Integrating non-linear radon transformation for diabetic retinopathy grading by Farida Mohsen, Samir Belhaouari, Zubair Shah

“…This study introduces RadFuse, a multi-representation deep learning framework that integrates non-linear RadEx-transformed sinogram images with traditional fundus images to enhance diabetic retinopathy detection and grading. …”
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A Non-Invasive Approach for Facial Action Unit Extraction and Its Application in Pain Detection by Mondher Bouazizi, Kevin Feghoul, Shengze Wang, Yue Yin, Tomoaki Ohtsuki

“…To address this, we present a quick, computationally efficient method for detecting action units (AUs) and their intensities—key indicators of health and emotion—using only 3D facial landmarks. Our proposed framework extracts 3D face landmarks from video recordings and employs a lightweight neural network (NN) to identify AUs and estimate AU intensities based on these landmarks. …”
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Multi-source remote sensing data for monitoring natural reserves: a case study on the Shaanxi Hanzhong crested ibis National Nature Reserve, China by Haoshan Wang, Yunwei Tang, Linhai Jing, Hui Li, Haifeng Ding, Changyong Dou

“…The paper proposes a framework to monitor human activities and evaluate habitat suitability for the Crested Ibis in the Shaanxi Hanzhong Nipponia nippon National Nature Reserve using multi-source satellite data. …”
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Unlocking transcranial FUS-EEG feature fusion for non-invasive sleep staging in next-gen clinical applications by Suneet Gupta, Praveen Gupta, Bechoo Lal, Aniruddha Deka, Hirakjyoti Sarma, Sheifali Gupta

“…Addressing the variations in electroencephalogram (EEG) and electrooculogram (EOG) signals across different sleep stages, this study introduces a transcranial focused ultrasound (tFUS) based multimodal feature fusion deep learning model (MFDL) for automated sleep staging. The proposed framework integrates two one-dimensional convolutional neural networks (1D-CNNs) to extract sleep-relevant features from EEG and EOG signals, followed by an adaptive feature fusion module that dynamically assigns weights based on feature significance. …”
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Graph-Based Radiomics Feature Extraction From 2D Retina Images by Ofelio Jorreia, Nuno Goncalves, Rui Cortesao

“…Within the radiomics framework, this study introduces a methodology to distinguish bifurcations from other structural variations in 2D local fragments of retinal vasculature. …”
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Enhancing Gamma Knife Cone-beam Computed Tomography Image Quality Using Pix2pix Generative Adversarial Networks: A Deep Learning Approach by Prabhakar Ramachandran, Darcie Anderson, Zachery Colbert, Daniel Arrington, Michael Huo, Mark B Pinkham, Matthew Foote, Andrew Fielding

“…Aims: The study aims to develop a modified Pix2Pix convolutional neural network framework to enhance the quality of cone-beam computed tomography (CBCT) images. …”
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Tree Species Classification at the Pixel Level Using Deep Learning and Multispectral Time Series in an Imbalanced Context by Florian Mouret, David Morin, Milena Planells, Cécile Vincent-Barbaroux

“…Validation on independent in situ data shows that all models struggle to predict in areas not well covered by training data, but even in this situation, the RF algorithm is largely outperformed by deep learning models for minority classes. The proposed framework can be easily implemented as a strong baseline, even with a limited amount of reference data.…”
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Human Scene Understanding Mechanism-Based Image Captioning for Blind Assistance by Jong-Hoon Kim, Sung-Wook Park, Jun-Ho Huh, Se-Hoon Jung, Chun-Bo Sim

“…In contrast to prior works that primarily rely on conventional convolutional architectures, the proposed model uniquely incorporates human-inspired visual perception principles and Vision Transformer-based global encoding, offering a novel and interpretable framework tailored for assistive image captioning.…”
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Learning Spectral–Spatial-Former Deep Prior for Hyperspectral Image Superresolution by Zeinab Dehghan, Jingxiang Yang, Abdolraheem Khader, Jian Fang, Liang Xiao

“…This study presents a multistage optimization framework that leverages high- and low-frequency components, along with a quadratic splitting method, to address the SR problem. …”
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Attention-enhanced StrongSORT for robust vehicle tracking in complex environments by Wei Xu, Xiaodong Du, Ruochen Li, Bingjie Li, Yuhu Jiao, Lei Xing

