Enhancing CNN-Based Signal Denoising: A Novel Metric Framework With Harmonic Suppression Through Hybrid Modeling by Omer Nacar, Turgay Koc

“…This paper introduces a comprehensive evaluation framework that merges traditional metrics like Signal-to-Noise Ratio (SNR) and Mean Squared Error (MSE) with proposed harmonic power-related metrics: Fundamental Power Ratio (FPR), Fundamental to Total Harmonic Power ratio (FTHPR), and Harmonic Power Ratio (HPR) to analyze the performance and generalization of four CNN architectures: Denoising Convolutional Neural Network (DnCNN), Deep Convolutional Neural Network (DCNN), Deep Convolutional Autoencoder (DDCAE), and a hybrid Convolutional Neural Network - Long Short-Term Memory (CNN-LSTM) model. …”
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The Prediction of Cerebral Palsy in Infants Using Pose Estimation Techniques Within a Hybrid Deep Learning Framework by Lama A. Alghamdi, Maryam A. Aldahri, Saja A. Alzahrani, Rowla A. Alaofi, Rawaf F. Nafea, Noura M. Alotaibi

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Automated classification of midpalatal suture maturation stages from CBCTs using an end-to-end deep learning framework by Omid Halimi Milani, Lauren Mills, Amanda Nikho, Marouane Tliba, Veerasathpurush Allareddy, Rashid Ansari, Ahmet Enis Cetin, Mohammed H. Elnagar

“…The feature extraction integrates Convolutional Neural Networks (CNN) architectures, such as EfficientNet and ResNet18, alongside our novel Multi-Filter Convolutional Residual Attention Network (MFCRAN) enhanced with Discrete Cosine Transform (DCT) layers. …”
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Gearbox Fault Diagnosis Under Noise and Variable Operating Conditions Using Multiscale Depthwise Separable Convolution and Bidirectional Gated Recurrent Unit with a Squeeze-and-Exc... by Xiaoteng Ma, Kejia Zhai, Nana Luo, Yehui Zhao, Guangming Wang

“…To address these limitations, this study proposes a novel fault diagnosis framework that integrates multiscale depthwise separable convolution, bidirectional gated recurrent units, and a squeeze-and-excitation attention mechanism. …”
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Spatiotemporal DeepWalk Gated Recurrent Neural Network: A Deep Learning Framework for Traffic Learning and Forecasting by Jian Yang, Jinhong Li, Lu Wei, Lei Gao, Fuqi Mao

“…In the framework, the spatial dependency between nodes of an entire road network is extracted by graph convolutional network (GCN), whereas the temporal dependency between speeds is captured by a gated recurrent unit network (GRU). …”
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Automatic model of sleep apnea detection using optimized weighted fusion process of hybrid convolution (1D/2D) efficient attention network from EEG signals by R. Nandakumar, Rajesh Arunachalam, R Pugalenthi, Karthikayen Arunachalam

“…Methods In this work, a hybrid deep learning framework for automated SA detection combines advanced feature extraction and efficient classification techniques. …”
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A Hybrid Framework for Photovoltaic Power Forecasting Using Shifted Windows Transformer-Based Spatiotemporal Feature Extraction by Ping Tang, Ying Su, Weisheng Zhao, Qian Wang, Lianglin Zou, Jifeng Song

“…Therefore, this paper proposes a hybrid framework based on shifted windows Transformer (Swin Transformer), convolutional neural network, and long short-term memory network to comprehensively extract spatiotemporal feature information, including global spatial, local spatial, and temporal features, from ground-based sky images and PV power data. …”
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Multi-stage framework using transformer models, feature fusion and ensemble learning for enhancing eye disease classification by Abdulaziz AlMohimeed

“…However, current methods mostly use single-model architectures, including convolutional neural networks (CNNs), which might not adequately capture the long-range spatial correlations and local fine-grained features required for classification. …”
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A Multi-Factor-Fusion Framework for Efficient Prediction of Pedestrian-Level Wind Environment Based on Deep Learning by Zhen-Zhong Hu, Yan-Tao Min, Shuo Leng, Sunwei Li, Jia-Rui Lin

“…This framework integrates Graph Convolutional Networks and Long Short-Term Memory networks to extract and fuse multiple factors and create an end-to-end neural network model capable of directly predicting wind fields. …”
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Motion blur aware multiscale adaptive cascade framework for ear tag dropout detection in reserve breeding pigs by Weijun Duan, Fang Wang, Xueliang Fu, Honghui Li, Buyu Wang

