BlindFG: Learning Contextual Fishing Trajectories for Unreported Fishing Gear Classification by Changha Lee, Chan-Hyun Youn

“…FCSom utilizes the completed trajectories within a 1D convolutional classification framework. By aggregating candidate predictions across multiple fishing type hypotheses, the system effectively discriminates among various fishing behaviors, even in the presence of partial AIS data. …”
Get full text

Deep Temporal and Structural Embeddings for Robust Unsupervised Anomaly Detection in Dynamic Graphs by Samir Abdaljalil, Hasan Kurban, Rachad Atat, Erchin Serpedin, Khalid Qaraqe

“…We introduce Temporal Structural Graph Anomaly Detection (<sc>T-StructGAD</sc>), an unsupervised framework that leverages Graph Convolutional Gated Recurrent Units (<monospace>GConvGRU</monospace>s) and Long Short-Term Memory networks (<monospace>LSTM</monospace>s) to jointly model both structural and temporal dynamics in graph node embeddings. …”
Get full text

Deep Learning Model-Based Detection of Anemia from Conjunctiva Images by Najmus Sehar, Nirmala Krishnamoorthi, C. Vinoth Kumar

“…This score demonstrates the framework’s exceptional capability in detecting anemia through a non-invasive approach. …”
Get full text

DASD- diagnosing autism spectrum disorder based on stereotypical hand-flapping movements using multi-stream neural networks and attention mechanisms by Theyazn H. H. Aldhyani, Theyazn H. H. Aldhyani, Abdullah H. Al-Nefaie, Abdullah H. Al-Nefaie

“…IntroductionThe early detection and diagnosis of autism spectrum disorder (ASD) remain critical challenges in developmental healthcare, with traditional diagnostic methods relying heavily on subjective clinical observations.MethodsIn this paper, we introduce an innovative multi-stream framework that seamlessly integrates three state-of-the-art convolutional neural networks, namely, EfficientNetV2B0, ResNet50V2, DenseNet121, and Multi-Stream models to analyze stereotypical movements, particularly hand-flapping behaviors automatically. …”
Get full text

GuardianMPC: Backdoor-Resilient Neural Network Computation by Mohammad Hashemi, Domenic Forte, Fatemeh Ganji

“…To ensure resilience against such backdoor attacks, this work presents GuardianMPC, a novel framework leveraging secure multiparty computation (MPC). …”
Get full text

Diverse behavior clustering of students on campus with macroscopic attention by Wanghu Chen, Zongjuan Wu, Siqi Zeng, Hongle Guo, Jing Li

“…To verify the effectiveness of the framework, this paper takes the university campus scene as a case study. …”
Get full text

VAE-Based Real-Time Anomaly Detection Approach for Enhanced V2X Communication Security by Yonas Teweldemedhin Gebrezgiher, Sekione Reward Jeremiah, Stefanos Gritzalis, Jong Hyuk Park

“…This paper proposes a real-time anomaly detection framework that integrates the reconstruction capabilities of Variational Autoencoders (VAEs) with the feature extraction power of Convolutional Neural Networks (CNNs). …”
Get full text

Uncertainty-Aware Adaptive Multiscale U-Net for Low-Contrast Cardiac Image Segmentation by A. S. M. Sharifuzzaman Sagar, Muhammad Zubair Islam, Jawad Tanveer, Hyung Seok Kim

“…This study proposes a novel segmentation framework Adaptive Multiscale U-Net (AMU-Net) combining Convolutional Neural Networks (CNNs) and transformer-based encoder–decoder architectures. …”
Get full text

HDL-ACO hybrid deep learning and ant colony optimization for ocular optical coherence tomography image classification by Shivani Agarwal, Anand Kumar Dohare, Pranshu Saxena, Jagendra Singh, Indrasen Singh, Umesh Kumar Sahu

“…This paper introduces HDL-ACO, a novel Hybrid Deep Learning (HDL) framework that integrates Convolutional Neural Networks with Ant Colony Optimization (ACO) to enhance classification accuracy and computational efficiency. …”
Get full text

Fast Identification and Detection Algorithm for Maneuverable Unmanned Aircraft Based on Multimodal Data Fusion by Tian Luan, Shixiong Zhou, Yicheng Zhang, Weijun Pan

