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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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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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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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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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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JMoE-FAP: A novel model for telecom network fraud victimization pattern analysis by Tuo Shi, Jing Hu, Danyang Li, Min Chen

“…The identified patterns can be used to design focused awareness campaigns, enhance fraud detection algorithms, and improve law enforcement training, thereby significantly increasing the effectiveness of anti-fraud initiatives.…”
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Flower Automata Pattern-Based Discrimination of Fibromyalgia From Control Subjects Using Fusion of Sleep EEG and ECG Signals by Prabal Datta Barua, Makiko Kobayashi, Sengul Dogan, Mehmet Baygin, Turker Tuncer, Jose Kunnel Paul, Thomas Iype, U. R. Acharya

Subjects: “…automata-based dynamic patterns…”
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Temperature and Precipitation Extremes in the Brazilian Legal Amazon: A Summary of Climatological Patterns and Detected Trends by Wanderson Luiz-Silva, Anna Carolina Fernandes Bazzanela, Claudine Pereira Dereczynski, Antonio Carlos Oscar-Júnior, Igor Pinheiro Raupp

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An improved multi‐scale YOLOv8 for apple leaf dense lesion detection and recognition by Shixin Huo, Na Duan, Zhizheng Xu

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SLEEP-SAFE: Self-Supervised Learning for Estimating Electroencephalogram Patterns With Structural Analysis of Fatigue Evidence by Wonjun Ko, Jeongwon Choe, Jonggu Kang

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STID-Net: Optimizing Intrusion Detection in IoT with Gradient Descent by James Deva Koresh Hezekiah, Usha Nandini Duraisamy, Kalaichelvi Nallusamy, Avudaiammal Ramalingam, Saranya Chandran, Murugesan Rajeswari Thiyagupriyadharsan, Periasamy Selvaraju, Rajagopal Maheswar

“…Unlike traditional models, STID-Net has an improved ability to identify irregular patterns in dynamic datasets. This work is also equipped with an attention mechanism for enhancing the detection of long-term dependencies in intrusion patterns. …”
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QPPLab: A generally applicable software package for detecting, analyzing, and visualizing large-scale quasiperiodic spatiotemporal patterns (QPPs) of brain activity by Nan Xu, Behnaz Yousefi, Nmachi Anumba, Theodore J. LaGrow, Xiaodi Zhang, Shella Keilholz

“…Quasi-periodic patterns (QPPs) are prominent spatiotemporal brain dynamics observed in functional neuroimaging data, reflecting the alternation of high and low activity across brain regions and their propagation along cortical gradients. …”
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Task-dependent eye-movement patterns in viewing art by Nino Sharvashidze, Alexander C Schütz

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A Deep Learning Method for Improving Community Multiscale Air Quality Forecast: Bias Correction, Event Detection, and Temporal Pattern Alignment by Ioannis Stergiou, Nektaria Traka, Dimitrios Melas, Efthimios Tagaris, Rafaella-Eleni P. Sotiropoulou

“…A comparison of four loss functions—Mean Square Error (MSE), Huber, Asymmetric Mean Squared Error (AMSE), and Quantile Loss—revealed that MSE offered balanced performance, Huber Loss achieved the highest reduction in systematic RMSE, and AMSE performed best in peak detection. Additionally, four deep learning architectures were evaluated: baseline CNN-LSTM, a hybrid model with attention mechanisms, a transformer-based model, and an End-to-End framework. …”
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Uncovering abnormal gray and white matter connectivity patterns in Alzheimer’s disease spectrum: a dynamic graph theory analysis for early detection by Juanjuan Jiang, Tao Kang, Ronghua Ling, Yingqian Liu, Jiuai Sun, Yiming Li, Xiaoou Li, Hui Yang, Bingcang Huang, the Alzheimer’s Disease Neuroimaging Initiative

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Transfer learning based deep architecture for lung cancer classification using CT image with pattern and entropy based feature set by Nithya R, Vidhyapathi C.M

“…Abstract Early detection of lung cancer, which remains one of the leading causes of death worldwide, is important for improved prognosis, and CT scanning is an important diagnostic modality. …”
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AI-Powered Eye Tracking for Bias Detection in Online Course Reviews: A Udemy Case Study by Hedda Martina Šola, Fayyaz Hussain Qureshi, Sarwar Khawaja

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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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Modeling and Classification of the Behavioral Patterns of Students Participating in Online Examination by B. J. Ferdosi, M. Rahman, A. M. Sakib, T. Helaly

“…Our system is robust since it observes the pattern of movement over a sequence of frames and considers the coordinated movement pattern of the head, eye, and lips rather than considering a single deviation as a cheating behavior which will minimize the false positive cases. …”
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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

“…Our model utilizes the temporal convolution neural network (CNN) architecture to extract high-resolution temporal features, while incorporating attention mechanisms to highlight disease-related patterns. …”
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