Self-Correlation Network With Triple Contrastive Learning for Hyperspectral Image Classification With Noisy Labels by Kwabena Sarpong, Mohammad Awrangjeb, Md. Saiful Islam, Islam Helmy

“…To address the above drawback, we propose an end-to-end self-correlation framework with triple contrastive learning (SCTCL) for HSI classification with noisy labels. …”
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A novel network with enhanced edge information for left atrium segmentation from LGE-MRI by Ze Zhang, Zhen Wang, Xiqian Wang, Kuanquan Wang, Yongfeng Yuan, Qince Li

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Post-disaster building damage assessment based on gated adaptive multi-scale spatial-frequency fusion network by Bo Yu, Yao Sun, Jiansong Hu, Fang Chen, Lei Wang

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Predicting microbe-disease associations via graph neural network and contrastive learning by Cong Jiang, Cong Jiang, Junxuan Feng, Junxuan Feng, Bingshen Shan, Bingshen Shan, Qiyue Chen, Jian Yang, Jian Yang, Gang Wang, Gang Wang, Xiaogang Peng, Xiaozheng Li, Xiaozheng Li

“…In this study, we propose a novel computational framework, called GCATCMDA, for forecasting potential associations between microbes and diseases. …”
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MMAgentRec, a personalized multi-modal recommendation agent with large language model by Xiaochen Xiao

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Artificial intelligence in vaccine research and development: an umbrella review by Rabie Adel El Arab, May Alkhunaizi, May Alkhunaizi, Yousef N. Alhashem, Alissar Al Khatib, Munirah Bubsheet, Salwa Hassanein, Salwa Hassanein

“…Deep learning architectures, including convolutional and recurrent neural networks, generative adversarial networks, and variational autoencoders, proved instrumental in multiepitope vaccine design and adaptive clinical trial simulations. …”
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A Day-Ahead PV Power Forecasting Method Based on Irradiance Correction and Weather Mode Reliability Decision by Haonan Dai, Yumo Zhang, Fei Wang

“…According to different weather modes, the existing research has established a classification forecast framework to improve the accuracy of day-ahead forecasts. …”
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HEAT: Incorporating hierarchical enhanced attention transformation into urban road detection by Ting Han, Chuanmu Li, Siyu Chen, Zongyue Wang, Jinhe Su, Yundong Wu, Guorong Cai

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Pure data correction enhancing remote sensing image classification with a lightweight ensemble model by Huaxiang Song, Hanglu Xie, Yingying Duan, Xinyi Xie, Fang Gan, Wei Wang, Jinling Liu

“…Existing advanced methods often require substantial modifications to model architectures to achieve optimal performance, resulting in complex frameworks that are difficult to adapt. To overcome these limitations, we propose a lightweight ensemble method, enhanced by pure data correction, called the Exceptionally Straightforward Ensemble. …”
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Frailty prediction in patients with chronic digestive system diseases: based on multi-task learning model by Sihan Hu, Xiaochuan Guo, Xiaobao Wang, Zixiang Jin, Chenyang Zhou, Lang Tu, Zhoulong Shi, Weiyi Ao, Xin Zhang, Jay Zheng, Xuezhi Zhang, Hui Ye

“…Utilizing the Multi-Gate Mixture-of-Experts (MMoE) framework, we built and evaluated five models: Tab Transformer, Convolutional Neural Network (CNN), Deep Neural Network (DNN), Extreme Gradient Boosting (XGBoost) and Random Forest (RF). …”
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Leveraging Complex-Valued Federated Learning for Accurate and Privacy-Respectful Threat Detection Based on Millimeter-Wave Imaging by Hadi Mahdipour, Jaime Laviada, Fernando Las-Heras Andres, Mehdi Sookhak

“…This paper introduces a novel high-resolution pseudo-image-based CO detection framework that leverages complex-valued convolutional neural networks (CV-CNNs) and their federated learning extension (CV-FL) to enhance detection accuracy while preserving user privacy. …”
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Vector Signals and Invariant Systems: Re-Tooling Linear Systems Theory by Mamta Dalal, Steven Sandoval

“…In a previous work, we identified the importance of rotation invariance in the standard complex-valued theory of linear time-invariant (LTI) systems and proposed a generalized vector-valued (VV) definition of convolution that reinterprets the complex-valued product of the traditional formalism as a scale rotation within the framework of geometric algebra. …”
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GDSN-CD: Graph-Guided Diffusion Synergistic Network for Remote Sensing Change Detection by Yunfei Zhu, Jintao Song, Jinjiang Li

“…To address this issue, this article innovatively combines the advantages of graph convolutional networks and diffusion models. First, a dynamic graph structure is created using a weight-sharing graph convolutional networks feature encoder, transforming discrete changes into topological relationships between nodes. …”
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Efficient and secure multi-party computation protocol supporting deep learning by Shancheng Zhang, Gang Qu, Zongyang Zhang, Minzhe Huang, Haochun Jin, Liqun Yang

“…Moreover, we introduce optimized protocols for two crucial deep learning operations: convolution and Softmax function computation. Our convolution protocol leverages the Winograd algorithm to significantly reduce multiplication gate count, yielding over 50% performance improvement. …”
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DHS-YOLO: Enhanced Detection of Slender Wheat Seedlings Under Dynamic Illumination Conditions by Xuhua Dong, Jingbang Pan

“…Our methodology builds upon the YOLOv11 architecture with three principal enhancements: First, the Dynamic Slender Convolution (DSC) module employs deformable convolutions to adaptively capture the elongated morphological features of wheat leaves. …”
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GANs for data augmentation with stacked CNN models and XAI for interpretable maize yield prediction by Ishaan Seshukumar Pothapragada, Sujatha R

“…Feature selection is carefully addressed via a combination of 14 statistical methods, tree-based methods, bio-inspired methods, and regularization methods so that only the most relevant features for modelling are chosen and included. The predictive framework is based on the ensemble of one-dimensional convolutional neural network (CNN) learning on the features selected, combining three parallel branches (processing features selected by Decision Tree, XGBoost, and Lasso methods), followed by a stacked refinement with residual connections. …”
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CBAM-SwinT-BL: Small Rail Surface Defect Detection Method Based on Swin Transformer With Block Level CBAM Enhancement by Jiayi Zhao, Alison Wun-Lam Yeung, Muhammad Ali, Songjiang Lai, Vincent To-Yee Ng

“…Experiment and ablation study have proven the effectiveness of the framework. The proposed framework has a notable improvement in the accuracy of small size defects, such as dirt and dent categories in RIII dataset, with mAP-50 increasing by +23.0% and +38.3% respectively, and the squat category in MUET dataset also reaches +13.2% higher than the original model. …”
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A Hybrid Quantum-Classical Approach for Multi-Class Skin Disease Classification Using a 4-Qubit Model by Aravinda C V, Emerson Raja Joseph, Sultan Alasmari

“…The framework integrates a Quantum Convolutional Neural Network (QCNN) for feature extraction and a Variational Quantum Classifier (VQC) for classification, processing a dataset of 770 images with significant class imbalance. …”
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Pothole detection and segmentation in the Bushveld Complex using physics-based data augmentation and deep learning by Glen T. Nwaila, Musa S.D. Manzi, Emmanuel John M. Carranza, Raymond J. Durrheim, Hartwig E. Frimmel

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Galaxy Morphology Classification via Deep Semisupervised Learning with Limited Labeled Data by Zhijian Luo, Jianzhen Chen, Zhu Chen, Shaohua Zhang, Liping Fu, Hubing Xiao, Chenggang Shu

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