RDW-YOLO: A Deep Learning Framework for Scalable Agricultural Pest Monitoring and Control by Jiaxin Song, Ke Cheng, Fei Chen, Xuecheng Hua

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HRNet Encoder and Dual-Branch Decoder Framework-Based Scene Text Recognition Model by Meiling Li, Xiumei Li, Junmei Sun, Yujin Dong

“…To solve the problems of STR for distorted, blurred, and low-resolution texts in natural scenes, this paper proposes a HRNet encoder and dual-branch decoder framework-based STR model. The model mainly consists of an encoder module and a dual-branch decoder module composed of a super-resolution branch and a recognition branch in parallel. …”
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VDTformer: A transformer-based framework for accurate deformation risk detection in cable tunnels by Ruipeng Liu, Jieyan Zhang, Pengfei Chen, Yunxun Liu, Wanlin Quan, Junliang Su

“…In this work, we propose VDTformer, a Transformer-based framework that integrates a Filter Bank Convolution (FBC) module for denoising and feature extraction with a Wavelet Transform-based Attention (WTA) mechanism for capturing non-stationary characteristics. …”
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Multitemporal Difference and Dynamic Optimization Framework for Multiscale Motion Satellite Video Super-Resolution by Qian Zhao, Youming Guo, Lei Min, Changhui Rao

“…Most existing methods rely on optical flow or deformable convolution for motion alignment. However, these methods perform poorly in handling complex satellite video scenes and diverse moving objects. …”
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Efficient Side-Tuning for Remote Sensing: A Low-Memory Fine-Tuning Framework by Haichen Yu, Wenxin Yin, Hanbo Bi, Chongyang Li, Yingchao Feng, Wenhui Diao, Xian Sun

“…To reduce memory requirements and training costs, this article proposes a low-memory fine-tuning framework, called efficient side-tuning (EST), for remote sensing downstream tasks. …”
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Interaction-Temporal GCN: A Hybrid Deep Framework For Covid-19 Pandemic Analysis by Zehua Yu, Xianwei Zheng, Zhulun Yang, Bowen Lu, Xutao Li, Maxian Fu

“…Therefore, we propose a novel framework, the Interaction-Temporal Graph Convolution Network (IT-GCN), to analyze pandemic data. …”
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DualDyConvNet: Dual-Stream Dynamic Convolution Network via Parameter-Efficient Fine-Tuning for Predicting Motor Prognosis in Subacute Stroke by Yunjeong Jang, Joohye Jeong, Yun Kwan Kim, Da-Hye Kim, Wanjoo Park, Laehyun Kim, Yun-Hee Kim, Minji Lee

“…In this study, we propose a novel framework, called dual-stream dynamic convolution network (DualDyConvNet), to predict motor recovery for two months using resting-state electroencephalogram data in the subacute phase. …”
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Data-driven non-intrusive reduced order modelling of selective laser melting additive manufacturing process using proper orthogonal decomposition and convolutional autoencoder by Shubham Chaudhry, Azzedine Abdedou, Azzeddine Soulaïmani

“…This approach effectively reduces the dimensionality of the high-fidelity snapshot matrix and constructs a regression framework for accurate predictions. Conversely, the CAE-MLP model employs a 1D convolutional autoencoder to reduce the spatial dimension of a high-fidelity snapshot matrix derived from numerical simulations. …”
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Toward Reliable Post-Disaster Assessment: Advancing Building Damage Detection Using You Only Look Once Convolutional Neural Network and Satellite Imagery by César Luis Moreno González, Germán A. Montoya, Carlos Lozano Garzón

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Deep convolutional neural network based archimedes optimization algorithm for heart disease prediction based on secured IoT enabled health care monitoring system by Sureshkumar S, Santhosh Babu A. V, Joseph James S, Maranco M

“…To tackle these issues, we proposed a novel IoT-enabled and secured healthcare monitoring framework (IoT-SHMF) for heart disease prediction. …”
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Enhancing Anti-Money Laundering Frameworks: An Application of Graph Neural Networks in Cryptocurrency Transaction Classification by Stefano Ferretti, Gabriele D'Angelo, Vittorio Ghini

“…The research specifically employs Graph Convolutional Networks (GCNs), Graph Attention Networks (GATs), the Chebyshev spatial convolutional neural networks, and GraphSAGE networks. …”
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Limits of Solar Flare Forecasting Models and New Deep Learning Approach by G. Francisco, M. Berretti, S. Chierichini, R. Mugatwala, J. Fernandes, T. Barata, D. Del Moro

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YOLOX-LS: Strong Gravitational Lenses Detection in the DECaLS with Deep Learning by Yangyang Liu, Liangping Tu, Jianxi Li, Jiawei Miao, Gengqi Lin, Chenying Zhao

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A twin CNN-based framework for optimized rice leaf disease classification with feature fusion by Prameetha Pai, S. Amutha, Mustafa Basthikodi, B. M. Ahamed Shafeeq, K. M. Chaitra, Ananth Prabhu Gurpur

“…Abstract This paper presents a novel Twin Convolutional Neural Network (CNN)-based framework for classifying rice leaf diseases. …”
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Diaproteo: A supervised learning framework for early detection of diabetes mellitus based on proteomic profiles by Hamza Shahab Awan, Fahad Alturise, Tamim Alkhalifah, Yaser Daanial Khan

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A Copula-Driven CNN-LSTM Framework for Estimating Heterogeneous Treatment Effects in Multivariate Outcomes by Jong-Min Kim

“…In this study, we propose a novel deep learning framework integrating empirical copula transformations with a CNN-LSTM (Convolutional Neural Networks and Long Short-Term Memory networks) architecture to capture nonlinear dependencies and temporal dynamics in multivariate treatment effect estimation. …”
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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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