation method that integrates attention UNet and multidimensional input. …”
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Analysis of China’s High-Speed Railway Network Using Complex Network Theory and Graph Convolutional Networks by Zhenguo Xu, Jun Li, Irene Moulitsas, Fangqu Niu

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Detection and Localization of Stationary Waves on Venus Using a Self‐Supervised Anomaly Detection Model by Husnu Baris Baydargil, Jose Eduardo Oliveira Silva, Yeon Joo Lee, Meeyoung Cha

“…Our model is developed over Akatsuki images of Venus and is trained on smaller cropped images containing features other than stationary waves, allowing it to learn the underlying patterns of normal cloud features. We enhance the model with spatial attention modules, skip connections, and latent space transformation to improve image reconstruction and avoid mixing stationary wave features with artifacts. …”
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Automated multi-class MRI brain tumor classification and segmentation using deformable attention and saliency mapping by Erfan Zarenia, Amirhossein Akhlaghi Far, Khosro Rezaee

“…To improve tumor characterization, we applied data augmentation techniques to MR images and developed a hierarchical multiscale deformable attention module (MS-DAM). This model effectively captures irregular and complex tumor patterns, enhancing classification performance. …”
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Paying attention to distortion: improving the accuracy of multistep-ahead carbon price forecasting with shape and temporal criteria by Tong Niu, Yuan Chen, Tao Li, Shaolong Sun, Weigang Zhao, Mingjian Cui, Jiakang Wang, Shiyu Han, Jiujiang Li, Yunkai Zhai

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Attention-based integrated deep neural network architecture for predicting the effectiveness of data center power usage by Yang-Cheng Shih, Sathesh Tamilarasan, Chin-Sheng Chen, Omid Ali Zargar, Yean-Der Kuan

“…The integration of convolutional layers processes hourly data inputs efficiently, reducing complexity and improving pattern detection. A subsequent flattening layer optimizes accuracy, while a dual-layered LSTM and a deep neural network delve into frequency, temporal dynamics, and complex data relationships. …”
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Towards interpretable drug interaction prediction via dual-stage attention and Bayesian calibration with active learning by Rongpei Li, Yufang Zhang, Heqi Sun, Shenggeng Lin, Guihua Jia, Yitian Fang, Chen Zhang, Xiaotong Song, Jianwei Zhao, Lyubin Hu, Yajing Yuan, Xueying Mao, Jiayi Li, Aman Kaushik, Dandan An, Dongqing Wei

“…Conclusion We present DABI-DDI, an integrated feature extraction framework that successfully addresses key challenges in DDIs prediction through three major innovations: Temporal pattern recognition, reducing false positives, and biological interpretability. …”
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CABAD: A video dataset for benchmarking child aggression recognition by Shehzad Ali, Md Tanvir Islam, Ik Hyun Lee, Mohammad Hijji, Khan Muhammad

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X-FASNet: cross-scale feature-aware with self-attention network for cognitive decline assessment in Alzheimer's disease by Wenhui Chen, Shunwu Xu, Yiran Peng, Yiran Peng, Hong Zhang, Jian Zhang, Huaihao Zheng, Hao Yan, Zhaowen Chen, Zhaowen Chen

“…Comprehensive experimentation across multiple neuroimaging datasets confirms that X-FASNet provides an effective computational framework for neurodegeneration assessment, characterized by enhanced detection of subtle anatomical variations and improved pathological pattern recognition.…”
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Gamma synchronization between the medial temporal lobe and medial frontal cortex for goal-directed visual attention in humans by Jie Zhang, Jing Xia, Huihui Zhou, Shuo Wang

“…We found that gamma-band synchronization between the MTL and MFC signaled target detection. Using two additional tasks, we dissociated the neural processes underlying working memory and search decision execution, revealing distinct patterns of synchronization. …”
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TFBlender: a hybrid time series attention model with data-driven macroeconomic perspectives for ELS Knock-In prediction by Kyungjun Lee, Juyeob Lee, Eunil Park

“…On the time-step token path, it captures both short- and long-term patterns in data, while on the feature token path, multi-head attention analyzes interactions among diverse features. …”
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MBFE-UNet: A Multi-Branch Feature Extraction UNet with Temporal Cross Attention for Radar Echo Extrapolation by Huantong Geng, Han Zhao, Zhanpeng Shi, Fangli Wu, Liangchao Geng, Kefei Ma

“…Additionally, we introduce a Temporal Cross Attention Fusion Unit to model the temporal correlation between features from different network layers, which helps the model to better capture the temporal evolution patterns of radar echoes. …”
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XABH-CNN-GRU: Explainable attention-based hybrid CNN-GRU model for accurate identification of common arrhythmias by Abduljabbar S. Ba Mahel, Fahad Mushabbab G. Alotaibi, Zenebe Markos Lonseko, Ni-Ni Rao

“…This model is designed to capitalize on the unique features of arrhythmic patterns and enhance classification metrics. Innovative techniques employed within the methodology are detailed to elucidate the rationale behind their selection and their anticipated contributions to improved model performance. …”
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Driving Fatigue Onset and Visual Attention: An Electroencephalography-Driven Analysis of Ocular Behavior in a Driving Simulation Task by Andrea Giorgi, Gianluca Borghini, Francesca Colaiuda, Stefano Menicocci, Vincenzo Ronca, Alessia Vozzi, Dario Rossi, Pietro Aricò, Rossella Capotorto, Simone Sportiello, Marco Petrelli, Carlo Polidori, Rodrigo Varga, Marteyn Van Gasteren, Fabio Babiloni, Gianluca Di Flumeri

“…This paper presents evidence that an EEG-driven approach can be used to detect the onset of fatigue while driving and to study the related visual attention patterns. …”
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Distributed Abnormal Activity Detection in Smart Environments by Chengliang Wang, Qian Zheng, Yayun Peng, Debraj De, Wen-Zhan Song

“…The abnormal activity detection in smart environments has experienced increasing attention over years, due to its usefulness in pervasive applications. …”
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