Detecting Fraudulent Transactions for Different Patterns in Financial Networks Using Layer Weigthed GCN by Shaziya Islam, Gagan Raj Gupta, Apu Chakraborty, Santosh Singh, Anisha Soni, Chhavi Patle

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Insider threat detection for specific threat scenarios by Tian Tian, Chen Zhang, Bo Jiang, Huamin Feng, Zhigang Lu

“…The multi-head attention mechanism simultaneously attends to multiple positions in the behavior sequence, capturing potential correlations between behaviors and user behavior patterns. …”
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Cross-attention swin-transformer for detailed segmentation of ancient architectural color patterns by Lv Yongyin, Yu Caixia

“…These methods struggle with balancing precision and computational efficiency, especially when dealing with complex patterns and high-resolution images.MethodsTo address these limitations, we propose a novel segmentation model that integrates a hierarchical vision transformer backbone with multi-scale self-attention, cascaded attention decoding, and diffusion-based robustness enhancement. …”
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Multiscale deformed attention networks for white blood cell detection by Xin Zheng, Qiqi Xu, Shiyi Zheng, Luxian Zhao, Deyang Liu, Liangliang Zhang

“…To tackle the large foreground-background differences in WBC images, this paper introduces a novel WBC detection method, named the Multi-Scale Cross-Deformation Attention Fusion Network (MCDAF-Net), which combines CNNs and Transformers. …”
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Voice-AttentionNet: Voice-Based Multi-Disease Detection with Lightweight Attention-Based Temporal Convolutional Neural Network by Jintao Wang, Jianhang Zhou, Bob Zhang

“…To address this challenge, we propose a voice-based multi-disease detection approach with a lightweight attention-based temporal convolution neural network (Voice-AttentionNet) designed to analyze speech data for multi-class disease classification. …”
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Graph-Attention Diffusion for Enhanced Multivariate Time-Series Anomaly Detection by Vadim Lanko, Ilya Makarov

“…In this article, we propose a novel reconstruction-based approach that enhances normal pattern learning through data masking and leverages diffusion models to capture both temporal and spatial interrelations via graph-attention layers. …”
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Development of the relationship between visual selective attention and auditory change detection by Yuanjun Kong, Xuye Yuan, Yiqing Hu, Bingkun Li, Dongwei Li, Jialiang Guo, Meirong Sun, Yan Song

“…Our one recent study has shown a positive correlation between the event-related potential (ERP) amplitudes associated with visual selective attention (posterior contralateral N2) and auditory change detection (mismatch negativity) in adults, suggesting an intimate relationship and potential shared mechanism between visual selective attention and auditory change detection. …”
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Adaptive Video Anomaly Detection by Attention-Based Relational Knowledge Distillation by Burcak Asal, Ahmet Burak Can

“…Detecting anomaly patterns in videos is a challenging task due to complex scenes, huge diversity of anomalies, and fuzzy nature of the task. …”
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Attention dual transformer with adaptive temporal convolutional for diabetic retinopathy detection by Mishmala Sushith, Ajanthaa Lakkshmanan, M. Saravanan, S. Castro

“…Abstract An Attention Dual Transformer with Adaptive Temporal Convolutional (ADT-ATC) model is proposed in this research work for enhanced detection of Diabetic Retinopathy (DR) from retinal fundus images. …”
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A linear oscillator model predicts dynamic temporal attention and pupillary entrainment to rhythmic patterns by Lauren K. Fink, Brian K. Hurley, Joy J. Geng, Petr Janata

“…During a deviance detection task, participants listened to continuously looping, multi- instrument, rhythmic patterns, while being eye-tracked. …”
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Temporal Logical Attention Network for Log-Based Anomaly Detection in Distributed Systems by Yang Liu, Shaochen Ren, Xuran Wang, Mengjie Zhou

“…Our approach makes three key contributions: (1) a temporal logical attention mechanism that explicitly models both time-series patterns and logical dependencies between log events across distributed components, (2) a multi-scale feature extraction module that captures system behaviors at different temporal granularities while preserving causal relationships, and (3) an adaptive threshold strategy that dynamically adjusts detection sensitivity based on system load and component interactions. …”
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Detecting Lameness in Dairy Cows Based on Gait Feature Mapping and Attention Mechanisms by Xi Kang, Junjie Liang, Qian Li, Gang Liu

“…This limitation is exacerbated by the distinct kinematic patterns exhibited across lameness severity grades, ultimately reducing detection accuracy. …”
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Vision transformer embedded video anomaly detection using attention driven recurrence by Ummay Maria Muna, Shanta Biswas, Syed Abu Ammar Muhammad Zarif, Philip Jefferson Deori, Tauseef Tajwar, Swakkhar Shatabda

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Harnessing self-supervised learning to boost malicious traffic detection with enhanced attention by SUN Jianwen, ZHANG Bin, LI Hongyu, CHANG Heyu

“…The existing deep learning-based malicious traffic detection methods generally suffered from three main problems: labeled sample scarcity, inadequate representation of malicious behavior traffic features, and a high false positive rate due to ineffective integration of behavioral association patterns during detection. …”
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ADA-NAF: Semi-Supervised Anomaly Detection Based on the Neural Attention Forest by Andrey Ageev, Andrei Konstantinov, Lev Utkin

“…In this study, we present a novel model called ADA-NAF (Anomaly Detection Autoencoder with the Neural Attention Forest) for semi-supervised anomaly detection that uniquely integrates the Neural Attention Forest (NAF) architecture which has been developed to combine a random forest classifier with a neural network computing attention weights to aggregate decision tree predictions. …”
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Attention-based multi-scale convolution and conformer for EEG-based depression detection by Ze Yan, Ze Yan, Ze Yan, Yumei Wan, Xin Pu, Xiaolin Han, Mingming Zhao, Haiyan Wu, Wentao Li, Xueying He, Yunshao Zheng

“…Depression is a common mental health issue, and early detection is crucial for timely intervention. This study proposes an end-to-end EEG-based depression recognition model, AMCCBDep, which combines Attention-based Multi-scale Parallel Convolution (AMPC), Conformer, and Bidirectional Gated Recurrent Unit (BiGRU). …”
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MTAD-TF: Multivariate Time Series Anomaly Detection Using the Combination of Temporal Pattern and Feature Pattern by Q. He, Y. J. Zheng, C.L. Zhang, H. Y. Wang

“…Our article proposes an unsupervised multivariate time series anomaly detection. In the prediction part, multiscale convolution and graph attention network are mainly used to capture information in temporal pattern with feature pattern. …”
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Video anomaly detection via cross-modal fusion and hyperbolic graph attention mechanism by JIANG Di, LAI Huicheng, WANG Liejun

“…Finally, a hyperbolic graph attention mechanism was incorporated to effectively capture the hierarchical relationships between normal and abnormal representations through the pattern separation property of hyperbolic space, thereby improving detection accuracy. …”
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Deep Learning-Based Video Anomaly Detection Using Optimised Attention-Enhanced Autoencoders by Anjali S, Don S

“…Through the reconstruction of normal patterns and the computation of reconstruction error in relation to ground truth, convolutional autoencoders detect anomalies. …”
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Rhythm-Based Attention Analysis: A Comprehensive Model for Music Hierarchy by Fangzhen Zhu, Changhao Wu, Qike Huang, Na Zhu, Tuo Leng

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