Using a Solution Construction Algorithm for Cyclic Shift Network Coding under Multicast Network to the Transformation of Musical Performance Styles by Xiuqin Wang, Jun Geng, Zhiyuan Li

“…This paper presents a theoretical framework of the circular shift network coding system through the study of nonmultiple clustered interval music performance style conversion and the analysis of music conversion by using circular shift topology, and a series of basic research results of circular shift network coding is obtained under this framework. …”
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Combining Deep Learning and Street View Images for Urban Building Color Research by Wenjing Li, Qian Ma, Zhiyong Lin

“…The framework is composed of two phases: “deep learning” and “quantitative analysis.” …”
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Integrating Machine Learning and IoT for Effective Plant Disease Management by Bhoi Manjulata, Dubey Ahilya

“…Then, this paper presents an innovative framework that utilizes ML and IoT technologies to improve the crop health and yield. …”
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Deep context-attentive transformer transfer learning for financial forecasting by Ling Feng, Ananta Sinchai

“…A transfer learning framework is incorporated to enhance generalization across markets through pretraining, encoder freezing, and fine-tuning. …”
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Low-Power Branch CNN Hardware Accelerator with Early Exit for UAV Disaster Detection Using 16 nm CMOS Technology by Yu-Pei Liang, Wen-Chin Chao, Ching-Che Chung

“…This paper presents a disaster detection framework based on aerial imagery, utilizing a Branch Convolutional Neural Network (B-CNN) to enhance feature learning efficiency. …”
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GOMFuNet: A Geometric Orthogonal Multimodal Fusion Network for Enhanced Prediction Reliability by Yi Guo, Rui Zhong

“…This paper introduces the Geometric Orthogonal Multimodal Fusion Network (GOMFuNet), a novel mathematical framework designed to address these challenges. GOMFuNet synergistically combines two core mathematical principles: (1) It utilizes geometric deep learning, specifically Graph Convolutional Networks (GCNs), within its Cross-Modal Label Fusion Module (CLFM) to perform fusion in a high-level semantic label space, thereby preserving inter-sample topological relationships and enhancing robustness to inconsistencies. (2) It incorporates a novel Label Confidence Learning Module (LCLM) derived from optimization theory, which explicitly enhances prediction reliability by enforcing mathematical orthogonality among the predicted class probability vectors, directly minimizing output uncertainty. …”
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Unsupervised Anomaly Detection for Volcanic Deformation in InSAR Imagery by Robert Popescu, Nantheera Anantrasirichai, Juliet Biggs

“…We test three different state‐of‐the‐art architectures, one convolutional neural network Patch Distribution Modeling (PaDiM) and two generative models (GANomaly and Denoising diffusion probabilistic models (DDPM)). …”
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Deep learning with ensemble-based hybrid AI model for bipolar and unipolar depression detection using demographic and behavioral based on time-series data by Naga Raju Kanchapogu, Sachi Nandan Mohanty

“…Machine learning (ML) and deep learning (DL) offer automated approaches to detect depression using behavioral and demographic data.Methods This study proposes a hybrid AI framework combining structured demographic features with synthetic actigraph time-series data. …”
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Deep Neural Networks for Accurate Depth Estimation with Latent Space Features by Siddiqui Muhammad Yasir, Hyunsik Ahn

“…In response to these challenges, this study introduces a novel depth estimation framework that leverages latent space features within a deep convolutional neural network to enhance the precision of monocular depth maps. …”
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Attention-fused residual transformer CNN for robust lower limb movement recognition by A. Anitha, D. Jeraldin Auxillia

“…To address these challenges, a new framework that combines an Attention-Fused Residual-Transformer Convolutional Neural Network (AF-RT-CNN) is proposed. …”
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A New Bearing Fault Diagnosis Method Based on Deep Transfer Network and Supervised Joint Matching by Chengyao Liu, Fei Dong, Kunpeng Ge, Yuanyuan Tian

“…To overcome these problems, by integrating the superiority of deep learning method and feature-based transfer learning method, this work proposes an innovative cross-domain fault diagnosis framework based on deep transfer convolutional neural network and supervised joint matching. …”
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Autoencoder-Augmented Graph Neural Networks for Accurate and Scalable Structure Recognition in Analog/Mixed-Signal Schematics by Mohamed Salem, Witesyavwirwa Vianney Kambale, Ali Deeb, Sergii Tkachov, Anjeza Karaj, Joachim Pichler, Manuel Ludwig Lexer, Kyandoghere Kyamakya

“…In this work, a novel framework has been proposed that combines the generative augmentation capabilities of convolutional Autoencoders with the structural analysis power of Graph Convolutional Networks (GCNs). …”
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Optimized AlexNet Pruning for Edge-Based Medical Diagnostics by Yasser A. Amer, Hassan I. Saleh, Omar A. Nasr

“…The results reveal a clear difference between fully connected (FC) and convolutional layers: pruning FC layers substantially reduces memory consumption, while pruning convolutional layers significantly boosts inference speed. …”
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Remote Sensing Image Segmentation Using Vision Mamba and Multi-Scale Multi-Frequency Feature Fusion by Yice Cao, Chenchen Liu, Zhenhua Wu, Lei Zhang, Lixia Yang

“…Therefore, this paper proposes CVMH-UNet—a hybrid semantic segmentation network that integrates the Vision Mamba (VMamba) framework with multi-scale feature fusion—to achieve high-precision and relatively efficient RS image segmentation. …”
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Power System Transient Stability Assessment Based on Intelligent Enhanced Transient Energy Function Method by Tianxiao Mo, Jun Liu, Jiacheng Liu, Guangyao Wang, Yuting Li, Kaiwei Lin

“…The example results show that the transient stability prediction framework proposed in this paper can improve the scope of the application of mechanism discrimination and enhance the interpretability of the results of the intelligent method.…”
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Leveraging spatial dependencies and multi-scale features for automated knee injury detection on MRI diagnosis by Jianhua Sun, Ye Cao, Ying Zhou, Baoqiao Qi

“…The proposed model consists of three main components: a graph construction module, graph convolutional layers, and a multi-scale feature fusion module. …”
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LE-YOLO: A Lightweight and Enhanced Algorithm for Detecting Surface Defects on Particleboard by Chao He, Yongkang Kang, Anning Ding, Wei Jia, Huaqiong Duo

“…Compared with other models, the proposed approach not only improved detection precision but also effectively reduced model complexity, achieving a lightweight and efficient detection framework.…”
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A Robust Scheme of Vertebrae Segmentation for Medical Diagnosis by Faisal Rehman, Syed Irtiza Ali Shah, Naveed Riaz, Syed Omer Gilani

“…In this paper, we propose a novel and efficient framework to address the subject problem by integrating a parametric level set approach in deep convolutional neural networks. …”
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Prototypical Few-Shot Learning for Histopathology Classification: Leveraging Foundation Models With Adapter Architectures by Kazi Rakib Hasan, Sijin Kim, Junghwan Cho, Hyung Soo Han

“…This study introduces a framework for the few-shot adaptation of self-supervised histopathology pretrained foundation models using multilayer perception adapters and convolutional adapters. …”
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A novel network-level fused deep learning architecture with shallow neural network classifier for gastrointestinal cancer classification from wireless capsule endoscopy images by Muhammad Attique Khan, Usama Shafiq, Ameer Hamza, Anwar M. Mirza, Jamel Baili, Dina Abdulaziz AlHammadi, Hee-Chan Cho, Byoungchol Chang

“…The proposed framework begins with dataset augmentation to enhance training robustness. …”
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