A Hybrid Long Short-Term Memory-Graph Convolutional Network Model for Enhanced Stock Return Prediction: Integrating Temporal and Spatial Dependencies by Songze Shi, Fan Li, Wei Li

“…This study proposes a hybrid model integrating long short-term memory (LSTM) networks and graph convolutional networks (GCNs) to enhance accuracy by capturing both temporal dynamics and spatial inter-stock relationships. …”
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Development of an Integrated System of sEMG Signal Acquisition, Processing, and Analysis with AI Techniques by Filippo Laganà, Danilo Pratticò, Giovanni Angiulli, Giuseppe Oliva, Salvatore A. Pullano, Mario Versaci, Fabio La Foresta

“…The electrical signals analyzed on healthy and unhealthy subjects are acquired using a meticulously developed integrated circuit system featuring biopotential acquisition electrodes. …”
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Combined Application of Deep Learning and Radiomic Features for Classification of Lung CT Images by Shariati Faridoddin, V. A. Pavlov

“…The ResNet18 architecture was adapted to integrate radiomic features directly into the deep learning workflow. …”
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Swin-ReshoUnet: A Seismic Profile Signal Reconstruction Method Integrating Hierarchical Convolution, ORCA Attention, and Residual Channel Attention Mechanism by Jie Rao, Mingju Chen, Xiaofei Song, Chen Xie, Xueyang Duan, Xiao Hu, Senyuan Li, Xingyue Zhang

“…The encoder uses a hierarchical convolution module to build a multi-scale feature pyramid, enhancing cross-scale geological signal representation. …”
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A Two-Stage Deep Fusion Integration Framework Based on Feature Fusion and Residual Correction for Gold Price Forecasting by Cihai Qiu, Yitian Zhang, Xunrui Qian, Chuhang Wu, Jiacheng Lou, Yang Chen, Yansong Xi, Weijie Zhang, Zhenxi Gong

“…Aiming to solve these limitations, an innovative two-stage hybrid deep integration framework that combines feature extraction and residual correction techniques is proposed with a view to predicting the gold price more accurately. …”
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AirQuaNet: A Convolutional Neural Network Model With Multi-Scale Feature Learning and Attention Mechanisms for Air Quality-Based Health Impact Prediction by Sreeni Chadalavada, Suleyman Yaman, Abdulkadir Sengur, Ravinesh C. Deo, Abdul Hafeez-Baig, Tracy Kolbe-Alexander, Niranjana Sampathila, U. Rajendra Acharya

“…AirQuaNet integrates deep learning (DL) innovations, namely Multi-Scale Convolutional Blocks (MSCBs), residual connections, and self-attention mechanisms, to enhance its feature extraction capabilities and enable it to learn long-range temporal dependencies. …”
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RoBERTa-Based Multi-Feature Integrated BiLSTM and CNN Model for Ceramic Review Analysis by LiHua Yang, Jun Wang, WangRen Qiu

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Collaborative Forecasting of Multiple Energy Loads in Integrated Energy Systems Based on Feature Extraction and Deep Learning by Zhe Wang, Jiali Duan, Fengzhang Luo, Xiaoyu Qiu

“…This method provides a high-precision tool for the planning and operation of integrated energy systems.…”
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An Ontology-Based Framework for Complex Urban Object Recognition through Integrating Visual Features and Interpretable Semantics by Xiao Xie, Xiran Zhou, Jingzhong Li, Weijiang Dai

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Hybrid CNN-Transformer Architecture With Xception-Based Feature Enhancement for Accurate Breast Cancer Classification by Alireza Zeynali, Mohammad Ali Tinati, Behzad Mozaffari Tazehkand

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Attention-Guided Sample-Based Feature Enhancement Network for Crowded Pedestrian Detection Using Vision Sensors by Shuyuan Tang, Yiqing Zhou, Jintao Li, Chang Liu, Jinglin Shi

“…To address this, we introduce a novel architecture termed the Attention-Guided Feature Enhancement Network (AGFEN), designed within the deep convolutional neural network framework. …”
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Cell-Type Annotation for scATAC-Seq Data by Integrating Chromatin Accessibility and Genome Sequence by Guo Wei, Long Wang, Yan Liu, Xiaohui Zhang

“…To address these challenges, we propose scAttG, a novel deep learning framework that integrates graph attention networks (GATs) and convolutional neural networks (CNNs) to capture both chromatin accessibility signals and genomic sequence features. …”
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Nonlocal and Local Feature-Coupled Self-Supervised Network for Hyperspectral Anomaly Detection by Degang Wang, Longfei Ren, Xu Sun, Lianru Gao, Jocelyn Chanussot

“…NL2Net employs a dual-branch architecture that integrates both local and nonlocal feature extraction. …”
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Spatial and Channel Attention Integration with Separable Squeeze-and-Excitation Networks for Image Classifications by Nazmul Shahadat, Shleshma Regmi, Anup Rijal

“…Among these, Squeeze-and-Excitation (SE) networks have shown significant effectiveness by adaptively recalibrating feature maps. This paper proposes a novel architecture by integrating spatial and channel attention mechanisms using separable SE (SC-SE) Layers. …”
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PI-ADFM: Enhancing Multimodal Remote Sensing Image Matching Through Phase-Integrated Aggregated Deep Features by Haiqing He, Shixun Yu, Yongjun Zhang, Yufeng Zhu, Ting Chen, Fuyang Zhou

“…Geometric distortions and significant nonlinear radiometric differences in multimodal remote sensing images (MRSIs) introduce substantial noise in feature extraction. Single-branch convolutional neural networks fail to capture global image features and integrate local and global information effectively, yielding deep descriptors with low discriminability and limited robustness. …”
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DualGCN-GE: integration of spatiotemporal representations from whole-blood expression data with dual-view graph convolution network to identify Parkinson’s disease subtypes by Wei Zhang, Zeqi Xu, Ruochen Yu, Mingfeng Jiang, Qi Dai

“…This DualGCN-GE method has proposed dual-view graph convolution network(GCN) to integrate temporal and topological features underlying whole-blood expression data, thus detecting PD-PACE subtypes. …”
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Diabetic Retinopathy Classification Using Hybrid Color-Based CLAHE and Blood Vessel in Deep Convolution Neural Network by Ammar Jawad Kadhim, Hadi Seyedarabi, Reza Afrouzian, Fadhil Sahib Hasan

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CAFU-Net: A Context-Aware Feature Aggregation Network for Lung Nodule Segmentation by Jiachen Hou, Yingqi Lu, Zhougui Ling, Tao Li, Xiangsuo Fan, Yanna Qin, Qingnan Huang

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Hybrid Deep Learning Architecture with Adaptive Feature Fusion for Multi-Stage Alzheimer’s Disease Classification by Ahmad Muhammad, Qi Jin, Osman Elwasila, Yonis Gulzar

“…Methods: This research proposes a novel deep learning framework for multi-stage Alzheimer’s disease (AD) classification using T1-weighted MRI scans. The adaptive feature fusion layer, a pivotal advancement, facilitates the dynamic integration of features extracted from a ResNet50-based CNN and a vision transformer (ViT). …”
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TransRNetFuse: a highly accurate and precise boundary FCN-transformer feature integration for medical image segmentation by Baotian Li, Jing Zhou, Fangfang Gou, Jia Wu

“…This study introduces a novel pathological image segmentation method, termed TransRNetFuse, which incorporates stepwise feature aggregation and a residual fully convolutional network architecture. …”
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