EEG-Based Attention Classification for Enhanced Learning Experience by Madiha Khalid Syed, Hong Wang, Awais Ahmad Siddiqi, Shahnawaz Qureshi, Mohamed Amin Gouda

“…These extracted features are then fed into a k-NN classifier, which accurately distinguishes between high and low attention brain wave patterns, with the labels derived from the quiz performance indicating high or low attention. …”
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Temporal-Aware Transformer Approach for Violence Activity Recognition by Rajdeep Chatterjee, Ritabrata Roy Choudhury, Mahendra Kumar Gourisaria, Sreejata Banerjee, Soumik Dey, Manoj Sahni, Ernesto Leon-Castro

“…Using advances in artificial intelligence (AI) and computer vision, this research presents a scalable deep learning architecture for real-time violence detection using two approaches. In the first approach, Convolutional Neural Networks (CNN) and bidirectional long-short-term memory (BiLSTM) networks are combined, where MobileNetV2 is used for spatial feature extraction and BiLSTM for temporal pattern recognition, achieving an accuracy of 95.6%. …”
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Global trends of depressive disorders among women of reproductive age from 1990 to 2021: a systematic analysis of burden, sociodemographic disparities, and health workforce correla... by Fangyi Dai, Yuzhou Cai, Min Chen, Yong Dai

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Cognitive mechanisms and temporal dynamics of negative emotion in facilitating congruency judgments by Yiheng Chen, Feier Fu, Qiwei Zhao, Yueyi Ding, Yingzhi Lu

“…Behavioral and hierarchical drift-diffusion model results showed that negative emotions enhanced judgments by accelerating evidence accumulation and improving incongruency detection. ERP analysis revealed larger P1 and late positive potential (LPP) components in response to negative emotions, which indicated stronger early attention capture and sustained emotional processing. …”
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Automatic Detection for Mining Subsidence Areas Using the CBAM-Enhanced VGG-UNet Model With Long Time Series InSAR Interferograms by Kegui Jiang, Keming Yang, Mengting Gao, Liuguo Zhu, Chuang Jiang

“…First, this study designs a VGG-UNet model enhanced by an attention mechanism module to learn and detect mining subsidence areas. …”
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Effect of attentional bias modification on pre-competition anxiety in athletes by Jing Zhao, Yuhan Yang, Heng Zhang, Yu Nie, Qiulin Wang

“…Attentional bias correction training (ABMT) aims to modify these attention patterns with the aim of alleviating anxiety symptoms. …”
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Transformer attention fusion for fine grained medical image classification by Danyal Badar, Junaid Abbas, Raed Alsini, Tahir Abbas, Wang ChengLiang, Ali Daud

“…This model uses self-attention mechanics to improve spatial connections between single scales and cross-attention to automatically match feature patterns across multiple scales, thereby developing a comprehensive information structure. …”
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Decoding dynamic brain networks in Parkinson’s disease with temporal attention by Salil B Patel, James J FitzGerald, Chrystalina A Antoniades

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ANALYSIS AND MINIMIZATION OF THE RISKS OF HARMFUL DEPENDENCIES IN THE PROCESS OF CONTINUOUS INTEGRATION AND IMPLEMENTATION OF SOFTWARE CODE by Danil N. Alekseev, Renat M. Khamitov

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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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Genomic technologies to improve variation identification in undiagnosed diseases by Joseph T.C. Shieh

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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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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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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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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

“…Together, these enhancements create a more effective process for identifying disease pattern in wide range of plant species. The model was evaluated using an extensive crop disease dataset and against state-of-the-art methods such as EffiNet-TS, PlantXViT, and MobileNet V2 to assess its effectiveness. …”
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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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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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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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