A digital twin framework with MobileNetV2 for damage detection in slab structures by Duong Huong Nguyen, Huan Nguyen, Xiaohong Gao

“…In this study, a digital twin framework is proposed for damage detection in a civil structure, which consists of a finite element model, neural networks, model updating methods, and signal processing. …”
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Multi-task deep learning framework for enhancing Mayo endoscopic score classification in ulcerative colitis by Jaehyuk Lee, Eunchan Kim

“…This study proposes a multi-task learning (MTL) framework inspired by the coarse-to-fine processing mechanism of the human brain to address these challenges. …”
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SFFNet: Shallow Feature Fusion Network Based on Detection Framework for Infrared Small Target Detection by Zhihui Yu, Nian Pan, Jin Zhou

“…To solve the above problems, we present a shallow feature fusion network (SFFNet) based on detection framework. Specifically, we design the shallow-layer-guided feature enhancement (SLGFE) module, which guides multi-scale feature fusion with shallow layer information, effectively mitigating the loss of information in deep networks. …”
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Enhanced Federated Learning Framework based on Deep Learning and Neutrosophic Set for Android Malware Classification by Mohamed Refaat Abdellah, Hasan H. Oudah, Ahmed Mohamed Ahmed Badawy, Mohamed AbdElFattah AbdElFattah M Hassan, Shady Ahmed Bedier, Ahmed A. Metwaly, Mohamed eassa, Ahmed Abdelhafeez

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A CNN-Transformer Hybrid Framework for Multi-Label Predator–Prey Detection in Agricultural Fields by Yifan Lyu, Feiyu Lu, Xuaner Wang, Yakui Wang, Zihuan Wang, Yawen Zhu, Zhewei Wang, Min Dong

“…To address this challenge, we propose a hybrid deep learning framework that integrates convolutional neural networks (CNNs) and Transformer architectures for multi-label recognition of predator–pest combinations. …”
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The Short-Term Wind Power Forecasting by Utilizing Machine Learning and Hybrid Deep Learning Frameworks by Sunku V.S., Namboodiri V., Mukkamala R.

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Hierarchical Multi-Scale Decomposition and Deep Learning Ensemble Framework for Enhanced Carbon Emission Prediction by Yinuo Sun, Zhaoen Qu, Zhuodong Liu, Xiangyu Li

“…The proposed framework not only improves predictive accuracy, but also enhances interpretability by revealing emission patterns across multiple temporal scales, supporting both operational and strategic carbon management decisions.…”
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Maize Seed Variety Classification Based on Hyperspectral Imaging and a CNN-LSTM Learning Framework by Shuxiang Fan, Quancheng Liu, Didi Ma, Yanqiu Zhu, Liyuan Zhang, Aichen Wang, Qingzhen Zhu

“…This study introduced an efficient method for maize variety identification by combining hyperspectral imaging with a framework that integrates Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. …”
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Decoding vocal indicators of stress in laying hens: A CNN-MFCC deep learning framework by Suresh Neethirajan

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DSI-ResCNN: A Framework Enhancing the Error-Tolerance Capacity of DNA Storage for Images by Cihan Ruan, Liang Yang, Rongduo Han, Shan Gao, Haoyu Wu, Qiming Yuan, Yanting Guo, Nam Ling

“…These two modules, namely the residual convolutional neural network (ResCNN) module and the DNA sequence dropout control (SDC) module, collectively constitute a framework, named DNA storage of images with residual CNN (DSI-ResCNN). …”
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A Bio-Inspired Learning Dendritic Motion Detection Framework with Direction-Selective Horizontal Cells by Tianqi Chen, Yuki Todo, Zhiyu Qiu, Yuxiao Hua, Hiroki Sugiura, Zheng Tang

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Hybrid transfer learning and self-attention framework for robust MRI-based brain tumor classification by Soumyarashmi Panigrahi, Dibya Ranjan Das Adhikary, Binod Kumar Pattanayak

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FSBNet: A Classifying Framework of Disaster Scene for Volcanic Lithology Through Deep-Learning Models by Lan Liu, Zhouyi Xiao, Jianpeng Hu, Jingxin Han, Jung Yoon Kim, Rohit Sharma, Chengfan Li

“…Experimental results show that the proposed FSBNet framework achieves 94.66% accuracy of volcanic lithology scene classification on the validation set compared with three traditional machine-learning methods and assists the remote sensing investigation of volcanic disaster scenes.…”
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A Fast Forward Prediction Framework for Energy Materials Design Based on Machine Learning Methods by Xinhua Liu, Kaiyi Yang, Lisheng Zhang, Wentao Wang, Sida Zhou, Billy Wu, Mengyu Xiong, Shichun Yang, Rui Tan

“…The results show that the proposed framework can predict material performance well toward rapid initial screening. …”
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An Efficient Semantic Segmentation Framework with Attention-Driven Context Enhancement and Dynamic Fusion for Autonomous Driving by Jia Tian, Peizeng Xin, Xinlu Bai, Zhiguo Xiao, Nianfeng Li

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Two-stream spatio-temporal GCN-transformer networks for skeleton-based action recognition by Dong Chen, Mingdong Chen, Peisong Wu, Mengtao Wu, Tao Zhang, Chuanqi Li

“…Abstract For the purpose of achieving accurate skeleton-based action recognition, the majority of prior approaches have adopted a serial strategy that combines Graph Convolutional Networks (GCNs) with attention-based methods. …”
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Individualized spatial network predictions using Siamese convolutional neural networks: A resting-state fMRI study of over 11,000 unaffected individuals. by Reihaneh Hassanzadeh, Rogers F Silva, Anees Abrol, Mustafa Salman, Anna Bonkhoff, Yuhui Du, Zening Fu, Thomas DeRamus, Eswar Damaraju, Bradley Baker, Vince D Calhoun

“…To do this, we develop a deep Siamese framework comprising three-dimensional convolution neural networks for contrastive learning based on individual-level spatial maps estimated via a fully automated fMRI independent component analysis approach. …”
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CausalCervixNet: convolutional neural networks with causal insight (CICNN) in cervical cancer cell classification—leveraging deep learning models for enhanced diagnostic accuracy by Zahra Taghados, Zohreh Azimifar, Malihezaman Monsefi, Mojgan Akbarzadeh Jahromi

“…This study introduces CausalCervixNet, a Convolutional Neural Network with Causal Insight (CICNN) tailored for cervical cancer cell classification. …”
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HMA-Net: a hybrid mixer framework with multihead attention for breast ultrasound image segmentation by Soumya Sara Koshy, L. Jani Anbarasi

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A novel optic disc and optic cup segmentation technique to diagnose glaucoma using deep learning convolutional neural network over retinal fundus images by H.N. Veena, A. Muruganandham, T. Senthil Kumaran

“…This proposed paper introduces an efficient glaucoma master framework to segment the optic cup and optic disc to find the Cup-to-Disc-Ratio (CDR). …”
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