“…To address the critical challenges of insufficient monitoring capabilities and vulnerable defense systems against drones in regional airports, this study proposes a multi-source data fusion framework for rapid UAV detection. Building upon the YOLO v11 architecture, we develop an enhanced model incorporating four key innovations: (1) A dual-path RGB-IR fusion architecture that exploits complementary multi-modal data; (2) C3k2-DATB dynamic attention modules for enhanced feature extraction and semantic perception; (3) A bilevel routing attention mechanism with agent queries (BRSA) for precise target localization; (4) A semantic-detail injection (SDI) module coupled with windmill-shaped convolutional detection heads (PCHead) and Wasserstein Distance loss to expand receptive fields and accelerate convergence. …”
Get full text

Deep learning based rapid X-ray fluorescence signal extraction and image reconstruction for preclinical benchtop X-ray fluorescence computed tomography applications by Amrit Kaphle, Sandun Jayarathna, Sang Hyun Cho

“…Here we propose a novel end-to-end deep learning (DL) framework that integrates a one-dimensional convolutional neural network (1D CNN) for rapid XRF signal extraction with a U-Net model for XFCT image reconstruction. …”
Get full text

Roof Geometrical Component Extraction Using Bimodal Data and Graph Neural Network by F. Soleimani Vostikolaei, S. Jabari

“…This study proposes a novel framework using optical imagery and Digital Surface Models (DSM) to extract these elements and construct 3D building models. …”
Get full text

Cross Attentive Multi-Cue Fusion for Skeleton-Based Sign Language Recognition by Ogulcan Ozdemir, Inci M. Baytas, Lale Akarun

“…We demonstrate how the proposed attention-based framework exposes distinct temporal patterns of visual cue representations extracted via Spatio-Temporal Graph Convolutional Network (ST-GCN) and exploits them for learning SL representations more effectively. …”
Get full text

An Integrated Preprocessing and Drift Detection Approach With Adaptive Windowing for Fraud Detection in Payment Systems by Hadi M. R. Al Lawati, Anazida Zainal, Bander Ali Saleh Al-Rimy, Mohammad Al-Azawi, Mohamad Nizam Kassim, Sultan Ahmed Almalki, Tami Abdulrahman Alghamdi

“…This study proposes a complete framework that brings together advanced data preprocessing, effective drift detection, and a reliable detection model to address these issues. …”
Get full text

Research on Bearing Fault Diagnosis Method for Varying Operating Conditions Based on Spatiotemporal Feature Fusion by Jin Wang, Yan Wang, Junhui Yu, Qingping Li, Hailin Wang, Xinzhi Zhou

“…To address this issue, this paper proposes a spatiotemporal feature fusion domain-adaptive network (STFDAN) framework for bearing fault diagnosis under varying operating conditions. …”
Get full text

Fast Anomaly Detection for Vision-Based Industrial Inspection Using Cascades of Null Subspace PCA Detectors by Muhammad Bilal, Muhammad Shehzad Hanif

“…In this study, we introduce a novel anomaly detection framework that leverages feature maps from a lightweight convolutional neural network (CNN) backbone, MobileNetV2, and cascaded detection to achieve notable accuracy as well as computational efficiency. …”
Get full text

Adaptive Cut Selection in Mixed-Integer Linear Programming by Turner, Mark, Koch, Thorsten, Serrano, Felipe, Winkler, Michael

“…We present a reinforcement learning framework for selecting cuts, and train our design using said framework over MIPLIB 2017 and a neural network verification data set. …”
Get full text

Assessing Revisit Risk in Emergency Department Patients: Machine Learning Approach by Wang-Chuan Juang, Zheng-Xun Cai, Chia-Mei Chen, Zhi-Hong You

“…ObjectiveThis study aims to develop an ML-assisted framework that identifies high-risk patients who may revisit the ED within 72 hours after the initial visit. …”
Get full text

Deep Learning for Hyperspectral Image Classification: A Critical Evaluation via Mutation Testing by Zhifei Chen, Yang Hao, Qichao Liu, Yuyong Liu, Mingyang Zhu, Liang Xiao

“…Recently, there has been a surge in the adoption of deep learning (DL) techniques, especially convolutional neural networks (CNNs), to perform hyperspectral image (HSI) classification. …”
Get full text

An Improved CEEMDAN-FE-TCN Model for Highway Traffic Flow Prediction by Heyao Gao, Hongfei Jia, Lili Yang

“…Finally, the Temporal Convolutional Network (TCN) is adopted to predict the recombined subsequences, and the final prediction result is reconstructed. …”
Get full text