Neural mechanisms by which attention modulates the comparison of remembered and perceptual representations. by Bo-Cheng Kuo, Duncan E Astle

“…A no-cue condition was also included. When attention cannot be effectively deployed in advance (i.e. following the simultaneous-cues), we observed a distributed and extensive activation pattern in the prefrontal and parietal cortices in support of successful change detection. …”
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MMG-Based Motion Segmentation and Recognition of Upper Limb Rehabilitation Using the YOLOv5s-SE by Gangsheng Cao, Shen Jia, Qing Wu, Chunming Xia

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Lightweight Deep Learning Model for Fire Classification in Tunnels by Shakhnoza Muksimova, Sabina Umirzakova, Jushkin Baltayev, Young-Im Cho

“…This model integrates MobileNetV3 for spatial feature extraction, Temporal Convolutional Networks (TCNs) for temporal sequence analysis, and advanced attention mechanisms, including Convolutional Block Attention Modules (CBAMs) and Squeeze-and-Excitation (SE) blocks, to prioritize critical features such as flames and smoke patterns while suppressing irrelevant noise. …”
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Separable sustained and selective attention factors are apparent in 5-year-old children. by Mette Underbjerg, Melanie S George, Poul Thorsen, Ulrik S Kesmodel, Erik L Mortensen, Tom Manly

“…Here we examine whether this pattern is detectable in 5-year-old children from the healthy population. …”
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Attention-fused residual transformer CNN for robust lower limb movement recognition by A. Anitha, D. Jeraldin Auxillia

“…The AF-RT-CNN architecture combines residual blocks, attention mechanism and Transformer Encoder aiding robust feature extraction, good generalization capability and pattern recognition. …”
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Spatial attention-guided pre-trained networks for accurate identification of crop diseases by Satrughan Kumar, S. S. Aravinth, Sreedhar Kollem, Munish Kumar, Quadri Noorulhasan Naveed, Azath Mubarakali, Abhishek Bhattacherjee, Addisu Frinjo Emma

“…To address these challenges, we introduce an enhanced crop disease classification framework that incorporates EfficientNet-B3 with an ancillary convolutional layer and a spatial attention module (ACSA). EfficientNet-B3 offers a strong foundation for feature extraction due to its compound scaling and efficient computation, while the spatial attention module improves classification accuracy by directing the model to focus on critical regions of diseased leaves. …”
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Segmentation of Low-Grade Brain Tumors Using Mutual Attention Multimodal MRI by Hiroyuki Seshimo, Essam A. Rashed

“…This study focuses on enabling multimodal MRI sequences to advance the automatic segmentation of low-grade astrocytomas, a challenging task due to their diffuse and irregular growth patterns. A novel mutual-attention deep learning framework is proposed, which integrates complementary information from multiple MRI sequences, including T2-weighted and fluid-attenuated inversion recovery (FLAIR) sequences, to enhance the segmentation accuracy. …”
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Nonparametric analysis of inter‐individual relations using an attention‐based neural network by Takashi Morita, Aru Toyoda, Seitaro Aisu, Akihisa Kaneko, Naoko Suda‐Hashimoto, Ikuma Adachi, Ikki Matsuda, Hiroki Koda

“…The high interpretability of the attention mechanism and flexibility of the entire neural network allow for automatic detection of inter‐individual relations included in the raw data, without requiring prior knowledge/assumptions about what modes/types of relations are included in the data. …”
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Can attention-deficit/hyperactivity disorder be considered a form of cerebellar dysfunction? by Valeria Isaac, Vladimir Lopez, Maria Josefina Escobar

“…We suggest considering more rigorous assessments in future ADHD studies, including cerebellar-associated skill evaluations to correlate with symptom severity and other detected outcomes, such as executive dysfunction, and study possible associative patterns that may serve as more objective measures for this diagnosis.…”
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Optimized classification of potato leaf disease using EfficientNet-LITE and KE-SVM in diverse environments by Gopal Sangar, Velswamy Rajasekar

“…EfficientNet-LITE improves the model's emphasis on pertinent features through Channel Attention (CA) and 1-D Local Binary Pattern (LBP), while preserving computational economy with a reduced model size of 12.46 MB, fewer parameters at 3.11M, and a diminished FLOP count of 359.69 MFLOPs.ResultsBefore optimization, the SVM classifier attained an accuracy of 79.38% on uncontrolled data and 99.07% on laboratory-controlled data. …”
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Statistical learning re-shapes the center-surround inhibition of the visuo-spatial attentional focus by Andrea Massironi, Carlotta Lega, Luca Ronconi, Emanuela Bricolo

“…Abstract To effectively navigate a crowded and dynamic visual world, our neurocognitive system possesses the remarkable ability to extract and learn its statistical regularities to implicitly guide the allocation of spatial attention resources in the immediate future. The way through which we deploy attention in the visual space has been consistently outlined by a “center-surround inhibition” pattern, wherein a ring of sustained inhibition is projected around the center of the attentional focus to optimize the signal–noise ratio between goal-relevant targets and interfering distractors. …”
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SDMA-Net: Swin Transformer-Based Dynamic Memory-Attention Network for Endoscopic Navigation by Runnan Zhang, Qi Tian, Jinghui Chu, Wei Lu

“…Nevertheless, endoscopic video data often exhibit low texture, variable lighting, and dynamic motion patterns, which poses significant challenges to existing methods. …”
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Task-dependent eye-movement patterns in viewing art by Nino Sharvashidze, Alexander C Schütz

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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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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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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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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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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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