Hybrid Deep Learning Approach for Automated Sleep Cycle Analysis by Sebastián Urbina Fredes, Ali Dehghan Firoozabadi, Pablo Adasme, David Zabala-Blanco, Pablo Palacios Játiva, Cesar A. Azurdia-Meza

“…This study presents a hybrid neural network architecture composed of convolutional neural network (CNN) layers, bidirectional long short-term memory (BiLSTM) layers, and attention mechanism layers in order to process large volumes of EEG data in PSG files. …”
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Enhanced Offline Writer Recognition System Employing Blended Multi-Input CNN and Bi-LSTM Model on Diverse Handwritten Texts by Naresh Purohit, Subhash Panwar

“…Due to the variety of text visuals, especially handwriting images, author recognition is challenging. Convolution Neural Network (CNN) excels in many fields. …”
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A New Efficient Hybrid Technique for Human Action Recognition Using 2D Conv-RBM and LSTM with Optimized Frame Selection by Majid Joudaki, Mehdi Imani, Hamid R. Arabnia

“…While deep learning models such as 3D convolutional neural networks (CNNs) and recurrent neural networks (RNNs) deliver promising results, they often struggle with computational inefficiencies and inadequate spatial–temporal feature extraction, hindering scalability to larger datasets or high-resolution videos. …”
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MSFF-Net: Multi-Sensor Frequency-Domain Feature Fusion Network with Lightweight 1D CNN for Bearing Fault Diagnosis by Miao Dai, Hangyeol Jo, Moonsuk Kim, Sang-Woo Ban

“…The vibration and acoustic signals are initially converted into the frequency domain using the fast Fourier transform (FFT), enabling the extraction of temporally invariant spectral features. These features are processed by a compact one-dimensional convolutional neural network, where modality-specific representations are fused at the feature level to capture complementary fault-related information. …”
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Deep Learning Framework for Oil Shale Pyrolysis State Recognition Using Bionic Electronic Nose by Yuping Yuan, Xiaohui Weng, Yuheng Qiao, Xiaohu Shi, Zhiyong Chang

“…The proposed solution integrates Graph Convolutional Network (GCN) and Long Short-Term Memory (LSTM) to capture the spatial correlations among different sensors in the electronic nose and the temporal characteristics of the data, respectively. …”
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WIVIDOSA-Net: Wigner–Ville distribution based obstructive sleep apnea detection using single lead ECG signal by Amit Bhongade, Tapan Kumar Gandhi

“…Then, SWVSs were fed as input to our newly developed DLM named WIgner–VIlle Distribution-based Obstructive Sleep Apnea convolutional neural network (WIVIDOSA-Net) as well as other standard pretrained ResNet-18 and ResNet-50 for comparison. …”
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Sound event detection by intermittency ratio criterium and source classification by deep learning techniques by Vidaña-Vila Ester, Brambilla Giovanni, Alsina-Pagès Rosa Ma

“…To fill this weakness, the DL-based classification system, which uses a MobileNet convolutional neural network, shows promise in identifying foreground sound sources. …”
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Research on Electric Vehicle Charging Load Prediction Methods Combining Signal Noise Reduction and Time Series Modeling by Liyun Liu, Xiaomei Xu, Jinsong Zhang, Dong Li

“…This study introduces a hybrid deep learning model combining Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Convolutional Neural Networks (CNN), Bi-directional Gated Recurrent Units (BiGRU), and Attention Mechanism (AM) to address the volatility in charging load patterns. …”
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A non-anatomical graph structure for boundary detection in continuous sign language by Razieh Rastgoo, Kourosh Kiani, Sergio Escalera

“…To enhance the model performance, replace the handcrafted feature extractor, and also consider the hand structure in these models, we propose a deep learning-based approach, including a combination of the Graph Convolutional Network (GCN) and the Transformer models, along with a post-processing mechanism for final boundary detection. …”
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SLEEP-SAFE: Self-Supervised Learning for Estimating Electroencephalogram Patterns With Structural Analysis of Fatigue Evidence by Wonjun Ko, Jeongwon Choe, Jonggu Kang