“…Second, Density-Aware Dilated Convolution (DA-DC) dynamically adjusts the convolutional receptive field to improve small ear tag detection. …”
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Innovative Framework for Historical Architectural Recognition in China: Integrating Swin Transformer and Global Channel–Spatial Attention Mechanism by Jiade Wu, Yang Ying, Yigao Tan, Zhuliang Liu

“…Focusing on the study of Chinese historical architecture, this research proposes an innovative architectural recognition framework that integrates the Swin Transformer backbone with a custom-designed Global Channel and Spatial Attention (GCSA) mechanism, thereby substantially enhancing the model’s capability to extract architectural details and comprehend global contextual information. …”
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Hybrid LDA-CNN Framework for Robust End-to-End Myoelectric Hand Gesture Recognition Under Dynamic Conditions by Hongquan Le, Marc in het Panhuis, Geoffrey M. Spinks, Gursel Alici

“…For these reasons, we propose a hybrid Linear Discriminant Analysis–convolutional neural network (LDA-CNN) framework to improve the gesture recognition performance of sEMG-based prosthetic hand control systems. …”
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Blur CLIP Context Model-Based Image Coding for Machines Framework With Down and Up Sample Pairs for Multitasks by Zhongpeng Zhang, Ying Liu

“…Inspired by CLIP, channel-wise context model and mask convolutional neural network (PixelCNN), we propose a Blur CLIP context model (BCcm) for reducing bitrate usage and a two-hyperprior multitask framework augmented by BCcm (TMFBC). …”
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MRF-Mixer: A Simulation-Based Deep Learning Framework for Accelerated and Accurate Magnetic Resonance Fingerprinting Reconstruction by Tianyi Ding, Yang Gao, Zhuang Xiong, Feng Liu, Martijn A. Cloos, Hongfu Sun

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An Explainable Deep Learning Framework for Sorghum Weed Classification Using Multi-Scale Feature Enhanced DenseNet by Armaano Ajay, S. Sandosh, Aryan Saji, Harsh Agarwal

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A Relative Attitude Detection Method for Unmanned Aerial Vehicles Based on You Only Look Once Framework by Jingting Qiu, Feifan Yu, Fengrui Xu, Xinmin Chen, Jiqiang Wang

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Replay-Based Incremental Learning Framework for Gesture Recognition Overcoming the Time-Varying Characteristics of sEMG Signals by Xingguo Zhang, Tengfei Li, Maoxun Sun, Lei Zhang, Cheng Zhang, Yue Zhang

“…This study proposes an incremental learning framework based on densely connected convolutional networks (DenseNet) to capture non-synchronous data features and overcome catastrophic forgetting by constructing replay datasets that store data with different time spans and jointly participate in model training. …”
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A Novel 3D Convolutional Neural Network-Based Deep Learning Model for Spatiotemporal Feature Mapping for Video Analysis: Feasibility Study for Gastrointestinal Endoscopic Video Cla... by Mrinal Kanti Dhar, Mou Deb, Poonguzhali Elangovan, Keerthy Gopalakrishnan, Divyanshi Sood, Avneet Kaur, Charmy Parikh, Swetha Rapolu, Gianeshwaree Alias Rachna Panjwani, Rabiah Aslam Ansari, Naghmeh Asadimanesh, Shiva Sankari Karuppiah, Scott A. Helgeson, Venkata S. Akshintala, Shivaram P. Arunachalam

“…To reduce computational complexity, a (2 + 1)D convolution is used in place of full 3D convolution. …”
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A comprehensive construction of deep neural network‐based encoder–decoder framework for automatic image captioning systems by Md Mijanur Rahman, Ashik Uzzaman, Sadia Islam Sami, Fatema Khatun, Md Al‐Amin Bhuiyan

“…Abstract This study introduces a novel encoder–decoder framework based on deep neural networks and provides a thorough investigation into the field of automatic picture captioning systems. …”
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SP-IGAN: An Improved GAN Framework for Effective Utilization of Semantic Priors in Real-World Image Super-Resolution by Meng Wang, Zhengnan Li, Haipeng Liu, Zhaoyu Chen, Kewei Cai

“…The framework consists of two branches. The main branch introduces a Graph Convolutional Channel Attention (GCCA) module to transform channel dependencies into adjacency relationships between feature vertices, thereby enhancing pixel associations. …”
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