“…To address these challenges, we propose AE-StrongSORT (Attention-Enhanced StrongSORT), an attention-enhanced tracking framework featuring three systematic innovations: first, the GAM-YOLO (global attention mechanism-YOLO)hybrid architecture integrates multi-scale feature fusion with a global attention mechanism (GC2f structure). …”
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Learnable Resized and Laplacian-Filtered U-Net: Better Road Marking Extraction and Classification on Sparse-Point-Cloud-Derived Imagery by Miguel Luis Rivera Lagahit, Xin Liu, Haoyi Xiu, Taehoon Kim, Kyoung-Sook Kim, Masashi Matsuoka

“…While cost effective, these sensors produce sparser point clouds, leading to poor feature representation and degraded performance in deep learning techniques, such as convolutional neural networks (CNN), for tasks like road marking extraction and classification, which are essential for HD map generation. …”
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Video Instance Segmentation Through Hierarchical Offset Compensation and Temporal Memory Update for UAV Aerial Images by Ying Huang, Yinhui Zhang, Zifen He, Yunnan Deng

“…On the self-built UAV-VIS dataset, our HT-VIS with PHOC surpasses the baseline SipMask by 2.1% and achieves the highest average segmentation accuracy of 37.4% in the CNN-based methods, demonstrating the effectiveness and robustness of our proposed framework.…”
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Randomization-Driven Hybrid Deep Learning for Diabetic Retinopathy Detection by A. M. Mutawa, G. R. Hemalakshmi, N. B. Prakash, M. Murugappan

“…This study pioneers an innovative framework, using Multi-Scale Discriminative Robust Local Binary Pattern (MS-DRLBP) features, combined with a hybrid Convolutional Neural Network-Radial Basis Function (CNN-RBF) classifier, to enhance the detection of DR. …”
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The spatial–temporal evolution characteristics and influencing factors of coordinated development in the Yellow River Basin: Based on the perspective of flood-sediment transport, e... by Ni Geng, Guiliang Tian, Hengquan Zhang

“…Considering the characteristics of the YRB, we proposed a complex basin-wide analytical framework consisting of FS-EE-SE (FES) system. Firstly, we used the entropy weight method to determine the indicators weights and built the coupling coordination degree (CCD) model. …”
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Spatiotemporal information conversion machine for time-series forecasting by Hao Peng, Pei Chen, Rui Liu, Luonan Chen

“…In this work, a neural network computing framework, the spatiotemporal information conversion machine (STICM), was developed to efficiently and accurately render a forecasting of a time series by employing a spatial-temporal information (STI) transformation. …”
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RETRACTED ARTICLE: An intelligent dynamic cyber physical system threat detection system for ensuring secured communication in 6G autonomous vehicle networks by Shanthalakshmi M, Ponmagal R S

“…So we present a novel approach to mitigating these security risks by leveraging pre-trained Convolutional Neural Network (CNN) models for dynamic cyber-attack detection within the cyber-physical systems (CPS) framework of AVs. …”
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Advanced Diagnosis of Cardiac and Respiratory Diseases from Chest X-Ray Imagery Using Deep Learning Ensembles by Hemal Nakrani, Essa Q. Shahra, Shadi Basurra, Rasheed Mohammad, Edlira Vakaj, Waheb A. Jabbar

“…This robust ensemble learning framework underscores its potential for reliable and precise disease detection, offering significant improvements over traditional methods. …”
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Multimodal Fusion Mamba Network for Joint Land Cover Classification Using Hyperspectral and LiDAR Data by Haizhu Pan, Ruixiang Zhao, Haimiao Ge, Moqi Liu, Quanxiu Zhang

“…Recently, the emerging deep learning framework Mamba has shown superior performance over traditional architectures, including transformers and convolutional neural networks. …”
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I-AIR: intention-aware travel itinerary recommendation via multi-signal fusion and spatiotemporal constraints by Xiao Cui, Zhihua Wang, Ping Li, Qiang Xu

“…To address these limitations, we propose an intention-aware deep learning framework that integrates diverse user signals into a unified itinerary planning model. …”
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Recognition of Handwritten Characters in Birch-Bark Manuscripts via Object Detection by Ivan P. Malashin, Vadim S. Tynchenko, Andrei P. Gantimurov, Vladimir A. Nelyub, Aleksei S. Borodulin

“…This study adapts the YOLO-based object detection framework for detection and recognition of handwritten characters in digital photographs of Novgorod birch-bark fragments. …”
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