“…In this regard, this work proposes a novel deep convolutional neural network architecture that can learn spectro-spatio-temporal representation of the vigilance EEG signals, thereby achieving a powerful mental status recognition ability. …”
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Enhancing Magnetotelluric Data Quality Using Deep Learning-Based Denoising Models: A Study of CNN and LSTM by Widya Utama, Maman Hermana, Dwa D. Warnana, Wien Lestari, Muhammad N. A. Zakariah, Sherly A. Garini, Rista F. Indriani, Dhea P. Novian Putra, M Ulin Nuha Abduh, Alif N. F. Insani, Dandi Syahtia Pratama, Khairul Arifin Mohd Noh, Abdul Halim Abdul Latiff

“…To address this critical issue, this study develops denoising models based on Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) to enhance the quality of MT signals while preserving their original structure. …”
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Conv1D-GRU-Self Attention: An Efficient Deep Learning Framework for Detecting Intrusions in Wireless Sensor Networks by Kenan Honore Robacky Mbongo, Kanwal Ahmed, Orken Mamyrbayev, Guanghui Wang, Fang Zuo, Ainur Akhmediyarova, Nurzhan Mukazhanov, Assem Ayapbergenova

“…This study proposes a hybrid IDS model combining one-dimensional Convolutional Neural Networks (Conv1Ds), Gated Recurrent Units (GRUs), and Self-Attention mechanisms. …”
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Intelligent prediction method of virtual network function resource capacity for polymorphic network service slicing by Julong LAN, Di ZHU, Dan LI

“…For the extraction of spatial features, by given an adjacency matrix and a feature matrix,graph convolutional network is used to reorganize the spatial distribution features of time series in the Fourier domain. …”
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Hybrid CNN-LSTM With Attention Mechanism for Robust Credit Card Fraud Detection by Iman Akour, Nour Mohamed, Said Salloum

“…This paper proposes a hybrid fraud detection model integrating Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and an attention mechanism to address these challenges. …”
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Adaptive period decomposition network with muti-scale temporal variation learning for water level prediction by Hui Wang, LiPing Wang, QiCang Qiu, Yuyan Gao, WenJuan Li, QiuShun Wang

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Real-Time Pipeline Leak Detection: A Hybrid Deep Learning Approach Using Acoustic Emission Signals by Faisal Saleem, Zahoor Ahmad, Jong-Myon Kim

“…The core of the framework combines convolutional neural networks (CNNs) with long short-term memory (LSTM), ensuring a comprehensive examination of both spatial and temporal features of AE signals. …”
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Enhanced melanoma and non-melanoma skin cancer classification using a hybrid LSTM-CNN model by Sara M. M. Abohashish, Hanan H. Amin, E. I. Elsedimy

“…This paper presents a novel approach for the automatic identification of cutaneous lesions by integrating convolutional neural networks (CNNs) with long short-term memory (LSTM) networks. …”
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soundClass: An automatic sound classification tool for biodiversity monitoring using machine learning by Bruno Silva, Frederico Mestre, Sílvia Barreiro, Pedro J. Alves, José M. Herrera

“…We introduce the r package soundClass, a tool to train convolutional neural networks, and employ them to classify sound events in recordings. soundClass provides a sound event classification pipeline, from annotating recordings to automating trained networks usage in real‐life situations. …”
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Trajectory Prediction and Intention Recognition Based on CNN-GRU by Jinghao Du, Dongdong Lu, Fei Li, Ke Liu, Xiaolan Qiu

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A Random Degradation Aggregation Network With Temporal-Spatial Attention for Satellite Video Super-Resolution by Lu Li, Mi Wang, Yingdong Pi

“…Finally, we introduce an optical flow-guided deformable convolution module for aligning sequential frames, integrated with a temporal-spatial attention mechanism to effectively fuse temporal and spatial features. …”
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